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

• Published in: Journal of Manufacturing and Materials Processing Vol. 6; no. 3; p. 58
• Main Authors: Outeiro, José, Cheng, Wenyu, Chinesta, Francisco, Ammar, Amine
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022; MDPI
• Subjects: Aerospace industry; Artificial intelligence; Carbide tools; Compression ratio; Compressive properties; Crack initiation; Crack propagation; Cutting edge radius; Cutting parameters; Cutting speed; Design of experiments; Engineering Sciences; Geometry; Machinability; Machine learning; Machine tools; Machining; Methods; modelling; Optimization; Rake angle; Regression analysis; Reliability aspects; Residual stress; residual stresses; Simulation; Ti-6Al-4V; Titanium alloys; Titanium base alloys; Tungsten carbide; Variance analysis; Industrial and Manufacturing Engineering; Mechanics of Materials; Mechanical Engineering
• ISSN: 2504-4494; 2504-4494
• DOI: 10.3390/JMMP6030058

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 Vc, uncut chip thickness h, tool rake angle γn, and the cutting edge radius rn) 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°) to positive (5°), the cutting edge radius should be doubled (from 16 µm to 30 µm), and the cutting speed should be reduced by 67% (from 60 to 20 m/min).