Prediction of tool wear using regression and ANN models in end-milling operation |
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Authors: | P Palanisamy I Rajendran S Shanmugasundaram |
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Affiliation: | (1) Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, 641006, Tamil Nadu, India;(2) Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, 638401, Tamil Nadu, India;(3) Government College of Technology, Coimbatore, 641013, Tamil Nadu, India |
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Abstract: | Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting
tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and
reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN)
models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during
milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique
is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear
based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values
are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values
are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted
values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural
network model was found to be capable of better predictions of tool flank wear within the trained range. |
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Keywords: | Tool wear Design of Experiments (DOE) Regression model Artificial neural network (ANN) |
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