A NEURAL NETWORK SYSTEM FOR SHAPE-BASED CLASSIFICATION AND CODING OF ROTATIONAL PARTS

被引：67

作者：

KAPARTHI, S

SURESH, NC

机构：

[1] School of Management, State University of New York at Buffalo, Buffalo, NY

来源：

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | 1991年 / 29卷 / 09期

关键词：

D O I：

10.1080/00207549108948048

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The classification and coding of parts for group technology applications continue to be labour intensive and time-consuming processes. In this paper a pattern recognition approach utilizing neural networks is presented for the automation of some elements of this critical activity. As an illustrative example, a neural network system is used to generate part geometry-related digits of the Opitz code from bitmaps of part drawings. It is found to generate codes accurately and promises to be a useful tool for the automatic generation of shape-based classes and codes.

引用

页码：1771 / 1784

页数：14

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