Predicting creep failure by machine learning - which features matter?

Hiemer S, Moretti P, Zapperi S, Zaiser M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Forces in Mechanics Elsevier

Book Volume: 9

Article Number: 100141

DOI: 10.1016/j.finmec.2022.100141

Abstract

Spatial and temporal features are studied with respect to their predictive value for failure time prediction in subcritical failure with machine learning (ML). Data are generated from simulations of a novel, brittle random fuse model (RFM), as well as elasto-plastic finite element simulations (FEM) of a stochastic plasticity model with damage, both models considering stochastic thermally activated damage/failure processes in disordered materials. Fuse networks are generated with hierarchical and nonhierarchical architectures. Random forests - a specific ML algorithm - allow us to measure the feature importance through a feature's average error reduction. RFM simulation data are found to become more predictable with increasing system size and temperature. Increasing the load or the scatter in local materials properties has the opposite effect. Damage accumulation in these models proceeds in stochastic avalanches, and statistical signatures such as avalanche rate or magnitude have been discussed in the literature as predictors of incipient failure. However, in the present study such features proved of no measurable use to the ML models, which mostly rely on global or local strain for prediction. This suggests the strain as viable quantity to monitor in future experimental studies as it is accessible via digital image correlation.

Authors with CRIS profile

Stefan Hiemer Lehrstuhl für Werkstoffsimulation Paolo Moretti Lehrstuhl für Werkstoffsimulation Michael Zaiser Lehrstuhl für Werkstoffsimulation

Additional Organisation(s)

Graduiertenkolleg 2423 Skalenübergreifende Bruchvorgänge: Integration von Mechanik, Materialwissenschaften, Mathematik, Chemie und Physik

Involved external institutions

Università degli studi di Milano IT Italy (IT)

How to cite

APA:

Hiemer, S., Moretti, P., Zapperi, S., & Zaiser, M. (2022). Predicting creep failure by machine learning - which features matter? Forces in Mechanics, 9. https://doi.org/10.1016/j.finmec.2022.100141

MLA:

Hiemer, Stefan, et al. "Predicting creep failure by machine learning - which features matter?" Forces in Mechanics 9 (2022).

BibTeX: Download