From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy
A method is developed to exploit data on complex materials behaviors that are impossible to tackle by conventional machine learning tools. A pairwise comparison algorithm is used to assess a particular property among a group of different alloys tested simultaneously in identical conditions. Even tho...
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MDPI AG
2024-12-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/14/12/1412 |
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| author | Rafael Herschberg Lisa Rateau Laure Martinelli Fanny Balbaud-Célérier Jean Dhers Anna Fraczkiewicz Gérard Ramstein Franck Tancret |
| author_facet | Rafael Herschberg Lisa Rateau Laure Martinelli Fanny Balbaud-Célérier Jean Dhers Anna Fraczkiewicz Gérard Ramstein Franck Tancret |
| author_sort | Rafael Herschberg |
| collection | DOAJ |
| description | A method is developed to exploit data on complex materials behaviors that are impossible to tackle by conventional machine learning tools. A pairwise comparison algorithm is used to assess a particular property among a group of different alloys tested simultaneously in identical conditions. Even though such characteristics can be evaluated differently across teams, if a series of the same alloys are analyzed among two or more studies, it is feasible to infer an overall ranking among materials. The obtained ranking is later fitted with respect to the alloy’s composition by a Gaussian process. The predictive power of the method is demonstrated in the case of the resistance of metallic materials to molten salt corrosion and wear. In this case, the method is applied to the design of wear-resistant hard-facing alloys by also associating it with a combinatorial optimization of their composition by a multi-objective genetic algorithm. New alloys are selected and fabricated, and their experimental behavior is compared to that of concurrent materials. This generic method can therefore be applied to model other complex material properties—such as environmental resistance, contact properties, or processability—and to design alloys with improved performance. |
| format | Article |
| id | doaj-art-9e40962bbdc244ee9dbdb146936b91bf |
| institution | DOAJ |
| issn | 2075-4701 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj-art-9e40962bbdc244ee9dbdb146936b91bf2025-08-20T02:43:49ZengMDPI AGMetals2075-47012024-12-011412141210.3390/met14121412From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design StrategyRafael Herschberg0Lisa Rateau1Laure Martinelli2Fanny Balbaud-Célérier3Jean Dhers4Anna Fraczkiewicz5Gérard Ramstein6Franck Tancret7Nantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN, 44000 Nantes, FranceNantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN, 44000 Nantes, FranceCEA, Service de Recherche en Corrosion et Comportement des Matériaux, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceCEA, Service de Recherche en Corrosion et Comportement des Matériaux, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceFramatome, 69006 Lyon, FranceMINES Saint-Etienne, Université de Lyon, CNRS, UMR 5307 LGF, Centre SMS, 42023 Saint-Etienne, FranceNantes Université, CNRS, Laboratoire des Sciences du Numérique de Nantes (LS2N), 44306 Nantes, FranceNantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN, 44000 Nantes, FranceA method is developed to exploit data on complex materials behaviors that are impossible to tackle by conventional machine learning tools. A pairwise comparison algorithm is used to assess a particular property among a group of different alloys tested simultaneously in identical conditions. Even though such characteristics can be evaluated differently across teams, if a series of the same alloys are analyzed among two or more studies, it is feasible to infer an overall ranking among materials. The obtained ranking is later fitted with respect to the alloy’s composition by a Gaussian process. The predictive power of the method is demonstrated in the case of the resistance of metallic materials to molten salt corrosion and wear. In this case, the method is applied to the design of wear-resistant hard-facing alloys by also associating it with a combinatorial optimization of their composition by a multi-objective genetic algorithm. New alloys are selected and fabricated, and their experimental behavior is compared to that of concurrent materials. This generic method can therefore be applied to model other complex material properties—such as environmental resistance, contact properties, or processability—and to design alloys with improved performance.https://www.mdpi.com/2075-4701/14/12/1412alloy designmachine learningoptimizationmolten salt corrosionwear |
| spellingShingle | Rafael Herschberg Lisa Rateau Laure Martinelli Fanny Balbaud-Célérier Jean Dhers Anna Fraczkiewicz Gérard Ramstein Franck Tancret From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy Metals alloy design machine learning optimization molten salt corrosion wear |
| title | From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy |
| title_full | From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy |
| title_fullStr | From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy |
| title_full_unstemmed | From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy |
| title_short | From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy |
| title_sort | from pairwise comparisons of complex behavior to an overall performance rank a novel alloy design strategy |
| topic | alloy design machine learning optimization molten salt corrosion wear |
| url | https://www.mdpi.com/2075-4701/14/12/1412 |
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