Universal Design Methodology for Printable Microstructural Materials via a New Deep Generative Learning Model: Application to a Piezocomposite

Mohammad Saber Hashemi; Khiem Nguyen; Levi Kirby; Xuan Song; Azadeh Sheidaei

doi:10.48550/arxiv.2402.11102

Back

Universal Design Methodology for Printable Microstructural Materials via a New Deep Generative Learning Model: Application to a Piezocomposite

Preprint

Open access

Universal Design Methodology for Printable Microstructural Materials via a New Deep Generative Learning Model: Application to a Piezocomposite

Mohammad Saber Hashemi, Khiem Nguyen, Levi Kirby, Xuan Song and Azadeh Sheidaei

arXiv.org

Cornell University

02/16/2024

DOI: 10.48550/arxiv.2402.11102

Files and links (1)

url

https://doi.org/10.48550/arxiv.2402.11102View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

We devised a general heterogeneous microstructural design methodology applied to a specific material system, elasto-electro-active piezoelectric ceramic embedded plastics, which has great potential in sensing, 5G communication, and energy harvesting. Due to the multiphysics interactions of the studied material system, we have developed an accurate and efficient FFT-based numerical method to find the multifunctional properties of diverse cellular microstructures generated by our HetMiGen code. To mine this big dataset, we used our customized physics-aware generative neural network in the format of a VAE with convolutional neural layers augmented by a vision transformer to learn long-distance features which may affect the properties of the 3D voxelized microstructures. In training, the decoder learns how to map the property distribution to the appropriate high-dimensional distribution of 3D microstructures. Therefore, it can be considered an online material designer within the explored design space during its inference phase.

Physics - Materials Science

Details

Title: Subtitle: Universal Design Methodology for Printable Microstructural Materials via a New Deep Generative Learning Model: Application to a Piezocomposite
Creators: Mohammad Saber Hashemi
Khiem Nguyen
Levi Kirby
Xuan Song
Azadeh Sheidaei
Resource Type: Preprint
Publication Details: arXiv.org
DOI: 10.48550/arxiv.2402.11102
eISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 02/16/2024
Academic Unit: Industrial and Systems Engineering; Injury Prevention Research Center; Mechanical Engineering
Record Identifier: 9984560420102771

Metrics

6 Record Views