Statistically equivalent surrogate material models and the impact of random imperfections on elasto-plastic response
Ustim Khristenko, Andrei Constantinescu, Patrick Le Tallec, Barbara, Wohlmuth

TL;DR
This paper introduces a new class of surrogate models for two-phase materials that incorporate random imperfections, enabling efficient uncertainty quantification of elasto-plastic responses in additive manufacturing structures.
Contribution
The work develops a flexible, interpretable surrogate modeling approach based on level-sets and Gaussian perturbations, tailored for two-phase materials with imperfections, and demonstrates its application in UQ of lattice structures.
Findings
Surrogate models accurately represent material imperfections.
Efficient sampling enables extensive uncertainty analysis.
Imperfections significantly influence effective Young's modulus.
Abstract
Manufactured materials usually contain random imperfections due to the fabrication process, e.g., the 3D-printing, casting, etc. These imperfections affect significantly the effective material properties and result in uncertainties in the mechanical response. Numerical analysis of the effects of the imperfections and the uncertainty quantification (UQ) can be often done by use of digital stochastic surrogate material models. In this work, we present a new flexible class of surrogate models depending on a small number of parameters with special focus on two-phase materials. The surrogate models are constructed as the level-set of a linear combination of an intensity field representing the topological shape and a Gaussian perturbation representing the imperfections. The mathematical design parameters of the model are related to physical ones and thus easy to interpret. The calibration of…
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Taxonomy
TopicsManufacturing Process and Optimization · Additive Manufacturing and 3D Printing Technologies · Advanced Multi-Objective Optimization Algorithms
