In the last decade, AISI 316L austenitic stainless steel has been considered one of the most useful materials for high-demand equipment in the industry due to its mechanical properties. This work shows the multi-criteria analysis of the dry and MQL turning process of AISI 316L steel using the non-dominated sorting genetic algorithms class II and III (NSAG-II and NSAG-III). The wear of the cutting tool (VB), the energy consumption (E), and the machining time (t) are used as study variables, with the aim of minimizing the wear of the cutting tool based on the optimal selection of machining parameters. When comparing the results obtained from both methods, we found that NSAG-III was the best alternative for selecting parameters in the turning operation, with fewer tool wear, more efficient use of energy consumption, and more variants of material removal rate.
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