Evolutionary algorithm for the prediction and optimization of SiCp/Al metal matrix composite machining


  • Rashid Ali Laghari School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 15000, China
  • Munish Kumar Gupta Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, PR China
  • Jianguang Li School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 15000, China


Particle swarm optimization, Metal matrix composite, Turning, Cutting parameters, Tool life, Surface roughness


This article attempts the optimization study of machining of SiCp/Al metal matrix composite (MMC) material to analyze the most suitable cutting parameters on tool life and surface quality. The research study integrated the effects of three machining parameters, cutting speed, feed rate, and depth of cut with carbide cutting tools. The optimization prediction model of machining parameters was established, and the influence of machining parameters on tool life and surface roughness and their interaction were studied. Through analysis, the key factors affecting tool life and surface roughness are determined. Combining the process parameters, advanced computation method was employed to predict responses and to compare it with the experimental data. The results show that, in this study, machining indexes such as tool life and surface roughness are mainly affected with increasing feed rate followed by the depth of cut. However, surface roughness reduces as the cutting speed increases slightly. Statistical methods have a level to a certain extent to determine its ability as a powerful method for analyzing SiCp/Al composite material processing.


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How to Cite

Laghari, R. A. ., Gupta, M. K. ., & Li, J. . (2020). Evolutionary algorithm for the prediction and optimization of SiCp/Al metal matrix composite machining. Journal of Production Systems and Manufacturing Science, 2(1), 59-69. Retrieved from http://imperialopen.com/index.php/JPSMS/article/view/46



Original Research Articles