Correcting geometric deviations of CNC Machine-Tools: An approach with Artificial Neural Networks

被引：10

作者：

de Oliveira Leite, Wanderson ^{[1
]}

Campos Rubio, Juan Carlos ^{[2
]}

Duduch, Jaime Gilberto ^{[3
]}

Maciel de Almeida, Paulo Eduardo ^{[4
]}

机构：

[1] Univ Fed Minas Gerais, Programa Posgrad Engn Prod, Belo Horizonte, MG, Brazil

[2] Univ Fed Minas Gerais, Dept Engn Mecan, Belo Horizonte, MG, Brazil

[3] Univ Sao Paulo, Escola Engn Sao Carlos, Dept Engn Mecan, Sao Carlos, SP, Brazil

[4] Ctr Fed Educ Tecnol Minas Gerais, Lab Sistemas Inteligentes, Belo Horizonte, MG, Brazil

来源：

APPLIED SOFT COMPUTING | 2015年 / 36卷

关键词：

Artificial Neural Networks; CNC Machine Tools; Error compensation; Design for Manufacturing; Precision technology; WEAR PREDICTION; FLANK WEAR; MODEL; QUALITY; COMPENSATION; OPTIMIZATION; MANUFACTURE; DESIGN; SYSTEM;

D O I：

10.1016/j.asoc.2015.07.014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an experimental methodology of Design for Manufacturing (DFM) used for survey and analysis of geometric deviations of CNC Machine-Tools, through their final product. These deviations generate direct costs that can be avoided through the use of Intelligent Manufacturing Systems (IMS), by the application of Artificial Neural Networks (ANNs) to predict the fabrication parameters. Finally, after the experiments, it was possible to evaluate the experimental methodology used, the equations, the variables of data adjustment and thus enable the validation of the methodology used as a tool for DFM with high potential return on product quality, development time and reliability of the process with wide application in various CNC Machines. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：114 / 124

页数：11

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