Johannes A. Österreicher , Dragan Živanović , Wolfram Walenta , Stefan Maimone , Manuel Hofbauer , Sindre Hovden , Zuzana Tükör , Aurel Arnoldt , Angelika Cerny , Johannes Kronsteiner , Miloš Antić , Gregor A. Zickler , Florian Ehmeier , Milomir Mikulović , Georg Kunschert
{"title":"利用原位电导测量法研究铝镁硅合金的均质化并通过机器学习预测挤压晶粒结构","authors":"Johannes A. Österreicher , Dragan Živanović , Wolfram Walenta , Stefan Maimone , Manuel Hofbauer , Sindre Hovden , Zuzana Tükör , Aurel Arnoldt , Angelika Cerny , Johannes Kronsteiner , Miloš Antić , Gregor A. Zickler , Florian Ehmeier , Milomir Mikulović , Georg Kunschert","doi":"10.1016/j.matdes.2024.113070","DOIUrl":null,"url":null,"abstract":"<div><p>In industrial practice, no sensors capable of obtaining microstructural information <em>in situ</em> during thermo-mechanical processing of Al alloys are commonly employed. Inductive electrical conductivity measurement is safe, inexpensive, and capable of acquiring valuable information about precipitation and dissolution processes. However, commercial eddy current sensors work only at low temperatures near room temperature and are thus not suitable for <em>in situ</em> conductometry during heat treatments of Al alloys. We designed a high-temperature eddy current sensor and performed <em>in situ</em> conductometry during the homogenization of six Al-Mg-Si wrought alloys, three of which are experimental recycling-friendly alloys with increased Fe content. The results are interpreted with regard to microstructural investigations, and the advantages and limitations of our approach are discussed. As a proof-of-concept, we show how the conductivity curves and extrusion process parameters can be combined to predict final extrudate grain structures using machine learning. To achieve this, we employed finite element simulation of extrusion coupled with microstructural simulation over a wide parameter range, validated by extrusion experiments and metallography, and trained a feedforward neural network. We believe our interdisciplinary approach can lead to improvements in the industrial processing of Al wrought alloys.</p></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":null,"pages":null},"PeriodicalIF":7.6000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0264127524004441/pdfft?md5=0e1da86550dadea84ee8922c97bf1290&pid=1-s2.0-S0264127524004441-main.pdf","citationCount":"0","resultStr":"{\"title\":\"In situ conductometry for studying the homogenization of Al-Mg-Si alloys and predicting extrudate grain structure through machine learning\",\"authors\":\"Johannes A. Österreicher , Dragan Živanović , Wolfram Walenta , Stefan Maimone , Manuel Hofbauer , Sindre Hovden , Zuzana Tükör , Aurel Arnoldt , Angelika Cerny , Johannes Kronsteiner , Miloš Antić , Gregor A. Zickler , Florian Ehmeier , Milomir Mikulović , Georg Kunschert\",\"doi\":\"10.1016/j.matdes.2024.113070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In industrial practice, no sensors capable of obtaining microstructural information <em>in situ</em> during thermo-mechanical processing of Al alloys are commonly employed. Inductive electrical conductivity measurement is safe, inexpensive, and capable of acquiring valuable information about precipitation and dissolution processes. However, commercial eddy current sensors work only at low temperatures near room temperature and are thus not suitable for <em>in situ</em> conductometry during heat treatments of Al alloys. We designed a high-temperature eddy current sensor and performed <em>in situ</em> conductometry during the homogenization of six Al-Mg-Si wrought alloys, three of which are experimental recycling-friendly alloys with increased Fe content. The results are interpreted with regard to microstructural investigations, and the advantages and limitations of our approach are discussed. As a proof-of-concept, we show how the conductivity curves and extrusion process parameters can be combined to predict final extrudate grain structures using machine learning. To achieve this, we employed finite element simulation of extrusion coupled with microstructural simulation over a wide parameter range, validated by extrusion experiments and metallography, and trained a feedforward neural network. We believe our interdisciplinary approach can lead to improvements in the industrial processing of Al wrought alloys.</p></div>\",\"PeriodicalId\":383,\"journal\":{\"name\":\"Materials & Design\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0264127524004441/pdfft?md5=0e1da86550dadea84ee8922c97bf1290&pid=1-s2.0-S0264127524004441-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials & Design\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0264127524004441\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials & Design","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264127524004441","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
In situ conductometry for studying the homogenization of Al-Mg-Si alloys and predicting extrudate grain structure through machine learning
In industrial practice, no sensors capable of obtaining microstructural information in situ during thermo-mechanical processing of Al alloys are commonly employed. Inductive electrical conductivity measurement is safe, inexpensive, and capable of acquiring valuable information about precipitation and dissolution processes. However, commercial eddy current sensors work only at low temperatures near room temperature and are thus not suitable for in situ conductometry during heat treatments of Al alloys. We designed a high-temperature eddy current sensor and performed in situ conductometry during the homogenization of six Al-Mg-Si wrought alloys, three of which are experimental recycling-friendly alloys with increased Fe content. The results are interpreted with regard to microstructural investigations, and the advantages and limitations of our approach are discussed. As a proof-of-concept, we show how the conductivity curves and extrusion process parameters can be combined to predict final extrudate grain structures using machine learning. To achieve this, we employed finite element simulation of extrusion coupled with microstructural simulation over a wide parameter range, validated by extrusion experiments and metallography, and trained a feedforward neural network. We believe our interdisciplinary approach can lead to improvements in the industrial processing of Al wrought alloys.
期刊介绍:
Materials and Design is a multi-disciplinary journal that publishes original research reports, review articles, and express communications. The journal focuses on studying the structure and properties of inorganic and organic materials, advancements in synthesis, processing, characterization, and testing, the design of materials and engineering systems, and their applications in technology. It aims to bring together various aspects of materials science, engineering, physics, and chemistry.
The journal explores themes ranging from materials to design and aims to reveal the connections between natural and artificial materials, as well as experiment and modeling. Manuscripts submitted to Materials and Design should contain elements of discovery and surprise, as they often contribute new insights into the architecture and function of matter.