Bayesian Modeling of Inconsistent Plastic Response due to Material Variability

TL;DR

This paper develops Bayesian plasticity models incorporating material variability, improving predictive accuracy and enabling better model selection and parameter assessment for materials with microstructural defects.

Contribution

It introduces a Bayesian framework for modeling the distribution of plastic responses, integrating uncertainty quantification techniques into traditional plasticity models.

Findings

Enhanced predictive realizations over traditional models

Effective use of UQ techniques for model selection

Improved assessment of calibrated physical parameters

Abstract

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. We demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.