Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/10692
Title: MACHINE CELL FORMATION THROUGH NEURAL NETWORK MODELS
Authors: Defersh, Fantahun Melaku
Keywords: MECHANICAL INDUSTRIAL ENGINEERING;MACHINE CELL FORMATION;NEURAL NETWORK MODELS;GROUP TECHNOLOGY
Issue Date: 1999
Abstract: This ME dissertation is a result of a 6 months research work in the area of machine cell formation in cellular manufacturing systems and submitted in partial fulfillment for the award degree of MASTER OF ENGINEERING in MECHANICAL ENGINEERING with specialization in Production and Industrials System Engineering. It is a demonstration of the practical use of neural network models in designing cellular manufacturing systems. Chapters 1, 2 and 3 have a major thrust on introducing group technology and reviewing the related literature. Chapter 1, the introductory chapter, presents the need for implementing group technology (GT) way of manufacturing and points out the neural network models that are considered in this dissertation. In Chapter 2 a brief introduction of GT is included. This chapter presents a brief introduction about the various types of GT-manufacturing systems and the advantages that are anticipated from its implementation. And also some of the factors that are preventing the wide spread application of GT are indicted in this chapter. Chapter 3 presents a review of related literatures in the area of machine cell formation in cellular manufacturing systems. Various cell formation techniques that are available in literature are discussed in brief and some of the commonly used quantitative grouping measures are pointed out. The conclusions of comparative studies of various cell formation techniques given by some researchers are briefly summarized in this chapter
URI: http://hdl.handle.net/123456789/10692
Other Identifiers: M.Tech
Research Supervisor/ Guide: Shan, H. S.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (MIED)

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