Datasets describing optimization of the cutting regime in the turning of AISI 316L steel for biomedical purposes based on the NSGA-II and NSGA-III multi-criteria algorithms

There are several methods of analysis used in the metalworking industry for dry machining processes and with Minimum Quantity Lubrication (MQL). Evolutionary methods [1] have been used in the decision-making process in the machining process to select the optimal data and to analyze the behavior of variables such as cutting speed (V), feed rate (f) and cutting depth (a(p)). This work addresses the use of evolutionary algorithms of low dominance class II and III (NSGA-II and NSGA-III) to analyze from the multicriteria approach the initial wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) in the turning process of the AISI 316L steel workpiece for biomedical purposes. As input variables to the algorithm with 54 records, there are: cutting speed (V: 200, 300, 40 0 m/min) and feed rate (f: 0.1, 0.15, 0.2 mm/rev). The experiment was developed for a dry (1) turning operation and with the use of MQL (-1). For the MQL lubrication regime, a TRI-COOL MD-1 lubricant was employed, a vegetable type used in ferrous and non-ferrous metal cutting operations. A BIDEMICS JX1 ceramic cutting tool was used. (C) 2023 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)