Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks

Equation-Informed Neural Networks (EINNs) are developed as an efficient method for extracting the coefficients of constitutive equations. Subsequently, numerical Bayesian Inference (BI) iterations were applied to estimate the distribution of these coefficients, thereby further refining them. We coul...

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Published inMaterials Vol. 16; no. 14; p. 4922
Main Authors Yuan, Cadmus, Su, Qinghua, Chiang, Kuo-Ning
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 10.07.2023
MDPI
Subjects
Accuracy
advanced electronic packaging
Analysis
Approximation
Back propagation
Bayesian analysis
Coefficients
Constitutive equations
Constitutive models
Constitutive relationships
Datasets
Efficiency
Equation-Informed Neural Networks
Graphical representations
Lead free
Neural networks
Normal distribution
numerical Bayesian Inference
Parameter identification
Pb-free SAC305 solders
Solders
Statistical inference
Strain rate
Temperature
Temperature dependence
Tin base alloys
constitutive equations
advanced electronic packaging
numerical Bayesian Inference
Equation-Informed Neural Networks
Pb-free SAC305 solders
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Summary:Equation-Informed Neural Networks (EINNs) are developed as an efficient method for extracting the coefficients of constitutive equations. Subsequently, numerical Bayesian Inference (BI) iterations were applied to estimate the distribution of these coefficients, thereby further refining them. We could generate coefficients optimally aligned with the targeted application scenario by carefully adjusting pre-processing mapping parameters and identifying dataset preferences. Leveraging graphical representation techniques, the EINNs formulation is implemented in temperature- and strain-rate-dependent hyperbolic Garofalo, Anand, and Chaboche constitutive models to extract the corresponding coefficients for lead-free SAC305 solder material. The performance of the EINNs-based extracted coefficients, obtained from experimental results of SAC305 solder material, is comparable to existing studies. The methodology offers the dual advantage of providing the coefficients' value and distribution against the training dataset.
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ISSN:1996-1944
1996-1944
DOI:10.3390/ma16144922