The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel
Abstract Traditional alloy design typically relies on a trial‐and‐error approach, which is both time‐consuming and expensive. Whilst physical metallurgical (PM) models offer some predictive capabilities, their reliability is limited by errors accumulating across space scales. To address this, this s...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-03-01
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| Series: | Materials Genome Engineering Advances |
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| Online Access: | https://doi.org/10.1002/mgea.70 |
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| author | Yong Li Hua Li Chenchong Wang Pedro Eduardo Jose Rivera‐Diaz‐del‐Castillo |
| author_facet | Yong Li Hua Li Chenchong Wang Pedro Eduardo Jose Rivera‐Diaz‐del‐Castillo |
| author_sort | Yong Li |
| collection | DOAJ |
| description | Abstract Traditional alloy design typically relies on a trial‐and‐error approach, which is both time‐consuming and expensive. Whilst physical metallurgical (PM) models offer some predictive capabilities, their reliability is limited by errors accumulating across space scales. To address this, this study proposes a novel framework that combines PM knowledge graphs (PMKGs) with graph neural networks (GNNs) to predict the tensile properties of quenching and partitioning steels, using genetic algorithms for dual‐objective optimization. Compared to traditional artificial intelligence (AI) models, this framework shows significant advantages in predicting ultimate tensile strength (UTS) and total elongation (TEL) with higher accuracy and stability. Notably, the R2 for TEL prediction improved by approximately 15%. Furthermore, this framework successfully balances UTS and TEL, resulting in the design of alloys with superior overall properties. The designed alloys, with a composition of approximately 0.3 wt.% C, 3 wt.% Mn, 1.2 wt.% Si, and minor amounts of Cr and Al, achieve a UTS exceeding 1500 MPa and TEL near 20%, aligning with PM principles and validating the rationality and feasibility of this method. This study offers new insights into applying AI in complex multi‐objective alloy design, highlighting the potential of integrating expert knowledge with GNNs. |
| format | Article |
| id | doaj-art-e634070bb75c4029b6abf252ebb6c631 |
| institution | Kabale University |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-e634070bb75c4029b6abf252ebb6c6312025-08-20T03:42:01ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972025-03-0131n/an/a10.1002/mgea.70The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steelYong Li0Hua Li1Chenchong Wang2Pedro Eduardo Jose Rivera‐Diaz‐del‐Castillo3School of Engineering and Physical Sciences University of Southampton Southampton UKSchool of Engineering and Physical Sciences University of Southampton Southampton UKState Key Laboratory of Rolling and Automation Northeastern University Shenyang Liaoning ChinaSchool of Engineering and Physical Sciences University of Southampton Southampton UKAbstract Traditional alloy design typically relies on a trial‐and‐error approach, which is both time‐consuming and expensive. Whilst physical metallurgical (PM) models offer some predictive capabilities, their reliability is limited by errors accumulating across space scales. To address this, this study proposes a novel framework that combines PM knowledge graphs (PMKGs) with graph neural networks (GNNs) to predict the tensile properties of quenching and partitioning steels, using genetic algorithms for dual‐objective optimization. Compared to traditional artificial intelligence (AI) models, this framework shows significant advantages in predicting ultimate tensile strength (UTS) and total elongation (TEL) with higher accuracy and stability. Notably, the R2 for TEL prediction improved by approximately 15%. Furthermore, this framework successfully balances UTS and TEL, resulting in the design of alloys with superior overall properties. The designed alloys, with a composition of approximately 0.3 wt.% C, 3 wt.% Mn, 1.2 wt.% Si, and minor amounts of Cr and Al, achieve a UTS exceeding 1500 MPa and TEL near 20%, aligning with PM principles and validating the rationality and feasibility of this method. This study offers new insights into applying AI in complex multi‐objective alloy design, highlighting the potential of integrating expert knowledge with GNNs.https://doi.org/10.1002/mgea.70alloy designdual‐objective optimisationgraph neural networksknowledge graphsphysical metallurgy |
| spellingShingle | Yong Li Hua Li Chenchong Wang Pedro Eduardo Jose Rivera‐Diaz‐del‐Castillo The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel Materials Genome Engineering Advances alloy design dual‐objective optimisation graph neural networks knowledge graphs physical metallurgy |
| title | The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel |
| title_full | The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel |
| title_fullStr | The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel |
| title_full_unstemmed | The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel |
| title_short | The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel |
| title_sort | role of physical metallurgical relationships in enhancing alloy properties prediction and design a case study on q p steel |
| topic | alloy design dual‐objective optimisation graph neural networks knowledge graphs physical metallurgy |
| url | https://doi.org/10.1002/mgea.70 |
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