{"title":"利用基于机器学习的阿伦尼乌斯模型加强热变形过程中镁合金的成分描述和加工性表征","authors":"","doi":"10.1016/j.jma.2024.01.011","DOIUrl":null,"url":null,"abstract":"<div><p>Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.</p></div>","PeriodicalId":16214,"journal":{"name":"Journal of Magnesium and Alloys","volume":null,"pages":null},"PeriodicalIF":15.8000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213956724000252/pdfft?md5=5c6d5ec98e130caba5b471dfeeee43b6&pid=1-s2.0-S2213956724000252-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model\",\"authors\":\"\",\"doi\":\"10.1016/j.jma.2024.01.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.</p></div>\",\"PeriodicalId\":16214,\"journal\":{\"name\":\"Journal of Magnesium and Alloys\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.8000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2213956724000252/pdfft?md5=5c6d5ec98e130caba5b471dfeeee43b6&pid=1-s2.0-S2213956724000252-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Magnesium and Alloys\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213956724000252\",\"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/S2213956724000252","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}
引用次数: 0
摘要
热变形是增强镁合金延展性和可加工性的常用加工技术。然而,镁合金的热变形对温度、应变率和应变等因素高度敏感,导致镁合金的流动行为复杂,加工窗口异常狭窄。为了克服传统阿伦尼乌斯(AT)模型的缺点,本研究结合遗传算法(GA)、粒子群优化(PSO)和人工神经网络(ANN),开发了基于机器学习的阿伦尼乌斯模型(ML-AT)。结果表明,在描述 AQ80 合金的流动行为时,在所有 ML-AT 和 AT 模型中,PSO-ANN-AT 模型的预测精度和泛化能力最为突出。此外,利用基于 PSO-ANN-AT 模型重建的流动应力场和活化能场,建立了活化能处理图(AEP)。实验验证表明,与传统的插值法相比,活化能加工图对微观结构演变的预测能力更强,最终有助于精确确定最佳加工窗口。这些发现为热变形过程中镁合金的精确构成描述和加工性表征提供了新的见解。
Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model
Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.
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.