Artificial-neural-networks-based surface roughness Pokayoke system for end-milling operations

被引：33

作者：

Huang, Bernie P. ^{[1
]}

Chen, Joseph C. ^{[1
]}

Li, Ye ^{[1
]}

机构：

[1] Iowa State Univ Sci & Technol, Ames, IA 50011 USA

来源：

NEUROCOMPUTING | 2008年 / 71卷 / 4-6期

关键词：

in-process adaptive control; surface roughness; milling operation; neural networks; Pokayoke;

D O I：

10.1016/j.neucom.2007.07.029

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Surface roughness is an important indicator of the quality of machined parts. Commonly, the off-line, manual technique of direct measurement is utilized to assess surface roughness and part quality, which is found to be very time-consuming and costly. For that reason, the neural network-based surface roughness Pokayoke (NN-SRPo) system is developed to keep the surface roughness within a desired value in an in-process manner. Both the surface roughness prediction and machining parameters control are performed online during the machining process. A testing experiment demonstrated the efficacy of this NN-SRPo system. (c) 2007 Elsevier B.V. All rights reserved.

引用

收藏

页码：544 / 549

页数：6

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