Experimental modeling and optimizing process parameters in the laser assisted machining of silicon carbide particle-reinforced aluminum matrix composites

Abstract

The process of laser assisted machining (LAM) of metal matrix composites (MMC) is experimentally studied. The effects of process parameters (cutting speed, feed rate and depth of cut), laser parameters (laser power, laser frequency and laser beam angle), and the percentage of reinforcing particles on the tool wear and the surface roughness are investigated. Furthermore, the effects of the mentioned parameters on the built-up edge (BUE), chip shape, and workpiece temperature are explored. The experiments were performed using uncoated tungsten carbide and PCD tools under dry conditions. In order to analyze the sensitivity of the process parameters, the Plackett-Burman method was used for screening of parameters. Then, for experimental optimization of the effective parameters, Taguchi optimization method and analysis of variance (ANOVA) were used. The surface roughness of the specimens and tool wear were considered as the main objectives in the optimization procedure. Photographs of the tool tip after cutting showed that the flank wear and BUE on the tool tip are the main wear mechanisms. The experiments revealed that at high feed rates, the BUE could occur at both low and high cutting speeds. As a part of this study, the mathematical models of the flank wear and the surface roughness in terms of the effective parameters has been formulated. Finally, based on the ANOVA study, an optimal parametric combination that minimizes the surface roughness and tool wear were obtained.