AN EVALUATION OF ART1 NEURAL MODELS FOR GT PART FAMILY AND MACHINE CELL FORMING

被引：23

作者：

LIAO, TW

CHEN, LJ

机构：

[1] Louisiana State University, Baton Rouge, LA

来源：

JOURNAL OF MANUFACTURING SYSTEMS | 1993年 / 12卷 / 04期

关键词：

NEURAL NETWORK; ART1 NEURAL MODEL; GT PART FAMILY FORMING; GT MACHINE CELL FORMING; CELLULAR MANUFACTURING SYSTEM DESIGN;

D O I：

10.1016/0278-6125(93)90319-O

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper describes ART1 neural models for GT part family and machine cell forming. An ART1 neural model was first implemented in C and was tested with examples taken from the literature. The ART1 model was then integrated with a feature-based design system for automatic GT coding and part family forming. It was finally incorporated into a three-stage procedure for designing cellular manufacturing systems. Our evaluation concludes that ART1, when compared with nonlearning algorithms, is best suited for GT applications due to its fast processing speed, fault tolerance and learning abilities, ease of classifying new parts, etc.

引用

页码：282 / 290

页数：9

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