Modelling and Optimization of Machining of Ti-6Al-4V Titanium Alloy Using Machine Learning and Design of Experiments Methods

Ti-6Al-4V titanium is considered a difficult-to-cut material used in critical applications in the aerospace industry requiring high reliability levels. An appropriate selection of cutting conditions can improve the machinability of this alloy and the surface integrity of the machined surface, including the generation of compressive residual stresses. In this paper, orthogonal cutting tests of Ti-6Al-4V titanium were performed using coated and uncoated tungsten carbide tools. Suitable design of experiments (DOE) was used to investigate the influence of the cutting conditions (cutting speed V-c, uncut chip thickness h, tool rake angle gamma(n), and the cutting edge radius r(n)) on the forces, chip compression ratio, and residual stresses. Due to the time consumed and the high cost of the residual stress measurements, they were only measured for selected cutting conditions of the DOE. Then, the machine learning method based on mathematical regression analysis was applied to predict the residual stresses for other cutting conditions of the DOE. Finally, the optimal cutting conditions that minimize the machining outcomes were determined. The results showed that when increasing the compressive residual stresses at the machined surface by 40%, the rake angle should be increased from negative (-6 degrees) to positive (5 degrees), the cutting edge radius should be doubled (from 16 mu m to 30 mu m), and the cutting speed should be reduced by 67% (from 60 to 20 m/min).

Automated summary

In this study, cutting tests on Ti-6Al-4V titanium were conducted using tungsten carbide tools to investigate the influence of cutting conditions on cutting forces, chip compression ratio, and residual stresses. Machine learning methods were used to predict residual stresses for different cutting conditions, and optimal cutting conditions were determined.