Optimization of turning process parameters using a new hybrid evolutionary algorithm

In this article, a new hybrid improved differential evolution and Nelder-Mead (IDE-NM) is introduced for optimizing the multi-objective machining process during the turning operation under three modes of lubrication conditions. The fitness functions are the tangential cutting force, the surface roughness, and the cutting power. Five mixed design variables are considered in the optimization procedure including the cutting speed, feed rate, and depth of cut, mode of lubrication, and the type of cutting material. The mathematical expressions of the three objectives are created based on experimental results and modeled using the artificial neural network (ANN). In the first step, the proposed method is examined by solving seven mechanical engineering design problems. The comparison results illustrate that the IDE-NM algorithm outperforms other state-of-the-art optimization methods considered in the literature. Moreover, for the turning operation problem, the results of IDE-NM are compared with those of four recent metaheuristics. The results show that the proposed method outperforms the four compared algorithms in terms of robustness, high success rate, and can provide effective solutions.