NSGA III for CNC End Milling Process Optimization

Abstract

Computer Numerical Controlled (CNC) end milling processes require very complex and expensive experimentations or simulations to measure the overall performance due to the involvement of many process parameters. Such problems are computationally expensive, which could be efficiently solved using surrogate driven evolutionary optimization algorithms. An attempt is made in this paper to use such technique for the end milling process optimization of aluminium block and solved using Non-dominated Sorting Genetic Algorithm (NSGA III). The material removal rate, and surface roughness are considered as the crucial performance criteria. It is shown that the regression driven NSGA III is efficient and effective while obtaining improved process responses for the end milling.