Authors: H Öktem
Publish Date: 2008/10/01
Volume: 43, Issue: 9-10, Pages: 852-861
Abstract
The aim of this study is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of AISI 1040 steel material with TiAlN solid carbide tools under wet condition A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of four cutting parameters on the surface roughness Artificial neural network ANN based on Backpropagation BP learning algorithm is used to construct the surface roughness model exploiting a full factorial design of experiments Genetic algorithm GA supported with the tested ANN is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process GA improves the surface roughness value from 067 to 059 μm with approximately 12 gain Then machining time has also decreased from 1282 to 10316 min by about 20 reduction based on the cutting parameters before and after optimization process using the analytical formulas The final measurement experiment has been performed to verify surface roughness value resulted from GA with that of the material surface by 3278 error From these results it can be easily realized that the developed study is reliable and suitable for solving the other problems encountered in metal cutting operations as the same as surface roughness
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