Multi-objective Optimization of Cutting Parameters for Machining Process of Ni-Rich NiTiHf High-Temperature Shape Memory Alloy Using Genetic Algorithm

Abstract

This study focuses on optimizing cutting parameters of NiTiHf high-temperature shape memory alloys using a multi-objective optimization method. The empirical models are used to predict outputs in a turning process of Ni-rich NiTiHf and compared to experimental data. Cutting parameters, such as cutting speed, feed rate, and depth of cut, have found a great effect on performance criteria of turning processes in machining process of this alloy. These parameters were optimized by using Genetic Algorithm to obtain maximum tool life, minimum energy consumption, and maximum surface quality.