Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yuan, Cadmus, Su, Qinghua, Chiang, Kuo-Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381129/
https://www.ncbi.nlm.nih.gov/pubmed/37512197
http://dx.doi.org/10.3390/ma16144922
_version_ 1785080367029420032
author Yuan, Cadmus
Su, Qinghua
Chiang, Kuo-Ning
author_facet Yuan, Cadmus
Su, Qinghua
Chiang, Kuo-Ning
author_sort Yuan, Cadmus
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10381129
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-103811292023-07-29 Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks Yuan, Cadmus Su, Qinghua Chiang, Kuo-Ning Materials (Basel) Article 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. MDPI 2023-07-10 /pmc/articles/PMC10381129/ /pubmed/37512197 http://dx.doi.org/10.3390/ma16144922 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yuan, Cadmus
Su, Qinghua
Chiang, Kuo-Ning
Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks
title Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks
title_full Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks
title_fullStr Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks
title_full_unstemmed Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks
title_short Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks
title_sort coefficient extraction of sac305 solder constitutive equations using equation-informed neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381129/
https://www.ncbi.nlm.nih.gov/pubmed/37512197
http://dx.doi.org/10.3390/ma16144922
work_keys_str_mv AT yuancadmus coefficientextractionofsac305solderconstitutiveequationsusingequationinformedneuralnetworks
AT suqinghua coefficientextractionofsac305solderconstitutiveequationsusingequationinformedneuralnetworks
AT chiangkuoning coefficientextractionofsac305solderconstitutiveequationsusingequationinformedneuralnetworks