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

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  • 1Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung 40724, Taiwan.

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Equation-Informed Neural Networks (EINNs) efficiently extract constitutive equation coefficients. Numerical Bayesian Inference (BI) refines these coefficients, providing both values and distributions for materials like SAC305 solder.

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Equation-Informed Neural NetworksPb-free SAC305 soldersadvanced electronic packagingconstitutive equationsnumerical Bayesian Inference

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Area of Science:

  • Materials Science
  • Computational Mechanics
  • Machine Learning

Background:

  • Constitutive models are crucial for material behavior prediction.
  • Extracting accurate material parameters can be challenging.
  • Existing methods may lack efficiency or comprehensive parameter insights.

Purpose of the Study:

  • To develop an efficient method for extracting constitutive equation coefficients.
  • To refine extracted coefficients using numerical Bayesian Inference.
  • To apply the method to lead-free SAC305 solder material.

Main Methods:

  • Equation-Informed Neural Networks (EINNs) for coefficient extraction.
  • Numerical Bayesian Inference (BI) for coefficient distribution estimation.
  • Implementation in Garofalo, Anand, and Chaboche models for SAC305 solder.

Main Results:

  • EINNs successfully extracted coefficients for temperature- and strain-rate-dependent models.
  • The performance of EINNs-derived coefficients is comparable to existing studies.
  • The methodology provides both the value and distribution of coefficients.

Conclusions:

  • EINNs offer an efficient approach to constitutive model parameter identification.
  • The combined EINNs and BI method enhances coefficient accuracy and provides uncertainty quantification.
  • This approach is effective for characterizing lead-free solder materials like SAC305.