Application of Evolutionary Computational Algorithms to Estimate Responses in Milling Al 7075

The study is on investigating the machinability aspects of Al7075, a high-strength and lightweight alloy commonly used in automobiles, aircraft structures, defense equipment, etc. The work presented includes cutting forces, specific power, surface roughness, and tool wear while milling Al7075 alloy. A central composite design in response surface methodology is used to determine the optimum parameters, i.e., cutting speed, feed rate, and depth of cut with above responses. The prediction models are developed followed by ANOVA analysis to understand the role of input parameters on responses. These models are in good agreement with experimental results, followed by parametric optimization using desirability approach. Simultaneously, the evolutionary algorithms like real-coded genetic algorithm, teaching–learning-based optimization, and JAYA are employed for selecting the machining parameters and corresponding responses. The optimized results show that JAYA and TLBO algorithms are better to effectively minimize the responses in milling Al-7075.