Multiobjective Optimization of Tool Geometric Parameters Using Genetic Algorithm

Abstract

Tool geometric parameters have a huge impact on tool wear. Up to now, there are only a few researches on tool geometric parameters and optimization, and the single objective function of parameter optimization used by researchers during high-speed machining (HSM) mainly is the minimum cutting force. However, the elevated cutting temperature also greatly affects tool wear due to the numerous cutting heat generation. Thus, to reduce tool wear, it is the most fundamental approach to taking into account the comprehensive control of the cutting force and cutting temperature because they are the two most important physical quantities in metal cutting processes. This work proposes a new optimization idea of the cutting-tool’s multi geometric parameters (three main parameters: rake angle, clearance angle, and cutting edge radius) with two objective functions (the cutting force and the temperature). Based on the response surface method (RSM), we have established the modified functional relation models of the influence of tool geometric parameters on the cutting force and temperature according to the finite element simulation results in high-speed cutting of Ti6Al4V. Then the models are solved by using a genetic algorithm, and the optimal tool geometric parameters values that can concurrently control the two objectives in their minimum values are obtained. The advantages lie in the strategy of the separate models of the cutting force and cutting temperature owing to their different dimensions and the solution of the models through giving the cutting force and cutting temperature different weight coefficients. The optimal results are verified by experiments, which shows that the optimal tool geometric parameters are very effective and vital for ensuring both the cutting force and the cutting temperature not too high. This work is of great significance to the cutting tool design theory and its manufacturing for reducing tool wear.