Fuzzy logic-integrated PSO methodology for parameters optimization in end milling of Al/SiCp MMC

Abstract

This paper deals with a novel integrated parametric optimization in end milling of Al-SiC p metal-matrix composites using carbide tool. Fuzzy logic modeling combined with particle swarm optimization (PSO) technique is applied as intelligent optimization methodology to obtain optimal process parameters in end milling process. Fuzzy rules-based surface roughness prediction model is developed with SiC p percentage as one the input parameter is addition to spindle speed, feed and depth of cut. Comparison of fuzzy rule model predictions with confirmation data sets showed 95.44% average prediction accuracy. The effect of process parameters on surface roughness is also studied. Analysis of variance established feed rate as the most significant process variable with 69.93% contribution followed by SiC p (19%) and spindle speed (9.73%), respectively. The integrated PSO optimizer optimizes the process parameters for an objective to minimize the machining time satisfying desired surface roughness to be obtained. A number of problems with different values of desired surface roughness are solved and algorithm converged in less than ten iterations.

Development of ANN modelling for estimation of weld strength and integrated optimization for GTAW of Inconel 825 sheets used in aero engine components

“…Tamang and Chandrasekaran [20] employed an integrated ANN-PSO methodology to optimize turning variables for obtaining desired surface finish with minimum machining time during machining of Inconel 825. A fuzzy logic modelling with PSO integrated methodology has been used by Devarasiddappa and Chandrasekaran [21] to obtain optimum process parameters satisfying desired value of surface finish during end milling of Al-SiC p MMCs.…”