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.publishe
