Process Capability Study and Identification of the Causes of Defects and Rejections in Galvanization Line of Corrugated Sheet Production
Case on: Adama steel factory (ASF)
By: Balem Limenie Adamu
Thesis submitted to Department of Mechanical Design and Manufacturing Engineering School of Mechanical Chemical and Materials Engineering Presented in Partial Fulfillment of the Requirements for the Degree of Master in Mechanical Design and Manufacturing Engineering
Office of Graduate Studies Adama Science and Technology University Adama, Ethiopia
June, 2019
I | P a g e
Process Capability Study and Identification of the Causes of Defects and Rejections in Galvanization Line of Corrugated Sheet Production
Case on: Adama steel factory (ASF)
By: Balem Limenie Adamu
Advisor Dr. Guteta Kabeta
Thesis submitted to School of Mechanical Chemical and Materials Engineering Presented in Partial Fulfillment of the Requirements for the Degree of Master in Mechanical Design and Manufacturing Engineering Office of Graduate Studies Adama Science and Technology University Adama, Ethiopia
June, 2019
II | P a g e
Approval Sheet of Board of Examiners
I, the undersigned, members of the Board of Examiners of the final open defense by Balem Limenie have read and evaluated her thesis entitled “process capability study and identification of sources of defects and rejections. Case of Adama steel factory” and examined the candidate. This is, therefore, to certify that the thesis has been accepted in partial fulfillment of the requirement of the Degree of Master’s in Mechanical Design and Manufacturing engineering.
______Advisor Signature Date ______Chairperson Signature Date ______Internal Examiner Signature Date ______External Examiner Signature Date
III | P a g e
Candidate Declarations
I hereby declare that the work, which is being presented in thesis, entitled’’ Process capability study and identification of the sources of defects and Rejection in Galvanization line of corrugated sheet production” in ASF. In partial fulfillment of the requirements for the award of the degree of Masters of Science in Manufacturing Engineering is an authentic record of my own work carried out from November 2018 to June 2019 under the supervision of Dr. Guteta Kabeta, Mechanical Design and Manufacturing Engineering program, Adama Science and Technology University, Ethiopia.
The matter embodied in this thesis has not been submitted by me for the award of any other degree or diploma. All relevant resources of information used in this thesis have been duly acknowledged.
Balem Limenie Adamu
Candidate Signature Date
This is to certify that above declaration made by the candidate is correct to the best of my knowledge and belief. This thesis has been submitted for examination with our approval:
Dr. Guteta Kabeta
Advisor Signature Date
IV | P a g e
ACKNOLOGNMENT
First of all, I would like to thank my almightily GOD to give my full health and her support to finalize my thesis study without any adversity.
Secondly, I like provision very grateful thank to my advisor GUTETA KABETA (PhD) in school of chemical, material and mechanical engineering in design and manufacturing program for exposing me to such kind of explorative and investigative thesis work. His encouragement, excellent guidance, creative suggestions and critical comments has a great contributed to accomplish my thesis work. I would like to thank Mr. Dagmawi Hailu for such great knowledge and experience in the quality domain and given me very valuable advice and ways to be easy clear my work along the way.
Finally, I would like to thank all ASF staff members. Specially Mr. Asabu Molla, a head technician and productivity department for his continuous help and advice, Mr. Mulatu Teshome head of general human resource manager, Mr. Mekonnen operator of quality control department and Mr. Melaku Ygeizu mechanical operator of machine and also to tank my lovely family and my best friends how are not parting in necessary time and support.
V | P a g e
Abstract
The process capability measurement is an important aspect in quality management to decide whether a process is capable of meeting the required specifications. The main objective were to study capability of the process and identify the possible source of defects and rejections in galvanized sheet production line and provide recommendations. The study has done through collected and analyzed sample data with the application of Microsoft excel and Minitab software to determine the capability of process, applied statistical quality control tool in order to determine the cause and rate of defects and examine the quality of different products using process capability indices. The result showed that the manufacturing process was incapable and in acceptable in both parameters of coating thickness and weight according to Thumb law and six sigma principle; all the values of indices and ppm were out of standard value. All indices values were less than 1.33 and 1.67 and ppm was much greater than 2700 (cp 1) and 0.545 (cp>1.5). The analyzed data, the mount of defects and rejections 3.23% and the overall profit losses are 74,724,240 birr due to the occurrences of different causes of defects and process variations.
Key words: Rejection, process capability analysis, Process Capability index, Statistical quality control, defect amount, rework, profit loss.
VI | P a g e
Table of Contents
Approval Sheet of Board of Examiners ...... I
Candidate Declarations ...... IV
ACKNOLOGNMENT ...... V
Abstract ...... VI
Table of Contents ...... VII
List of figures ...... XI
List of tables ...... XII
Abbreviations and Acronyms ...... XIII
CHAPTER ONE ...... 1
1. INTRODUCTION ...... 1
1.1 Back grounds of the study...... 1
1.2 Back ground of the company ...... 3
1.3 Process description...... 3
1.4 General Hot dip galvanizing Defects ...... 4
1.5 Zinc coating section and its defect ...... 6
1.5.1 Factors affecting Zn coating ...... 7
1.6 Pickling Section and Its Defects ...... 7
1.6.1 Under Pickling ...... 8
1.6.2 Over Pickling ...... 8
1.7 Sources of Defects and Rejections ...... 8
1.8 Defect Reduction Methodology ...... 10
1.8.1 Six Sigma Method...... 10
1.8.2 Process Capability Analysis and Indices ...... 12
1.8.3 Statistical Quality Control l (SQC) ...... 14 VII | P a g e
1.9 Statement of the Problem ...... 18
1.10 Objectives of the study...... 19
1.10.1 General Objective ...... 19
1.10.2 Specific objectives ...... 19
1.11 Significance of the Study ...... 19
1.12Motivation of the study ...... 20
1.13 Scope of the Study ...... 20
1.14 Limitation of the study ...... 20
1.15 Thesis Structures ...... 20
CHAPTER TWO ...... 22
2. LITERATURE RIVEW ...... 22
2.1 Introduction ...... 22
2.2 Theoretical backgrounds ...... 22
2.2.1 Hot Dip Galvanization Defects ...... 22
2.2.2 Process Capability Analysis ...... 24
CHAPTER THREE ...... 28
3. RESERCH METHODS,TOOLS AND MATERIALS ...... 28
3.1 Research Method ...... 28
3.2 Data Collection ...... 29
3.2.1 Primary Data Collection ...... 29
3.2.2. Secondary Data Collection ...... 29
3.3 Data Analysis and Interpretation ...... 29
3.4 Sampling Technique ...... 30
3.5 Measurement Method ...... 30
3.6 Materials ...... 30
VIII | P a g e
3.7 Methdology / Project Flow Chart ...... 31
3.8 Capability Indices Analysis ...... 32
3. 9 Process Capability Assumption ...... 33
CHAPTER FOUR ...... 34
4. RESULTS AND DISSCUSION ...... 34
4.1 Introduction ...... 34
4.2 Defects of Hot Dip Galvanization Process in ASF ...... 34
4.2.1 Coating Defects ...... 34
4.2. 2 Wrinkle Pattern Defect ...... 36
4.2.3 Surface defects ...... 37
4.3 Defects and Rejection Rates Analysis ...... 41
4.3.1 Results of Defect Analysis ...... 50
4.4 Capability Analysis/ study of process ...... 52
4.4.1 Capability analysis of coated weight on two sample data ...... 53
4.5 Process capability analysis of coated thickness on two sample data ...... 57
4.5.1 Process capability analysis thickness of 1st sample data ...... 57
4. 5.2 Process capability analysis of thickness on 2nd sample data ...... 58
4. 6 Process capability result anlysises of coating weight of two sample data ...... 60
4.6.1 PCA of coating weight on first sample data ...... 60
4.6.2 PCA of coating weight on second sample data ...... 63
4.7 Process capability result anlysis of coating thickness of two sample data ...... 66
4.7.1 PCA of coating thickness on first sample data (thickness one) ...... 66
4.7.2 PCA of Coating Thickness on second Sample Data ...... 69
CHAPTER FIVE ...... 73
5. SUMMARY, CONCLUSION AND RECOMMENDATION ...... 73
IX | P a g e
5.1 Summary ...... 73
5.2 Conclusion ...... 74
5.3 Recommendations ...... 74
5.4 Future work ...... 75
REFERENCE ...... 76
APPENDIX ...... 81
X | P a g e
List of figures
Figure 1.1 Flow chart of DMAIC tools (Linderman et.al 2003) 11 Figure 1.2 Diagram of Pareto tool (source Kirti Singh 2016) 18 Figure 3.1 Project flow charts 31 Figure 4.1 Photos of dross defect: (a) damaged sheet and (b) dross cumulative 35 Figure 4.2 Photo of ash defec 35 Figure 4.3 Photos wrinkle sheet defect 37 Figure 4.4 Photo of scrath and bent defects 40 Figure 4.5 Parteo diagarm of defects 51 Figure 4.6 Defect cause diagram 52 Figure 4.7 Process capability report for weight one 60 Figure 4.8 Xbar R chart of coating weight weight one of product 61 Figure 4.9 Histogram for coating weight one of the product 61 Figure 4.10 Propability pilot for coating weight one of the product 62 Figure 4.11 Process capability report for weight two (W2) 63 Figure 4.12 X bar- R charts for weight two 64 Figure 4.13 Frequency histogram for weight two of product 65 Figure 4.14 Probability pilot for W2 of the product 65 Figure 4.15 Process capability report for thickness one 66 Figure 4.16 X bar- R chart for thickness 1 of product 67 Figure 4.17 Frequency histograms for thickness 1 of product 68 Figure 4.18 Probability pilot for T1 of product 69 Figure 4.19 Process capability analysis reports for thickness two 69 Figure 4.20 X bar-R chart for T2 of product 70 Figure 4.21 Frequency histogram for thickness two 71 Figure 4.22 Probability pilot for t2 of product 72
XI | P a g e
List of tables
Table 1.1 Standard values of six- sigma tool 12 Table 1.2 Capability index definition (Adeoye2013) and (Yerriswamy 2014) 12 Table 1.3 Over all capability indexes and their meaning (Belokar, 2012) 14 Table 1.4 Constant A2, d2, D3 and D4 values (table 8A SPC) 16 Table 2.1 Literature summary 27 Table 4.1 Summary of defects 40 Table 4.2 Collected data of first month defect frequency 42 Table 4.3 Collected data on second month (Sept.) with actual production 44 Table 4.4 Third month (Nov) collected data actual frequency of 666,916 .75 45 Table 4.5 4th month (Jan) data collection of 996,861 actual production 47 Table 4.6 5th month (Feb) data collection of 1,080,094 actual production 48 Table 4.7 Over all data representations of five-month frequency records 49 Table 4.8 Ethiopian standard values of coating thickness and weight of GS 53 Table 4.9 Measured value of coating weigh on first sample 53 Table 4.10 Measured value of coating weight on second sample 55 Table 4.11 Measured value of coating thickness on first sample 57 Table 4.12 Measured value of coating thickness on second sample 58
XII | P a g e
Abbreviations and Acronyms
Xi Individual mean N Number of sample size UCL Upper control limit
LCL Lower control limit Al Aluminum Pb Lead 6 σ Six sigma Al2O3 Aluminum oxide Na OH Sodium oxide SEM Scanning microscopy STDEV Standard deviation σ S.T Estimated standard deviation HDGCL Hot dip galvanizing continuous line PPM Part per million MSCR Mild steel cold roll EDX Energy dispersive x ray spectroscopy AGA American galvanization association ASTM American standard thickness measurement DMADV Define ,measure, analysis ,design and verify DMAIC Define ,measure, analysis ,improve and control Cp Process capability Cpk Process capability index PCIS Process capability indices Pp Process performance SQC Statistical quality control USL Upper specification limit
XIII | P a g e
LSL Lower specification limit ASF Adama steel factory CGL Continuous galvanizing line HDG Hot dip galvanizing SPC Statistical process control PCA Process capability analysis PPK Process performance index CED Cause effect diagram M/TON Metric ton GS Galvanized sheet
XIV | P a g e
CHAPTER ONE 1. INTRODUCTION
1.1 Back grounds of the study
Steel factory is one of the manufacturing industries in Ethiopia that produce different products having various functions, shapes and sizes. One of these products is steel sheet metal with different shape and thickness based on customers demand. The aim of manufacturing industries is to meet customer requirement by providing high quality products relatively low cost and increase their competency and market share. In order to satisfy the customer requirements; different mechanisms might be used such as: designing a new process, or modify existing to ensure the process is capable and consistently produce according to specification and prevent defect and rejection. Improving capability of process helps to reduce manufacturing time, increase yield, reduce defect, lower rework cost, and reduce inspection time (Ravi et.al 2018). Defect is an imperfection that creates rejection and rework in the production process that impairs the productivity of a factory in increase unit cost of products. It is an identified deviation from specified tolerance limit or specification that has been set during design stage. Defects in steel galvanized process create different problems for the manufacturers and users. Therefore, variety of systems must develop to cope with this problem. Identification of defect type and its causes helps to raise profit margin, reduce lead-time, improve quality of output and reduce unit cost of product. Rejection is losing of quality, quantity of items or pieces that created by different processes. Rejection Analysis is a process of identification of amount and frequency of rejected output related problems in manufacturing process. The application of rejection analysis allows studying the amount and causes of failed parts. Now a day’s, rework of defective parts are common but rework adds losses on net profit of the company Ghazia Zaine (2011). The vision and mission of almost all industrial sectors is to be effective, competitive and maximize their profit in the product output. However, sometimes fall due to different reasons caused by individual or group members of company employees or other personnel. Quality improvement tools is a tools/ techniques or approaches used to improve the process to produce defect free product, avoid or reduce non- conforming items and increase the quality of products. There are several techniques used to improve quality and rejection and rework such as; six
1 | P a g e
sigma, process capability analysis and statistical process. Process capability is the ability of process to produce the required product that will fulfill the requirement characteristics or specifications. It is a measure of inherent variability in a process as compared to the requirements of the product. Capability analysis PCA is engineering study to calculate and investigate the process capability. It is about how well a process meets its specification limits. PCA helps to estimate, monitor and reduce the variability in the processes. An important part in capability analysis is the use of capability indices. Process capability index (PCIs) is the main indicator used to measure the capability analysis and to evaluate a production process and indicate if the process is capable or not i.e., it is prepared to produce items with the required specifications. The objective of a process capability study is to identify non-random variability, examine the sources and intervene, in an effort to eliminate the cause Maline Albing (2006).
The capability analysis is very important aspect in many manufacturing industries, and several researchers conducted related to capability indices Anis et.al (2008). In process capability index, there are two types of specification namely lower specification limit (LSL) and upper specification limit (USL), those two limits indicate ranges of acceptance quality characteristics. (PCIs) as Cp and Cpk are a quantitative measurement to evaluate the process capability Montgomery (2001). Cp and CPK are process capability and process capability indices used to determine the performances or quality of outputs to produce acceptable products. Other technique used to minimize rejection is statistical process control (SPC) by using different tools like probability plot, control chart, histogram, causes and effect diagram, cheek sheet, process flow diagram and parteo diagram to improve quality of products. In order to improve the manufacturing process and to reduce defect, waste and rejection in galvanized process capability analysis done by preparing suitable methodology. Galvanization is a process of applying molten zinc metal in steel or iron to prevent rest or corrosion. This thesis in tends to identify causes of different variability (defects) in galvanization process line of Adama Steel Factory (ASF), measure the capability of the indicated line and suggest solution for improvement Akhil and Deote (2012).
2 | P a g e
1.2 Back ground of the company
Adama steel factory (ASF) was established with a capital of Br. 40 million in the year 2003 E.C by local investor, at Oromia Regional State Adama town, 90 km far from the capital city, Addis Ababa, found west of Adama Town, bounded by main rail and express way with a total area 40,000m2 land through recruitment of 65 employees. ASF supplying Ega sheet, tiles profile and corrugated sheet for governmental, private company, individual house builders and wholesale traders through importation of galvanized and pre-painted iron sheet on coil. The factory expands to produce normal nail, so that, today ASF has expanded by inclusion of higher capacity galvanization industry established with a capital of birr 570 million, producing different types of steel sheet products, various diameter wire, different size nail, and twisted nail. Currently the factory was at implementation of different integrated steel industries to meet the customer requirement, increase value addition and to be best competitor in the sector, through producing galvanized, color coated iron sheet producing other products for constraction industries. This company introduced diverse product for local market and plan to supply for foreign market. It is the largest market shareholder of steel sheet and nails products in the country and open branches in different regions of Ethiopia, such as Hawassa , Bahirdar and four showrooms at Addis Ababa Bole backside of Millennium hall; CMC St. Michael Zelalem building second floor; Gotera Condominium and Lebu around Haile garment to make easy access for customers. In order to fast and effective, the work there are many employees in different departments are presents in ASF.
1.3 Process description
This part deals about hot dip galvanization process. Galvanization is a process of applying pure zinc or zinc alloy coating in steel through immersing in molten zinc at a temperature close to 450-465°C, to prevent corrosion and increase service life. The most common methods for processing coating are hot dip galvanizing (HDG), electro galvanizing and zinc spraying. From those methods, HDG is the most powerful and effective way to steel product. In HDG process other production sections are involved to accomplish the task, the most critical and important processes are cleaning (degreasing, fluking and pickling) and zinc coating. In the degreasing process, the steel is immersed in alkaline solution bath typically sodium hydro oxide. NaoH
3 | P a g e
naturally have the ability to remove oil contaminates such as dirt, grease and oil. Similarly in pickling bath, the steel is immersed in acidic solution bath typically Hcl to remove stains, rust and scale or oxidation surface to reduce corrosion. It contains salty aqueous solutions such as ammonium chloride. This chemical solution is responsible for prepared steel surface to obtain proper metallurgical reaction with molten zinc bath. Coating section used to apply zinc in to steel sheet to prevent rust or corrosion through apply temperature around 460-470 and a purity of zinc at least 98% (ASTM B6, 2013). Furnace is critical section to make coating and including different important elements such as ammonia or flux, lead, tin, zinc, antinomy, aluminum and alloy (Aluminum and antinomy) in their specific box namely flux box, zinc box and lead box. Flux box used to carry flux to better cleaning of sheet, sometimes extra impurity pass to sheet through insufficient cleaning. Because, the cleaning is poor it is difficult to coat with uniform, good and smooth, these are the major source for poor appearance and low life time of sheet products. Zinc box contains the requirement substrates such as Zn, Al, Pb, tin and antinomy. Al, tin and antinomy used to shine, fine and attractive the sheet product. Lead box contains only lead used to protect kittle box from damage. Furnace section has control mechanisms to control the thickness of coated sheet, such as air wiping, Zn and Pb layer and air pressure regulator. Air wiping mechanism help to be uniform and smooth the coated sheet through remove excess-coated amount. Applying air pressure depends on the thickness of cold roll; the input material thickness is less than 0.185mm; the air pressure gap reduced form standard values (0.185mm) otherwise remove excess amount of coated. Zinc and lead layer used to control required amount of Zn and Pb because, the Zn amount is high increased zinc consumption and sheet coated is thicker, in opposite side Pb amount is high lead pass on sheet since, reduce the appearance and service time of product.
1.4 General Hot dip galvanizing Defects
Blending defects: is leaching out of the paint film. The possible cause of this defect is frequent brushing on the same spot, improper pickling adjustment and use of incompatible coat. Gelling defect: this defect formed through decreasing viscosity caused by bacteria degradation of the protein binder or other thickness agent and by the use of contaminated tool or water solvent and mixing of different type of paint. Water contaminated occurred due to poor or insufficient water treatment or inhibitor.
4 | P a g e
Settling defect: is settlement of the pigment to the bottom and failure of re-disprese. The main casuses of this defects are insuficent string during storage period of materials and put under excessive warm condition. General Roughness: This is usually due to excessive growth or unevenness of the alloy layers that can be attribute to the steel chemical composition or original surface condition. Heavy coatings are usually rougher than lighter coatings because irregularity of alloy layers tends to increase with thickness Luigi Solazzi (2017). Matte Gray or Mottled Coating defect Usually appears as a localized dull patch or wed-like area on a normal surface, and develops when there is a lack of free zinc layer on the coating surface during the cooling process. A matte gray coating occurred mostly in steel with relatively high silicon or phosphorous content, since they are heavier sections that cool slower. Galvanizers generally do not have prior knowledge of the steel’s chemical composition, and has no control over its occurrence (American organization association 02). Surface defects Surface defect is a defect present in the surface of galvanized sheet through different factors and affects the quality of coated product. As a matter of fact, most defects occur because of a rough or mechanically damaged substrate surface, insufficient cleaning of the substrate, poor bath chemistry management and line equipment maintenance Saravanan (2018). Mahieu et.al (200) proposed the categories of surface defect encountered in galvanized sheet based on its originality as; defects originating from steel substrate, defects associated with chemical cleaning, defects originating from annealing furnace, defects related to zinc bath ,defects originating from air jet wiping and defects related to temper rolling. Bulge particle defect is formed under surface defect and it is the small granular defect sticking out of the coating surface and randomly distributes on the surface of HDG steel sheet. It is one of the main surface defects result in quality degradation, because it reduces the appearance quality of hot-dip galvanizing steel sheet Taixiong et.al (2014). It is creates due to presences of residual of iron and oil in the surface of sheet through improper surface cleaning. To protect sheet from bulge defect through the mechanism of removing residual of iron and oil by proper surface cleaning before hot dip galvanizing, with adjustable concentration of acid amounts (electrolytic degreasing) and by applying inspection surface before coating.
5 | P a g e
1.5 Zinc coating section and its defect
Zinc first use in construction was in 1811 in Belgium. However, it was not until 1829 that the galvanic action of zinc protecting iron was discovered. In an experiment by Michael Faraday, he found that zinc ports iron by sacrificial corrosion to itself when the two metals are in contact in the presence of an electrolyte. Zinc coating has been used to corrosion mitigation since the 18th century. Because there was no proper surface cleaning of steels at that, time due to the high cost of the process Maaß et.al (2011). The dross formation is a major source of coating defects. Almost all the cause of coating defects is presence of surface oxides in the steel sheet. The defects could originate from the process itself, the geometry of the metals that are being dipped, the surface covering the metal (oil, grease, slag, etc.), from transport, storage and assembly Maaß (2011). Pimples defect This defect produce rough region on the surface of galvanized coating and produced because of blowing air pattern in air knives. The main factor that often leads to pimple is embedded particle in coating. Such particle are generally composed of dross on top of the galvanizing bath and include some inter metallic compounds or snout dust containing vaporized zinc particles. Outburst in inhibition layer can be another source of pimples due to the growth of intermetallic compounds through the coating. Air knife is an instrument to blow off liquid products as they travel of conveyers. It is functional to separate lighter or smaller particle from other component to use in sub sequential steps, post-manufacturing part to drying and conveyer cleaning. An industrial air knife is a pressurized air plenum contains a serious of holes or continuous slots through which pressurized air exist in a laminar flow pattern. This tool used to remove liquid, control the thickness of coating, dry the coating liquid, remove region particle and cool the surface of coating galvanized process Azimi (2012). Blowout or welding blowout Blowout defect occurred due to poor welding quality or chemicals entering unsealed overlaps, in this case will be boiling out of the connection when dipped in the molten zinc (450 ). This defect can be controlled through proper assembled of welded part and affirmation quality of welded area to supply conformation to absence of fluid penetration through prepare vent between welded plate to control distortion of weld parts.
6 | P a g e
1.5.1 Factors affecting Zn coating Different factors are affecting the quality of coating product so; the final coating might be thin or thick. Coating thickness has a great role to justify the quality and life time of final products. Thicker zinc coating, make longer corrosion protection and thick coating has less adherence and bond than coating of normal thickness. Coating thickness is the thickness of final product and coating weight is the amount of zinc applies for a given surface area. Farid Hanna (1984) states about factors affect zinc coating are zinc bath chemistry, surface roughness, immersion into the zinc bath, bath temperature, mass, shape and improper design of kittle. The chemistry contains the levels of Si, Mn, Al and Pb that influence coating characteristics. During the coating of steel proper selection of chemical composition, provide effective output according to different aspects like appearance, uniform thickness and smoothness by reduce the defects come from composition. Si or Al added to remove oxygen, since the content of Si affect the GL reaction. The phosphorus content of the steel also influences on the reactivity, especially for cold rolled steels and with drawl and quenching affect microstructure and appearance of the coating. When the withdrawal or quenching slowly, it require more time for metallurgical reactions and influence time and fuel wastes. Additionally, coating thickness and uniformity are influenced by mass, shape and degree of cold working components. The other factor for coating defect is non- exactness of steel surfaces. Rough surface often produce thinner coatings because the intermetallic layers will grow into each other. In time of coating, as much as possible use pure zinc is candidate, because alloys make some dark and matte gray appearance.
1.6 Pickling Section and Its Defects
Acid pickling section is a metal surface treatment used to clean surface of steel or iron to avoid impurities like oil, grease, dirty or oxides /mill scales and any trace of iron salts by using hydrochloric acid (Hcl). It may be carryout through any methods using either acid, alkaline baths or in combination to provided adequate effective product by addition of sufficient inhibitor. Mostly the available acid amount up on 5- 10 % Hcl, and the main target is to efficiently aggress and dissolve rust and mill scale. Defects can occur in pickling section due to improper process parameter, insufficient acid concentration, unskilled operator and improper box etc. According to those and other related parameters different types of defects, occur in pickling section Valentina Colla (2011).
7 | P a g e
1.6.1 Under Pickling This type of defect occurred through acidic concentration is weak or increase the amount of iron salt in the cleaning batch. To achieve standard quality of steel product amount of iron salt under normal parameter should not be greater than 8-10% in the bath. In the case of unbalancing acid concentration in the batch, another defect is happen like black spot. The concentration amount less than standards, poor surface cleaning will be happen and pass iron or other hindrance in to sheet product and it is challenged because higher amount of Hcl may attack sheets, in other side loss much cost because if the sheet is damaged it might be need rework or automatically reject L. Mraz1 (2009).
1.6.2 Over Pickling In order to fabricate quality of sheet surface balanced amount of inhibitor is added in pickling section but high rate inhibitor has effect in sheet. Due to this reason increase sheet coating weight so, excess amount of zinc is consume and accelerated attack of molten zinc with the steel surface. Inhibitor is chemical substance that used to reduce or prevent corrosion or other imperfection. One type of corrosion inhibitor that helps to eliminate metal dissolution due to corrosiveness and aggressiveness of acid solution and prevent outcome at the time of remove scales and other obstacles to effectiveness is termed as pickling inhibitor L. Mraz1(2009).
1.7 Sources of Defects and Rejections
Process parameters consideration Process parameter has a great rule in order to decrease or increase the quality of product. The necessity of proper selection and adjustment of process parameters are used to produce defect free product and increase the quality of outcomes through properly and effectively check before apply and run the process. The types, amounts and characteristics of process parameters are very critical to effectiveness of process. Those process parameters differ from one process to another since, proper usage is necessary. Process parameters are; pressure, temperature, speed and immersion time etc. Raw material batch In production process, the system contains three steps; input, process and output. For any organization, raw material type and quality has a great advantage to increase the performance of
8 | P a g e
quality requirements. The available input quality is not standard the final product is not entrancement or non-smug. To produce desirable quality product proper selection of input is first essential requirement because the final output quality is examined by raw material efficiency. Because it has a major contribution to the occurrences of different types of defects, scraps, reworks and rejections. Surface preparation In order to provide defect free product proper surface preparation is basic requirement. Kreibich (2007) states about good surface can be prepared through apply proper cutting, welding, cleaning, etc. The removal of organic substances (oils, fats and grease), corrosive products and roughening surface according to their requirements lead to improvement of conditions, quality and quality galvanizing Inspection mechanism Proper inspection mechanism is the most essential and power full method to investigate quality, reduce brittleness, rework, waste and defect. It might be apply in different production processes or departments for better inspection. The inspection mechanism in coating line used to determine thickness, uniformity and appearance by using simple physical visualization and laboratory test. The inspection method and instruments are not scientifically proofed much amount of products may be loss since, to reduce non conforming items as much as possible properly designed instrument is necessary in each production departments. Steel chemistry To obtained higher efficiency and performance of product especially in galvanized process, steel chemistries have major contribution including; aluminum, zinc, silicon, manganese, phosphorus, tin and lead. However, silicon and phosphorus has influences, if the amount of silicon is too much, the result goes through coating defect including thicker coating thickness. Silicon added during the steel-making process to deoxidize the molten steel. Saravanan and (Srikanth 2018) determine the amount of steel substrates in order to reduce galvanizing defect and increase quality of items as per customer satisfactions. The recommended silicon amount to steel is either less than 0.04% or between 0.15% and 0.22%. Similar to silicon, the presence of phosphorus influence the reaction between molten zinc and steel. Phosphorus level over than 0.04%, happening of matte gray coating appearance and a rough surface with ridges of thicker coating. The amount of steel chemistry bath in HDG are 0.18- 0.20% Al, 0.10-0.20% Pb while restricting
9 | P a g e
Fe level is 0.30-0.40% maximum for achieving high-quality coating. Al has major function of increasing brightness of sheet and reduces dross formation by forming an oxide (Al2O3) in surface of molten zinc Saravanan and Srikanth (2018).
1.8 Defect Reduction Methodology
1.8.1 Six Sigma Method Six-sigma is an organized and systematic process improvement technique to minimize defect and achieve stable and predictable result. Antony (2005) told about six -sigma, is a powerful business strategy that helps in yielding a dramatic reduction of waste, defects, errors, or mistakes in service processes. Gutierrez et.al (2017) state that, six-sigma is the strategy of continuous improvement of the organization to find and eliminate the causes of the errors, defects and delays in business organization processes. Six-sigma is a powerful methodology that was develops to accelerate quality improvement in service sectors by focusing relentlessly on reducing process variation and eliminating non-value added steps or tasks Kwak and Anbari (2004). Kholopane (2016) describe the necessary tools to apply six sigma approaches (DMICA and DMIDV). DMICA tools explain as define, measure, improve, control and analysis respectively. Define phase used to define the work objectives, boundaries and choices, a team and establish metrics to monitor the path to achievement of goals. Measure phase used to measure the existing system of the process to define correctly the base line. Improvement phase used to improve the area that is most familiar to defect, error or variations in the process or products. Control phase used to control the new systems and establish the plans and procedure to ensure that the changes sustained. These tools used to give answer to what are the areas that need to improve in order to overcome the obstacles that are hindering the progress of employees under operation. Linderman et.al (2003) describes flow chart for DMICA quality improvement process described in Figure 1.1.
10 | P a g e
Process Yes Define capability ok? No
Measure Analysis
Redesign YES Modify No Improvement Control design?
No Yes Process capability ok?
Figure 1.1 Flow chart of DMAIC tools (Linderman et.al 2003) The other tool for implementation of six sigma is DMADV (define, measure, analysis, design and verify) the meaning of this method is the same as the DMICA tool while the only difference is D and V (design and verify). Design phase apply to produce new process that will be provide better product or a way of creation or modify new technology to reduce non value added to improve the final income. Verify used to verify or cheek available process is sufficient or determine its effectiveness for future extensions. It is a part of monitoring and a part of simulation. If the plan to be implement must try to simulate, to cheek the error is eliminate or not. The principle of six sigma methodology is to identify the problem and remove the wastage, defect and other related non-conforming units to improve the process and final products and finally reduce defect amounts in 0.002 part per million (PPM) and 99.9997% product free from defects.
11 | P a g e
Table1.1Standard values of six- sigma tool Sigma Mean on target DPMO Process mean DPMO Cp(center) shifted 1.5σ Cpk(not centered) 6 2.00 0.00197 1.50 3.4 5 1.67 0.57330 1.17 233 4 1.33 63 0.83 6210 3 1.00 2700 0.53 66,811 2 0.67 45,500 0.17 308,770 1 0.33 317,311 -0.17 697,672
1.8.2 Process Capability Analysis and Indices PCA helps to estimate, control and minimize the variability and evaluate the process data follow in normal distribution and process is stable or not. This tool can applicable in many manufacturing area in process, product design, product cycle and manufacturing planning to determine the ability or verify its performance. The basic backbone help to measure process capability is capability indexes to proof if the process is capable or not and specification limit has their own values because, the analysis of capability index depend on SL. Capability indices Capability indices are a unit less measurement used to provide quantitative measure of performance and potential of process. Capability indexes are Cp, Cpk, Cpkm and Cpm. Cp and Cpk are the most capability index used to measure the ability of process for manufacturer at a given product.
Table1.2 Capability index definition (Adeoye2013) and (Yerriswamy 2014) Capability Equation Usage index
Cp Cp = Describes that process is capable and process output
approximately normally distributed
Cpl Cpl = Describes capability for specifications for lower limit
only
12 | P a g e
assume process output approximately normally distributed
Cpu Cpu = Describes capability for specifications for upper limit
only assume process output approximately normally distributed Cpk Describes whether process, is capable or not consider CPK=min ( ,
the process centered between specifications.
Cpm Cpm= In this case describes, capability around target T,
always assume process output approximately normally distributed C In this case describes, capability around target T, and pkm Cpmk=
accounts for an off center process mean assume process output approximately normally distributed
The first process capability index appearing in the engineering literature was presumably the simple precision index Cp Juran et.al (1974). This index used to consider the over all process variability relative to manufacturing tolerance as a measure process precision. If the value of Cp is one, not more than 2700-ppm defect occurred and Cpk level equals to1.33 the defect rate drops to 66 ppm. To achieve less than 0.544-ppm defect rate, Cpk level must be equal to1.67 and Cp is
1.33. Cpk and Cpkm indexes used to examine multi- process performance analysis. Montgomery (2001) proposed about PCIs such as Cp and Cpk are quantitative measurements to evaluate the process capability. Process capability indices also depend on the process standard deviation (σ) and mean ( ). Huang (2002) and Chen (2006), states about the most frequently used univariate PCIs including CP, CPK, CPM, and CPMK proposed in the manufacturing industry to provide numerical measures on process capability and performance that are effective tools for quality assurance.
13 | P a g e
Process Capability Rules of Thumb 1. If Cp >one, process is capable (product will fit between customer’s upper and lower specification limit if the process is centered). 2. If Cpk >one, process is capable and centered between the LSL and USL. 3. If Cp = Cpk the process is centered at the mid-point of the specification limits. 4 If Cp >Cpk the process is off-center. 5. If Cpk =1.66, the process is barely capable. 6. If Cpk Table1.3 over all capability indexes and their meaning (Belokar, 2012) Index Estimation of process Classes or process categories Cp cpk Process is inadequate, new 6 design or process parameter must chosen Cp=Cpk Process is placed exactly at the 5 center of specification limit Cp<1 Process is incapable 4 1 Cpk>1.33 Process is satisfactory enough 2 Cpk>1.66 Process is very satisfactory. 1 1.8.3 Statistical Quality Control l (SQC) SPC is analytical decision making engineering tool provide away of identify the process capability to produce product with the requirements and control the continuous improvement process by using different statistical tools like soft ware, histogram, parteo chart or control chart. Sokovic et.al (2009) noted that the basic quality control tools can be used from the beginning of the product development process to the last phase of production and delivery be sides the continuous improvement process. Varsha et al (2014) provides introduction about 7 QC; those are cause and effect diagram, control chart, cheek sheet, flow chart, histogram, Pareto chart and scatter diagram explained below. These Tools comprise graphical method help to transform the 14 | P a g e data into easily understandable diagrams or to identifying and analysis the problem easily and leads to developing solutions which aim towards quality improvement. Flow chart is a graphical approach to show the sequence or order of the system or operations to reduce the frequency of defect, rework rejection. By using this tool, we can represent the procedure/step of process with the helping of pictorial symbols. Histogram applied in many research areas to investigate and identify the underlying distribution of the variable explored. It is very useful tool to describe a sense of the frequency distribution of observed values of a variable. It is bar chart visualizes attribute and variable data of a process, also assists users to show the normality of distribution data and the amount of variation within a process. Cheek sheet designed to fast, simple and enough forms with certain format that can aid to record desired information data in a firm systematically and arrange the data for utilization later. It is a structured, prepared form for collecting and analyzing data and adapted for a wide variety of purposes. Cause effect diagram explained by Behnam (2017) is a problem solving tool that investigate and analyze systematically all the potential or real causes that result in a single effect. CED identifies many possible causes for an effect or problem, sort ideas into functional group and a graphic representation of relationship between a given product outcome and all factors affect outcome. CED explained by causes and sub-causes, the main causes are men, material, machine, method, environment, measurement and the sub causes are other causes source for main causes. Control chart tool used to distinguish process variation between the special and common causes and determine stability or non-stability of process. Process stability is a process has displayed a certain degree of consistency in the past and expected to continue the future. Common cause variation is a result of numerous and ever-present differences in the process while special causes may be the result of causes which are not normally present in the process. This tool also used to examine whether or not the process is in statistical control or not and show the process lying between specification limits. Two classes of control chart are attribute data and variable data. Variable data demonstrate values gate from measuring continuous variable. Attribute data also demonstrate data result from counting the number of item in a single categories of similar items. X-bar and R control charts are functional to controller mean and variation of process. In order to estimate X-bar and R control charts mean, range and grand mean is an important parameters obtained from the collected data. 15 | P a g e Mean ( ) the ratio of individual sum to number of size and represent as: 1 Where, n represent size of the sample of observation and k represent number of selected samples of subgroups or average of the observations in the i -th sample (where i = 1, 2... n) grand average or average of the averages (mean of mean). x1, x2, x3……xn represent individual sample, . Control limit for bar chart the upper and lower control limit calculated as follows: Where represent the mean of ranges, mean of mean (grand mean) and A2 is factor of mean from control limit standard table depending up on sample size (n) listed in table 4 and R1, R2, R3…. Rn represents the number of individual ranges in each sub group. Table1. 4 constant A2, d2, D3 and D4 values (table 8A - variable data &AIAG manual for SPC) Charts for X bar R chart average Control limit Divisors to Charts for control factor Estimate σx limit(d3&d4) Subgroup A2 d2 D3 D4 size(n) 2 1.880 1.128 - 3.267 3 1.023 1.693 - 2.574 16 | P a g e 4 0.729 2.059 - 2.282 5 0.577 2.326 - 2.114 6 0.483 2.534 - 2.004 7 0.419 2.704 0.076 1.924 8 0.373 2.847 0.136 1.864 9 0.337 2.970 0.184 1.816 10 0.308 3.078 0.223 1.777 11 0.285 1.744 12 0.266 1.717 13 0.249 1.693 14 0.235 1.672 15 0.223 3.472 0.347 1.653 Similar to the above analysis of control limit of mean, the upper and lower control limit for range can be calculated as; The values of D3 = zero and D4=2.114 Ducan(1986), where D3 & D4 represent the lower and upper control limit for R chart respectively. Pareto diagram is the other SQC tool, named after Vilfredo Pareto an Italian economist, is a specialized bar graph that used to show the relative frequency of events such as defects, repairs and rejection in the descending order and used to classify and identify defects according to percentage significant. In order to determine the percentage amount of defect we can apply the following formula. Percentage of overall defective pieces in week, month or year calculated by the equation: Total production number of pieces per day×7, 30 or 365. Total numbers of defective pieces determined by adding defective items occur in month, week or years. 17 | P a g e When the gathered data collected in two or above two week, month or year the average percentage of defect calculated by using; Where n, represents the number of month; this analysis used to determine the amount of defect percentages among the defective amounts. This indicated by the help of statistical tool of parteo chart as represented in the figure 1.2. Figure 1.2 diagram of Pareto tool (source Kirti Singh 2016) 1.9 Statement of the Problem Efficient and effective usage of production improvement techniques are very important to obtain the quality of manufactured product and to reduce wastes. The problems are raised on product quality in manufacturing industries due to various degrees of variations (process variation, defect, scrap or rework) and inefficiency, which can be root cause of rejection. These can be achieved through proper selection of quality raw materials, proper usage of machine and 18 | P a g e technique, proper material handling and plant lay out are the most common way of producing product with the requirements. Now a day, many manufacturing companies strive for the quality and required of their products but, failing short of production rate and quality of requirement due to process incapability. The reasons may be poor machine efficiency, poor method /techniques applied, unskilled employee, etc according to this challenged the companies is under its expected optimum profit. Based on the observation at Adama steel factory production line has problem such as: Wrinkling, delaminating and shaving of coating layer from sheet in different sections. Bending /deflection and wrinkle of sheet (loss product shape). Poorly cleaned products are observed. Due to poorly cleaned surface preparation and wrinkle of sheet can not apply uniform and smooth coating thickness and good appearances of final sheet. 1.10 Objectives of the study 1.10.1 General Objective The main objective is to investigate the capability of the process and identify possible causes of defects and rejections on galvanized sheet production line and recommending possible solution to Adama steel factory. 1.10.2 Specific objectives i. To study the capability of galvanization process whether or not the existing process meets the specification as per the standard of the organization. ii. To investigate the possible causes of defects and variations of galvanization process. iii. To apply statistical method to identify the possible causes and percentage of defects of a process in galvanizing production line. 1.11 Significance of the Study The significance of the study is multi-face, it improves the process capability of Adama steel factory, improve productivity and competitiveness and increase profit and customer satisfaction 19 | P a g e by reduce rate of rejections and defects. The profitability of one company has a great contribution to firstly owner itself, secondly employees and finally the country. 1.12Motivation of the study During my internship in ASF much amount of rejection was observed in different production department that affects process capability and quality of outputs. This motivated me to study the major causes of defects and rejections and their impacts in ASF production departments. 1.13 Scope of the Study The study was limited to the process capability study, identification of source of defects and rejections and defect analysis through statistical method; Process capability indices and statistical quality control tools (histogram, cause and effect diagram, control chart and probability pilot). The capability analysis done through collecting and analyzing sample data by using Microsoft excel and Minitab 17 software. 1.14 Limitation of the study Almost all workers and operators were busy due to continuous production line because, many pieces produce at a time,so its make challenge to discuss and gain required information from operators and teams. Science, ASF was recently estabilished there was no docemneted resrch work related to capability and defects of galvanization process and reluctance to provide sufficient data from industry side. 1.15 Thesis Structures The thesis study organized in six chapters and the outlines described as below: Chapter1: Describes about introduction, statement of the problem, objectives, significance, motivation, scope and limitation are disuse briefly. Chapter2: Describes about literature review mainly presents literatures supporting my thesis study like overview of research work done on causes and remedies of defects in GL, back ground 20 | P a g e of the company, process capability study, statistical quality control tools and finally discuss related ideas about galvanization. Chapter 3: Describes about methodology, this part refer to away of thesis is done including method of data collection and analysis, soft ware to study capability analysis and required material for measurement sample data. Chapter 4: Describes about result and discussion, data validation statistical soft ware Minitab 17 and also discussion and interpretation of the results. Chapter 5: Describes about conclusion and recommendation, in this chapter briefly discussed about conclusion, recommendation for the future suggestion in order to reduce defects and loss amounts. 21 | P a g e CHAPTER TWO 2. LITERATURE RIVEW 2.1 Introduction In the review part of the thesis, detail discussed about different related works regardless to various defect types and sources of galvanization process and process capability study in order to determine process capability or process performance. 2.2 Theoretical backgrounds 2.2.1 Hot Dip Galvanization Defects Pranay et.al (2012) studied on minimization of dross defect formation during continuous galvanized line in steel industry to reduce waste material. The researchers work through developing MATLAB soft ware program in order to find the optimum percentage of parameters used to minimize the source of waste and develop simulation mathematical model improve suitable parameters to reduce dross formation. Finally, they found the causes of dross defect and influence of improper parameters on the quality. The researchers provide recommended idea to reduce dross like properly adjust strip entry temperature (470 ) and effectively applying aluminum content in the bath (0.18%wt). Singh and Singh (2012) reported on defect of galvanized coating on steel wire. The main objective was to investigate the causes of lump defect through EXDA experimental procedures. The finding shows lump deposition in steel caused for shaving in galvanized coating and shaving problem galvanized wire due to catalytic effects of surface segregated Si on reactivity of steel with molten zinc resulting in lumpy deposition of coating. The research did not indicating the relative amount of lump defect with shaving that helps to estimate the impact of lump in galvanization process. Saravanan and Srikanth (2018) investigate the surface defects and their control in hot dip galvanized and galvannealed Sheets. The researcher focused on types of galvanized defects and their causes especially surface defects. The main objective of the research is to study the causes of surface defect and finds the control mechanism in GL.SEM, EDS and optical microscopy 22 | P a g e instruments used to study surface defects. The result of this study conclude that the main causes of most surface defects are poor substrate surface quality, insufficient strip surface cleaning, poor bath chemistry management and inadequate line equipment maintenance. Nevertheless, in the research the final indispensible point is not including that is not any mechanism is applying or suggests for controlling and avoiding surface defect in both galvanized and galvannealed sheets. Marcello et.al (2017) studies the influence of manufacturing process on defects in the galvanized coating of high carbon steel wires. The main target was to analysis the failure of galvanized high carbon steel wires, which developed coating cracks. The reporters can investigate the formation of crack on coating before torsion test by visual inspection and examine the origin of these cracks, systematic metallographic investigations were performed by means of optical and scanning electron microscope. They found factors related to failure relatively high content of silicon in the steel and unsuitable cooling rate of the rods at the exit from the galvanizing bath. While the researches not recommend any solution that help to reduce or eliminate this coating problem. Michal and dostal (2014) study the adhesion of zinc in hot dip coating process. The main aim of the study was to proposed methodology for adhesion test of zinc coating by nondestructive diagnostic method which would monitor characterize progress of coating delimitation of hot-dip zinc from basic material in way to adhesion tests to verification quality adhesion of zinc coating to investigates the basic element which affects both adhesion and quality in coating. The researchers can determine the result of factors affect the coating adhesion which including surface adjustment, chemical composition, straining and acoustic emission. While this paper has large limitation, that is no proper result found according to their objective in general, there is miss-mach of their target and their work. Taixiong et.al (2014) investigates the causes and remedies of bulge particle defect in surface of hot-dip galvanized sheets. The aim was to investigate the problem of bulge particle defect on the surface of hot-dip galvanized sheets; the causes analyzed by investigating defect characteristics and surface cleanliness of cold-rolled steel sheets .To identify the defects use microscopes of X- 650 scan electron microscopy and EDAX spectrum analyzer to analyze the microscopic characteristics of the defect. Finally they founds the causes of defect that is poor surface cleaning in the decreasing and pickling section, residual of iron in the surface and finally provide suggestion to prevention of bulge particle defects. 23 | P a g e Azimi et.al (2012) discussed on metallurgical analysis of pimple defect and their influence on properties of hot dip galvanized steel sheet. The main aim was to determine the main causes of pimple defect and their impacts on mechanical properties of steel. Using SEM can identify microstructure, main causes and identify the impact of defect on mechanical properties use tensile test and corrosion resistance of defect. The result show the major causes of pimple defect are adherence of metal chips and embedded particles to the sheet surface before the formation of zinc coating. Nevertheless, after determine the impact of corrosion resistance, mechanical properties and quality reduction of defect there is no any possible recommendation guided to eliminate and prevent this defect. Lmraz and Lesay (2009) reported on Problems with reliability and safety of hot dip galvanized steel structures. The main target was to study the influence of chemical composition in the hot dip galvanization process through regression analysis by taking different amount of zinc and other element. Additionally study the fracture mechanism in HDG process, by taking samples study the failed steel structures after hot dip galvanizing. To examine the fracture mechanism of steel structures investigated using optical microscope METAVAL. The result of this study shows that coating process affects the reliability of the required product due to welded structures, failures appeared before putting the structure into service and were associated with higher hardness, diffusible hydrogen, liquid zinc and local residual stresses as the consequences of welding. Finally put the factors responsible for failures of zinc-coated products including local stress, high hardness and diffusible hydrogen. Nevertheless, did not put any method or mechanism is suggesting for the future work to reduce failure and failed product. 2.2.2 Process Capability Analysis Tanvir et.al (2013) studies an application of cause and effect diagram to minimize sewing defect. The main objective was to reduce defect percentage that will reduce rejection and rework through applying approaches of cause - effect diagram and Pareto analysis. The experimental study work on two types of product; Woven shirt and Woven pant through data collection, experiment analysis by 25 sample defect types and five production section and apply cause and effect diagram and parteo analysis of quality tool determine the wastages amounts and finally provides suggestion to improve their productivity. The result reflects in 115 major concerning areas, which are responsible for 71.40% defects. While the mistake of is done, all the things for one product i.e. woven pant. 24 | P a g e Yerriswamy et.al (2014) studies on process capability analysis tools for process performance measure and metrics. The aim is to conduct PCA for boring operation through applies the steps of identified process capability indices, operation, material, and critical characteristic of the process. For 20 subgroup of sample size of five, all relevant calculations are done and for validating critical assumptions x-bar, R-chart, histogram and normal probability plot developed using Minitab 14 software. Finally, process capability evaluated by shifting process mean to specification mean by collecting data again and all analysis has done again after shifting the process mean and rejection scrap and rework of gear reduced. They found the process capability of rolling process is incapable, but there is no identified optimum machining process and there is no suggestion for improvement for future. Hewan (2016) investigate on minimization of defect in sewing section through DMAIC methodology of six - sigma. The main objective was to investigate defect types and propose a basic solution to minimize or reduce defects to better productions. The study was done through the application of phase of six sigma approach with critical six sigma tools by collecting and analyzing data by the helping of Microsoft excel and Minitab for forming ANOVA, Pareto diagram, binomial process capability ,control chart and DOE from the selected companies. The result of this study reflects the range of defect before and after optimization that is 3.51852 to 1.51852 respectively. Bharat et.al (2016) studies on statistical process control tool in small manufacturing company. The main objective was to explain the function of SPC tool in order to reduce variation and rejection and to improve quality. The researchers apply a method of data collection and analysis, component selection and specification review using CED, control chart and PCA. PCA implemented in different standards of drive shafts to decide the capability (Cp &Cpk) for 20 subgroup of sample size 5, data collected at various stage of manufacturing process for diameter of shaft. Finally, they conclude the process is incapable in all sample data and through applying SPC the rejection rate reduced from 9% to 2%. Nevertheless, not use additional parameter, interaction effects studied and simply process capability studied without software analysis. Jospeh (2013) discussed on control charts as a productivity improvement tool in construction. The main aim was to investigate the application of control chart to facilitate productivity improvement in construction process and the study done through collecting data in random sampling method on cycle times for excavation, forming, and reinforcing processes. He founds 25 | P a g e all the interventions showed improvement, only one decrease in the mean cycle time during the excavation process proved to be statistically significant, while reliable significant results were not found, this study demonstrates the principles of using control charts in construction to identify process inefficiencies. Adeoye et.al (2013) reported on process capability analysis as means of decision making in manufacturing company. The main objective was to study process capability of the process through examine satiability of process and investigate whether the specification is properly centered or not. The work finalized through X bar R control chart analysis by taking sample data by the parameter of weight of the drug in gram manufactured in five different machines. There is difference in the weight of drug produced due to variation in machine. After necessary the calculation, conclude the process incapable and off centered. However, use only one parameter to study the capability of process and no cause and effect diagram is apply to show causes that can enables to inadequate the process and corrective suggestion and recommendation is not providing. Gabriele and Stefano (2017) reported on review of the fundamentals on process capability, process performance, process sigma and process sigma split. The target was to investigate the fundamental concept and application of capability indexes to evaluate the process yield and performance. The study done by data collection and experimental analysis on nine sample size and 1125 sample data collection on measuring the diameter of ball bearings. After accomplish all relevant analyses they found the result that shows all index values below standards or the process is incapable. Vaibhav et.al (2013) studies on analysis of sand casting drop defect. The main target was to analyze and minimize the rejection level of sand casting drop defect in automobile cylinder through apply different statistical tools like why-why analysis, cause and effect diagram, cheek sheet and cause-and-effect matrix. Through the application of why-why analysis tool the researchers get the result that show, the level of reduction in rejection of sand drop defect from 37.17% to 16.3%. 26 | P a g e Table 2.1 literature summary Authors Finding Limitation name Adeoye et.al Studies the process capability Not applied soft ware for capability (2013) through statistical tools to examines analysis. stability and centering of process. Used one parameter for analysis. Not applied any statistical method. Yerriswamy Studied the process capability Not provide any suggestions. et.al (2014) analysis through statistical tools. Not defined causes for process incapability. Bharat et.al Studied the process capability study Used one parameter for analysis. (2016) through statistical process control Not applied soft ware used. tools Not showed the possible causes for incapability. Azimi et.al Studied on causes and influences of Possible recommendation and suggestion (2012) pimple defect. idea are not recommended to eliminate and prevent. Identification was limited by one defect of HDG process. Saravanan Investigated of surface defects and The research was not fit with its objective and Srikanth their control mechanism. , any method were not suggeseted to (2018) control defects. Depending up on the literatures, limitations are visualized in different research works, the study is limited on one or few defect identification, use one parameters for analysis and capability analysis done for other manufacturing factories. In addition, the capability analysis mostly focused on investigation of capability, but if the process is incapable only showed the result, rather than investigates the sources that can enable to process was inadequate. Therefore, this reserch worked on collected and analyzed sample data and identify different types of defects, main sources of defect and rejection and their influences on the capability on process and required products. 27 | P a g e CHAPTER THREE 3. RESERCH METHODS,TOOLS AND MATERIALS 3.1 Research Method In this chapter, different tools or techniques were applied to perform the study in order to study capability of process, defect identification and determine rejection rate in a particular production line in steel manufacturing company. It includes data collection, literature review, experimental measuring by digital micrometer and analytical balances, various quality tools specially control chart, Pareto analysis, probability pilot and cause-effect diagram to decide whether a process is capable or not finally provide recommended suggestion for the company to solve the problems. Additionally to make PCA some steps are required to be complete the thesis: i. Select critical parameters: in order to reduce challenge of study selection of critical parameters before starting the study is decisiveness. ii. Collect data: data collection needed to establish and assure assumed problem after research problems are defined and generate solution depending up on input data. iii. Establish techniques to analyze data: different approaches are applied to analyze collected data and calculate capability of process to get the result through experimental testing, apply any software or fabrication. iv. Identifying the sources of defect and variation: identification of basic source of variation and loss involve determine what process factor affect natural process spread and process centering. v. Provide recommended point: after collect and analyze data and determine process capability some suggested solution is essential to improve the process but it is acceptable when the process is inadequate or not fit with upper and lower customer specification limit and presences of defect and rejection rate is much or above 0.002% in six sigma level. 28 | P a g e 3.2 Data Collection Data collection is an activity or way of gathering useful information from different source in the form of direct or indirect ways. It is enables a team to formulate and test working assumptions about a process and develop information that will lead to the improvement of the key quality characteristics of the product or service. It is also improves decision making by helping focus on objective information about what is happening in the process, rather than subjective opinions. 3.2.1 Primary Data Collection Primary data collection is a system of obtained relevant data from direct sources and data collected from the communities. The user use different mechanism that help to obtained the most critical and powerful information in easy and suitable way by using; observation method, interviews of skilled person and questionnaires, surveys and focus group. Form those methods of primary data collection, the first two are help in this study that is direct observation and interviews and questionnaires. Observation becomes a scientific tool and the method of data collection for the researcher, when it serves a formulated research purpose, is systematically planned and recorded and is subjected to checks and controls on validity and reliability(C.R. kothan 2004). 3.2.2. Secondary Data Collection In this type of data collection, data gathered indirectly from different sources. Such as reference books, different journals and article papers, manuals and internet sources. 3.3 Data Analysis and Interpretation The collected data analyzed using Microsoft excel and Minitab 17 software through the helping of statistical tool like quality control tool and process capability analysis. After getting, the relevant data other related activities done using the analysis techniques and specified software. Calculating required collected data statistics. Validate the critical assumptions using control charts, histograms and probability plots. Determine the process capability or incapability. By applying SQC, tools identify the possible root causes of defects and rejections. Suggesting optimum defects with their causes and losses minimization methods. 29 | P a g e The capability study done in the parameters of coating weight and thickness. For every critical quality characteristics, the data grouped in to two sample data in 20 subgroups of five sample sizes. 3.4 Sampling Technique In order to obtain easy and simple work in measuring study proper preparation of sampling method before collect and analysis data is important. In order to be able to record all possible influences acting under standard production condition Konzer (2005) suggested the following condition should be considering sampling. The random samples are taken from ongoing production process under standard production condition i.e. five samples are taken from every shift per day. During random sampling, the parts with considered characteristics are taken as much as possible in direct sequence. For each critical quality characteristics, the data grouped in minimization of four samples in 20 groups or five-sample size and 20 subgroups. The statistical data’s are collected by random sampling techniques due to considering factory access and time access. The reason is the process in production since, available measuring instrument used for their purpose in order to investigate and control the quality. 3.5 Measurement Method In experimental study work, different measurements are applied to measure precisely the required parameters in effectively and scientifically in order to get relatively accurate result. The instrument used for this study are provided from ASF quality control department, the critical parameters of this thesis are coating weight and thickness of galvanized sheet to study/ investigate process capability or performances. The measuring instruments are Vernier caliper, digital micrometer and analytical balance. 3.6 Materials Minitab software 2017 30 | P a g e Steel sheet specimen to measure and record required values of parameters of coating thickness and weight in order to study the capability of the process and identify the defect and rejection sources. Different types of instrument to measure and read the evaluation parameters; digital micrometer and analytical balance instruments. 3.7 Methdology / Project Flow Chart This flow chart indicates the flows or steps of work to accomplish the main objective of the study and shortly explain the methods. Select the critical Parameters and production line Primary data Data collection Secondary data Process capability analysis Establish techniques for analyzed and defect rate analysis PCIS Analyzed process data to study capability SQC tools and determine defect and loss rate Minitab software Identify sources of variation and defects In different department by collecting data Provide suggestion to better solution Figure 3 .1 project flow charts 31 | P a g e 3.8 Capability Indices Analysis The capability indices are cp and cpk used to calculate the capability of process by using other parameters analyzed from collected sample data; σ, USL, LSL. In order to analyzed capability indices mean and standard deviation are important parameters and gate from collected data. Rezaie et.al (2015) states the process capability indices CP and CPK that are the major judgment of the process. Cp is process capability index used to indicate the potential performance by relating the natural process spread to the specification (tolerance) spread. Where USL, LSL , σ, UCL, LCL are upper specification limit, lower specification limit ,standard deviation, upper control limit and lower control limit respectively. 2. Cpk (two-side specification limit):- process capability index is an adjustment of process capability through: According to Ranganadha Kumar, we can calculate the values of CPU and Cpl Moreover, we can calculate capability index in one side of specification in either maximum or minimum side. Cpk (one side of specification) specification might be in upper or low side. Similar to above capability analysis, Cpm and Cpkm calculated by the equation chen (2006). 32 | P a g e , If the values of Cpk> Cp the distribution is centered otherwise it is off centered. 3. 9 Process Capability Assumption Process capability used to measure process variability relative to SL based on three key assumptions. Such as: The process stablity or in statistical control. Quality characteristics followed with normal distribution and Large sample data and target value and specifications of quality characteristics are specified. The process is out of statistical control (unstable) it is special cause of variation is assigned; the process cannot be improved because the variation is unpredictable. 33 | P a g e CHAPTER FOUR 4. RESULTS AND DISSCUSION 4.1 Introduction In this chapter the results were discussed in detail. Investigate the main defective production sections, investigate capability of process and determine amount of defect rate according to the company standard. 4.2 Defects of Hot Dip Galvanization Process in ASF 4.2.1 Coating Defects Dross Defect Dross is accumulative of arduous impurities floating on a molten metal and mainspring in the final products. Causes of dross defect The quality of input zinc: Different types of zinc input with different performance range is available in the international market, while due to improper selection makes many problems such as poor quality product, increase extra expensive through rework, waste , defect, increase loss and rejection, reducing suppler contestant etc. Superfine input material provides great contribution to produce outcomes with their customer need and to enlargement their effectual in the world markets. Therefore, to prevent and control product and process cross bar the selected input must be no vulgarize in design parameters and other necessary required points. Residual of iron in CR sheets: Iron residual in steel through improper cleaning of sheet due to imbalance of alkaline or acidic solution, poor washing system and operators. This dross defect first formed in the zinc batch and pass in to final product during coating listed in the following photo (a) and (b). These photos represent the influences of dross defect in input zinc and quality final outcomes. This has a great impact on facility of cost of material, machine, labor and quality. The arrow indicates the places that are more damaged by dross defect. 34 | P a g e Damaegd sheet by dross Dross cumulative area (a) (b) Figure 4.1 photos of dross defect: (a) damaged sheet and (b) dross cumulative. Ash defect Zinc ash formed in galvanizing process as the steel immersed in the zinc bath due to air contamination; in zinc box, zinc is melt and burn, dried and converted in to ash show in the above photo 4.2. The ash formed skimmed off the surface of the molten zinc prior to withdrawing the sheet from the galvanizing bath. Ash may leave a dull surface appearance or a light brown stain after removal. Zinc ash defect Figure 4.2 photo of ash defect Lead separation Due to different reasons lead pass in to steel sheet product and make deficit in cost of machine, labor, equipment and material. This problem happens when the amount of zinc is less and Pb is 35 | P a g e higher and the impacts are directly related to appearance, quality and service time. This problem can prevent through apply scientific instrument to know the amounts rather than visual inspection. Sources /causes Stabilizer roll: This problem occurs when stabilizer roll not properly tighten and sink roll push much amount of Pb, go out in steel sheet. Stabilizer roll equipment is functional to control the amount of lead and zinc conjunction with the sink roll to establish the strip pass line through the galvanizing bath. It used to enable stable, center and to reduce vibration at the production section. The main function is to provide a consistent and stable strip pass line through control by air wiper to provide a uniform coating mass distribution. This roll operates on the opposite side of the strip at the sink roll and the strip does not have the same wrap around, therefore this roll may require more frequent changing than sink roll. Less amount of zinc: When small amount of zinc added in the kittle at steel immersed in zinc bath, product has less amount of zinc that is challenge to good appearances, durability and quality. Zn amount may be less due to operator error. The method used to find out the amount of zinc in the kittle through simple visual inspection. The melting temperature range between 460- 470 the zinc amount is enough but decreasing and increasing from listed value addition of Zn must required. 4.2. 2 Wrinkle Pattern Defect It is a deformation of shapes and sizes due to improper selection of parameters (water temperature, wiping pressure and material quality) in process of production and occurred due to different reasons. Sources /causes of wrinkle defect Wiping jet: Wrinkle occurs sheet coated steel strip is vibrating due to wiping jet, and flow of molten zinc is irregular results a wave-shaped flow pattern known as wrinkles. To prevent and eliminate wrinkle generate a solution like monitoring and adjust wiping pressure, balance gap between steel strip and nozzle. 36 | P a g e Water pressure: Proper treatment of water is the most essential parameter in steel organization used to remove dirty, grease, oil, and scale to better cleaning and minimize unnecessary wastages. The amount of water pressure has a great contribution in effectiveness of outcome. The pressure is increased sheets deflect in one side due to applied pressure and dip enter in to the machine, this is causes to sheet deformations and machine damages. pressure also decreased the required amount of water is not fall down properly, in this case production time increase, increase rework and defect and poor surface neatness are happens because of improper removal of impurities. As seen in the following photo much amount of item is loss, because in one coil much amount of pieces is here. Roller conditions: roller is a mechanical device designed to carry heavy loads. The rollers are damaged; problems occurred that affects the sheets and machine. For instance, piston distance difference occurred. Piston distance formation was the result of deformation of sheet shape because the sheet moves in improper direction. The rollers damaged by various reasons as poor layout and input impurity. In order to prevent deformation of sheet rollers maintain and repair in time interval before breaks and investigate performance of input sheet before apply the galvanized process. Wrinkled part Figure 4.3 photo of wrinkle sheet defect 4.2.3 Surface defects Bare spot defect Bare spot is uncoated areas on the steel surface occurred because of inadequate surface preparation and due to reaction between steel and molten zinc vapor when the quality of input cold roll sheets are insufficient and not enough. The presence of bare spot defect was challenge 37 | P a g e to provide uniform coating thickness and quality product, because some times higher or thin coated and often uncoated sheets are produced. Additionally presences by poor surface preparation i.e. improper reduction of oxide scale and poor wet ability of steel surface by molten zinc, welding slag, unbalanced batch composition. Sources of bare spot defect Thickness of sheet: raw material has great value for producing acceptable product with required amount between specifications. The final grade coating thickness evaluated through thickness of input. If the thickness is small, the final coated product is thin, this is also one causes for corrosion reversely the thickness of the raw material is thick with standard it is enable to producing desirable quality products. Therefore, to eliminate bare spot defect cheek the performance of input is sufficient to produce acceptable output or not and adjust wiper spanner before hot dip galvanizing process. Poor surface cleaning: Surface preparation must be good and attractive otherwise it is difficulties to get defect free products. To produce better surface quality apply all the necessary process parameters and materials like water, water pressure, chemical composition rate, machine quality and operator skill. If the water cleanness and water pressure is not smooth, fixed in their specification range and enough the surface is not good and suitable. Low temperature condition: If the entire temperature is too low below standard specification and the batch temperature is unbalanced, the required substrates are not properly melting in required amount and not suitable for coating the steel surface therefore cannot achieve quality item with appearance and lifetime. Generally, the impact goes through bare spot defect. For the occurrence of this defect some causes has a great sketches including over drying, extra aluminum blowout, flux deposition or stains. Over drying and excess Al: over drying was cause for bare spot defect; the drying temperature was very high, at that condition the barrier protection lost flux. This (flux lost) also make another problem like improper adhesion and coating. The impact of excess aluminum in coated sheet was make black and spat appearance. 38 | P a g e Welding situation: In continuous production process, all production line moves without stooping to accomplish the task by using different production mechanism form that welding is the one, often some problem occurred during welding due to thickness difference between two welding coil, machine efficiency and operators. There is a thickness difference between CRS, create error in the time of coating because the final coating thickness evaluate by thickness of CRS. In addition, the welding process is not properly apply blowout might be happen due to get high temperature or steel strip enter in the zinc batch at high temperature. The other cause to blowout is Pre-treatment chemicals penetrating sealed overlap areas through the required vent holes and escaping during immersion in the molten zinc. This impact damages the flux coating and causing localized uncoated areas. To provide resolution pre-heat item prior to immersion in zinc bath to dry out overlap area as much as possible. Pimple defect Pimple defect formed in the surface of steel sheet through demerit of different factor as dross inclusion. The agitation of dross layer occurred at the bottom of the bath or from dragging material through the dross layer. Blisters formed by hydrogen, which is absorbed daring pickling and diffused at galvanizing temperatures. Sources of pimple defect Sink roll: Sink roll rotates to mixed zinc and lead to better-coated product and lead help to protect kittle from damaged. If sink roll is not properly and correctly designed and controlled in production line there will be bare product produced. This equipment damaged due to bolt tightens up and not tighten, entering any dirty, oil and strips of different metal or others. Operators and production technique: To staying and develop any organization employees has a large place when they are rich with knowledgeable and experienced on their working department. Often some problem occurred due to workers through less skilled and carless to operate the work. In addition to operator often, operation technique creates problem through sudden machine stack and sudden problems. 39 | P a g e Scratch and bent Defects Scratch happened due to improper cleaning of sheet, poor wiping adjustment and coated was thicker than formal value. This problem were much costly, because remove all coated after coating. As seen the photo below (a and b) reflects the impact of scratch defect. In (a) indicated the shaving amount less than b, so this product is not totally rejected used in min- work out of roofing but (b) all coats are remove this sheet automatically rejected. Photo c represents bents defect. All those defect impacts on general performance and reduce the capability of the process. (a) (b) Shaving of coats (c) Bent 4.4 photos of (a&b) scratch and c bent defects The other one is bents defect, this defect present in final shaping section due to operator problem by improper setting on their specific baral forming machine. This is not used for roofing purpose because difficulties to good overlapping. 40 | P a g e Table 4.1 summary of defects Processing Types of defects observed Possible cause of defects section Coating Dross , ash, lead separtation Improper cleaning of sheet and poor appearances. Non availability of instrument Operators skill (less experaniced) Pickling Wrinkling of sheet Spray type roller damaged Improper cleaning Pump and valves capacity Shaping bents(shape improper) Operators (lack of skill and knowledge) Others Scratch, Pimple, shaving of Technique, operators, method and raw coating and bare spot material performance 4.3 Defects and Rejection Rates Analysis Defect and rejection analysis used for anymanufacturing organization in order to: . To determine amount of waste and defect during production time in each defected sections. . To reach proper conclusions concerning how to handle defects when they occur or how to prevent them from happening. . To eliminate root causes of defects, scraps and show more defected section . To determine profit, deficit and performances depending on defect rate. . To generates basic solution for better improvement of performance and quality in order to increase profitability and satisfaction. The analysis done on Aug, Sept, Nov, Jan and Feb and in 4 sections including coating, pickling, exit and shaping section. The first sample was done in Aug at the actual production of 485,078.04 pieces(m/ton) per month. 41 | P a g e Table 4.2 collected data of first month defect frequency Number Defect type Out of 18656 (m/ton) pieces for Total no. of defects 1st month frequency 1 Coating 4346 2 Pickling 560 3 Exit 530 4 Shaping 350 5786 After determine the above necessary and important data, calculate defect percentages in each defected sections. For first month, data collected to analysis rejection rate per month use different steps to achieve better results. In first month production of steel pieces per day 18656 m/ton One-month production of steel daily production 26 m/ton /month For one month the frequency of each defects in first month calculated through direct multiplication of daily defective items by 30, this formula help to all five month data analysis: 1. In addition, to calculate the amount of percentage of defect from one month production pieces in coating section to be: The frequency of defects or defects per month and defect percentage of five month data’s are analyses by similar procedures in each defective section. 42 | P a g e 2. 3. 4. S Total numbers of defect calculated from the defects happens after and before galvanized (defects in raw material). For this case the amount of defect before galvanization is 1840 and defects after galvanization was listed in table 4.2. All the five month data analysis done by the same procedures the only differences is the final values. = After determine total no. of defects, then calculate the percentage of individual defects to investigate the most defected sections. 5. 6. 7. % 8. 43 | P a g e In order to determine profit loss due to defective items in each production months. The 1st month profit loss can be calculated as follows: 1st month profit loss = average of defective items of 1st month prices of items = 112,996 +14,560 +13,780 +9100 =150,436 defective items. Profit loss = 150,436 150 =22,565,400 birr loss. Table 4.3 collected data on second month (Sept.) with actual production of 926,017.57 Number Defect type Out of 38,584 m/ton for 2nd Total no. of defect month frequency 1 Coating 50,078 2 Pickling 2378 3 Exit 1980 4 Shaping 1000 55,436 Similar to the above step all parameters calculate from the second month data. In second month production of steel pieces per day 38,584 m/ton Second month production of steel = daily production 24 = m/ton /month For second month the frequency of each defect are calculated in the above formula. Therefore the value of each defects per months are listed under. 44 | P a g e 3. 4. Then, calculate total number of defectives in second month production to determine the overall percentage of defective pieces form the data. In this case, the raw material defect is 2,270. Depending up on total number of defect, determine the overall percentage defect on a month by the equation. After evaluate total no. of defects then, we can calculate the percentage of individual defects. 5. 6. 7. 8. 2nd month profit loss = average of defective items of 2nd month prices of items = 1302,028 +61826 +51480 +26000= 1441,334 defective items. Profit loss = 1441,334 150 =216,200,100 birr loss Table 4.4 third month (Nov) collected data actual frequency of 666,916 .75 Number Defect type Out of 25,650.6 m/ton month Total no. of defect frequency 1 Coating 7743.7 2 Pickling 1102 3 Exit 650 4 Shaping 450 9945.7 45 | P a g e In third month production of steel pieces per day 25,650.62 m/ton Third month production of steel daily production 26 /month For third month, the frequency of each defects calculated values are listed under by apply the same formula the same as 1st and 2nd months. 1. According to frequency of defect, can calculate percentage of all defects in each section. % % 4. After determine all the necessary data then, calculate total number of defectives in third month production to determine the overall percentage of defective pieces form the data. In the 3rd case the total number of defect can be calculated through addition of 1750 MCSR defect in individual defect frequency in the above table 4.4. Depending up on total number of defect in the process, determine the overall percentage of defect in 3rd month. After calculate total no. of defects then, the values of percentage of individual defects are calculated as follows: 5. 6. 3rd month profit loss = average of defective items of 3rd month prices of items = 201,336 +28652 +16900 +11700 = 258,588 defective items. 46 | P a g e Profit loss = 258,588 150 =387,88, 200 birr loss Table 4.5 4th month (Jan) data collection of 996,861 actual production of defect frequency Number Defect type Out of 39874.4 m/ton for Total no. of defects 4th month frequency 1 Coating 12,092 2 Pickling 2100 3 Exit 1106 4 Shaping 1080 16,378 In 4th month production of steel pieces per day meter pieces 4th month production of steel daily production 25 / month For 4th month the frequency of each defects calculated as similar to the above procedures. 1. According to frequency of defect, calculate all percentage of defects in each section. 30.33% 2. 3. % 4. Then, calculate total number of defectives in forth month production to determine the overall percentage of defective pieces of 4th month form the data. In 4th month case defect rate before galvanization is 2060. Depending up on total number of defect, the overall percentage defect of 4th month is: 47 | P a g e After evaluate total no. of defects then, we can calculate the percentage of individual defects. 5. % 6. 7. P 8. 4th month profit loss = average of defective items of 4th month prices of items (m/ton) = 302,300 + 12,500 +27,650 +27,000 = 369,450 defective items. Profit loss = 369,450 150 =55,417, 500 birr loss Table 4.6 5th month (Feb) data collection of 1,080,094 actual production of defect frequency Number Defect type Out of 25,203.76 m/ton Total no. of defect pieces for 1st month frequency 1 Coating 8132 2 Pickling 1920 3 Exit 588 4 Shaping 200 10840 In 5th month production of steel pieces per day 25,203.76 m/ton Five-month production of steel daily production 25 pieces /month For 5th month, the frequency of each defects calculated as In addition, to calculate the amount of percentage of defect from 5th month production pieces in each section as follows: % 2. % 3. 48 | P a g e % 4. % Then, calculate total number of defectives in 5thmonth production the overall percentage of defective pieces of 5th month form the data. In 5th month case defect rate before galvanization is 1730. After evaluate total no. of defects then, we can calculate the percentage of 5th month individual defects. 5. % 6 . 7. 8. 5th month profit loss = average of defective items of 5th month prices of items (m/ton) = 203,300 + 48,000 +14,700 +5000 = 271,000 defective items. Profit loss = 271,000 150 =40650, 000 birr loss Therefore, the average percentages of five month defective pieces are the ratio of overall defective part to total number of months. Table 4.7 over all data representations of five-month frequency records. No. Defects 1st 2nd. 3nd 4th 5th Total defect frequency 1 Coating 4346 50,078 7743.7 12,092 8132 82,391.7 2 Pickling 560 2378 1102 2100 1920 8060 49 | P a g e 3 Exist 530 1980 650 1106 588 4854 4 Shaping 350 1000 450 1080 200 3080 Total 485,078.04 926,017.57 660,916.75 996,861.00 1,080,094.00 production Total no. 13,146 64,516 16,945.7 24,618 17,760 of defective parts %of 2.71% 6.7% 2.56% 2.5% 1.64% defective part Additionally the five month over all profit loss = = = 7472, 4240 birr loss. 4.3.1 Results of Defect Analysis This analysis used to investigates the impacts of sources of defect in critical production process. Because those amount of defects are happened due to the above causes. The result of this analysis properly proof that coating and pickling sections are familiar to produce much amount of defective items than other sections. The coating and pickling section defect from 1st month production are 26.8% and 3.46% respectively. The coating and pickling section defect from 2nd month production are 16.2% and 7.7% respectively. The coating and pickling section defect from 3rd month production are 35.2% and 5.002% respectively. The coating and pickling section defect from 4th month production are 36.4% and 6.3% respectively. The coating and pickling section defect from5th month production are 22.58% and 5.33% respectively. By applying parteo diagram, from collected and analyzed data, determine cumulative rejection percentage based on different types of defects as shown the figure 8. 50 | P a g e Commulative value Total Vital view Defect percentage value Figure 4.5 Pareto diagrams of five-month defective data This chart showing the major defective sections those are contributing in major percentage rejections. From the above result, absolutely coating and pickling defects identified as major defectives. The anlyized data showed the rate of profit loss is higher due to much amount of defect rates ineach production period. Therefore, the total amount of losses in 5 (five) months are 74,724,240 birr profit loss, this loss can be reduced by eliminating various sources of causes and varition in each production departments. The main causes for much amount of defect percentages are described in the defect cause diagram listed in the diagram below. 51 | P a g e Figure 4.6 defect- cause diagrams 4.4 Capability Analysis/ study of process The quality and performance of steel product can be examined through different parameters such as coating thickness, weight, surface neatness and other related parameters that affect the quality. In this case, the analysis has done by coating thickness and weight of sheet. 52 | P a g e Table 4.8 Ethiopian standard values of coating thickness and weight of galvanized sheet. Product type(mm) Thickness standard (mm) Permissible deviation(mm) GI- 0.02 0.20 % GI- 0.25 0.25 % GI- 0.32 0.32 GI- 0.40 0.40 While in ASF uses to provide sufficient quality products. 4.4.1 Capability analysis of coated weight on two sample data Capability analysis of coated weight on 1st sample data Table 4.9 Measured value of coating weigh (g/m2) on first sample This table recors the stastical measured values of parameter of weight in the foirst sample data help to make capability analysis of galvanized process. Number of days S. No Time 1 2 3 4 5 Sum Mean Range St dev 1 2:00 180 182 178 176 183 899 179.8 7 2.56 2 3:30 182 180 183 180 178 903 180.6 5 1.743 3 5:00 181 181 180 177 180 899 179.8 4 1.469 4 6:30 178 180 177 183 179 897 179.4 5 2.059 5 8:00 178 179 182 182 182 903 180.6 4 1.743 6 9:30 181 182 180 179 181 903 180.6 3 1.019 7 11:00 179 183 181 181 179 903 180.6 4 1.45 8 2:00 180 179 176 184 179 898 179.6 8 2.58 9 3:30 176 181 179 177 176 889 177.8 5 1.94 10 5:00 183 178 181 182 178 902 180.4 5 2.058 11 6:30 179 180 183 180 177 899 179.8 4 1.94 12 8:00 181 181 178 181 174 895 179 7 2.76 13 9:30 184 175 180 183 177 899 179.8 9 3.43 53 | P a g e 14 11:00 182 182 177 179 184 904 180.8 7 2.48 15 2:00 177 180 178 183 179 897 179.4 6 2.06 16 3:30 182 183 179 180 175 899 179.8 8 2.78 17 5:00 176 178 178 180 184 896 179.2 8 2.71 18 6:30 184 182 180 178 182 906 181.2 6 2.03 19 8:00 184 181 183 178 185 911 182.2 7 2.48 20 9:30 175 181 177 176 181 890 178 6 2.52 Total 17093 3598.4 118 2.47257 Depending up on the above measured value and standard data can calculate all the necessary requirements that can enable to say whether the process is capable or not. From the above standard table those values are taken to determine the control limit for both X bar –R charts for n=5.therefore the values are A2 0.577, D3 0, D4 2.110 and d2 2.326 Upper and lower control limits for averages or means calculated by: UCL +A2 = 179.92+0.577 =183.32 4.15 LCL - A2 179.92-0.577 =176.51 4.16 Upper and lower control limits of range calculated by: UCL D4× 2.110×5.9 =12.449 4.17 LCL D3× 5.9×0 = 0 of σ= 2.48503 After get this values we can calculate the capability indexes Cp and Cpk to determine their 2 capability of products. The tolerance limit of coating weight is 180 g/m to produce 0.2mm thickness sheet and the tolerance limit is 4%. According to the above statistical data number can calculate their capability 54 | P a g e Capability analysis of weight on second sample data Table 4.10 Measured value of coating weight on second sample Sample Number of days No. Time x1 x2 x3 x4 x5 Sum Mean Range St dev 1 2:00 177 180 178 183 181 899 179.8 6 1.66 2 3:30 182 177 182 178 176 895 179 6 2.52 3 5:00 179 185 183 184 182 913 182.6 6 2.708 4 6:30 175 176 179 177 178 885 177 4 1.414 5 8:00 178 183 181 185 183 910 182 7 2.366 6 9:30 182 176 180 182 179 899 179.8 6 2.225 7 11:00 175 184 177 185 184 905 181 10 4.147 8 2:00 176 179 184 180 184 903 180.6 8 3.072 9 3:30 183 184 178 182 179 906 181.2 5 2.315 10 5:00 174 181 176 179 183 893 178.6 9 3.261 11 6:30 181 177 181 183 180 902 180.4 6 1.956 12 8:00 186 183 180 177 177 903 180.6 9 3.498 13 9:30 177 180 182 178 182 899 179.8 5 2.0396 55 | P a g e 14 11:00 182 182 179 181 176 900 180 6 2.2803 15 2:00 178 180 183 176 177 894 178.8 7 2.481 16 3:30 175 184 181 178 179 897 179.4 9 3.007 17 5:00 184 182 176 177 181 900 180 8 3.033 18 6:30 177 184 180 183 182 906 181.2 7 2.482 19 8:00 179 180 182 185 180 906 181.2 6 2.1354 20 9:30 185 175 181 179 182 902 180.4 10 3.322 Total 18017 3603.4 140 2.9361 Upper and lower control limits of average or mean calculated by the equation of: UCL +A2 = 180.17+ 0.577 =184.21 4.25 LCL - A2 = 180.17- 0.577 =176.131 4.26 Upper and lower control limits for ranges calculated through the following equations: UCL D4× = 2.110×7 =14.77 4.27 LCL D3× = 7×0 = 0 4.28 Process standard σ is = 2.95096 After get this values can calculate the capability indexes Cp and Cpk from collected data .So According to the above standard number can calculate their capability Cpk =min (0.82, 0.78) =0.78 4.32 56 | P a g e 4.5 Process capability analysis of coated thickness on two sample data 4.5.1 Process capability analysis thickness of 1st sample data Table 4.11 Measured value of coating thickness (mm) on first sample Sample Time x1 x2 x3 x4 x5 Sum Mean Range St.dev. No. 1 2:00 0.015 0.0151 0.0148 0.0146 0.0152 0.0747 0.0149 0.0006 0.0005 2 3:30 0.0151 0.015 0.0152 0.015 0.0148 0.0751 0.0150 0.0004 0.000158 3 5:00 0.0151 0.0151 0.015 0.0147 0.015 0.0749 0.0149 0.0004 0.00012 4 6:30 0.0148 0.015 0.0147 0.0152 0.0149 0.0746 0.0149 0.0005 0.000049 5 8:00 0.0148 0.0149 0.0151 0.0151 0.0151 0.0751 0.0150 0.0003 0.000713 6 9:30 0.0151 0.0151 0.015 0.0149 0.0151 0.0752 0.015 0.0002 0.000098 7 11:00 0.0149 0.0153 0.0151 0.0151 0.0149 0.0753 0.015 0.0004 0.000147 8 2:00 0.015 0.0149 0.0146 0.0153 0.0149 0.0747 0.0149 0.0007 0.000225 9 3:30 0.0146 0.0151 0.0149 0.0147 0.0146 0.0738 0.0148 0.0004 0.0001512 10 5:00 0.0153 0.0148 0.0151 0.0151 0.0148 0.0751 0.015 0.0005 0.000203 11 6:30 0.0149 0.0153 0.0150 0.015 0.0147 0.0596 0.011 0.0003 0.000272 12 8:00 0.0151 0.0151 0.0148 0.015 0.0145 0.0743 0.0149 0.0005 0.000196 13 9:30 0.0153 0.0145 0.015 0.0153 0.0147 0.0748 0.015 0.0008 0.00032 14 11:00 0.0151 0.0151 0.0147 0.0149 0.0153 0.0153 0.0151 0.0006 0.000225 15 2:00 0.0147 0.015 0.0148 0.0153 0.0149 0.0745 0.0149 0.0005 0.00448 16 3:30 0.0151 0.0153 0.0149 0.015 0.0149 0.0759 0.0152 0.0003 0.0000978 17 5:00 0.0146 0.0148 0.0148 0.015 0.0153 0.0742 0.01484 0.0004 0.00448 18 6:30 0.0153 0.0151 0.015 0.0152 0.0148 0.0754 0.01508 0.0005 0.00017 19 8:00 0.0153 0.0152 0.0153 0.0148 0.0153 0.0755 0.0151 0.003 0.000253 20 9:30 0.0145 0.0146 0.0153 0.0147 0.015 0.0606 0.01212 0.0008 0.00029 Where, X1, X2, X3, X4 and X5 represents the numbers of observations representing sample for each subgroups. To get the values of average or mean from the collected data by adding each individual values and divided by the number of sample sizes and ranges through subtraction from maximum to minimum values from data. 57 | P a g e = 0.00021506 4.35 Upper and lower control limit for averages or means of coating thickness can be calculated by UCL +A2 = 0.0149+0.577 =0.01523 4.36 LCL - A2 0.0149 -0.577 =0.0146 4.37 Upper and lower control limit for averages or means calculated by: UCL D4× 2.110×0.00057 =0.00012 4.38 LCL D3× 0.00057×0 = 0 4.39 Depending up on the above calculate value can calculate the required capability indexes Cp and Cpk. Therefore 4. 5.2 Process capability analysis of thickness on 2nd sample data Table 4.12 Measured value of coating thickness on second sample Sample No. Time x1 x2 x3 x4 x5 Sum Mean Range St dev. 1 2:00 0.0147 0.015 0.015 0.0152 0.015 0.0747 0.01494 0.0005 0.0002 2 3:30 0.0152 0.0147 0.015 0.0148 0.0146 0.0745 0.0149 0.0006 0.0002 3 5:00 0.0149 0.0154 0.015 0.0153 0.0152 0.076 0.0152 0.0005 0.0045 4 6:30 0.0145 0.0146 0.015 0.0147 0.0148 0.0735 0.0147 0.0004 0.0063 5 8:00 0.0148 0.0152 0.015 0.0154 0.0152 0.0756 0.01512 0.0006 0.0002 6 9:30 0.0152 0.0146 0.015 0.0152 0.0149 0.0749 0.01498 0.0006 0.0002 7 11:00 0.0145 0.0153 0.015 0.0154 0.0153 0.0752 0.01504 0.0009 0.0004 8 2:00 0.0146 0.0149 0.015 0.015 0.0153 0.0751 0.01502 0.0007 0.0003 58 | P a g e 9 3:30 0.0152 0.0153 0.015 0.0152 0.0149 0.0754 0.01508 0.0005 0.0002 10 5:00 0.0145 0.015 0.015 0.0149 0.0152 0.0742 0.01484 0.0007 0.0003 11 6:30 0.015 0.0147 0.015 0.0152 0.015 0.0749 0.01498 0.0005 0.0001 12 8:00 0.0155 0.0152 0.015 0.0147 0.0147 0.0751 0.01502 0.0008 0.0067 13 9:30 0.0147 0.015 0.015 0.0148 0.0152 0.0749 0.01498 0.0005 0.0002 14 11:00 0.0152 0.0152 0.015 0.015 0.0146 0.0749 0.01498 0.0006 0.0002 15 2:00 0.0147 0.0152 0.015 0.0152 0.0148 0.0749 0.01498 0.0005 0.0002 16 3:30 0.0145 0.0153 0.015 0.0148 0.0149 0.0745 0.0149 0.0008 0.0003 17 5:00 0.0153 0.0152 0.015 0.0147 0.015 0.0748 0.01496 0.0007 0.0132 18 6:30 0.0153 0.0147 0.015 0.0152 0.0152 0.0754 0.01508 0.0006 0.0002 19 8:00 0.0149 0.015 0.015 0.0147 0.015 0.0748 0.01496 0.0005 0.0002 20 9:30 0.0149 0.0145 0.015 0.0148 0.0153 0.0749 0.01498 0.0009 0.0003 Upper and lower control limit for average or means of coating thickness calculated by: UCL +A2 = 0.01498+0.577 =0.0153 4.46 LCL - A2 0.01498 -0.557 =0.0146 4.47 Upper and lower control limit for averages or means calculated by: UCL D4× 2.110×0.00062 =0.000131 4.48 LCL D3× 0.00062×0 = 0 4.49 From the collected data, σ is 0.0002428 then calculates the capability of thickness (cp and cpk) from collected sample data through the equation of Cpk = min (cpl, cpu) 0.8 4.53 59 | P a g e 4. 6 Process capability result anlysises of coating weight of two sample data 4.6.1 PCA of coating weight on first sample data Figure 4.7 Process capability report of weight one Depending up on the above Minitab result value, the characteristics of coating weight of galvanized process based on capability, stability and normality tests. Since to determine the capability of process must depends on indices values and number of defective units (ppm). In this case all values are below standard values and the values of ppm greater than six sigma principles. The stability and normality tests are used to identify whether or not the process is stable and normally distributed as seen in figure 4.7 and 4.8 respectively. The stability test shows that the process is stable in X bar R chart, since the control limit of X bar R chart is acceptable. Because the values fall with the upper and lower control limits. Therefore, the process can be improved by apply different quality improvement tools. 60 | P a g e Stability and normality tests for weight one (X bar R chart for weight one) Xbar-R Chart of W1, ..., C5 184 UCL=183.410 182 n a e _ M _ e l 180 X=179.92 p m a S 178 LCL=176.430 176 1 3 5 7 9 11 13 15 17 19 Sample UCL=12.79 12 e g 9 n a R _ e l 6 R=6.05 p m a S 3 0 LCL=0 1 3 5 7 9 11 13 15 17 19 Sample Figure 4.8 X bar R chart of coating weight of product Frequency histogram and probability pilot for weight one (W1) Histogram of C5 Normal Mean 179.7 5 StDev 3.083 N 20 4 y c n 3 e u q e r F 2 1 0 174 176 178 180 182 184 186 C5 Figure 4.9 Histogram for coating weight W1 of the product 61 | P a g e The histogram mean of 179.7 seems to be approximately symmetrical about to means, all most all the points fall within a straight line small data far from the line due to special case, although those points under their ranges. Therefore the data is normal distributed that the capability indices can be calculated. Propability pilot for weight one Figure 4.10 Probability pilot for weight W1 of the product From the above Minitab result the values of mean 179.6, st dev. 2.448, AD 0.403 and p value is 0.351. Probability pilot represents the data is normal distributed through p values. The values of p-calculated greater than significance level =0.05reflect process is normal distribution. 62 | P a g e 4.6.2 PCA of coating weight on second sample data Figure 4. 11 Process capability report for weight two (W2) The capability result clears that high process variation happened due to different causes and affect the capability and performances of process as well products. From this result all capability indices values are less than 1.3 (cp) and 1.67 (cpk) and also the number of defective units are 16,153. 95. This ppm value has a great impact on productivity and profit. 63 | P a g e Stability and normality tests for weight 2 (X bar R chart for weight two) Xbar-R Chart of weight 2, ..., C5 185.0 UCL=184.24 182.5 n a e _ M e X=180.17 l 180.0 p m a S 177.5 LCL=176.10 175.0 1 3 5 7 9 11 13 15 17 19 Sample 16 UCL=14.91 12 e g n a R 8 _ e l R=7.05 p m a S 4 0 LCL=0 1 3 5 7 9 11 13 15 17 19 Sample Figure 4.12 X bar- R charts for weight two This result show the process of weight in the second sample data is stable or in control and the process variations are tolerable because there is no any point out of control limit in chart and no assignable cause of variation is occurred and the process can be improved to achieve better production. Assignable causes of variation happen when the process is instable and difficult to improve the process because the variations are predictable. Frequency histogram for weight two The frequency diagram with process mean of 180.3 and standard deviation of 2.552 with in a population(N) of 100 as seen in Figure 4.12 seems normal distributed, since the histogram distribution fall within specification limits but represent process variations and high amounts of rejected items. 64 | P a g e Histogram of C5 Normal 4 Mean 180.3 StDev 2.552 N 20 3 y c n e u 2 q e r F 1 0 176 178 180 182 184 186 C5 Figure 4.13 Frequency histogram for weight two of product Probability pilot for weight two Figure 4.14 Probability pilot for W2 of the product 65 | P a g e This probability pilot constructed using Minitab 17 for the input data of W2 in Figure 4.13, with process mean of 179.9, st.dev 3.155, AD (Anderson –Darling test statistics value) 0.458 and p value of 0.259.In the probability pilot can determine the normal distribute depend on p value > significance value. Therefore we can proof this process is normal distributed because 0.296>0.05. 4.7 Process capability result anlysis of coating thickness of two sample data 4.7.1 PCA of coating thickness on first sample data (thickness one) Figure 4.15 Process capability report for thickness one The thickness parameter result shows, the distribution defect in the lower point of nominal value (0.015) means that almost data are record in thin coating thickness. The reason for increasing of 66 | P a g e ppm (7728.79) and lower indices values (<1.33 and 167) are amount of process variation in the process due to the sources of defect in the process. Stability and normality tests for Thickness one (X bar-R chart for thickness one (T1) Xbar-R Chart of thickness 1, ..., C5 UCL=0.0152856 0.0152 n a e _ M 0.0150 X=0.014977 e l p m a S 0.0148 LCL=0.0146684 0.0146 1 3 5 7 9 11 13 15 17 19 Sample UCL=0.001131 0.00100 e g n 0.00075 a R _ e l R=0.000535 p 0.00050 m a S 0.00025 0.00000 LCL=0 1 3 5 7 9 11 13 15 17 19 Sample Figure 4.16 X bar- R charts for thickness one of product In order to determine the stability of process X bar –R chart were created using Minitab17, the X bar- R chart shows process was stable and acceptable. Because, the all process data are fall within process limit(UCL and LCL),in this case common casue of varaitaions are occurred.Therefore the process can be improved. 67 | P a g e Frequency histogram for thickness one Histogram of C5 Normal 5 Mean 0.01494 StDev 0.0002349 N 20 4 y c 3 n e u q e r F 2 1 0 0.0144 0.0146 0.0148 0.0150 0.0152 0.0154 C5 Figure 4.17 Frequency histograms for thickness one of product The center of histogram was estimated by calculating the average of 100 readings (N). The frequency histogram with process mean of 0.01494 and standard deviation of 0.0002349 in Figure 4.17 seems normally distributed, since the frequency histogram is properly centered and narrows enough to fit within the specification limits. Probability pilot for thickness one The probability pilot for thickness one value, the process mean of 0.01501, st.dev 0.0002234, AD 0.201and p value 0.878 in figure below. Similar to the above principle this process is distributed normally since p value > significance level and data are mostly fit within a straight line. 68 | P a g e . Figure 4.18 Probability pilot for thickness one of product 4.7.2 PCA of Coating Thickness on second Sample Data Figure 4.19 Process capability analysis reports for thickness two 69 | P a g e The capability study result showed that the capability of process in the parameter of thickness in the second sample collected data was lower process performance and process capability (indices values) and much amount of defective items. Those points are the main back bone to determine the capability and acceptability of the process to meet products with in specifications.In the second sample data the proceses was in capable and accepatable. Stability and normality test for thickness two X-bar R chart for thickness two Xbar-R Chart of thickness 2, ..., C5 0.0154 UCL=0.0153301 0.0152 n a e _ M _ e l 0.0150 X=0.014984 p m a S 0.0148 LCL=0.0146379 0.0146 1 3 5 7 9 11 13 15 17 19 Sample UCL=0.001269 0.0012 e g 0.0009 n a R _ e l 0.0006 R=0.0006 p m a S 0.0003 0.0000 LCL=0 1 3 5 7 9 11 13 15 17 19 Sample Figure 4.20 X bar-R chart for T2 of product To investigate the stability and normality of process through second sample of thickness value the mintab17, showed the result decided the stability and normality of process. This chart proofed that, the process was stable or in control limits with in upper and lower control limits because, all points fall within specification limits and the varations are predictable and accepatable. 70 | P a g e Frequency histogram for thickness two Histogram of C5 Normal 5 Mean 0.01500 StDev 0.0002282 N 20 4 y c 3 n e u q e r F 2 1 0 0.0146 0.0148 0.0150 0.0152 0.0154 C5 Figure 4.21 Frequency histogram for thickness two The ferequceny histogram result showed that the normality or non normality of process data. In this parameter the data are normally distributed because, the process distrbuction fall with in company upper and lower specification limits (172.8 and 187.2). Probability pilot for thickness two The probability pilot for thickness two values, the process mean of 0.01501, stdev 0.0002696, AD 0.520 and p value 0.182 from figure 4.21. This process distributed normally since p value > significance level (0.05) and almost data are fall within strainght lines.some data are far from straight line due to special causes. 71 | P a g e Figure 4.22 Probability pilot for t2 of product 72 | P a g e CHAPTER FIVE 5. SUMMARY, CONCLUSION AND RECOMMENDATION 5.1 Summary From the Capability analysis /study result, the final value examines the capability of process in terms of capability indices and six sigma levels according to standard values. The coating weight parameter of two sample dates’ shows that the process not capable. The first sample weight one (W1) result show all indices values are Cp, Cpl, CPU (Cpk), Pp and Ppk are 0.95, 0.94, 0.97 and 0.96 respectively, since the values of cp less than 1.33 and cpk less than 1.67. According to the law of thumbs all values are below standard value (1.33 and 1.67) and the value of cpk less than cp indicate the process is off-centered. Similarly, the second data weight two (W2) shows the values of cp 0.8, cpk 0.78, pp 0.81 and ppk 0.79.Therefore process are incapable and off- centered. According to the science of six- sigma, the capability of process determined by all indices values are greater than 1.5 and the number of defective units are 0.545. In the statistical data analysis weight parameter in two sample data’s the values of ppm is 4555.82 for W1 and 16,153.95 for W2. Therefore, the process is incapable and not acceptable because not centered and defect and loss amounts are high. Therefore, depending up on PCIS and 6 sigma principle can conclude that coating weight process is incapable to produce desired defect free items. The thickness parameter were investigated the capability based on the above principles. From the above Minitab result, the first sample data thickness one (T1) the capability indices values are cp 0.89, cpk 0.86, pp 0.921and ppk 0.88 and cp# cpk and pp# ppk it implies that the process is incapable and off centered. The second sample data (T2) values of indices are cp 0.82, cpk 0.8, pp 0.83 and ppk 0.81. In this case the process is off centered (cpk 73 | P a g e of defects has a great impact on the process (capability) and product (quality) and rejections and losses.through those causes of defects or varation the company was losses many amount of profit as per each production months.The analysed data result showed the rate of profit losses in five month production i.e. 74,724,240 birr loss,it is chalned to their profitability and productivity. 5.2 Conclusion In order to produce right quality product with relatively low cost, investigation of their existing performance with the help of statistical tool is very powerful technique. SQC tool is the most important technique to determine performance range, identify and reduce amounts of defective items to produce zero defective outputs. Manufacturing high quality product at low cost has a great task to sustain in the global market competition and to improve product quality, satisfy both customers and suppliers, control production cost and to save required facility. The statistical analysis result showed that, the performance of process through two parameters, the values of all capability indices (cp, cpk, pp and ppk) were below 1.33 and 1.67 and six sigma principle ppm values are greater than 2700, the amount of defects and rejections were 3.23%.The process were incapable, off centered and does not meet 100% customer’s requirement. This study was achieved the objectives and statement of problem of the study. In other case the overall profit loss of the five month productions are 7472, 4240 birr. This loss of birr has a great impact on productivity and profitability. 5.3 Recommendations This research is carried out on coating, pickling, shaping and exit sections of ASF to investigate the amount of defect rates in order to determine loss percentage and study the performance of process. The collected and analyzed data indicated that the process is not capable due to different sources of defects. Therefore, the company forwards the following recommendations so as to improve the capability of the industry through reduction of loses due to reduce rejections and reworks by eliminating different causes of defects. . To reduce pickling and coating section defect and loss spry type cleaning mechanism shall be converted to deep type mechanism because has advantage to industry through different economic and safety aspects. 74 | P a g e . Develop proper quality management system in order to quick detection and solution of the quality problems. . Upgrade the experience and knowledge of operators and supervisors through different training and other inventive works. . Apply statistical quality control and appropriate instruments in coating section as SEM and optical microscope to determine the chemical substrates rather than visual inspection and guess. . To prevent and control surface defects apply prpoer surface roughness measuring instrument. . The Company needs to identify and prioritize critical areas and work on elimination of root causes of defects instead of correcting defective parts. . Implement inspection before and after each section helps to minimize and correct defects than inspecting final product just to reject the output. . The company needs to open its own research and development center for sustainable improvement in production process. . In pickling section apply proper water pressure gauge and proper roller alignments to reduce deformation (wrinkle) defect. 5.4 Future work According to capability study,defect rate and profit loss analysis the result showed that the process needs further improvement. With the help of parteo diagram analysis the top defective areas are identified. Therefore, these two critical sections need further study. 75 | P a g e REFERENCE A. Azimi et.al (2012), metallurgical analysis of pimples and their influence on the properties of hot dip galvanized steel sheet, department of materials science and engineering, Shiraz University of technology, shiraz 7155713876, pp 1-30. Adan Valles et.al, (2007), implementation of six sigma in a manufacturing process, international journal of industrial engineering, [email protected]; ISSN 1943-670X ©. Adeoye Akeem.O.,et.al, (2013), process capability analysis as a means of decision making in manufacturing company, international journal of advanced research in computer science and technology (IJARCST) Vol. 1 Issue 1,pp 1-18. Ajit Goswami1 & Harendra Narayan Dutta, (2013), some studies on normal and non-normal process capability indices, International Journal of Mathematics and Statistics Invention (IJMSI) E-ISSN: 2321 – 4767 P-ISSN: 2321 - 4759 www.ijmsi.org Volume 1 Issue 2, PP-31-40. Anis (2008) basic process capability indices an expletory review, international statistical review banner, volume 76, issue 3, pp 347-367, doi:10.111 l/j.l751-5823.2008.00060. Antonio Galvao Naclerio Novaesa, (2016), Process capability index CPK for monitoring the thermal performance in the distribution of refrigerated products, Universidad Campinas, SP Brasil, pp54-65. Aysun Sagbas, (2008), improving the process capability of a turning operation by the application of statistical techniques, Professional article/Strokovni clanek, ISSN 1580-2949, MTAEC9, 43(1)55, pp 1-5. Behnam Neyestani, (2017), seven basic tools of quality control: the appropriate techniques for solving quality problems in the organizations, Behnam Neyestani @ yahoo.com, pp 1-12, https://doi.org/10.5281/zenodo.400832. 76 | P a g e B. Naveen and Dr. T. Ramesh Babu, (2018), productivity improvement in manufacturing industry using industrial engineering tools, journal of mechanical and civil engineering (IOSR- JMCE), e-ISSN: 2278-1684, p-ISSN: 2320-334X, volume 15, PP 11-18, www.iosrjournals.org. Dr Bharat Chede, et.al (2016), statistical process controls a rejection control tool -a case study, international journal of research and engineering (IJORT), volume 3, Issue 2, PP34-60, DOI: 10.13140/RG.2.1.5057.1126. Dr.Vinod (2016), process capability a manager tool for six sigma quality advantage, international research journal publisher, global journals inc.(USA),volume 16, Issue 3 version1.0, ISSN:2249- 4588,pp 3-20. Gabriele Arcidiacono and Stefano Nuzzi, (2017), a review of the fundamentals on process capability, process performance and process sigma, international journal of applied engineering research, ISSN 0973-4562 ,Volume 12,pp. 4556-4570, http://www.ripublication.com. Gutierrez et.al, (2017), logistics services and lean six sigma implementation: a case study, international journal of lean six sigma, vol.7 issue: 3, pp.324 – 342, doi: 10.1108/ijlss-05-2015- 0019. Hewan Taye Beyene, (2016), minimization of defects in sewing section at garment and textile factories through DMAIC methodology of six sigma, Ethiopian institute of technology, mekelle university, pp 11-98. Hossein Taheri,(2017), Powder-based additive manufacturing – a review of types of defects, generation mechanisms, detection, property evaluation and metrology, electrical and computer engineering publications, Iowa State University,[email protected] pp 1-9. J K Singh & D. D N Singh, (2012), defects in galvanized coating on steel wire, Indian journal of chemical technology, volume 19, pp 361-365. Jospeh Hubert Ault, (2013), control charts as a productivity improvement tool in construction, [email protected],pp 1-89, http://docs.lib.purdue.edu/techmasters. 77 | P a g e Kevin Linderman, et.al,(2003), six- sigma definition and underlying theory, journal of operations management 26 (2008), pp 536–554, www.elevier.com/locate/com. Kirti Singh, (2016), defects reduction using root cause analysis approach in gloves manufacturing unit, international research journal of engineering and technology (IRJET), e- ISSN: 2395 -0056 Volume: 03 Issue: 07, pp 173-186. K. Rezaie, et.al, (2015), a practical implementation of the process capability indices, journal of Applied Sciences ,Volume 6 (5), pp, 1182-1185, DOI: 10.3923/jas.20015.1182.1185. L. Mraz and J. Lesay, (2009), Problems with reliability and safety of hot dip galvanized steel structures, welding research institute – industrial SR, volume 14, number 2, ISSN: 0104-9224, pp, 1-10, http://dx.doi.org/10.1590/S0104-92242009000200011. Marcello Gelfi, et.al, (2017), influence of the manufacturing process on defects in the galvanized coating of high carbon steel wires, department of Mechanical and Industrial engineering, Via Branze38,25123 Brescia, pp 1-12, doi: 10.3390/ma10030264. Marzena Nowicka,(2018), influence of hot-dip galvanized coating morphology on the adhesion of organic coatings depending on the zinc bath Pb content and the post galvanizing cooling method, international journal of corrosion, article ID 2102086, pp 1-9, https://doi.org/10.1155/2018/2102086. Md. Mazedul, et.al , (2013), minimization of reworks in quality and productivity improvement in the apparel industry, international journal of engineering and applied sciences, [email protected], Volume 1, number 4, pp147-164. Michal and petr, (2014), adhesion of zinc hot dip coating, university of agricultural, volume 62, issue 1, number1, pp 53-64,https//doi.org/10.11118/ actaun 20142010053. Milind et.al, (2017), improve quality and reduce rejection level through quality control, department of mechanical engineering, international journal on recent and innovation trends in computing and communication, volume: 5 issues: 7, pp 764-768. 78 | P a g e Montgomery, (2001), introduction to statistical quality control book six edition, quality improvement in the modern business environment, ISBN 978-0-471-65631-9, OCLC 56729567 pp 3-650. Mousumi Rahaman, (2016), different types of defects identification and controlling method for quality and productivity improvement, journal of polymer and textile engineering (IOSR-JPTE), e-ISSN: 2348-019X, p-ISSN: 2348-0181, Volume 3, Issue 2, PP 01-18. M. Shanmugaraja and M. Nataraj, (2011), on defect control analysis for improving quality and productivity: an innovative Six Sigma case study, international journal on quality and Innovation, Vol. 1, No. 3. Peter Maaß, et.al, (2011), Handbook of Hot-dip Galvanization 1st Edition, pp1-19, doi: 10.1002/97832763884.ch1. P Mahesh Babu et.al,(2014), reduction of reworks and rejections in manufacturing of a thin walled aerospace component, international journal on mechanical and robotics research, volume 3, number 4, ISSN 2278 – 0149, www.ijmerr.com, pp 604-611. Pranay Sawaitul, et.al,(2012), minimization of dross formation during the continuous galvanizing process in the steel industry, international journal of emerging technology and advanced engineering, ISSN 2250-2459, Volume 2, Issue 1, pp.1-7. P. Saravanan and S.Srikanth,(2018), surface defects and their control in hot dip galvanized and galvannealed Sheet, international journal of advanced research in chemical science (IJARCS), Volume 5, Issue 11, pp 11-23, ISSN: 2349-0403 DOI: http://dx.doi.org/10.20431/2349- 0403.0511002. Pyzdek et.al, (2003), The Six Sigma Handbook, Fourth Edition, ISBN-13: 978-0071840538, pp 704-720. Rdestan,(2011) , multivariate process capability indices on the presence of priority for quality characteristics, African journal of business management Vol.5 (26), pp. 10900-10907. 79 | P a g e Shaik muteeb Irfan, shekhar (2017), process capability improvement of routing machines through standardization of compensation, international journal of mechanical and production engineering, ISSN: 2320-2092, Volume- 5, Issue-7. S. Zadake and S. Dambhare, (2016), modification of passivation unit in continuous galvanizing line, international journal of current engineering and technology, E-ISSN, 2277 – 4106, pp 6-45. Srikanth, et.al, (2005), metallurgical investigation in to the genesis of bare spot, exfoliations and matte coating appearance in hot dip galvanization steel sheet, pp1-10. Tanvir Ahmed, et.al, (2013), an application of parteo analysis and cause-effect diagram for minimizing defect percentage in sewing section of a garment factory, Bangladesh university of engineering and technology, international journal of modern engineering research (IJMER) Vol. 3, Issue 6. Tatjana V, et.al,(2009), SPC and process capability analysis– case study, international journal of total quality management and excellence, volume 37,number 1-2, pp1-7, https://www.reserchgate .net/puplication/254891. Taixiong Guo, et.al (2014), causes and remedies of bulge particle defect on surface of hot dip galvanized steel sheet, 1662-8985, pp 330-334, doi: 10.4028/www.scientific .et/ AMR. Vaibhav and Sachin, (2013), analysis of the sand drop defect to reduce the rejection level of cylinder block casting, a case study, international journal of engineering research and technology (IJERT), Vol. 2, Issue 9, pp1-6. Varsha M. Magar, (2014), application of 7 quality control tools for continuous improvement of manufacturing processes, international journal of engineering research and general science, Volume 2, Issue 4, ISSN 2091-2730, pp 364-371. Yerriswamy, te.al, (2010), the process capability analysis - a tool for process performance measures and metrics, international journal for quality research 8(3), ISSN 1800-6450: pp 399- 416, https://www.researchgate.net/publication/267512995. 80 | P a g e APPENDIX Current man-power of ASF Number Department Current man power 1 Production and techniques 129 2 Finance 19 3 Human resource management (HRM) 29 4 Security 100 5 Oddity 3 6 Director 1 7 Vis directors 1 Supply and procurement 8 88 9 General manager 1 10 Total 372 81 | P a g e