Impact of Feature-Selection in a Data-Driven Method for Flow Curve Identification of Sheet Metal

Abstract

This study presents an innovative data-driven methodology to model the hardening behavior of sheet metals across a broad strain range, crucial for understanding sheet metal mechanics. Conventionally, true stress-strain data from such tests are used to analyze plastic flow within the pre-necking regime, often requiring additional experiments to inverse finite element methods, which demand extensive field data for improved accuracy. Although digital image correlation offers precise data, its implementation is costly. To address this, we integrate experimental data from standard tensile tests with a machine-learning approach to estimate the flow curve. Subsequently, we conduct finite element simulations on uniaxial tensile tests, using materials characterized by the Swift constitutive equation to build a comprehensive database. Loading force-gripper displacement curves from these simulations are then transformed into input features for model training. We propose and compare three models-Models A, B, and C-each employing different input feature selections to estimate the flow curve. Experimental validation including uniaxial tensile, plane strain, and simple shear tests on the DP590 and DP780 sheets are then carefully considered. Results demonstrate the effectiveness of our proposed method, with Model C showing the highest efficacy.