Strategies for automatic constitutive model selection and recommendation

Abstract

The efficient development of high-quality metal products requires realistic numerical simulations prior to manufacturing. The choice of the constitutive model significantly influences material behaviour prediction and manufacturing process simulation. Numerous models exist to describe mechanical phenomena, making model selection challenging and prone to errors. This leads to costly delays in manufacturing. To address this issue, an automated material constitutive model selection and recommendation tool is essential. This research aims to develop a systematic strategy in three steps: analysis of variance (ANOVA), identifiability analysis, and identification quality. The approach is validated through simulations using a known material, including a hole expansion test and two strain heterogeneous tests for calibration. Step one establishes a ranking for model importance, aiding decision-making and accurate parameter calibration. Step two excludes models with inadequate identifiability and sensitivity measures. Step three confirms the methodology by showing improvements in calibration.