Direct sensitivity analysis on the parameterization of crystal plasticity models

• Sensitivity analysis (SA) was performed directly on the crystal plasticity results using the elementary effects test (EET) method, avoiding errors commonly introduced when using surrogate-based approaches. • This proposed direct framework, enabled by a GPU implementation of crystal plasticity, effectively captures the non-linear influence of parameterization on the material’s mechanical response. • An efficient methodology is presented for future calibration and SA of crystal plasticity model parameters. • The study also reveals that parameters contribution to uncertainty in single crystals are strongly governed by the number of active slip systems. Various methods for calibrating crystal plasticity finite element (CPFE) models lead to non-unique input parameter values, which subsequently introduce uncertainty in the predicted mechanical response. Sensitivity analysis (SA) conducted on crystal plasticity models is used to identify how variability in these parameters contribute to output uncertainty. Traditional SA on CPFE parameters uses simplified surrogate models to save computational time. However, the accuracy of the surrogate models depends on the quantity of training data used, and any modeling error can propagate into the SA results, potentially affecting their reliability. In this work, the elementary effects test (EET) method, a global SA technique using direct CPFE simulations was employed, and the results obtained were compared with the First Order Second Moment (FOSM) method. ExaConstit, an open-source GPU-enabled CPFE code, was used to perform the simulations and direct SA. The EET method was accurately able to capture the non-linear effects of all the input parameters on the output and is a valuable approach for reliably attributing parameter sensitivities in CPFE models. Based on the results, efficient strategies to perform future parameter calibration and SA are discussed. Additionally, the SA trends observed in different single crystal orientations closely mirrored the activity of the slip systems.

Authors

Institutions

Publication Details

Journal
Materials & Design
Published
2025-11-21
DOI
https://doi.org/10.1016/j.matdes.2025.115187
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Direct sensitivity analysis on the parameterization of crystal plasticity models

James Belak, Robert Carson, Michael D. Sangid, Leonidas Zisis et al.
Materials & Design
Model Reduction and Neural Networks
article

Direct sensitivity analysis on the parameterization of crystal plasticity models

James Belak, Robert Carson, Michael D. Sangid, Leonidas Zisis, Harshit Gaddam
article en

Abstract

• Sensitivity analysis (SA) was performed directly on the crystal plasticity results using the elementary effects test (EET) method, avoiding errors commonly introduced when using surrogate-based approaches. • This proposed direct framework, enabled by a GPU implementation of crystal plasticity, effectively captures the non-linear influence of parameterization on the material’s mechanical response. • An efficient methodology is presented for future calibration and SA of crystal plasticity model parameters. • The study also reveals that parameters contribution to uncertainty in single crystals are strongly governed by the number of active slip systems. Various methods for calibrating crystal plasticity finite element (CPFE) models lead to non-unique input parameter values, which subsequently introduce uncertainty in the predicted mechanical response. Sensitivity analysis (SA) conducted on crystal plasticity models is used to identify how variability in these parameters contribute to output uncertainty. Traditional SA on CPFE parameters uses simplified surrogate models to save computational time. However, the accuracy of the surrogate models depends on the quantity of training data used, and any modeling error can propagate into the SA results, potentially affecting their reliability. In this work, the elementary effects test (EET) method, a global SA technique using direct CPFE simulations was employed, and the results obtained were compared with the First Order Second Moment (FOSM) method. ExaConstit, an open-source GPU-enabled CPFE code, was used to perform the simulations and direct SA. The EET method was accurately able to capture the non-linear effects of all the input parameters on the output and is a valuable approach for reliably attributing parameter sensitivities in CPFE models. Based on the results, efficient strategies to perform future parameter calibration and SA are discussed. Additionally, the SA trends observed in different single crystal orientations closely mirrored the activity of the slip systems.

Materials & DesignVol. 260
Lawrence Livermore National Laboratory (US), Purdue University West Lafayette (US)
Openalex Percentile: Top 71%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.