Modelling of surface roughness in CNC face milling of alloy stellite 6
by Saeed Zare Chavoshi
International Journal of Computational Materials Science and Surface Engineering (IJCMSSE), Vol. 5, No. 4, 2013

Abstract: In this study, soft computing techniques including multilayer perceptron (MLP), generalised feed forward (GFF), modular neural network (MNN) and co-active neuro-fuzzy inference system (CANFIS) are presented for the prediction of surface roughness during CNC face milling of alloy stellite 6. Prediction model based on regression analysis (RA) is also presented for comparison. Input variables consist of cutting speed, feed rate and depth of cut while output variable is surface roughness. The trained models using experimental data are tested using the set of validation data. Modelling results presented using machining data demonstrate that the CANFIS is reasonably more accurate. For determining the effects of machining parameters on surface roughness, statistical analysis using main effect and interaction plots are performed.

Online publication date: Sat, 21-Jun-2014

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