Title: Modelling of cutting forces as a function of cutting parameters in milling process using regression analysis and artificial neural network
Authors: Harshit K. Dave, Harit K. Raval
Addresses: Department of Mechanical Engineering, S.V. National Institute of Technology, Surat – 395007, India. ' Department of Mechanical Engineering, S.V. National Institute of Technology, Surat – 395007, India
Abstract: 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.
Keywords: milling; full factorial DOE; design of experiments; regression analysis; artificial neural networks; ANNs; multilayer perceptron; MLP; cutting forces; cutting parameters.
International Journal of Machining and Machinability of Materials, 2010 Vol.8 No.1/2, pp.198 - 208
Published online: 05 Aug 2010 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article