Prediction of surface roughness in turning of PEEK cf30 by using an artificial neural network CF30 Issam Hanafi* Analysis and Modelling of Systems Laboratory Faculty of Sciences at Tetouan - Morocco Abdellatif Khamlichi Analysis and Modelling of Systems Laboratory Faculty of Sciences at Tetouan - Morocco Francisco Mata Cabrera Polytechnic School of Almaden - Spain Pedro J. Nuñez López School of Mechanical Engineering of Ciudad Real - Spain

摘要

Surface roughness parameters Ra and Rt are mostly used as an index to determine the surface finish quality in the process of machining. Because of the strong nonlinear character of relationships between the process inputs and outputs, it is difficult to accurately estimate roughness characteristics by using traditional modeling techniques. In this work, accurate prediction of the Ra and Rt values during machining of reinforced poly ether ether ketone (PEEK) CF30 with TiN coated tools is achieved. The modeling is performed by using artificial neural network approach to represent the complex relationships between cutting conditions and surface roughness parameters. The input cutting parameters include cutting speed, depth of cut and feed rate. The network was trained with pairs of inputs and outputs datasets generated by machining experimental results that were obtained according to a full factorial design of experiment table. Predictions of the ANN based model were found to fit experimental data very well with a correlation coefficient as high as 99%. Complementary results that were not used during derivation of the ANN model have enabled one to assess the validity of the obtained predictions. RaRt TiN CF30RaRt View full textDownload full textKeywordsANN, modeling, machining, PEEK CF30, TiN coated inserts, surface roughness parametersKeywords CF30 TiN Related var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10170669.2012.702690