Intelligent Modeling, Diagnosis And Control Of Manufacturing Processes
eBook - PDF

Intelligent Modeling, Diagnosis And Control Of Manufacturing Processes

  1. 272 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Intelligent Modeling, Diagnosis And Control Of Manufacturing Processes

Book details
Table of contents
Citations

About This Book

This volume demonstrates that the key to the modeling, diagnosis and control of the next generation manufacturing processes is to integrate knowledge-based systems with traditional techniques. An up-to-date study is given here of this relatively recent development.The book is for those working primarily with traditional techniques and those working in the knowledge-based systems field. Both sets of readers will find it to be a source of many specific ideas about the integration of knowledge-based systems with traditional techniques, and carrying a wealth of useful references.

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Yes, you can access Intelligent Modeling, Diagnosis And Control Of Manufacturing Processes by B-t Chu, Su-shing Chen in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Science General. We have over one million books available in our catalogue for you to explore.

Information

Publisher
World Scientific
Year
1992
ISBN
9789814503570
Topic
Biological Sciences
Subtopic
Science General
Index
Biological Sciences

Table of contents

  1. CONTENTS
  2. PREFACE
  3. CHAPTER 1 Manufacturing Diagnosis and Control: A Task-Specific Approach*
  4. CHAPTER 2 The Theory and Application of Diagnostic and Control Expert System Based on Plant Model
  5. CHAPTER 3 Integrated Problem Solving for the Diagnosis of Interacting Process Malfunctions
  6. CHAPTER 4 A Neural Network Model for Diagnostic Problem Solving
  7. CHAPTER 5 PROCESS CONTROL SYSTEM FOR VLSI FABRICATION
  8. CHAPTER 6 Development and Application of Equipment-Specific Process Models for Semiconductor Manufacturing
  9. CHAPTER 7 Continuous Equipment Diagnosis Using Evidence Integration- An LPCVD Application
  10. CHAPTER 8 Equipment/Instrument Diagnosis with Continuous and Discrete Causal Relationships
  11. CHAPTER 9 INTELLIGENT CONTROL OF SEMICONDUCTOR MANUFACTURING PROCESSES
  12. CHAPTER 10 A Machine Learning Approach to Diagnosis and Control with Applications in Semiconductor Manufacturing
  13. Bibliography