Lizhe Wang , Fuyuan Liu , Min Chen , Zhiyun Mao , Geng Chen , Zhichao Zhang , Zhouyi Xiang
{"title":"Synergizing machine learning and multiscale shakedown method for shakedown loading capacity evaluation of parameterized lattice structures","authors":"Lizhe Wang , Fuyuan Liu , Min Chen , Zhiyun Mao , Geng Chen , Zhichao Zhang , Zhouyi Xiang","doi":"10.1016/j.eml.2025.102297","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, advanced soft computing methodologies have emerged as more effective than traditional approaches in estimating fatigue properties. However, a significant research gap remains in efficiently and accurately evaluating the multiaxial loading capacities of lattice structures and the impact of mesoscale design parameters, especially under unknown cyclic conditions during operation. To address this, we propose a data-driven methodology that integrates a multiscale shakedown evaluation method with a hybrid machine learning (HML) model. Our HML model, incorporating ensemble learning techniques and hyperparameter tuning via random search, accurately predicts the multiaxial shakedown fatigue loading capacity of a representative peanut-shaped auxetic lattice structure with parameterized geometry. The HML model's exceptional performance, demonstrated by a Normalized Root Mean Squared Error (NRMSE) of 0.018 and a coefficient of determination (R<sup>2</sup>) of 0.945, underscores its reliability, precision, and practicality. Additionally, sensitivity-based parametric analyses reveal the significant influence of center distance and edge width on the multiaxial fatigue properties of the lattice structure. This work offers an efficient tool for quantifying the contributions of various design parameters and loading conditions to multiaxial shakedown loading capacities.</div></div>","PeriodicalId":56247,"journal":{"name":"Extreme Mechanics Letters","volume":"75 ","pages":"Article 102297"},"PeriodicalIF":4.3000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Extreme Mechanics Letters","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352431625000094","RegionNum":3,"RegionCategory":"工程技术","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
In recent years, advanced soft computing methodologies have emerged as more effective than traditional approaches in estimating fatigue properties. However, a significant research gap remains in efficiently and accurately evaluating the multiaxial loading capacities of lattice structures and the impact of mesoscale design parameters, especially under unknown cyclic conditions during operation. To address this, we propose a data-driven methodology that integrates a multiscale shakedown evaluation method with a hybrid machine learning (HML) model. Our HML model, incorporating ensemble learning techniques and hyperparameter tuning via random search, accurately predicts the multiaxial shakedown fatigue loading capacity of a representative peanut-shaped auxetic lattice structure with parameterized geometry. The HML model's exceptional performance, demonstrated by a Normalized Root Mean Squared Error (NRMSE) of 0.018 and a coefficient of determination (R2) of 0.945, underscores its reliability, precision, and practicality. Additionally, sensitivity-based parametric analyses reveal the significant influence of center distance and edge width on the multiaxial fatigue properties of the lattice structure. This work offers an efficient tool for quantifying the contributions of various design parameters and loading conditions to multiaxial shakedown loading capacities.
期刊介绍:
Extreme Mechanics Letters (EML) enables rapid communication of research that highlights the role of mechanics in multi-disciplinary areas across materials science, physics, chemistry, biology, medicine and engineering. Emphasis is on the impact, depth and originality of new concepts, methods and observations at the forefront of applied sciences.