Optimization through NSGA-II during machining of A356Al/20%SiCp metal matrix composites using PCD Tool. (July 2019)

• Record Type: Journal Article
• Authors: Seeman, M.; Kanagarajan, D.; Sivaraj, P.; Seetharaman, R.; Devaraju, A.
• Is Part Of: IOP conference series. Volume 574(2019)
• Publication Date: 2019-07
• Subjects: metal matrix composite (MMC) -- tool flank wear (VBmax) -- average surface roughness (Ra) -- polycrystalline diamond (PCD) -- non-dominated sorting genetic algorithm (NSGA-II); Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/574/1/012008
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Metal matrix composite (MMC) has established growing usages in the engineering for lightweight high-strength application. However, because of abrasive nature of the reinforcement particles in MMC, machinability is reduced, tool wear is high, yet only diamond tools are appropriate for machining MMC. Being a complex process, it is very complicated to establish optimal parameters for improving cutting performance. Tool flank wear ( VBmax ) and Average surface roughness ( Ra ) are the most significant output responses, which decide the machinability of a material. The effect of cutting parameters on output responses of tool flank wear and average surface roughness are conflict with each one another so there is no single optimal mixture of cutting parameters. In this study multi regression model function, based on NSGA-II was used to optimize the machining of Al/20%SiCp MMC using tipped polycrystalline diamond (PCD) tool and also characterize the relationship among input parameters and output performance. The NSGA-II based non dominated solutions of 30 combinations chosen from 100 and presented; none of them superior to any other each and every one are best based on engineer requirement.