0- STATISTICAL QUALITY CONTROL SUPPORT SYSTEM TO FACILITATE ACCEPTANCE SAMPLING AND CONTROL CHART PROCEDURES/

A Thesis Presented to The Faculty of Russ College of Engineering and Technology Ohio University

In Partial Fulfillment of the Requirements for the Degree Master of Science

by

Mohammed Nadeem August, 1'~994 Table of Contents

Page No.

1.0 Introduction 1

1.1 Statement of the Purpose 5

2.0 Literature Review 6

2.1 Statistical Process Control Studies 6

2.2 Intelligent Systems · 7

2.3 Expert System Applications to Statistical Quality Control. 8

2.4 Present and Future Trend 15

3.0 Background 17

3.1 History ofthe Organization 17

3.2 Acceptance Sampling 19

3.2.1 Types of Sampling Plan 20

3.3 Military Standards 21

3.4 Statistical Process Control 22

3.4.1 Preparation Measures 23

3.5 LEVELS OBJECT 25

3.6 Need for the System 26

4.0 Development of the System

4.1 Systems Design 31

4.1.1 Selecting an Expert Systems ShelL 31

i 4.0 Development of the System (contd.)

4.1.2 Software Requirement 38

4.1.3 Hardware Requirement 39

4.2 Knowledge Acquisition and Representation 39

4.3 Acceptance Sampling 44

4.4 Statistical Process Control. 56

4.5 Testing and Maintaining the System 66

5.0 Results and Conclusions " 71

6.0Recommendations 74

6.1 Drawbacks and Limitations 75

6.2 Further Scope ofResearch 75

Bibliography 78

Appendix A: Pareto Analysis Charts 85

Appendix B: AQL Determination Sheets 93

11 List of Figures

Page No.

Figure 1. Cost Incurred due to Work-In-Process Inventory Rejection 29

Figure 2. Flow Chart of the Knowledge Acquisition Procedure 41

Figure 3. Acceptance Sampling Menu 47

Figure 4. Foil Menu 48

Figure 5. Paper Menu 49

Figure 6. Adhesives Menu 50

Figure 7. Inks/Coatings Menu 51

Figure 8. Menu to Enter the Lot Size 52

Figure 9. Sample Size Menu 53

Figure 10. Flow Chart ofthe Acceptance Sampling Procedure 54

Figure 11. Measurement Menu 55

Figure 12. Recommendation Menu 57

Figure 13. Flow Chart of the Control Chart Plotting and Analysis Procedure 58

Figure 14. Main Menu 59

Figure 15. Variable Selection Menu 60

Figure 16. Control Chart Measurement Menu 61

Figure 17. X-Chart Display 63

Figure 18. R-Chart Display 64

Figure 19. Control Chart Analysis I 67

Figure20. Control ChartAnalysisII 68

iii List of Tables

Page No.

Table 1. Total Work-In-Process Rejected 28

Table 2. Acceptable Quality Level (AQL) for Different Sampling Parameters

of the In-coming Raw Material 43

Table 3. Types of Defects found in the Raw Material 45

iv 1

1.0 Introduction

America has always been the pioneer in the field of quality assurance. Names

like W. A. Shewart, W.E. Deming among others instantly come to mind at mere

mention of the word quality. The 19th century was the era of the beginning of quality

assurance in the United States. Fredrick Taylor pioneered scientific management,

removing work planning from the purview of workers and foremen and placing it in

the hands of industrial engineers. In the 20th century, Henry Ford introduced the

moving assembly line into the manufacturing environment ofFord Motor Company that broke down complex operations so they could be performed by unskilled labor which resulted in the manufacturing of highly technical products at low cost. The field of quality control witnessed rapid progress as George Edwards and Walter Shewart, Bell

System employees, led the way. Walter Shewart introduced the concept of statistical quality control and was basically involved with methods for economically controlling quality in mass production environments. George Edwards was the founding President ofthe American Society for Quality Control (ASQC). In 1950's, W. Edwards Deming, another Bell System employee, worked in Japan on the invitation of the Union of

Japanese Scientists and Engineers and now Deming's methods are implemented allover the world. Other noted contributors from the US are Armand V. Feigenbaum, who introduced the concept of Total Quality Control (TQC), and Joseph Juran.

When computers were introduced to the manufacturing industry, quality assurance was an obvious field yet to be exploited by automation. Translation of 2 parameters, required in automation procedures, into an operational system at any point ofthe manufacturing process is an extremely time-consuming iterative action that could

be solved with the incorporation of new technologies, quality control and intelligent

techniques. Today's industry is now oriented towards state-of-the-art technology,

including among others automatic identification systems, computer integrated

manufacturing, artificial intelligent simulation techniques, etc. With the advent of

microcomputers in manufacturing applications, quality' control became an obvious task to be automated. In the past, much work has been done on automated inspection ofin­ process and finished product. Techniques such as machine vision, ultrasonic sensors, robots, robot vision, X-ray testing, etc., have been commonly used over the years for industrial inspection applications.

Lately expert systems have been coming into use for quality control applications. An expert system may be defined as a software that seeks to model the expertise of a human expert within a specific problem domain. As defined by

Modesitt (1987), "expert systems are computer-based software systems which attain or surpass human expertise, currently in very narrow and specialized domains. " An expert system is a computer program that achieves high levels of performance on problems that normally require years of education and training for human being to solve. In an expert system, the knowledge of a human expert, including factual, judgmental and procedural knowledge is developed into a "knowledge base" for a particular problem solving domain. The knowledge base is manipulated using a control mechanism, commonly called the "inference engine", to reach conclusions or provide 3 solutions to complex problems which could ordinarily only be solved by a human expert. State-of-the-art expert systems solve problems, explain the reasoning behind the solution and can modify the knowledge base to include new information.

Expert systems are part of the wider field of study known as 'artificial intelligence' (AI). AI is the field of subjects concerned with making computers behave intelligently and includes areas such as robotics, pattern recognition, neural networks, and expert systems. Basically, an artificial intelligence program is a program like any other. It is a set of procedures which the computer follows, with inputs and outputs.

These programs are based upon a model of the problem they seek to solve. In conventional information systems, this model is constructed in terms of numbers. In an AI program, the model is constructed in terms nearer to a human view using symbols, such as text or pictures. The computer's view is still a model, but is simply expressed in different terms.

A few characteristics of an intelligent system are:

they solve complex problems

they behave logically

they make effective use of existing information

they provide non-linear program navigation

they are responsive and adaptive

they are user-friendly and highly interactive

Expert systems are perhaps the most common form ofthe AI system. The label

'expert system' is not applied to one single type ofsystem, or to even a class ofsystems 4 but to a whole spectrum of systems. It covers systems ranging from conventional information systems with hardly any symbolic reasoning content, to systems which entirely depend upon symbolic reasoning, and bear little resemblance to conventional information systems. Similarly, it is applied to systems ranging from those which use a very superficial symbolic model of a problem, the so-called toy domains, to those which use very sophisticated symbolic models.

The use of expert systems in modem industry is not a new concept and is derivative of the advantages derived from such applications. There are numerous benefits of using expert system applications, to name a few:

Expert systems save time.

- Expert systems can cut costs and increase revenues.

- Expert systems can preserve endangered knowledge.

- Expert systems can propagate knowledge.

- Expert systems can improve consistency.

- Expert systems are useful in training situations.

Expert systems can easily integrate with other software.

- Expert system shells considerably reduce development time. 5

1.1 Statement of the Purpose

In pursuit of ways to improve the efficiency of quality control procedures, a research study was conducted at the Belpre plant of International Converter, Inc. (ICI). leI is a manufacturer ofaluminum foil related products. ICI has been facing problems relating to the quality ofin-coming raw material which was in-tum affecting the quality of in-process goods and the production process on the whole. A huge quantity of raw material was scrapped during production because of inferior quality and added to the cost in terms ofinterruptions in production processes and resources already used on that particular raw material. This study was to specifically concentrate and deal with similar problems and to enhance and improve quality management at the production floor.

Since the organization lacks trained quality personnel, a quality system, with the expertise of a quality engineer, best suited the needs of the organization. The major objective ofthis study was to provide a user-friendly object-oriented quality system that will automate the acceptance sampling procedures and exercise automatic process control in terms of establishing, maintaining, and interpreting control charts to counter quality-related problems at the plant. 6

2.0 Literature Review

2.1 Statistical Process Control Studies

Companies in the US are moving towards adopting a number of strategies such as JIT, TQM, and automation to gain competitive advantage in an increasingly competitive environment. Among such strategies is Statistical Process Control (SPC).

SPC is a technique to increase quality and decrease costs. SPC theory is based on the postulate that process performance is dynamic and fluctuates over time. It relies on the use of aids such as control charts to analyze these fluctuations.

David E. Keys and Kurt F. Reding [1992], have described in detail how such fluctuations were analyzed and controlled by explaining the construction and interpretation of control charts. They also provide a few rules of thumb to assist in the analysis of control charts. (1) Control limits are established at three standard deviations from the mean of the variable, (2) With-in limit observations should be investigated under certain conditions, (3) 100% inspection is seldom 80% or more effective, and (4) A minority of the processes will account for the majority of the problems. They state, "SPC is a complex and long term strategy. When it is adopted initially, the short term results will not be impressive to accountants and mangers who are used to emphasizing the short run". They further add, "Organizations with a long term perspective, without excessive emphasis on the short run, will have a better chance of educating their people to use the cost of quality report and SPC appropriately as an 7 integrated part of the whole world."

2.2 Intelligent Systems

While comprehensively explaining the design strategy for an intelligent system development, Larry Bielawski and Robert Lewand [1991] have described a five-step procedure that has evolved into a standard methodology for intelligent system construction:

a. Problem Identification

In this step, a complete diagnosis of the problem is made in the sense

such as ifthe problem is cognitive or computational; the scope ofthe problem,

if the domain is specific and narrow; existence of resources to solve the

problem; and worthiness of the problem

b. Knowledge Acquisition and Representation

The second step is to define clearly as to how the knowledge will be

acquired (from interviews, experts, etc.) and how the knowledge will be

represented, from the expert to a paper and then to machine readable form.

c. Tool Selection

A methodological approach is suggested for the tool selection where four

environments are accounted for. These are: applications environment,

development environment, user environment, and run-time environment.

d. Prototyping and Development

Prototyping is required to test the feasibility of the project before it is

completely developed. 8

e. Testing and Maintenance

The last step is to determine the usefulness of the system, assess its

completeness, resolve the issue of user friendliness, calculate efficiency and

payoff, and write a maintenance policy.

2.3 Expert System Applications to Statistical Quality Control

John F. Affisco and Mahesh Chandra [1990] have proposed a conceptual model of an expert system for quality assurance purposes in "Quality Assurance and Expert

Systems - A Framework and Conceptual Model". In this paper they presented a framework for the use of expert systems in quality assurance and also proposed a conceptual model for the integration ofknowledge and calculation capabilities in expert systems for quality assurance. In a brief review of expert systems they defined the feature of an expert system as emphasizing consultation and reasoning rather than performing calculations with three different user modes, namely; (a) getting answers to user's problems, (b) allowing increase in system's knowledge by the user, and (c) harvesting the knowledge base for human use. In an overview of quality assurance, they discuss Deming's Triangle according to which quality must be measured by the interaction of three participants: (1) the product (2) the users and how he uses the product; how he installs and maintains it; and his expectations from the product, and

(3) instructions for use, warranty and availability of parts etc. Also discussed are

Taguchi's classification of noise factors or variables which cause the functional characteristic of a product to deviate from its target values. 9

Quality assurance is then classified in terms of the organizational functions

responsible for the execution ofpertinent quality activities as Off-Line (Product design,

process design), On-Line (production management), and After-Market (customer

service), and a different type of expert system support is suggested for each of them.

This support is of various natures such as diagnosis, planning, monitoring, instructing,

controlling, etc. Thus, for instance, an On-Line system for the department ofproduction

management would require a consulting expert system support for process control, and

debugging and interpreting support for control chart selection, design and

implementation.

A frame work for expert system development in statistical quality control

applications has been proposed by James R. Evans and William M. Lindsay [1988].

The knowledge base is partitioned into three sets: domain-independent, analysis rules, which determine whether or not the sample observations indicate a lack of control:

interpretive rules which analyze the patterns in the chart in terms of process changes: and domain-dependent diagnostic rules, which assist in determining assigned causes and corrective action. This structure allows some portability between applications and customizing to specific applications.

Further, typical patterns indicating lack ofcontrol have been described as, points tending to hug center line; sudden in the level ofthe ;*p+5Xaverage value of X or R; systematic, non random fluctuations such as sawtooth patterns; correlation between X and R statistics; and trends up or down in the process characteristics. In any case, the diagnosis of a control chart pattern involves human expertise. The purpose of this 10 expert system is to capture such expertise in a knowledge base. The first phase of diagnosis deals with the analysis ofunstable or out-of-control conditions automatically.

The second phase of decision making requires a more complex search process for an assignable cause. This process usually requires interaction between an observer and the expert system to complete the diagnosis and offer a prescription for correction of the problem.

Various.changes that may affect the X chart are described as: materials defects, operator error, inspector error, machine setting, supplier changes, and unusual tool wear. Some assignable causes for R chart variability are suggested as: poorly trained operator, non-uniform materials, machine out of adjustment, non-standard, parts and beginning or end of run.

James R. Evans and William M. Lindsay [1988] further state, "we have developed demonstration expert systems using the commercial development software

EXSYS. OUf system is designed to perform an analysis ofa control chart as each new sample observation is taken. One ofthe powerful features of EXSYS is that it allows an external program interface. Our system first queries the user for the latest observation and plots the chart prior to calling the expert system routine. The knowledge base is divided into three groups: analysis rules for determining conditions which signifies the potential lack ofstatistical control; interpretative rules for analyzing patterns in the control chart in terms of process changes; and diagnostic rules for determining assignable causes of out-of-control conditions. The analysis rule base is portable and domain-independent, and is based upon concepts of statistical inference." 11

Two different types of approaches have been discussed by Hakong & Hickman

[1985] to building statistical expert systems. The first approach is to incorporate expertise in the use of a chosen method where assumptions underlying the statistical method chosen are not violated. For instance, while using a system built for regression analysis, the user will have to determine that linear regression analysis is appropriate for that particular problem. The second approach is to incorporate expertise in guiding the user to select an appropriate statistical method wherein it is ensured that the right method is chosen in the first place.

Vasser A. Hosni and Ahmad K. Elshennawy [1988] have proposed a system which tries through dialog with the user to direct him to the proper chart(s) in the package. An analysis component for each chart determines statistical phenomena and provides a general explanation. Another component in the system accesses an accompanying knowledge data base that provides possible explanation and advice. The user may add to the knowledge base at any time assisted by the statistical data base management system.

There have been many instances where successful application ofexpert systems for quality control applications has resulted in numerous benefits to the organizations.

Tecknowledge, Inc., a California based AI firm, reports the development ofComponent

Evaluation, an expert system that evaluates proposed changes in a component mix for quality control. It was built using Tecknowledge's M.I on a PC AT clone. Quality

Control Adviser is another expert system developed using Tecknowledge's M.l. Its application is in the area of batch manufacturing. 12

The State College, PA, plant of Coming Glass has developed an expert system to diagnose breakage problems that occur at the lehr. Product defects caused by other processing steps manifest themselves when the ware cools as it leaves the lehr.

Diagnosing breakage helps isolate the upstream problem that requires correction. The system was developed using Texas Instrument's Personal Consultant. These expert systems have been operational for a long time and have justified the trust of management to install such systems.

Michael Stock [1989] states, "In order to build an intelligent statistical process control (ISPC) environment implementing the intelligent reasoning cycle, it is necessary to build a highly structured knowledge base that encompasses the key aspects of the problem." He asserts that classes of knowledge must include the process, statistical process control methods, quality control tests, raw materials, plant operating conditions, goals and objectives, constraints, recipes, and control configuration. He further explains the essential modules ISPC should include as:

"An object system for structuring the declarative component of the

knowledge base.

An inference engine component for providing rule-based logic in

addition to methods and other procedural constraints.

A sophisticated stable storage architecture built on top of relational

database technology which will allow the generation of reports,

status's, justification, and explanations for any batch and across the

history of batches. 13

A sophisticated knowledge acquisition interface for parameterizing,

controlling, and maintaining the ISPC system by plant personnel.

A communications facility for interconnecting On-Line the ISPC system

with other host computers for data acquisition.

A foreign system interface which allows the ISPC system to make use

of other existing and future applications and systems software, a

sophisticated user .interface through which the tools can be used easily

in the plant environment by routine personnel."

Finally he states that the design goals of such a system should have the characteristics such as flexibility, integration, high performance, ease of use, extensibility and maintainability, and sophisticated reporting.

Cambell Soup Company boasts of an expert system "Simon" which saves the company about $5 million annually. This expert system was installed in 1988 and became fully operational in 1989 after one complete year oftesting. Simon was written using Aion Corp.'s Aion Development System shell. The thermal process development department sets the standards for how soup should be cooked to ensure sterility and to avoid conditions which lead the soup to be discarded. It also makes the calls about what to do when something goes wrong. For example, a batch of cream ofmushroom needs to be cooked for 50 minutes at 250 degrees and halfway through the cooking, the plant looses steam pressure or the conveyor belt slows down. The department then decides whether to OK that batch for sale or to destroy it. The goal is to destroy as little as possible while guaranteeing the quality of the product. 14

Before using Simon, the company was using a General Electric Co. time sharing arrangement. This process took up to eight weeks to make a decision and the product in question had to sit in the warehouse for that period. However, Simon has cut the decision making time to only three minutes in tum saving more money for the company. Moreover, since the expert system can separate the business rules from the mathematical calculations, either can be changed without affecting the other. This is more important in situations where the mix of product changes very frequently.

In Hoosick Falls, New York, Lovejoy Chaplet, a screw machine products shop faced difficulties in implementing Statistical Process Control (SPC) as the operators and setup men had difficulty in understanding the X-bar and R charting techniques. As a result, a High Tech Research (HTR) system, HT 3000 was installed at the heart of which is an expert system. The expert system features the ability to adapt to an ever­ changing machining process (Adaptive Quality Control) using a variety of functions such as determination of total measurement error (TME), statistical approval of setup, and generation of sampling plans. Inspection intervals and sample size vary on each dimension as the process drifts out of control. A program in the HTR software called the In-Process Stat Adviser makes recommendations regarding sample size and the need to compensate for a dimensional drift in the process.

Another feature of the HTR system is that it uses Specification limits to determine the location ofControl Limits rather than the Spec Normal. Through a series of calculations, the expert system calculates the three sigma value location of the

Control Limits in relation to the Spec Limits of the dimensions. The system also 15

calculates a second set of control limits and creates a yellow caution zone to signal the

operator when an operation is drifting out of control.

The management feels that the system allows the operational theory to be '.

present without making operators feel uncomfortable. Moreover, the machine operator

requires little training and doesn't perform unnecessary inspections on all of the

dimensions.

2.4 Present and Future Trend

In article "Personal computers move into quality control", Anthony J. Demers

[1989] wrote, " Although mini and mainframe computers are necessary for many applications in business and industry, the personal computer, especially in laptop form, offers a convenient, cost effective method for implementing many applications. Today many industries are adopting aggressive programs in JIT, Quick

Response, Fault-Free and World Class Manufacturing. The success of such programs most often depends on the people in the plant, no matter how few, and their ability to adapt and thrive with these new philosophies." This fact has already been proven by the Japanese work force. The quality standards which originated in US were implemented more successfully in Japan. This quality trend provided a new approach to the global manufacturing which is getting more and more conscious ofthe demands of the consumer.

"Many manufacturers realize the potential value ofusing diagnostic and decision support software on personal computers to help inexperienced associates feed with 16

complex problems concerning process control, machine maintenance and product

quality. Some ofthese programs are expert of knowledge-based systems which capture

and emulate the reasoning of a recognized expert in a specific domain." As a result of

this fact, knowledge-based expert systems are finding more applications in

manufacturing situations.

In his article "Automatic Inspection in Industry Today", author Richard A.

Brook [1988] summarizes, " The paper concludes that future developments will

combine measurement technology having an increasingly high degree ofprecision and

flexibility with more intelligent information handling and control systems. Automated

inspection will become integrated at a fundamental level into the basic design of

automated production systems and shop floor data communications. The latter

development, aimed at factory-wide, automatic quality measurement and process

control, will be limited initially to the comparatively few situations where there are

opportunities for a complete rethink of production facilities; the most frequent

requirement, however, is to enhance production systems which are already in place,

normally with fairly sever constraints on costs and very restricted scope for modifying

existing plants and manufacturing procedures."

Automation has come to stay. Critiques of automation may have their own reservations, but the fact is that today's industry has more to gain from automation than to lose. Automation has become a key concept in the field of quality control and its

benefits are now only becoming clearer to management. Expert systems being such

an intricate part ofautomation, will have a major role to play in the tomorrow's industry. 17

3.0 Background

3.1 History of the Organization

Products: International Converter, Inc.'s Belpre, Ohio plant produces a wide range of foil, laminating and converting products in a variety of gauges and tempers (0.0002" to 0.008" gauge and dead soft to intermediate tempers) in web width up to 64" and a maximum roll O.D. of 72 inches.

_ Sandwich wraps - colored prints, spot and solid laminates.

_ Foodservice Packaging - foil board laminates.

_ Insulation foil - sheathing and backs for various types of insulation panels.

_ Groundwood labels - beer label substrate and candy wrappers.

_ Caul Stock - finishing and release membrane for wall panels, tabletops and

cabinets.

_ Miscellaneous packaging - stock for composite cans & liners, bag stock,

gift wrap, greeting cards and folding cartons.

_ Foil coating - heavy gauge unsupported foil.

_ General Label - graphic arts foil/paper laminates for sophisticated high

quality & embossed labels.

_ Cigarette .wraps - inner wraps, hinged lid cartons, outer carton and wrap

(gold and plain). 18

Capabilities:

FACILITY: Plant versatility provides customers with many options for production

planning.

Laminating - high speed modem laminating equipment (64" web width & 72" roll

O.D.) handles:

Foil/paper laminations of 0.0002" minimum foil and 15 lb. paper with a

maximum of 0.0035" foil and 32 pt. board. Bare foil ranges from 0.0015" to 0.008".

Other capabilities on the laminator include applying coating and printing one color.

Tri-laminate, film to foil, film to paper and board or paper to paper.

Slitting - a variety ofslitters provide a wide range ofslitting capabilities from edge trim to multiple cuts with a minimum I.D. of 3" and an O.D. to suit most customer requirements. Additional capabilities include multi-width single pass slitting, sheeting, perforating, guillotining, easy open tear tape and state-of-the-art packaging equipment.

Experience is the key to consistent quality and performance of coated foils.

International Converter has more than three decades of knowledge to call upon in recommending the best combination of coatings, types ofpreparation and inks used to produce each customer's individual specification. Coatings range from shellac washes, through waterbase acrylics and specialty types like slip and release coatings. Through technical and management involvement with vendors, customers and industry contacts, the latest foil printing and laminating processes and equipment is researched and used throughout the industry. 19

Quality Assurance:

A dedication to product excellence IS evident in ICI's product quality. A superior product quality is assured by:

International Converter's commitment to quality as its number one priority.

Involvement of all employees and their adherence to the fundamentals of modem

process and quality assurance procedures.

_ ICI representatives working with all customers to fully understand their

specifications, process and special requirements.

3.2 Acceptance Sampling

Although the best quality standard would be one which eliminates the need for inspection, yet for an efficient feedback inspection is still desirable. Inspection has become an important part of quality assurance including revision of raw materials, in­ process goods, and finished products. In manufacturing plants where goods are produced or received in large quantities, it is most desirable to examine a random sample from the entire lot. This is necessary to avoid 100 % inspection which is often time consuming, expensive and has certain restrictions for such products where inspection is destructive. Moreover inspection fatigue due to repetitive inspection procedures will counter the possibility that all accepted products conform to the standards. As a matter offact, no sampling procedure can eliminate acceptance ofnon­ conforming product. Such a condition may only arise when product is manufactured within specification limits in the first place. Rejecting entire lots based on acceptance 20

sampling plans will exert enormous pressure on the vendor to strive for better quality

products. This sampling may be performed in many ways. Inspection performed for

the purpose ofacceptance or rejection ofgoods is known as acceptance sampling. Lot­

by-lot acceptance sampling can be carried out using single sampling, double sampling,

or multiple and sequential sampling plans.

3.2.1 Types of Sampling Plans:

Single Sampling Plan: To briefly explain a sampling plan, every plan should have

three figures available, namely the total number of items N in a lot, the number of

random samples n, and the acceptance number c which is the maximum allowable number of defective in a lot. For example, in a lot of N=50 items, a random sample ofn=3 is picked for inspection and ifthere are c=l or more defective, we reject the lot; else accept the lot. For the purpose of this study only single sampling acceptance plans were used. Under these plans military standards MIL-STD 105E and MIL-STD

414 were used to determine sample sizes for a given Acceptable Quality Level (AQL).

Double Samplin~ Plan: Single sampling calls for a decision on acceptance or rejection of a lot on the basis of the evidence of one sample from that entire lot. Double sampling, however, involves the possibility of putting off the decision on the lot until a second sample has been taken. A lot may be accepted at once if the sample taken is good enough and can be rejected at once ifthe sample is bad enough. But ifthe first sample is neither good nor bad enough, the decision to accept or reject the lot will be based on the evidence of the first and second samples combined. 21

Multiple and Sequential Samplin~: As mentioned above, a double sampling plan

requires evidence from two sample to reach a decision to accept or reject a lot.

Similarly a plan which requires evidence from three or more samples to accept or reject

a lot, is known as multiple sampling plan. Sequential sampling is one where a decision

is possible after each item has been inspected and when there is no specified limit on

the total number of units to be inspected.

3.3 Military Standards

The AQL (Acceptable Quality Level) concept was first devised in connection with the development of statistical acceptance sampling for the Ordnance Department of the U.S. Army. The Ordnance procedures and tables were developed in 1942 by a group under the direction ofengineers from the Bell Telephone Laboratories. In 1945, statistical sampling tables and procedures were developed for the Navy by the

Statistical Research Group of Columbia University. After the unification ofthe armed services, these Navy tables were adopted by the Department ofDefense in 1949 as JAN

(Joint Army Navy) Standard 105.

MIL-STD 105A superseded JAN-STD 105 in 1950, MIL-STD 105B in 1958,

MIL-STD 105C in 1961. In 1963, military agencies from U.S.A., Great Britain, and

Canada, collectively developed common standards and named it ABC-STD 105, which was known as MIL-STD 105D in the United States. MIL-STD 414 are used for variables and the procedures are very much similar to those ofother military standards for attributes.. 22

3.4 Statistical Process Control

Among other techniques ofQuality Assurance, Statistical Process Control (SPC) has been the epitome of Quality Assurance for a long time. Statistical studies are critical component of quality improvement efforts and are known to be the foundation for studying variance in any process. Variance is a "natural" characteristic of any process in this world. No two things in the world are exactly identical. Variance creeps in every process. Process variation can be as a result of "common causes" or "special causes". These causes can be identified with the help of statistical process control.

Major tools used to establish SPC are:

1. Histogram

2. Check Sheet

3. Pareto Chart

4. Cause and Effect Diagram

5. Defect Concentration Diagram

6. Scatter Diagram

7. Control Chart

The most common SPC technique to study a process in progress is the use of

Variables Control Charts such as an X-bar chart (to control the process average), R chart (to control the process range), s chart (to control the process standard deviation), median chart (an alternative to the combination of x-bar and R chart), and individuals chart. The X-bar charts plot process averages of a random sample from the production against three horizontal lines. The central line represents the average value of the 23 quality characteristic corresponding to the in-control state. The other two lines are known as Upper and Lower Control Limits (UCL & LCL). The R chart plots points against DCL and LCL as a measure of variation of a process. The control limits are calculated as follows:

UCLx = X, + A2R

LCLx = Xo - A2R

UCLR = D4R

LCL R = D3R

, , where A2 D4 and D3 are constants and predetermined values obtained from a table.

3.4.1 Preparation Measures Shewart suggested certain decisions that should be made before plotting the control charts as follows:

Objectives of the Charts

1. To analyze a process to gather information required in establishing or

changing specifications or production procedures, or in determining

whether a given process can meet specifications.

2. To decide as to when to look for causes of variation and take corrective

action and when to leave a process alone.

3. To provide a basis for decisions on acceptance or rejection of manufactured

or purchased product. This will ultimately lead to reduction in inspection

costs by using the control chart for variables for acceptance. 24

Choice of the Variable

The real basis of choice is the prospect of reducing or preventing costs. The

variable chosen should be one that is causing rejections or rework involving

substantial costs. However, the variable must be something that can be

expressed and measured in numbers, such as dimensions, basis weight, tensile

strength, etc.

Decision on the Basis of Subgrouping

Subgroups should be selected in a way that makes each subgroup as

homogeneous as possible and that gives the maximum possible opportunity for

variation from one subgroup to the other.

Decision on the Size and Frequency of Subgroups

Shewart suggested four as the ideal group size and in the industrial use of the

control chart, five seems to be the most common size. The subgroup should

be as small as possible for minimum variation within a subgroup and large

enough for sound statistical results. The larger the subgroup, the narrower the

control limits on charts for X and the easier it is to detect small variations. No

rules are laid down for the frequency ofthe subgroups and each case is decided

on its own merits considering the cost of taking and analyzing measurements

and the benefits derived from actions based on the control charts.

Setting up the Forms for Recording the Data

Usually data forms are required to document all the observations while

performing measurement. For this application, in particular, the need for data 25

forms was eliminated by requiring one to enter the subgroup measurements

directly into the expert system, using the keyboard.

Determining the Method of Measurement

Decisions must be made as to the measuring instruments to be used and the

way in which the measurements are to be made.

3.5 LEVELS OBJECT

Expert systems are the most emphasized area in Artificial Intelligence these

days. Expert systems usually consist of a knowledge base, an inference procedure, and

a working memory which is a central data base. There are several expert systems

shells available commercially in the market today. In 1990, Information Builders, Inc.

(IBI) introduced LEVEL5 expert system development package which empowered the developers to build more sophisticated expert systems. Unlike traditional expert systems, which store information in records, columns and rows, LEVEL5 OBJECT has the ability to organize data as interrelated objects, promising the delivery of faster applications that can handle larger knowledge bases. It also has the capability to simplify the process of constructing new expert systems. For instance, users are able to assemble new applications simply by clicking on object icons with a mouse. The code is automatically assembled as the user maneuvers through the development system using a mouse to point and pick icons.

LEVEL5 OBJECT is an application development environment that combines expert system technologies, object-oriented programming, relational database models, 26

and hypertext capabilities. LEVELS OBJECT provides read/write access to dBASE III

databases and views these databases as objects, thereby simplifying the procedures a

developer uses for data exchanges and relationships between complex data structures. .

The LEVELS OBJECT Database Interface Task controls the interaction between a

knowledge base and an external database. LEVELS OBJECT also provides a function

of hypertext application development through simple tools such as displays,

pushbuttons, hyperregions, expands and the object-oriented structure ofthe knowledge

in a LEVELS OBJECT application.

LEVELS OBJECT Release 2.5 has an ability to access remote mainframe data

in addition to direct data access capabilities with personal computer databases. The

Windows development tool also enables users to achieve distributed data access with

IBI's Enterprise Data Access/SQL 2.0, a client/server software package and a part of

IBM's Information Warehouse. Release 2.5 also supports full implementation of SQL

and offers a Dynamic Data Exchange interface, which is one of the several interfaces

included with the new package. Moreover, in addition to its ability to run in Windows

3.0 and 3.1 environments, Release 2.5 also works in Windows running under IBM's

OS/2 2.0 operating system.

3.6 Need for the System

As discussed earlier, automation has reduced time, effort, and the dollar function

In most of the manufacturing applications. Areas such as production (design, 27

fabrication, assembly, etc.), material resource planning, and production control are

already automated in many organizations. Organizations such as General Motors,

Eastman Kodak Company, Saab, etc. have been using machine vision for many years

for the purpose of inspection. Expert systems have also been developed in the past for

control chart applications. Little has been done in the past to automate basic quality

procedures such as acceptance sampling and control charts in terms of providing a

simple-to-use, object-oriented system that would enhance the cause of reducing time

and effort on the part ofretraining the inspectors, and hence provide additional savings

to the industry. The economic justification for developing and installing such an expert

system is fairly difficult without previous application experience. Hence, it requires

long term vision and confidence on the part of management to make such a decision.

Such an opportunity was provided by the management of International

Converter, Inc. by contracting a project to develop a quality control system at their

Belpre, Ohio plant. Prior to the establishment of this quality control support system, there was no documented acceptance sampling plan for in-coming raw material. The in-coming lots were inspected only for the package integrity and a decision to reject was only made if any material was found to be defective during production. This led to the acceptance ofall raw material at the initial stage and problems were encountered during production due to sub-standard or non-conforming raw material. Rejection of raw material at that stage resulted in costs incurred due to time, labor, and effort spent on the material till that stage of production, stopped production time (interruptions), lower employee morale, etc. 28

Table 1. Total Work-in-Process Rejected during the year 1992 (in lbs.)

Product Code Month 1200's 1300's 1400's 1500's 1600's 3000's Total January 4800 4491 45156 4622 0 0 59069 February 1013 16578 32141 2260 0 3207 55199 March 6201 26792 37072 4074 0 0 74139 April 0 27691 43857 4931 1452 2923 80854 May 1194 2635 47047 3079 1050 2268 57273 June 6414 20871 68608 10512 515 0 88136 July 5877 2394 73026 6772 0 2196 90265 August 0 0 93088 7581 7151 3543 111363 September 0 2437 53449 5510 2455 0 63851 October 859 0 67690 7715 0 806 77070 November 0 5884 22044 315 0 0 28243 December 0 2296 43553 2254 0 0 48103 Total 26358 932851 6267311 59625 12623 14943 833565 29

Qi 0) :i .c :; co co co CI) m o ...J .5 [J II II :>30 es:I ~ hON ~ -;- .LJO == t' ...e d3S ~ ~= ! DnV Bu lflC e =-I ..s= xnr ~ ..e AVW ~ ...e ~ 'lIdV = "~ ~ 'llVN t: =C.I 83.::1 ..... erIJ U Nyr . ~

0 0 0 0 0 0 0 0 0 0 0 0 0 e § ~ 0 08 0 8 0 0 § V') .,...... co r-- \0 M N ...... i -.~ spoon ~~f~~Jo ~nI~i\ AJ01U~AUI 30

Table 1 illustrates a monthly breakdown of Work-In-Process (WIP) inventory rejected in pounds during the year 1992. Figure 1 depicts cost in terms of WIP rejected during each month of 1992. Moreover no efforts had been made to control the variable critical to maintain quality at the production level and hence control charts were not established. In short, little, if any, statistical quality control was practiced at any stage of production or shipment. However, strict quality control and inspection was performed only at the finished goods or dispatching level to screen defective or sub­ standard product from being shipped.

International Converter, Inc. has a small staff involved with quality procedures such as routine inspection, calibration oftest equipment, etc. However, the staff is not supported by a Quality Assurance Engineer or Statistical Analyst. In such a situation, an attempt to establish a Quality Control (QC) program would require at least an expert in the field of statistics. These constraints led to the decision to develop a system that would eliminate the need of an expert and at the same time be easy enough for those employees to operate who lack exposure to statistical quality control. Hence, accumulated effect was the end result of establishing the need for a statistical quality control support system to facilitate acceptance sampling and control chart procedures. 31

4.0 Development of the System

4.1 Systems Design

Design of the expert system is a complex affair, keeping in view the complex nature of expert systems. The foremost question was to choose an expert system shell for the purpose of development of the system.

4.1.1 Selecting an Expert Systems Shell

Commercially, a whole range of expert system development toolkits are being offered in today's market. To name a few; ART (by Inference Corporation), Envisage

(by Systems Designers), G2 (by Gensym), Goldworks (by Gold Hill Computers, Inc.),

KEE (by Intellicorp), KES (by Software A&E), Knowledge Craft (by Carnegie Group,

Inc.), LEVEL5 OBJECT (by Information Builders, Inc.), Nexpert-Object (by Neuron

Data, Inc.), NEXUS (by Human Intellect Systems), POPLOG (by Computable

Functions, Inc.), and VP-Expert (by Paperback Software International).

While selecting an expert systems shell for an application, there might be numerous criterion which would affect the selection of an appropriate shell. We can break down these factors by asking ourselves a few questions, such as: What expert systems features does the application require? What kind of data base or file access does it require? Will the application require to be on a network?

Another good move might be to develop a checklist. A checklist of expert 32

systems features which may include things such as forward chaining, backward

chaining, objects, graphical interfacing, help functions, etc. Another checklist might

have questions regarding the data base such as whether the application requires real­

time data or whether it just requires time-delay data. The final checklist will have

questions such as whether the system has to be run on a mainframe, or if it can be run

on a PC, or even if the data is on mainframe, do you want to connect the PC to the

mainframe, and so on.

Following is a list of various features of LEVEL5 OBJECT which might be

helpful in building an application:

THE APPLICATIONS ENVIRONMENT

Procedural Knowledge

The system classes in LEVEL5 OBJECT control much of the behavior in

applications written with this tool and, along with rules, account for all procedural

knowledge representations. Each system class has a default "instance," which is simply

described as the occurrence of the system class itself with values for its attributes.

These default instances are useful templates for developers who wish to create new object classes but who do not want to construct these objects from scratch. For example, in the case of an intelligent data-base application, the system object class might define the basic interface to the data-base. Then the developer only needs to describe the location of the needed data from an external data base. The instances of these pre-defined object classes will then contain the actual values retrieved from the 33 data base. In essence, the data-base object transparently becomes a system class in the intelligent application. Once the data-base information takes this form, it can then trigger other events or actions to occur within the application or be used in subsequent logical processes. In this sense, the data-base information can reflect procedural knowledge as expressed in a set of system objects or classes for a particular domain.

Since logic processes, data, and procedural knowledge are expressed as object classes in LEVELS OBJECT, these objects can form a semantic network that takes advantage of the property of inheritance. Thus, object classes are not only reusable within an application, but can inherit properties from other objects, such as a "parent" or "sibling" object. This ability to build quickly more specialized objects from more general ones offers a significant productivity advantage in building intelligent system applications.

As applications grow more complex, the need for using alternative structures over rules for procedural knowledge representation becomes apparent. LEVELS

OBJECT does, however, support for working with Production Rule Language (PRL) code.

For LEVELS OBJECT applications that involve rules, the tool supports both forward- and backward-chaining inferencing techniques, even within a single knowledge base. LEVELS OBJECT also supports a special type of rule called a "Demon" that watches the state ofall known facts within a working application and looks for certain patterns or events to occur. When they do, then the Demon rules are fired, forcing the inference engine to driven approach but can change midstream to a forward-chaining, 34 data-driven that can respond dynamically to new situations.

LEVEL5 OBJECT also supports a unique editor for creating Dynamic Agendas.

These Agendas allow developers to set out an overall course of events, perhaps to prescribe certain procedures or to test hypotheses. When behaving dynamically,

Agendas allow developers to change the goal of a system during program execution based on the outcomes of certain rules. This means that applications written in

LEVEL5 OBJECT can temporarily pursue alternative .goals, even ifthe first goal is still valid. The result is that the working application can allow for ad hoc explorations and redirect the system focus at any time.

For uncertainty handling, LEVEL5 OBJECT supports multiple confidence schema, allowing developers to deal with uncertain information on a per-fact basis.

Developers can express confidence factors as numeric or relative values that are attached to system rules or classes, or users can indicate at run time the amount of confidence of a system's conclusion or recommendation by choosing from one ofthree complex calculation methods: Product-Space Confidence, Bayesian, or Averaging techniques.

Relational Knowledge

LEVEL5 OBJECT represents relational knowledge primarily through the tool's object orientation; that is, LEVEL5 OBJECT configures objects (that is, system classes) into a semantically related network. In some cases, this object network itself can form the basis for system intelligence, where patterns of inheritance within object classes along with procedural attachments can account for much of the system logic. Such 35

methods can simplify design by eliminating the need to develop complex sets of rules.

Moreover, these object networks can aid in complex pattern-matching tasks often

associated with relational knowledge representation. When developers implement

hypermedia in LEVELS OBJECT, they can easily link hypermedia components to

object attributes. This means that hypermedia activities can cause events or actions to

occur within system classes, such as the firing of Demon rules.

THE DEVELOPMENT ENVIRONMENT

Expert System Building

To build expert system components within LEVELS OBJECT, developers

typically interact with a series of specialized editors that are invoked within windows.

Three critical editors are the "Object Editor," the "Rule Editor" "Display Editor." The

Object editor allows developers to create and edit system classes, as well as launch the

Rule Editor. The Rule Editor interacts with developers to create and keep track of

system rules within object classes, execution statements, displays, and knowledge trees.

The Display Editor gives developers full control over check boxes, radio buttons, text windows, hyperregions, and value boxes for system classes or rules.

Hypermedia Implementation

LEVELS OBJECT limits hypermedia implementation to hypergraphic regions, which means that the technology is primarily used as an interface device. The reason for this is that the object structure in LEVELS OBJECT offers all of the flexibility needed to create semantically related networks, so hypermedia is left to enhance 36

application displays. Here hypermedia in LEVEL5 OBJECT can invoke a form,

display, or dialog windows from within any other currently open window in the

running application.

Knowledge Visualization and Tracing

LEVEL5 OBJECT offers a host of visualization and tracing tools that allow

developers to monitor sessions by providing historical races, single-stepping of rules,

and breakpoints. A special tool called the "knowledge tree" offers a visual

programming environment that allows developers to diagram graphically a knowledge

base much like a flowchart or decision tree, where rules, object classes, Demons, and

facts all have distinct icons. From these icons, developers can open specialized editor

windows to code in system functions that are later automatically converted into binary

code. In this sense, LEVEL5 OBJECT offers a form of built-in Computer-Aided

Software Engineering (CASE).

Integration with Existing DatalPrograms

LEVEL5 OBJECT offers an object-oriented data-base management interface that

can directly access dBASE III +, Lotus 1-2-3, SQL, and ASCII file formats. This feature gives the tool a wide range of applicability in the PC world.

THE USER ENVIRONMENT

Screen Design

In the Microsoft Windows version of LEVELS OBJECT, developers can take full advantage ofthe graphical user interface, including pull-down menus, buttons, radio 37 buttons, icons, check boxes, and more. Hyperregions give users access to auxiliary textual and graphical information as needed. The net result is that LEVEL5 OBJECT has a complete click-and-shoot mouse control throughout running program sessions.

This graphical capability was one of the most important requirements for our application since the end-users were not experts in quality concepts.

Navigational Aids

LEVEL5 OBJECT offers both users and developers a "toolbox" full of devices that help with navigation through a working session. In addition to pull-down menus and standard Windows devices (such as scroll bars, arrows, check boxes, radio buttons and so forth), LEVEL5 OBJECT offers a complete set of default screen attributes that have special meanings to developers. For example, the hyperregion attachment allows the developer to create a new hyperregion, and an iconic device can tell users at run time that this hyperregion is present. Similarly, other on-screen devices can aid users in navigation, and LEVELS OBJECT takes full advantage of the standard Microsoft

Windows interface. Users can also invoke a special Session Monitor that reveals the current status of the inference engine.

Custom Report Generation

LEVEL5 OBJECT provides users with default line-of-reasoning reports that include complete access to the inferential process during program execution. This means users can watch how decisions are reached, while they are being reached, and have the same access to the trace, debugging, and analytical-reasoning tools as developers. The program also provides full audit trails that document how an 38 application arrives at its conclusions.

THE RUN-TIME ENVIRoNMENT

Portability to Other Hardware Platforms

Since LEVEL5 OBJECT observes standards for IBM's Systems Application

Architecture and Microsoft's Windows with Dynamic Data Exchange (DDE), developers can integrate LJ;:VEL5 OBJECT applications with other programs that run under these environments. Systems developed with LEVEL5 OBJECT under Windows are also easily ported to IBM mainframe, DEC VAX, Apple Macintosh, and UNIX workstation platforms that run LEVEL5 OBJECT because of the tool's modular construction and object orientation. This support system made extensive use of DDE to link LEVEL5

OBJECT and Microsoft Excel applications for data exchange.

Cost, Policy, and Licensing of Run-Time Systems

LEVEL5 OBJECT comes with one oftwo possible run-time licenses. The first is a single run-time license that would enable a developer to build a system intended for a single user. The second type of license is for multiple run-time copies without limit.

4.1.2 Software Requirements

_ Expert System Development tool, LEVEL5 OBJECT 2.5 by Information

Builders, Inc. 39

Microsoft Windows 3.0 or 3.1 environment.

Microsoft Excel 4.0.

Paintbrush or any other Bit Map Processor.

4.1.3 Hardware Requirements

IBM PC or 100% compatible with a 286 or 386 processor.

A 3 1/2" 720K or 5 1/4" 1.2 M floppy disk drive.

A hard disk drive with a minimum of 4 MB free hard disk space.

At least 2 MB of memory.

An EGA or VGA graphics board and monitor.

Mouse

4.2 Knowledge Acquisition and Representation

From the point of view of computer science, the notion of data is unstructured sets of numbers, facts and symbols. These data can convey information only in virtue of some structure or decoding mechanism. Knowledge is seen as a concept at a higher level of abstraction and is derived from information. For example, '2' is a datum, '2 defective products' could be information about an inspection at some situation but

'Reject the lot when 2 out of 3 samples are defective' is knowledge about acceptance sampling.

A knowledge engineer is a person who bridges the gap between knowledge and 40

explicit rules and codes the human expert's knowledge into an expert system. To build this support system, three types of knowledge representation were used. (1)

Declarative Knowledge. This knowledge is gathered through a dialogue with the user to establish what facts are true at that time. For example, the system gathers

information on inspection measurements from the end user and procedural knowledge decides how to use these facts. (2) Procedural knowledge. This knowledge forms the core of the knowledge base and the reasoning part of the system which infers conclusions. This knowledge is collected in the form of Production Rule Language

(PRL). For example, "IF lot OF size := 12 THEN action OF DDE 3 IS TRUE" (3)

Control Knowledge. The system needs to have a variety ofcontrol strategies available so that alternatives can be tried out at run-time. For example, various types of strategies such as 'Demons', 'When Changed Methods', and 'Rules' are used in this support system to control the reasoning of the system. Figure 2 depicts a flow chart of knowledge acquisition process.

In the initial stage of the development of acceptance sampling plan, extensive data was collected. In-coming raw material were identified as foil, paper, adhesives, and inks & coatings. Parameters critical to maintain the quality ofwork-in-process and of the finished product were established. These parameters were categorized as variables (such as tensile strength) and attributes (such as surface finish) and are as follows:

Foil

Gauge (Variable) Knowledge Manuals, Quality Engineer Procedures, Engineer & Tables

Knowledge Base

Inference Engine

Expert System

Figure 2. Knowledge Acquisition Flow Chart

~ J-l 42

Oil Spots (Variable)

Edge (Attribute)

Pin holes (Variable)

Moisture content (Variable)

Streaks (Variable)

Paper

Moisture Content (Variable)

Tensile Strength (Variable)

Basis Weight (Variable)

Dirt Count (Variable)

Adhesives

Temperature (Variable)

Viscosity (Variable)

Inks & coatings

Temperature (Variable)

Color (Attribute)

The next logical step was to decide on Acceptable Quality Levels (AQL) for each ofthese parameters. Table 2 lists the AQL's decided for the parameters. The type 43

Table 2. Acceptable Quality Level (AQL) for different Sampling Parameters of the. In-Coming Raw Material.

Material SampljDI: Parameter AoL (Percent)

Foil Edge 0.0 Foil Streaks 3.0 Foil Wettability 1.0 Foil Oil Spots 1.0 Foil Pin Holes 0.0 Foil Gauge 10.0

Paper Moisture Content 1.0 Paper Dirt Count 2.0 Paper Tensile Strength 3.0 Paper Package Integrity 0.0 Paper Basis Weight 10.0

Adhesive Viscosity 10.0 Adhesive Temperature 2.0

Ink/Coatings Temperature 2.0 Ink/Coatings Color 0.0 44 of sampling plan was then chosen as a single sampling plan and MIL-STD 105E were used for attributes and MIL-STD 414 for the variables. The factors affecting the choice of a sampling plan were:

- Administrative efficiency

- Type of information produced

Average amount of inspection required

- Impact of procedures on material flow

Secondly data forms were prepared to collect information on the progress ofthe production process in relation to the production machines; namely the laminator, slitting machines and the glutonizer. These data forms, or check sheets, were designed as a combination ofattribute, variable and defect-location check sheets. This data collection was the first step towards establishing control charts. The check sheet provided a list of defects occurring during the production run. A Pareto analysis was conducted on the data obtained. Pareto analysis is a tool used to identify and prioritize problems for solutions. It is based on the concept of "the vital few versus the trivial many".

Appendix A contains the Pareto charts and Table 3 lists all major defects encountered.

4.3 Acceptance Sampling

The user-interface displays were designed and created to allow the user to manually select desirable type of raw material and parameters to be inspected. The menu provides the initial choice of four raw materials (foil, paper, adhesives, inks and 45

Table 3. Types ofDefects Found in the Raw Material Product Code DEFECTS 1200's 1300's 1400's 1500's 1600's 3000's Total Foil Splice Tot 143 1 118 101 6 80 449 SPL.Fail 45 2 13 50 0 7 117 Sticky 22 6 115 15 0 21 179 Sawtooth 16 0 12 13 0 28 69 Baggy 1 1 33 5 0 31 71 Streeks 6 5 27 13 0 28 79 Pin Holes 24 3 60 12 . 0 6 105 Chatter 0 0 1 0 0 10 11 Br. Matte 1 3 11 4 0 0 19 Corrosion 7 4 199 1 0 10 221 Wettable 1 0 0 1 0 0 2 Wrinkles 26 0 11 8 0 12 57

Paper Splices 24 0 47 23 3 27 124 Wrinkles 20 0 38 3 0 8 69 Baggy 12 1 134 17 0 26 190 Slime Spot 0 1 10 0 0 0 11 Wild Spot 0 0 6 0 0 0 6 Welts 8 0 76 1 0 3 88

Coat.llnks Color 0 0 0 0 0 0 0 Viscosity 24 0 0 0 0 1 25 Appearance 0 0 3 1 0 1 5 Adhesion 0 0 0 1 0 1 2 Flow Out 0 0 1 1 0 1 3

Adhesive Temperature 0 0 0 0 0 0 0 Viscosity 0 0 2 0 0 0 2 Dirt Count 0 0 5 10 0 0 15

Total 380 27 922 280 9 301 1919 46

coatings). The user is prompted to select either ofthese four raw-materials by pointing and clicking the mouse on the picture of the raw material displayed on the screen

(Figure 3). These pictures are on pushbuttons which are linked to different screens, each of those screens provide similar pushbuttons for the parameters available for that particular raw material to be inspected (Figure 4, 5, 6, and 7). Again the user points and clicks on the pushbutton of the required parameter to be inspected and a different screen appears which prompts the user to enter the number of packages of that particular raw material on the shipment (Figure 8). This is helpful in determining the sample size for that particular lot. The lot size entered by the user is matched with the

Military Standard tables containing sample sizes for various lot sizes in the Microsoft

Excel file and the appropriate sample size is displayed back to the user (Figure 9).

Figure 10 illustrates the working of acceptance sampling procedure.

Now inspection has to be performed on 'n' number of samples taken from the entire lot, where n is the sample size suggested by the system. After the inspection has been performed on the samples, the user is now prompted to enter the measurements in prompt boxes provided on the screen (Figure 11). These measurements are exported to the Microsoft Excel file where statistical calculations are performed. The calculations for variables are performed on the assumptions of the sample of the parameter to be from a population of unknown standard deviation and two-sided specification limits. The Microsoft Excel file runs preset macro commands and performs the calculation to accept or reject that lot. This recommendation is then 47

1= Quality Assurance k~ file

To start Acceptance Sampling, Click on the picture oCthe Material to be Inspected

Figure 3. Acceptance Sampling Menu 48

t:f~ Quality Assurance ~", - r~; Eile Click on picture of the-parameteryou wish to inspect:

-

Figure 4. Foil Menu 49

!~ Quality Assurance r-- file

Click on picture of the parameter you wish to inspect:

-x.

Figure 5. Paper Menu 50

1= Quality Assurance r-.- file

Click on picture oCthe parameter you wish to inspect:

Figure 6. Adhesives Menu 51

l~ Quality Assurance A Eile

Click on picture of the parameter you 'Wish to inspect:

Figure 7. InksICoatings Menu 52

;] Quality Assurance ~ Eile S86Iplq Procedure forPara6le/er GAUGE

Enter the Total Number ofRolls I&J ofFoDontlUsSmppment ,----~

Click on OK button to see Sample Size III

Figure 8. Menu to Enter the Lot Size 53

;~~ Quality Assurance r- file Calculationsfor Gauge

For this Shipment SAMPLE SIZE is 3

Click OK to Enter the Measurements -

Figure 9. Sample Size Menu Lot Size Knowledge Base ----...... Sample Size CD CD eDt • -- Inspection of Samples ® Inspection Measurements @ Recommendations to Accept/Reject the Lot

Figure 10. Flow Chart of the Acceptance Sampling Procedure

U1 ~ 55

1= Quality Assurance C file Enter the GAUGE Measurements Performed on the Samples Gauge Type Gauge Type 1 10.00024 @ 000245 0 000285 o 00025 0 0003 2 10.00025 o 000265 0 00035 3 IDI!mI o 00021 0 000485 o 000215 0 0005 o 00028 0 001

Upper

LOW6 I . ------" After eJl1erDc the Yalues,clicko. OK 1D proceed ~====---:ll furtkeraJUltJleJlclickoJl-CoatiJlue".

.' . _.'.

Figure 11. Measurement Menu 56

displayed to the user (Figure 12) with a color scheme of red (for rejection) and green

(for acceptance).

For example, let us suppose that we are receiving 12 rolls of foil and we need

to inspect the gauge ofthe foil (which should be 0.00025). If the lot size is entered as

12, the system will respond back and tell you that the sample size is 3. Now gauge

measurements are performed on three random samples from the lot of 12 and these

measurements are entered in the promt boxes. We then click on the radio button group

for the foil type to be 0.00025 and click on 'OK'. The program will respond within

seconds and recommend whether we should accept the entire lot or reject it.

4.4 Statistical Process Control

The process of plotting and analyzing the control charts by the system is

illustrated in Figure 13. For the purpose of SPC, the support system offers an option

in the main menu (Figure 14). The SPC menu then offers the user to either review an

old control chart or to plot a new control chart with fresh data.

After selecting the process to be monitored, to create a new chart, the user has

to enter the averages obtained from the sample taken from the on-going process. A

radio button group allows the user to choose the parameter to plot the chart for (Figure

15). The system then prompts the end user to enter the measurements on random production samples collected at regular intervals oftime (Figure 16) and conveys these

values to the Microsoft Excel file using the "poke" command ofDDE. In the Microsoft

Excel file calculations are performed to determine the average and range of the data 57

r"l Quality Assurance b file

/ ~ .... /,v... ,.. • Jo,. )"" ... \. " ...... '- :~1~ ~>~s ~v' ~ ~ ~ ,,~ ~' ,~ ~,~ ,'~ ~ >~ If1 \ 1 ' >A '-; :' y j

;\oJ _>h.,)\,." ... ~" ....."",.i .... " , ~ ,...... ,(0,...... ~~..~"A-J,.~ ",_",::'

Based on the calcuJ.adons perfonned using MlL-SlD 414 for the purpose of inspection perfonned by acceptance sampling, recommendation is to -

-

Figure 12. Recommendation Menu veL, ~/ a M x \~ Random Measurements Knowledge LCL wi} Base Control Chart of-----~ the Parameter Being Controlled

Analysis of the • -- Control Chart

Figure 13. Flow Chart of the Control Chart Plotting and Analysis Procedures

Ul 00 59

i~l Quality Assurance P file

) '( ~ .. 'J ...

"...... __"'-...... "" "' ...... ~ ....N

Click on one of the pictures below:

-

Figure 14. Main Menu 60

'k~~ Quality Assurance r~- file

Sele ct by clicking the typ e of material and the variable you wish to control and then Click on PROCEED Type-----, Operator-----­ Special Req---- @ '1100 @ JUne Speed! o Number of Rolls o '1200 o Oven Temperature o Moisture 5 to 6" o '1300 o Adhesive Viscosity o MaxOD o '1400 o '1500_3100 Quality Control--- o Total Basis Wt o '3200 o Moisture o F_P_GWt -

Figure 15. Variable Selection MenD 61

i~~~5~ Quality Assurance [0 file

Enter 10 measurements performed on Random Samples from the Current Lot

1 ~~~~ 6 ~~~~ 2 ~~~~ 7 ~~~~ 3 ~~~~ 8 ~~~~ 4 ~~~~ 9 ~~~~ 5 ~~~~ 10 ~~~~

You are controlling the Variable LINE SPEED by Operator and Material Type is 1100

Figure 16. Control Chart Measurement Menu 62

entered, and to determine the trial control limits. With these values, the Microsoft

Excel file then plots X-bar chart and R chart using the macros initiated by "execute"

command of DDE and the graphical capabilities of Microsoft Excel 4.0 . These charts

are then exported to the knowledge base using the "request" command and displayed

on the relevant displays (Figure 17 and 18). The above mentioned "poke", "execute",

and "request" commands are automatically initiated by the support system.

The sys~em is capable of.analyzing these data points on the control chart. The

control charts exhibit various patterns. Western Electric Company engineers identified

fifteen such characteristic patterns as: natural patterns, shift in level patterns (sudden

shift in level, gradual shift in level, trends), cycles, wild patterns (freaks and

grouping/bunching), multi-universe patterns (mixtures, stable mixtures, associated with freaks and grouping/bunching), instability patterns, and relationship patterns (interaction and tendency of one chart to follow another). Unnatural patterns are found when one ofthese fifteen natural pattern is not visible. The rules ofthumb for unnatural patterns are based on probability calculations that indicate, for a stable process, the proportion ofpoints that will fall near the centerline, near the control limits, or beyond the control limits; or the expected number of runs above average, the expected number of runs below average, the length ofa run up, or the length ofa run down. The support system makes use of such rules of thumb to decide if a process is in control or lacks control.

There are seven such rules as listed below: 63

:~?~ Quality Assurance p Eile X-CHART

24 uc en 23 ...-..-. ---. .-..-- - ~ ~ x -a 22 ....--'-"'- ~ ----- > 21 c LC ~ 20 ~ 19 18 1 2 3 4 5 6 7 8 9 10 Da.ta Point # You are controlling the Variable LlNE SPEED by Operator and Material Type is 1100

Figure 17. X-Chart Display 64

~::-:;~ Quality Assurance I file R-Chart

2 3 4 5 6 7 8 9 08t8#

You are controllini the Variable LINE SPEED by Operator and Material Type is 1100

Figure 18. R-Chart Display 65

Rule 1. A process exhibits lack of control if any single value falls outside of a

control limit.

Rule 2. A process exhibits lack ofcontrol ifany two out ofthree consecutive points

fall in zone A or beyond.

Rule 3. A process exhibits lack of control if four out of five consecutive points fall

in zone B or beyond.

Rule 4. A process exhibits lack of control if eight or more consecutive points lie

on one side of the centerline.

Rule 5. A process exhibits lack ofcontrol ifeight or more consecutive points move

upward in a value or if eight or more consecutive points move downward

in value. The run of eight points can be on either side of the centerline or

cross over the centerline.

Rule 6. A process exhibits a lack of control if an unusually small number of runs

above and below the centerline are present (like a saw-tooth pattern).

Rule 7. A process exhibits a lack of control if 10 consecutive points fall within

zone C on either side of the centerline.

Note:

Boundary between

Upper Zones A & B = x, + (2/3) A2R

Boundary between

Lower Zones A & B = Xo - (2/3) A2R

Boundary between 66

Upper Zones B & C = x, + (1/3) A2R

Boundary between

Lower Zones B & C = Xo - (1/3) A2R

The support system makes use ofthese rules to analyze the control charts. The system scans each data point on the chart and informs whether the process is in control or exhibiting any lack of control ( Figure 19 and 20).

4.5 Testing and Maintaining the System

Any software system that is to be deployed has to undergo evaluation to determine if it performs acceptably. The system evaluation was separated into two components: validation and verification. Validation refers to determining whether the right system was built, that is, whether the system does what it was meant to do and at an acceptable level of accuracy. Validating an expert system involves confirming that the expert system performs the desired task with a sufficient level of expertise.

Verification refers to determining whether the system was built right, that is, whether the system implementation correctly corresponds to its specifications. Therefore verifying an expert system means confirming that the program accurately implements the acquired expert knowledge as documented.

The validation and verification of the support system was performed in two stages. 67

Figure 19. Control Chart Analysis I 68

Figure 20. Control Chart Analysis n 69

One was during the development of the support system and the other was after the development was completed. The knowledge acquisition process automatically performs verification tests, checking the evolving software for implementation bugs.

Each time the partially completed program is run to test the knowledge in the program, all aspects of the operation of the program are tested as well. The knowledge acquisition cycle was used to develop the system cumulatively, and the entire program was run and evaluated many times as it was developed. Thus most parts of the developing program were evaluated and re-evaluated repeatedly.

After the complete development of the system, the program was validated against the mathematical results of Military Standards and Control Chart procedures.

This was equivalent to testing against expert performance and field testing. Built-in features of LEVEL5 OBJECT were also used in system verification. The development tool provides an automatic check for rule systems, syntax errors, redundant rules, rules with same name and many other programming errors.

The system was tested at the manufacturing plant ofIntemational Converter, Inc. at Belpre, Ohio for a period of three months and is still under the testing period. The intelligent system has so far operated 'successfully' in the sense of development environment, application environment, and user environment. The run-time environment is still under testing conditions. The system is being maintained and monitored by the developer. The system will also be evaluated for economical benefits derived from installing the expert system in the long run. For this purpose, it is suggested that a long-term maintenance group be designated or formed and trained. 70

Maintenance encompasses not only fixing problems found during system operation but also revising internal data and knowledge such as switching from 'regular inspection' to 'tightened inspection' when required. 71

5.0 Results and Conclusions

The statistical quality control support system was established at International

Converter, Inc., Belpre, Ohio in January, 1993. While the system was being developed,

workshops were arranged at the manufacturing plant to introduce quality concepts to

the employees working on the floor. The management strengthened its commitment

to quality by arranging these workshops and this led to a 'quality awareness' among the

employees. The first step to a successful quality program in any environment is quality

consciousness. The organization, as a whole, became quality conscious and this served

as the foundation to the establishment of a statistical quality control support system.

Since the support system is in the stage ofbeing tested, it is too early to provide any financial or any other type of benefit arising out of its implementation. However, it is expected that the knowledge-based system can provide many economic advantages to an organization such as:

1. Enhancement ofproduct reliability

With superior quality raw material and improved production processes, the product reliability will be enhanced.

2. Reduction in manpower costs in terms of

a) Number of employees: By automating the acceptance sampling procedure, fewer employees will have to be devoted to perform the tedious task of acceptance sampling. It also indicates that quality experts can be spared from performing inspection 72

of raw material and their time can be devoted to other areas.

b) Number of hours allocated for complex calculations: Inspection personnel

will have to devote less time performing complex calculations to figure out whether to '.

accept the lot or to reject it. By entering the measurements performed on the sample

of raw material, the system will automatically provide such decisions within no time

at all.

c) Error on part ofhuman operator: Since the calculations are being performed

by the system, chances of human error is further minimized.

3. Reduction in costs resulting from 100% inspection:

Since acceptance sampling plans allows inspection of a required sample size from the entire lot, cost in terms of time and effort is reduced by not performing 100% inspection.

4. More pressure on suppliers to provide quality raw-material:

Rejected goods exert tremendous pressure on a supplier. In today's competitive market, suppliers avoid rejection of goods as it drives the customer to other potential suppliers.

5. Reduction in spoilage and rework:

Since the raw material is "screened" by acceptance sampling, production department receives "better quality" material which reduces scrap and rework.

6. Decrease in number ofproduction interruptions:

Better quality raw material also means less problems during production. Number of interruptions will greatly reduce with an increase in raw material quality. 73

7. Better employee morale and hence improved productivity:

Worker's morale improves because they are not seen as the problem. This aspect leads to further benefits such as: (a) less employee absence, (b) less burnout, (c) more interest in the job, and (d) motivation to improve work.

8. Improved product quality:

Improved quality ofraw material, improved production processes and higher employee morale leads to an improvement in the product quality.

9. Larger share in the market as a result oflowered costs and better quality:

Improved product quality will result in lower cost per unit, satisfied customers and hence larger share in the market. 74

6.0 Recommendations

The statistical quality control system discussed above has been custom designed

and developed for the sole purpose of quality control applications at International

Converter, Inc., Belpre, Ohio. Because ofthe specific application use, the system menu

and screens have been designed to suite the needs of the organization. Although the

system model can be used for general purposes by modifying the menu and screens, it

is advisable to redesign the system to meet the requirements of other organizations.

The system developed is practically maintenance free (in short-term) and easy to modify. It has been designed to be used even by people not very comfortable with statistical data. While performing the calculations for acceptance sampling, the system avoids exposure of complicated data to the user and directly provides the recommendations based on statistical calculations. Similarly, while plotting the control charts, the user is excused from determining trial control limits. The system determines the control limits itself by performing statistical calculations on the input data and revises these limits every time new data is entered. This feature helps in keeping track ofcontrol limits and provides real-time results as it determines new control limits every time the data is entered.

However, ifthe pre-set standards (such as MIL-STD l05E, calculation routines, etc.) have to be changed, the system does not require any modifications and only the

Microsoft Excel file has to be modified accordingly. Maintenance of the program will require changes to the implementation of knowledge and to the knowledge itself in a 75 long-term perspective. Thus a domain expert should be involved in the maintenance process, at least on a consulting basis.

In short, skills needed for operating and maintaining the expert systems differ from those needed to develop the prototype system. For systems operators, high degree ofdevelopment expertise is not required. People with less experience and background can be utilized to run the system and to maintain it. Of course, if large revisions or expansions of the system are to be done, personnel with greater expertise usually will be needed.

6.1 Drawbacks and Limitations

A certain limitation of this system is that the application is limited to the specific use by International Converter, Inc. A broader and more generic use is restricted in the sense that the display screens are custom designed for International

Converter, Inc. and its applications to other situations will require changes and modifications in the original system.

A different type of drawback related to the performance ofthe system is forced on by the statistical procedures used by the knowledge base, such as military standards, control chart procedures, etc. which have an in-built margin of error due to the probabilistic nature of these procedures.

6.2 Further Scope of Research

Although the applications ofthis systems are in relatively well-understood task 76

areas, in future an expert system might be required to operate in more complex areas

wherein no 'objective' solution exist.

I. System Expansion

(1) It may be desirable, during the years that the system operates, to make major changes to the system (perhaps expanding coverage), adding major new features and capabilities, or developing other expert systems or conventional systems that will integrate with or communicate with the original system. Onesuggestion is to expand the scope of this system to encompass all acceptance sampling plans such as double sampling, multiple sampling and so on. This might be done in conjunction with the availability ofother Military Standards that might be applicable to the future situations.

Such an expert system will cover all the types of acceptance sampling and will decide which type ofa sampling plan best meets all the requirements ofa given problem. The expert system will also be capable of switching between 'regular inspection' and

'tightened inspection' procedures.

(2) Further, to reduce manual data entry, the system can be integrated with bar code identification of the incoming raw material packages. The bar codes on the packages will identify the contents and this information can be directly provided to the expert system instead of manually entering the data through a keyboard.

(3) Another suggestion is to make available all types of control charts such as an s-chart, moving range chart, median chart, etc. to the system. The expert system will then provide a recommendation as to which type of control chart best suits the process. It is also left to the scope of further studies to expand the system's capability 77 to determine if a process is in control (or not) using a wider range ofset ofrules than what the system uses at present.

II. Related Research

An interesting branch of this system would be a study to integrate the system to the machines on the production floor through a servo-control mechanism via serial port of a computer. In this case, while performing control chart analysis, the system will not provide recommendations. Instead, the output will be directed to the servo­ control mechanism which, in-tum, will adjust the required parameters to bring the process under control, if needed. For example, if the variable under study is temperature and the expert system determines that the temperature has shifted from its average by 10°F (say, from 175° to 185° F), the expert system will activate the servo­ control which will adjust the temperature knob to decrease the temperature by 10°F. 78

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APPENDICES 85

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------