Statistically equivalent surrogate material models: Impact of random imperfections on the elasto-plastic response

Ustim Khristenko, Andrei Constantinescu, Patrick Le Tallec, Barbara Wohlmuth

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 and a calibration strategy ensuring that the constructed model fits to the available observation data, 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, allowing for fast sampling strategies. The mathematical design parameters of the model are related to physical ones and thus easy to interpret. The calibration of the model parameters is performed using progressive batching sub-sampled quasi-Newton minimization, using a designed distance measure between the synthetic samples and the data. Then, employing a fast sampling algorithm, an arbitrary number of synthetic samples can be generated to use in Monte Carlo type methods for prediction of effective material properties. In particular, we illustrate the method in application to UQ of the elasto-plastic response of an imperfect octet-truss lattice which plays an important role in additive manufacturing. To this end, we study the effective material properties of the lattice unit cell under elasto-plastic deformations and investigate the sensitivity of the effective Young's modulus to the imperfections.

Original languageEnglish
Article number115278
JournalComputer Methods in Applied Mechanics and Engineering
Volume402
DOIs
StatePublished - 1 Dec 2022

Keywords

  • Additive manufacturing
  • Elasto-plastic material
  • Random fields
  • Stochastic optimization
  • Surrogate model
  • Uncertainty quantification

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