TAILIEUCHUNG - Neural Network Applications
Neural Network Applications for Group Technologyand Cellular Manufacturing | Suresh Nallan C. Neural Network Applications for Group Technology and Cellular Manufacturing Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton CRC Press LLC 2001 4 Neural Network Applications for Group Technology and Cellular Manufacturing Nallan C. Suresh State University of New York at Buffalo University of Groningen Introduction Artificial Neural Networks A Taxonomy of Neural Network Application for GT CM Conclusions Introduction Recognizing the potential of artificial neural networks ANNs for pattern recognition researchers first began to apply neural networks for group technology GT applications in the late 1980s and early 1990s. After a decade of effort neural networks have emerged as an important and viable means for pattern classification for the application of GT and design of cellular manufacturing CM systems. ANNs also hold considerable promise in general for reducing complexity in logistics and for streamlining and synergistic regrouping of many operations in the supply chain. This chapter provides a summary of neural network applications developed for group technology and cellular manufacturing. Group technology has been defined to be in essence a broad philosophy that is aimed at 1 identification of part families based on similarities in design and or manufacturing features and 2 systematic exploitation of these similarities in every phase of manufacturing operation Burbidge 1963 Suresh and Kay 1998 . Figure provides an overview of various elements of group technology and cellular manufacturing. It may be seen that the identification of part families forms the first step in GT CM. The formation of part families enables the realization of many synergistic benefits in the design stage process planning stage integration of design and process planning functions production stage and in other stages downstream. In the design stage classifying parts into families and creating a database that is .
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