Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm

被引：111

作者：

Kant, Girish ^{[1
]}

Sangwan, Kuldip Singh ^{[1
]}

机构：

[1] Birla Inst Technol & Sci, Dept Mech Engn, Pilani 333031, Rajasthan, India

来源：

15TH CIRP CONFERENCE ON MODELLING OF MACHINING OPERATIONS (15TH CMMO) | 2015年 / 31卷

关键词：

Roughness; Artificial neural network; Genetic Algorithm; Optimization; Predictive modelling; CUTTING CONDITIONS; POWER-CONSUMPTION; TEMPERATURE; STRESSES; STEEL;

D O I：

10.1016/j.procir.2015.03.043

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

genetic algorithm - as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：453 / 458

页数：6

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