Regression and ANN models for estimating minimum value of machining performance

被引：55

作者：

Zain, Azlan Mohd ^{[1
]}

Haron, Habibollah ^{[1
]}

Qasem, Sultan Noman ^{[1
]}

Sharif, Safian ^{[2
]}

机构：

[1] Univ Teknol Malaysia, Soft Comp Res Group, Fac Comp Sci & Informat Syst, Utm Skudai 81310, Johor, Malaysia

[2] Univ Teknol Malaysia, Dept Mfg & Ind Engn, Fac Mech Engn, Utm Skudai 81310, Johor, Malaysia

来源：

APPLIED MATHEMATICAL MODELLING | 2012年 / 36卷 / 04期

关键词：

Modeling; Regression; ANN; Minimum surface roughness; End milling; ARTIFICIAL NEURAL-NETWORKS; MINIMIZING SURFACE-ROUGHNESS; MULTIPLE-REGRESSION; CUTTING PARAMETERS; GENETIC ALGORITHM; OPTIMIZATION; PREDICTION;

D O I：

10.1016/j.apm.2011.09.035

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R-a) must be formulated in the standard mathematical model. To predict the minimum R-a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R-a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R-a value. The results show that regression and ANN models have reduced the minimum R-a value of real experimental data by about 1.57% and 1.05%, respectively. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：1477 / 1492

页数：16

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