JOM最新文献_第2页

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-30 DOI: 10.1007/s11837-026-08818-0

引用次数: 0

Calculation of Electrode Gap and Prediction of Limiting Slag Resistivity During Electroslag Remelting 电渣重熔过程中电极间隙计算及极限渣电阻率预测

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-28 DOI: 10.1007/s11837-026-08726-3

Yi-ming Li, Zhou-hua Jiang, Ao Wang

{"title":"Calculation of Electrode Gap and Prediction of Limiting Slag Resistivity During Electroslag Remelting","authors":"Yi-ming Li, Zhou-hua Jiang, Ao Wang","doi":"10.1007/s11837-026-08726-3","DOIUrl":"10.1007/s11837-026-08726-3","url":null,"abstract":"<div><p>During electroslag remelting (ESR), the preset slag resistance determines the electrode gap at a given melting rate, thereby governing process stability and ingot surface quality. However, the electrode gap cannot be monitored in real time and is typically optimized by operator experience. To overcome this limitation, a predictive model for the electrode gap was developed. When the crystallizer dimensions, electrode geometry, and slag composition are fixed, a single remelting experiment is sufficient to establish a real-time calculation equation for the electrode gap, enabling its visualization in subsequent heats and facilitating adjustment of electrical parameters. Based on the calculated electrode gap, a critical melting rate was identified. When the electrode gap is maintained constant, increasing the melting rate beyond this value no longer decreases slag resistivity, and the critical rate is generally lower than that in the steady-state stage of ESR. Based on this relationship, a method is proposed to predict the limiting slag resistivity of new slags, allowing direct determination of the optimal slag-resistance setting under a target electrode gap and melting rate without extensive trial and error.</p></div>","PeriodicalId":605,"journal":{"name":"JOM","volume":"78 11","pages":"10476 - 10492"},"PeriodicalIF":2.3,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149250639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Evolution of ICME in the Aerospace Industry: Past, Present, and Future 航空航天工业中ICME的演变：过去、现在和未来

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-23 DOI: 10.1007/s11837-026-08734-3

Manish Kamal, Adam Pilchak, Vasisht Venkatesh, David Furrer, Lee Semiatin

{"title":"The Evolution of ICME in the Aerospace Industry: Past, Present, and Future","authors":"Manish Kamal, Adam Pilchak, Vasisht Venkatesh, David Furrer, Lee Semiatin","doi":"10.1007/s11837-026-08734-3","DOIUrl":"10.1007/s11837-026-08734-3","url":null,"abstract":"<div><p>Integrated computational materials engineering (ICME) has emerged as a formalized discipline to link material and manufacturing models to support system-level performance models. ICME offers many advantages, including reduced cost and development time, by avoiding trial and error-based approaches. The aerospace industry is particularly interested in ICME tools to take advantage of these benefits, as well as for improving quality and performance by developing enhanced material definitions. This paper provides a brief overview of the history and emergence of ICME, provides insights into the present state, and discusses future opportunities. Of key importance to the aerospace field is the ability to make component-scale predictions for both process and performance models, and advancements in these areas will be discussed. Characterization methods have matured in parallel with developments in computational tools which are enabling a deeper understanding of structure–property relationship and also offering robust datasets to facilitate model validation. The paper concludes with a discussion of some current challenges in the implementation of ICME.</p></div>","PeriodicalId":605,"journal":{"name":"JOM","volume":"78 11","pages":"10493 - 10504"},"PeriodicalIF":2.3,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149250540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Atomistic Machine Learning for Grain Boundaries in Advanced Manufacturing: From Metastable Structures to Processing–Structure–Property Design 先进制造中晶界的原子机器学习研究综述：从亚稳态结构到加工-结构-性能设计

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-17 DOI: 10.1007/s11837-026-08693-9

Anqi Qiu, Yue Fan

{"title":"A Review of Atomistic Machine Learning for Grain Boundaries in Advanced Manufacturing: From Metastable Structures to Processing–Structure–Property Design","authors":"Anqi Qiu, Yue Fan","doi":"10.1007/s11837-026-08693-9","DOIUrl":"10.1007/s11837-026-08693-9","url":null,"abstract":"<div><p>Grain boundaries are central to the performance of polycrystalline materials, yet their structures and properties become especially difficult to predict in advanced manufacturing, where rapid thermal cycling, steep gradients, severe deformation, and chemical complexity drive interfaces far from equilibrium. Machine learning offers new opportunities to represent, predict, and ultimately design such complex grain-boundary states. This review surveys recent progress in atomistic machine learning for grain boundaries, with emphasis on manufacturing-relevant challenges. We first discuss digital representations of grain boundaries, including crystallographic descriptors, SOAP-based local atomic environment fingerprints, and physics-informed descriptors for predicting grain-boundary energy and segregation behavior. We then review machine learning interatomic potentials and active-learning strategies that enable larger-scale, higher-fidelity simulations of chemically complex and dynamically evolving interfaces. Particular attention is given to frontier challenges in predicting finite-temperature and kinetic properties, including grain-boundary mobility, shear coupling, defect absorption, disordering, and complexion transitions. Finally, we outline an outlook in which interpretable machine learning, uncertainty-aware models, grain-boundary diagrams, and inverse design workflows connect processing history to interfacial structure and materials performance.</p></div>","PeriodicalId":605,"journal":{"name":"JOM","volume":"78 11","pages":"10252 - 10280"},"PeriodicalIF":2.3,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11837-026-08693-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149250691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mechanism and Kinetics of Scandium Extraction from High-Pressure Leachate of Iron-Aluminum Residue 铁铝渣高压渗滤液中钪的萃取机理及动力学研究

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-15 DOI: 10.1007/s11837-026-08616-8

Yunpeng Du, Chunmin Zhang, Yihui Chen, Jingning Yang, Wenwei Wu

{"title":"Mechanism and Kinetics of Scandium Extraction from High-Pressure Leachate of Iron-Aluminum Residue","authors":"Yunpeng Du, Chunmin Zhang, Yihui Chen, Jingning Yang, Wenwei Wu","doi":"10.1007/s11837-026-08616-8","DOIUrl":"10.1007/s11837-026-08616-8","url":null,"abstract":"<div><p>Laterite nickel ore, a crucial resource for nickel production, contains associated rare earth scandium (Sc) at 50–80 g/ton, which is difficult to extract directly from the raw ore. Currently, the high-pressure acid leaching (HPAL) process for laterite nickel ore is becoming mainstream; during this primary HPAL step, Sc remains in the solid residue because the leaching conditions are optimized for Ni and Co recovery. The Sc-bearing iron‑aluminum residue is then subjected to a second HPAL step with higher acidity and temperature, which effectively dissolves Sc. However, this second HPAL step dissolved not only Sc but also Fe and Al, necessitating selective Sc extraction from the leachate. This study systematically investigated the extraction efficiency of Sc using a binary system of bis(2,4,4-trimethylpentyl)phosphinic acid (Cyanex 272) and di(2-ethylhexyl) (2-ethylhexyl) phosphonate (Cextrant 230), employing leachate generated from prior experiments. At a Cyanex 272 molar fraction of 0.6 (total extractant concentration 0.1 mol/L, room temperature, aqueous-to-organic phase ratio 1:1), the system exhibited optimal synergistic extraction for Sc, achieving separation coefficients of 2.89 and 2.35 for Fe and Al, respectively. Based on these findings, the chemical formula of the final extracted complex was determined via the slope analysis method. Furthermore, the kinetics and mass transfer processes of the extraction reaction were systematically studied using the constant interfacial cell technique. The extraction of Sc<sup>3+</sup> by the Cyanex 272-Cextrant 230 system was identified as a first-order reaction. The forward extraction rate was found to be governed by a mixed diffusion-and-chemical-reaction mechanism, with the reaction occurring at the organic-aqueous interface. A rate equation was derived based on the extraction mechanism calculations. This research provides valuable insights for Sc recovery from laterite nickel ore.</p></div>","PeriodicalId":605,"journal":{"name":"JOM","volume":"78 10","pages":"9055 - 9066"},"PeriodicalIF":2.3,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149027698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivity of a Multi-Physics DC Casting Model to Material Database Selection: Implications on Hot-Tearing and Cold-Cracking Prediction in an AA7050 Alloy 多物理场直流铸造模型对材料数据库选择的敏感性：对AA7050合金热裂和冷裂预测的影响

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-10 DOI: 10.1007/s11837-026-08703-w

Tungky Subroto, Alexis Miroux, Dmitry G. Eskin, Laurens Katgerman

{"title":"Sensitivity of a Multi-Physics DC Casting Model to Material Database Selection: Implications on Hot-Tearing and Cold-Cracking Prediction in an AA7050 Alloy","authors":"Tungky Subroto, Alexis Miroux, Dmitry G. Eskin, Laurens Katgerman","doi":"10.1007/s11837-026-08703-w","DOIUrl":"10.1007/s11837-026-08703-w","url":null,"abstract":"<div><p>Numerical process simulation is increasingly used to optimize direct-chill (DC) casting of aluminum alloys, yet crack-susceptibility predictions depend critically on the constitutive–property databases employed. This study presents a sensitivity analysis of the ALSIM DC casting model with respect to material database selection, using AA7050 billet (315 mm diameter) casting as a benchmark. Three casting scenarios—hot tearing (HT), cold cracking (CC), and healthy billets—were simulated using five databases spanning semi-solid and fully-solid regimes: Al-2 wt.% Cu, AA7050, and artificially strengthened AA7050XTR semi-solid databases, alongside AA1050 and AA7050 fully-solid (hardening) databases. The results show that the fully-solid database dominates the first principal-stress field, which governs the critical crack size (CCS) used as the CC susceptibility indicator, while the semi-solid database has limited effect on the integrated critical strain (ICS) used as the HT indicator. This insensitivity may arise because accumulated strain in DC casting is governed primarily by process conditions rather than material properties, revealing a fundamental limitation of strain-based HT criteria for alloy-to-alloy comparison. To address this limitation, an improved version of reserve strain value (RSV) concept is discussed as a complementary, material-sensitive HT indicator that may support future development of coupled HT-CC assessment methods. Practical recommendations for industrial database development are provided.</p></div>","PeriodicalId":605,"journal":{"name":"JOM","volume":"78 11","pages":"10376 - 10391"},"PeriodicalIF":2.3,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11837-026-08703-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149250509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The 2025 TMS Annual Financial Report TMS 2025年度财务报告

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-09 DOI: 10.1007/s11837-026-08721-8

Dan Miracle, Trudi Dunlap

引用次数: 0

Investing in Tomorrow's Leaders: The TMS Foundation 2025 Annual Report 投资明日领袖：TMS基金会2025年年度报告

IF 2.3 4区材料科学

JOM Pub Date : 2026-09-08 DOI: 10.1007/s11837-026-08723-6

引用次数: 0

Cast Shop 2026: A Course Report—and an Invitation to the Next 铸造车间2026：课程报告和下一个邀请

IF 2.3 4区材料科学