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Although the tenn quality does not have a precise and universally accepted definition, its meaning is generally well understood: quality is what makes the difference between success and failure in a competitive world. Given the importance of quality, there is a need for effective quality systems to ensure that the highest quality is achieved within given constraints on human, material or financial resources. This book discusses Intelligent Quality Systems, that is quality systems employing techniques from the field of Artificial Intelligence (AI). The book focuses on two popular AI techniques, expert or knowledge-based systems and neural networks. Expert systems encapsulate human expertise for solving difficult problems. Neural networks have the ability to learn problem solving from examples. The aim of the book is to illustrate applications of these techniques to the design and operation of effective quality systems. The book comprises 8 chapters. Chapter 1 provides an introduction to quality control and a general discussion of possible AI-based quality systems. Chapter 2 gives technical information on the key AI techniques of expert systems and neural networks. The use of these techniques, singly and in a combined hybrid fonn, to realise intelligent Statistical Process Control (SPC) systems for quality improvement is the subject of Chapters 3-5. Chapter 6 covers experimental design and the Taguchi method which is an effective technique for designing quality into a product or process. The application of expert systems and neural networks to facilitate experimental design is described in this chapter.
List of contents
1 Introduction.- 1.1 Quality Assurance Systems.- 1.2 Knowledge-Based Systems for Quality Control.- 1.3 Neural Networks for Quality Control.- 1.4 Integrating Expert Systems and Neural Networks for Quality Control.- 1.5 Summary.- References.- 2 Artificial Intelligence Tools.- 2.1 Expert Systems.- 2.2 Neural Networks.- 2.3 Summary.- References.- 3 Statistical Process Control.- 3.1 Statistical Process Control (SPC) and Control Charting.- 3.2 XPC: An On-line Expert System for Statistical Process Control.- 3.3 Intelligent Advisors for Control Chart Selection.- 3.4 Summary.- References.- 4 Control Chart Pattern Recognition.- 4.1 Control Chart Patterns.- 4.2 A Knowledge-Based Control Chart Pattern Recognition System.- 4.3 Using Neural Networks to Recognise Control Chart Patterns.- 4.4 Composite Systems for Recognising Control Chart Patterns.- 4.5 Summary.- References.- 5 Integrated Quality Control Systems.- 5.1 The Integration Process.- 5.2 An Example of Integrating an Expert System with Neural Networks for Quality Control.- 5.3 Summary.- References.- 6 Experimental Quality Design.- 6.1 Taguchi Experimental Design.- 6.2 Neural Networks for the Design of Experiments.- 6.3 Summary.- References.- 7 Inspection.- 7.1 Role of Inspection in Quality Control.- 7.2 Automated Visual Inspection.- 7.3 Knowledge-Based Systems for Automated Visual Inspection.- 7.4 Neural Networks for Automated Visual Inspection.- 7.5 Discussion.- 7.6 Summary.- References.- 8 Condition Monitoring and Fault Diagnosis.- 8.1 Condition Monitoring.- 8.2 Diagnosis.- 8.3 Discussion.- 8.4 Summary.- References.- Author Index.
