Multi-Objective Optimization Design of Micro-Texture Parameters of Tool for Cutting GH4169 during Spray Cooling

Abstract

This study explores the performance of micro-textured tools when cutting GH4169 during spray cooling. First, the morphologies of the micro-textures were selected according to the simulation and experiments. Secondly, cutting experiments were carried out during spray cooling. As appropriate for each experiment, regression models of cutting force, cutting temperature, or tool wear area were established, and variance analysis was conducted. The cutting force, cutting temperature, and tool wear area functions were obtained from the respective regression models. Based on these functions, the micro-texture parameters were optimized using the response surface method with the cutting force, cutting temperature, and rake face wear area as the objectives. Finally, a full factor experiment on the micro-texture parameters was designed using Minitab, and cutting experiments were conducted using micro-textured tools with these parameters. Taking a relatively low cutting force, cutting temperature, and tool wear as the objectives, a genetic algorithm multi-objective optimization model for the micro-texture parameters of the tools was established, and the model was solved using the NSGA-II algorithm to obtain a Pareto solution set and micro-texture parameters with a good, comprehensive cutting performance. The micro-texture morphology and parameters obtained in this study can also be used for cutting other high-temperature alloy materials with similar properties to GH4169. This research method can also be used to optimize micro-textured tools for cutting other materials.