Modelling of cutting forces as a function of cutting parameters in milling process using regression analysis and artificial neural network

被引：8

作者：

Dave H.K. ^{[1
]}

Raval H.K. ^{[1
]}

机构：

[1] Department of Mechanical Engineering, S.V. National Institute of Technology

来源：

International Journal of Machining and Machinability of Materials | 2010年 / 8卷 / 1-2期

关键词：

ANN; Artificial neural network; Full factorial design of experiment; Milling; MLP; Multi layer perceptron; Regression equation;

D O I：

10.1504/IJMMM.2010.034496

中图分类号：

学科分类号：

摘要：

In the present work, an effort has been made to explore the potentialities of application of regression analysis and artificial neural network (ANN) in milling process. Optimum setting of horizontal and vertical cutting forces for a particular tool-work piece combination is found using three levels of speed, feed and depth of cut. The parameter combination is worked out using full factorial design of experiment methods (DOE). Experiments are conducted for all the combinations and forces are measured using a milling tool dynamometer. Based on the observations, regression equations are derived. The present investigation was further extended with the application of ANN using an architecture consisting of three input and two output nodes and a hidden layer. The network training is carried out and then trained network is tested with few experimental results, which are not used during training. The results obtained during the study are critically discussed and reported. Copyright © 2010 Inderscience Enterprises Ltd.

引用

页码：198 / 208

页数：10

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