Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals

TL;DR

This paper presents a machine learning approach to rapidly predict parameters for ductile damage models in polycrystalline metals, reducing computational costs and setup time compared to traditional inverse modeling techniques.

Contribution

The work introduces a machine learning inverse model for parameterizing the TEPLA ductile damage model, enabling faster and more efficient predictions.

Findings

Good accuracy on synthetic data

Validated against experimental data

Significantly faster than Bayesian calibration

Abstract

Damage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. This work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.