Multi-Objective Optimization in Hot Machining of Al/SiCp Metal Matrix Composites

2nd International Manufacturing Engineering Conference; 3rd Asia-Pacific Conference on Manufacturing Systems

Jadhav, M.R.^1 ; Dabade, U.A.^1

Department of Mechanical Engineering, Walchand College of Engineering, Sangli, Maharashtra

416 415, India^1

关键词: Al/sicp metal matrix composites;    Analysis of variance method;    Flank wear;    Force component;    Grey relational analysis;    Machined surface quality;    Surface finishes;    Taguchi grey relational analysis;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/114/1/012122/pdf
DOI  :  10.1088/1757-899X/114/1/012122

来源: IOP

【 摘 要 】

Metal Matrix Composites (MMCs) have been found to be useful in a number of engineering applications and particle reinforced MMCs have received considerable attention due to their excellent engineering properties. These materials are generally regarded as extremely difficult to machine, because of the abrasive characteristics of the reinforced particulates. These characteristics of MMCs affect the machined surface quality and integrity. This paper presents use of Taguchi Grey Relational Analyses (GRA) for optimization of Al/SiCp/10p (220 and 600 mesh) MMCs produced by stir casting. Experiments are performed using L16 orthogonal array by using hot machining technique. The objective of this study is to identify the optimum process parameters to improve the surface integrity on Al/SiCp MMCs. The machined surface integrity has been analyzed by process parameters such as speed, feed, depth of cut and preheating temperature. The significance of the process parameters on surface integrity has been evaluated quantitatively by the analysis of variance (ANOVA) method and AOM plots. The grey relational analysis shows optimum machining conditions as 0.05 mm/rev feed, 0.4 mm depth of cut and 60 °C preheating temperature to enhance surface integrity for both Al/SiCp/10p (220 and 600 mesh) MMCs except for cutting speed 50 and 25 m/min respectively.