DIC-informed microscale elastoplastic flow states in machining

Abstract

Characterizing material flow states is essential for understanding metal cutting mechanisms and improving machine tool performance, particularly near the tool cutting edge (TCE). Accurately modeling these flow states is challenging due to the rapid and severe deformations in this region. To address this, a hybrid approach combining experimental observation and theoretical modeling was developed for machining of Inconel 718 alloy. An in-situ imaging system with high-speed filming capability was set up on a CNC lathe. The captured images were processed using a digital image correlation (DIC) algorithm based on the Gauss-Newton nonlinear iterative method to obtain incremental displacement and strain fields. DIC analysis revealed the evolution of the shearing zone from concave-convex to near-linear, the transition of the stagnation point/zone ahead of the TCE, the dynamic sticking-sliding contact at the chip-tool interface, and the ploughing-induced stretching-like springback at the tool flank face. These observations supported the development of an extended slip-line field model for cutting with rounded TCE, incorporating microscale elastoplastic material flow states into forces prediction. Orthogonal cutting tests with various uncut chip thickness (UCT) and cutting velocities validated the hybrid model. Compared with a previous one, the proposed approach reduced the average prediction error of forces from 21.6% to 14.5% in the cutting direction and from 61% to 5.1% in the thrust direction. This study provides a comprehensive characterization of microscale material flow states and significant process signatures, offering guidance for machine tool design and manufacturing process optimization.