Research on numerical simulation and prediction of tool wear in cutting ultra-high-strength aluminum alloys

The objective of this research is to predict tool wear by quickly and accurately analyzing the cutting parameters range of the machinable area that the typical hard-to-machine material, 7075 ultra-high-strength aluminum alloy. Firstly, the four-factor and four-level cutting finite element simulation model is established, and the range analysis of the orthogonal experiment results is carried out. Secondly, a tool wear experimental platform is set up in order to perform gray correlation analysis on the experimental data and compare or validate the findings with the finite element simulation results. In the end, mathematics and statistics principles or the machine learning and artificial intelligence theories are used to develop the tool wear prediction model. Regression analysis and performance evaluation analysis are then conducted on the prediction model. The findings demonstrate that cutting experiments may be precisely and successfully guided by the cutting finite element simulation model. The feed per tooth has the greatest influence on tool wear, followed by cutting speed, cutting depth and cutting length. The tool wear prediction model based on mathematical and statistical principles, machine learning and artificial intelligence theories can effectively guide the tool wear prediction, and the prediction accuracy of the optimized prediction model has been greatly improved. Engineering applications for research on numerical simulation and tool wear prediction in ultra-high-strength aluminum alloy cutting are particularly valuable.