Study of machinability and parametric optimization of end milling on aluminium hybrid composites using multi-objective genetic algorithm

  • B. RajeswariEmail author
  • K. S. Amirthagadeswaran
Technical Paper


Metal matrix composites offer a substantial surety to meet the present and future demands spanning from automobiles to aerospace. Hybrid metal matrix composites are a new choice of materials involving several advantages over the single reinforcement. In this present study, three specimens possessing aluminium 7075 reinforced with particulates of silicon carbide (5, 10, 15% weight percentage) and alumina (5% weight percentage) were developed using stir casting. The purpose of the study was to investigate the effect of reinforcement particles of silicon carbide on the machinability of hybrid metal matrix composites. These materials are engineered to match the requirements of optimal output responses such as low surface roughness, less tool wear, a less cutting force with the high rate of material removal under a set of practical machining constraints. Multi-objective parametric optimization using genetic algorithm obtained optimal cutting responses. The spindle speed, feed rate, depth of cut and weight percentages of SiC were selected as the influencing parameters for meeting the output responses in end milling operation. Based on the Box–Behnken design in response surface methodology, 27 experimental runs were conducted and nonlinear regression models were developed to predict the objective function. The adequacy of the model was checked through ANOVA and was found to be significant. The optimum settings of the parameters were found using multi-objective genetic algorithm. The predicted optimal settings were verified through confirmatory experiments, and the results validated.


Composites End milling Genetic algorithm Interaction effects Multi-objective 

List of symbols


Surface roughness (µm)


Material removal rate (mm3/min)


Tool wear (mm)


Cutting force (N)


Spindle speed (rpm)


Feed rate (mm/rev)


Depth of cut (mm)


Weight percentage of silicon carbide


Degree of freedom


Confidence interval


Variance inflation factor



The authors thank Government College of Technology, Coimbatore, India, for funding this research work under Technical Education Quality Improvement Programme—Phase II.


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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringGovernment College of TechnologyCoimbatoreIndia
  2. 2.PrincipalUnited Institute of TechnologyCoimbatoreIndia

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