Joshi Milan, Ghadai Ranjan Kumar, Madhu S, Kalita Kanak, Gao Xiao-Zhi
Department of Applied Science and Humanities, MPSTME SVKM'S Narsee Monjee Institute of Management Studies, Shirpur 425 405, India.
Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737 136, India.
Materials (Basel). 2021 Sep 6;14(17):5109. doi: 10.3390/ma14175109.
The popularity of micro-machining is rapidly increasing due to the growing demands for miniature products. Among different micro-machining approaches, micro-turning and micro-milling are widely used in the manufacturing industry. The various cutting parameters of micro-turning and micro-milling has a significant effect on the machining performance. Thus, it is essential that the cutting parameters are optimized to obtain the most from the machining process. However, it is often seen that many machining objectives have conflicting parameter settings. For example, generally, a high material removal rate (MRR) is accompanied by high surface roughness (SR). In this paper, metaheuristic multi-objective optimization algorithms are utilized to generate Pareto optimal solutions for micro-turning and micro-milling applications. A comparative study is carried out to assess the performance of non-dominated sorting genetic algorithm II (NSGA-II), multi-objective ant lion optimization (MOALO) and multi-objective dragonfly optimization (MODA) in micro-machining applications. The complex proportional assessment (COPRAS) method is used to compare the NSGA-II, MOALO and MODA generated Pareto solutions.
由于对微型产品的需求不断增长，微加工的普及程度正在迅速提高。在不同的微加工方法中，微车削和微铣削在制造业中被广泛使用。微车削和微铣削的各种切削参数对加工性能有显著影响。因此，优化切削参数以从加工过程中获得最大收益至关重要。然而，经常可以看到许多加工目标具有相互冲突的参数设置。例如，一般来说，高材料去除率（MRR）伴随着高表面粗糙度（SR）。在本文中，利用元启发式多目标优化算法为微车削和微铣削应用生成帕累托最优解。进行了一项比较研究，以评估非支配排序遗传算法II（NSGA-II）、多目标蚁狮优化（MOALO）和多目标蜻蜓优化（MODA）在微加工应用中的性能。采用复杂比例评估（COPRAS）方法比较NSGA-II、MOALO和MODA生成的帕累托解。
