Statistical learning and optimization of the helical milling of the biocompatible titanium Ti-6Al-7Nb alloy

Helical milling has been applied for hole-making in titanium alloys, especially in the Ti-6Al-4V alloy, considering the aims of the aeronautic, automobile, and other sectors. When considering hole-making in Ti-alloys for biomedical applications, few studies have been carried out. Besides, intelligent approaches for modeling and optimization of this process in these special alloys are demanded to achieve the best results in terms of hole surface quality, and productivity. This work presents an approach for modeling and optimization of helical milling for hole-making of Ti-6Al-7Nb biocompatible titanium alloy. The surface roughness of the holes was measured to quantify the hole quality. Principal component analysis was performed for dimensionality reduction of the roughness outputs. For modeling, a learning procedure was proposed considering polynomial response surface regression, tree-based methods, and support vector regression. Cross-validation is used for learning and model selection. The results pointed out that the support vector regression model was the best one. Multi-objective evolutionary optimization was performed considering the support vector regression model and the deterministic model of the material removal rate. The Pareto set and the Pareto frontier were plotted and discussed concerning practical aspects of the helical milling process. The proposed learning and optimization approach enabled the achievement of the best results of the helical milling in the biocompatible Ti-6Al-7Nb alloy and can be applied to other intelligent manufacturing applications.