Intelligent Methods for Quantifying and Determining Friction in the Cutting Process - a Review

In this paper some recently proposed possibilities of integration of AI (Artificial Intelligence) methods with FEM (Finite Element Method)-based modelling in terms of coefficient of friction (COF) prediction are overviewed and discussed. In particular, the implementation of the Grey-Box model and some regression testing methods are discussed. Some results of the integration of Python interface with FEM DEFORM package regarding componential cutting forces and cutting temperature using predicted COF values are given. The results of implementation of different friction models in FEM and SPH (Smoothed Particle Hydrodynamics) simulation packages are compared. A number of different friction models are considered in quantifying friction and predicting tool wear rate. It was documented that both dedicated tribo-tests and advanced ML prediction algorithms increase visibly the simulation accuracy. New trends and future research directions are overviewed.