statistical quality control support system to facilitate acceptance sampling and control chart ...

100 Pages·2007·6.11 MB·English

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STATISTICAL QUALITY CONTROL SUPPORT
SYSTEM TO FACILITATE ACCEPTANCE
SAMPLING AND CONTROL CHART
PROCEDURES/
A Thesis Presented to
The Faculty ofRuss College ofEngineering and Technology
Ohio University
In Partial Fulfillment ofthe 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 ofthe 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 ofSampling 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 NeedfortheSystem 26
4.0 Development ofthe System
4.1 Systems Design 31
4.1.1 Selecting an Expert Systems ShelL 31
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4.0 Development ofthe System (contd.)
4.1.2 SoftwareRequirement 38
4.1.3 HardwareRequirement 39
4.2 KnowledgeAcquisitionand Representation 39
4.3 AcceptanceSampling 44
4.4 StatisticalProcess Control. 56
4.5 Testing and Maintaining the System 66
5.0 ResultsandConclusions " 71
6.0Recommendations 74
6.1 DrawbacksandLimitations 75
6.2 FurtherScope ofResearch 75
Bibliography 78
AppendixA:Pareto AnalysisCharts 85
AppendixB: AQL DeterminationSheets 93
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List of Figures
Page No.
Figure 1. Cost Incurred due to Work-In-Process Inventory Rejection 29
Figure 2. Flow Chart ofthe 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 ofthe 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. ControlChartAnalysisII 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
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1.0 Introduction
America has always been the pioneer in the field ofquality assurance. Names
like W. A. Shewart, W.E. Deming among others instantly come to mind at mere
mention ofthe word quality. The 19thcentury was the era ofthe beginning ofquality
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 assemblylineintothemanufacturingenvironmentofFordMotor 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. GeorgeEdwards was the founding President
ofthe American Societyfor QualityControl (ASQC). In 1950's,W. Edwards Deming,
another Bell System employee, worked in Japan on the invitation of the Union of
Japanese ScientistsandEngineersandnow Deming'smethods 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
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parameters, required in automation procedures, into an operational system at any point
ofthe manufacturing process is an extremelytime-consuming iterative actionthat 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), "expertsystems are computer-basedsoftware 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
particularproblem solving domain. The knowledge base is manipulated using a control
mechanism, commonly called the "inference engine", to reach conclusions or provide
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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 ofsubjectsconcernedwith 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 ofnumbers. 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 ofan intelligent system are:
they solve complex problems
they behave logically
they make effective use ofexisting information
they provide non-linear program navigation
they are responsive and adaptive
they are user-friendly and highly interactive
Expert systems are perhapsthe most common form ofthe AI system. The label
'expert system' is not appliedto one singletype ofsystem, orto even a class ofsystems
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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 ofusing 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.
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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 ofInternational 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
ofin-process goods and the production process on the whole. A huge quantity ofraw
material was scrapped during production because of inferior quality and added to the
cost in terms ofinterruptions inproduction processes andresources already used on that
particularraw material. This study wasto 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 ofthe organization. The major
objective ofthis study was toprovide a user-friendly object-orientedquality systemthat
will automate the acceptance sampling procedures and exercise automatic process
control in terms ofestablishing, maintaining, and interpreting control charts to counter
quality-related problems at the plant.