{"title":"A deep learning-based crystal plasticity finite element model","authors":"","doi":"10.1016/j.scriptamat.2024.116315","DOIUrl":null,"url":null,"abstract":"<div><p>This study presents an innovative deep learning-based surrogate model for the Crystal Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of mechanical properties such as stress-strain curves in the study of crystal plasticity. Stress-strain curves are pivotal in understanding material deformation, elucidating the intricate relationship between a material's structure and its properties. Traditional CPFE methods, though thorough in their analysis, face significant computational challenges, largely due to the complexity of the crystal plasticity framework. The proposed model circumvents this bottleneck by utilizing an autoencoder architecture to learn intermediate data representations, which are then used to predict the plastic component of deformation. This predicted plastic component serves as a foundation for computing stress-strain curves, effectively bypassing the most time-intensive aspect of traditional CPFE methods, the plasticity self-consistency procedure (achieving a 29.3x speed increase without compromising accuracy).</p></div>","PeriodicalId":423,"journal":{"name":"Scripta Materialia","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1359646224003506/pdfft?md5=18b173b0796c149fc482156671ff8bd3&pid=1-s2.0-S1359646224003506-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scripta Materialia","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359646224003506","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This study presents an innovative deep learning-based surrogate model for the Crystal Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of mechanical properties such as stress-strain curves in the study of crystal plasticity. Stress-strain curves are pivotal in understanding material deformation, elucidating the intricate relationship between a material's structure and its properties. Traditional CPFE methods, though thorough in their analysis, face significant computational challenges, largely due to the complexity of the crystal plasticity framework. The proposed model circumvents this bottleneck by utilizing an autoencoder architecture to learn intermediate data representations, which are then used to predict the plastic component of deformation. This predicted plastic component serves as a foundation for computing stress-strain curves, effectively bypassing the most time-intensive aspect of traditional CPFE methods, the plasticity self-consistency procedure (achieving a 29.3x speed increase without compromising accuracy).
期刊介绍:
Scripta Materialia is a LETTERS journal of Acta Materialia, providing a forum for the rapid publication of short communications on the relationship between the structure and the properties of inorganic materials. The emphasis is on originality rather than incremental research. Short reports on the development of materials with novel or substantially improved properties are also welcomed. Emphasis is on either the functional or mechanical behavior of metals, ceramics and semiconductors at all length scales.