Optimisation of work roll grinding using Response Surface Methodology and evolutionary algorithm

被引：10

作者：

Mohanasundararaju, N. ^{[1
]}

Sivasubramanian, R. ^{[2
]}

Alagumurthi, N. ^{[3
]}

机构：

[1] Department of Mechanical Engineering, Sona College of Technology

[2] Department of Mechanical Engineering, Coimbatore Institute of Technology

[3] Department of Mechanical Engineering, Pondicherry Engineering College

来源：

International Journal of Manufacturing Research | 2008年 / 3卷 / 02期

关键词：

GA; Genetic Algorithm; Non-linear programming; Optimisation; Response Surface Methodology; RSM; Surface roughness; Work roll grinding;

D O I：

10.1504/IJMR.2008.017418

中图分类号：

学科分类号：

摘要：

Process modelling and optimisation are two important issues in manufacturing. The prediction of optimal grinding conditions for desired surface finish and dimensional accuracy plays a vital role in process planning. This work deals with the study and development of a surface roughness prediction model for grinding of work rolls made of D2 steel using Response Surface Methodology (RSM). The experimentation was carried out in a work roll traverse grinding machine with six grinding parameters such as wheel speed, work speed, traverse speed, in feed, dress depth and dressing lead using Box-Behenken design matrix with six central points. A second-order mathematical model, in terms of machining parameters, was developed for surface prediction using RSM. The computer-aided single-objective optimisation was solved by two approaches, namely non-linear programming and Genetic Algorithm (GA). © 2008, Inderscience Publishers.

引用

页码：236 / 251

页数：15

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