Intelligent machining methods for Ti6Al4V: A review

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision-making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence-based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.

Automated summary

Digital manufacturing is essential for future manufacturing systems in the fourth industrial revolution, integrating intelligent features and algorithms to improve efficiency and predictive capabilities. Modeling techniques for Ti6Al4V machining process include numerical modeling and AI-based models, with digital image processing used to analyze machining response and validate data in modeling techniques.