Multi-scale predictive modeling of tool wear for machining Inconel 718 with cemented carbide tools

Accurate prediction of tool wear offers great opportunity to study the effects of tool wear on cutting performance and to reduce real production costs. However, as a complex process, the accurate prediction of tool wear remains a challenging project. In this paper, the 2D modeling and simulation of tool wear during cutting Inconel718 are studied by using the coupled Euler-Lagrange (CEL) finite element method. Based on the temperature effect, a mathematical model of the wear rate of cemented carbide tools was established with comprehensive consideration of the abrasive, adhesive, and diffusive wear mechanisms. The corresponding mathematical model of abrasive wear rate was established during cutting by combining with the thermal hardness data and focusing on the influence of the abrasive wear in the abrasive grain movement mode on the wear rate. In the prediction of flank wear, the volume loss of the tool flank surface is related to the wear rate, and the corresponding mathematical model of flank wear is established based on temperature effect. Finally, the secondary development of ABAQUS is carried out using Python language, and tool wear subroutine is written to extract relevant parameters from the ABAQUS output database to calculate the tool wear rate. Through studying the evolution of the geometric shape of the rake and flank tool face wear and the change of the rake and tool flank face wear, and the orthogonal turning test which is carried out to observe and analyze the tool wear, the accuracy of the established tool wear model was verified to achieve the prediction of the tool wear. Compared with the test results, the prediction error of tool wear is less than 15 %. The equation of tool wear rate and cutting model proposed in this paper are helpful to improve the prediction accuracy of tool wear in cutting process.