Stress Field Prediction and DIC-Informed Spatial Regularization for AlSi10Mg Alloy Tensile Specimens Based on Graph Neural Networks.

Zhongying Dong, Xingbo Xie, Guili Yang, Zhangdong Li, Xinghua Li, Huayuan Ma

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Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Zhongying DongCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Xingbo XieCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Guili YangCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Zhangdong LiCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Xinghua LiCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Huayuan MaCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China.

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No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-fidelity finite-element (FE) simulation of nonlinear full-field responses in AlSi10Mg tensile specimens is computationally expensive, while the available digital image correlation (DIC) data in this study consist only of engineering-strain screenshots rather than exportable numerical fields. This work therefore develops a simulation-to-experiment framework for rapid full-field stress prediction and weakly supervised DIC-informed spatial regularization. Two hundred independent Abaqus/Explicit loading cases generated 5600 graph samples, which were used to train an eight-layer FiLM-conditioned graph neural network for nodal von Mises stress and three-component displacement prediction, together with a decoupled structured force-displacement branch. On independent FE tests, von Mises stress prediction achieved R

Indexed as

ablation studyAlSi10MgDIC-informed spatial regularizationdigital image correlationfull-field stress predictiongraph neural network

Identifiers

PMID42795199

DOI10.3390/ma19183979

PMCPMC13608456

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.