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0- Statistical Quality Control Support System to Facilitate Acceptance Sampling and Control Chart Procedures

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0- Statistical Quality Control Support System to Facilitate Acceptance Sampling and Control Chart Procedures 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.
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