Jinyeong Yu , Seong Ho Lee , Seho Cheon , Sung Hyuk Park , Taekyung Lee
{"title":"基于机器学习和能量建模的低循环疲劳寿命替代预测方法","authors":"Jinyeong Yu , Seong Ho Lee , Seho Cheon , Sung Hyuk Park , Taekyung Lee","doi":"10.1016/j.jma.2024.10.014","DOIUrl":null,"url":null,"abstract":"<div><div>Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machinability. The applications in these industries necessitate the accurate prediction of fatigue life under cyclic loading. However, this is challenging for many wrought Mg alloys owing to their pronounced plastic anisotropy. Conventional predictive methods such as the Coffin-Manson equation require manual parameter adjustment for different conditions, thus limiting their applicability. Accordingly, a novel predictive model for low-cycle fatigue (LCF) life that combines machine learning (ML) with an energy-based physical model, referred to as the hybrid ML/E model, is proposed herein. The hybrid ML/E model leverages a substantial hysteresis-loop dataset generated from LCF tests on a rolled AZ31 Mg alloy to effectively predict fatigue life. The proposed approach addresses the inherent challenges of small fatigue datasets, hysteresis-loop perception, and algorithm selection. The hybrid ML/E model demonstrates superior predictive accuracy and robustness in various loading directions, based on validation against conventional methods. The integration of ML and physical principles offers a unified framework for the LCF life prediction of anisotropic materials and represents a significant advancement for industrial applications.</div></div>","PeriodicalId":16214,"journal":{"name":"Journal of Magnesium and Alloys","volume":"12 10","pages":"Pages 4075-4084"},"PeriodicalIF":15.8000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Alternative predictive approach for low-cycle fatigue life based on machine learning and energy-based modeling\",\"authors\":\"Jinyeong Yu , Seong Ho Lee , Seho Cheon , Sung Hyuk Park , Taekyung Lee\",\"doi\":\"10.1016/j.jma.2024.10.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machinability. The applications in these industries necessitate the accurate prediction of fatigue life under cyclic loading. However, this is challenging for many wrought Mg alloys owing to their pronounced plastic anisotropy. Conventional predictive methods such as the Coffin-Manson equation require manual parameter adjustment for different conditions, thus limiting their applicability. Accordingly, a novel predictive model for low-cycle fatigue (LCF) life that combines machine learning (ML) with an energy-based physical model, referred to as the hybrid ML/E model, is proposed herein. The hybrid ML/E model leverages a substantial hysteresis-loop dataset generated from LCF tests on a rolled AZ31 Mg alloy to effectively predict fatigue life. The proposed approach addresses the inherent challenges of small fatigue datasets, hysteresis-loop perception, and algorithm selection. The hybrid ML/E model demonstrates superior predictive accuracy and robustness in various loading directions, based on validation against conventional methods. The integration of ML and physical principles offers a unified framework for the LCF life prediction of anisotropic materials and represents a significant advancement for industrial applications.</div></div>\",\"PeriodicalId\":16214,\"journal\":{\"name\":\"Journal of Magnesium and Alloys\",\"volume\":\"12 10\",\"pages\":\"Pages 4075-4084\"},\"PeriodicalIF\":15.8000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Magnesium and Alloys\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221395672400344X\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Magnesium and Alloys","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221395672400344X","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
Alternative predictive approach for low-cycle fatigue life based on machine learning and energy-based modeling
Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machinability. The applications in these industries necessitate the accurate prediction of fatigue life under cyclic loading. However, this is challenging for many wrought Mg alloys owing to their pronounced plastic anisotropy. Conventional predictive methods such as the Coffin-Manson equation require manual parameter adjustment for different conditions, thus limiting their applicability. Accordingly, a novel predictive model for low-cycle fatigue (LCF) life that combines machine learning (ML) with an energy-based physical model, referred to as the hybrid ML/E model, is proposed herein. The hybrid ML/E model leverages a substantial hysteresis-loop dataset generated from LCF tests on a rolled AZ31 Mg alloy to effectively predict fatigue life. The proposed approach addresses the inherent challenges of small fatigue datasets, hysteresis-loop perception, and algorithm selection. The hybrid ML/E model demonstrates superior predictive accuracy and robustness in various loading directions, based on validation against conventional methods. The integration of ML and physical principles offers a unified framework for the LCF life prediction of anisotropic materials and represents a significant advancement for industrial applications.
期刊介绍:
The Journal of Magnesium and Alloys serves as a global platform for both theoretical and experimental studies in magnesium science and engineering. It welcomes submissions investigating various scientific and engineering factors impacting the metallurgy, processing, microstructure, properties, and applications of magnesium and alloys. The journal covers all aspects of magnesium and alloy research, including raw materials, alloy casting, extrusion and deformation, corrosion and surface treatment, joining and machining, simulation and modeling, microstructure evolution and mechanical properties, new alloy development, magnesium-based composites, bio-materials and energy materials, applications, and recycling.