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.
Part 1 Knowledge-based statistical approach: chronological equipment diagnosis using evidence integration, N. Chang and C. Spanos; process control system for VLSI fabrication, E. Sachs et al. Part 2 Model-based approach: model-based plant diagnosis and control expert system, J. Suzuki et al; knowledge representation for multiple fault resolution and corrective action planning in chemical diagnosis, J. McDowell and J. Davies. Part 3 Machine learning and neural network approach: intelligent diagnosis systems for manufacturing using abductive technology, G. Mongermory et al; a connectionist approach to diagnostic problem solving, Y. Peng and J. Reggia; and others.