{"title":"基于CALPHAD和数据分析的奥氏体合金nsamel温度预测模型","authors":"Rangasayee Kannan , Peeyush Nandwana","doi":"10.1016/j.scriptamat.2025.116773","DOIUrl":null,"url":null,"abstract":"<div><div>The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. The implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.</div></div>","PeriodicalId":423,"journal":{"name":"Scripta Materialia","volume":"266 ","pages":"Article 116773"},"PeriodicalIF":5.3000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics\",\"authors\":\"Rangasayee Kannan , Peeyush Nandwana\",\"doi\":\"10.1016/j.scriptamat.2025.116773\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. The implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.</div></div>\",\"PeriodicalId\":423,\"journal\":{\"name\":\"Scripta Materialia\",\"volume\":\"266 \",\"pages\":\"Article 116773\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scripta Materialia\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1359646225002362\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scripta Materialia","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359646225002362","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}
Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics
The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. The implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.
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.