Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge makes it unable to guarantee the quality required during machining. Knowing and measuring the wear of tools is possible through artificial intelligence (AI), a branch of information technology that, by interpreting the behaviour of the tool, predicts its wear through intelligent systems. AI systems include techniques such as machine learning, deep learning and neural networks, which allow for the study, construction and implementation of algorithms in order to understand, improve and optimize the wear process. The aim of this research work is to provide an overview of the recent years of development of tool wear monitoring through artificial intelligence in the general and essential requirements of offline and online methods. The last few years mainly refer to the last ten years, but with a few exceptions, for a better explanation of the topics covered. Therefore, the review identifies, in addition to the methods, the industrial sector to which the scientific article refers, the type of processing, the material processed, the tool used and the type of wear calculated. Publications are described in accordance with PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols).

The phenomenological models for material flow stress and fracture, typically used in the Finite Element simulations of machining Nickel-based alloys, are often deemed to represent only certain metallurgical material states. In contrast, these models are not suitable to describe the constitutive behavior of the workpiece for different metallurgical states (i.e., annealed, aged, etc.) and, consequently, different hardness values.Since the description of the material behavior requires correct formulation of the constitutive law, new flow stress models which include also the hardness effect should be developed and used, for computer simulation of machining Nickel-based alloys.This paper describes the development of a hardness-based flow stress and fracture models for machining Inconel 718 alloy which can be applied for a wide range of work material hardness. These models have been implemented in a non-isothermal viscoplastic numerical model to simulate the influence of work material hardness on the chip formation process. The predicted results are being validated with experimental results properly carried out for this research. They are found to satisfactory predict the cutting forces, the temperature and the chip morphology from continuous to segmented chip as the hardness values change