Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation
Copper alloys, valued for their excellent electrical conductivity and mechanical properties, are widely applied in electronics, power systems, and related fields. However, the extensive diversity and compositional range of alloying elements pose substantial challenges in alloy design. To address thi...
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Elsevier
2025-05-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525003685 |
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| author | Li Feng Jiangnan Li Qiong Lu Yuanqi You Zunyan Xu Liyuan Liu Li Fu Peng Gao Jianhong Yi Caiju Li |
| author_facet | Li Feng Jiangnan Li Qiong Lu Yuanqi You Zunyan Xu Liyuan Liu Li Fu Peng Gao Jianhong Yi Caiju Li |
| author_sort | Li Feng |
| collection | DOAJ |
| description | Copper alloys, valued for their excellent electrical conductivity and mechanical properties, are widely applied in electronics, power systems, and related fields. However, the extensive diversity and compositional range of alloying elements pose substantial challenges in alloy design. To address this challenge, this study applied a machine learning approach: a Support Vector Regression (SVR) based “composition-conductivity” model was constructed to predict the impact of individual elements on the alloy’s electrical conductivity. According to the prediction results, Zn element was added to Cu-0.4Cr-0.06Ti alloy. Through experimental validation, it was shown that adding 0.05 wt% Zn achieves an ultimate tensile strength of 507 MPa, an electrical conductivity of 79 % IACS, and an elongation of 23 %. Morphology characterization revealed the role of Zn in the alloy: Zn was present in the matrix as a substitutional solid solution, while Cr was present as an interstitial solid solution. The addition of Zn promoted Cr precipitation and accelerated the transformation of Cr-rich phases, altering the interface between the matrix and precipitates from coherent to incoherent, thus reducing lattice distortion. This adjustment in solute elements and interfacial relationships enhanced both electrical conductivity and strength, breaking through the inverted relationship between strength and conductivity of copper alloy Furthermore, this study demonstrated that machine learning-based composition optimization effectively guides experimental design, providing new insights for the development of high-performance copper alloys. |
| format | Article |
| id | doaj-art-3c5508b762e0426fbf0090a48cfaf967 |
| institution | OA Journals |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-3c5508b762e0426fbf0090a48cfaf9672025-08-20T02:38:30ZengElsevierMaterials & Design0264-12752025-05-0125311394810.1016/j.matdes.2025.113948Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validationLi Feng0Jiangnan Li1Qiong Lu2Yuanqi You3Zunyan Xu4Liyuan Liu5Li Fu6Peng Gao7Jianhong Yi8Caiju Li9Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, China; Corresponding authors at: Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China.Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China; Yunnan Engineering Research Center of Metallic Powder Materials, Kunming University of Science and Technology, Kunming 650093, China; Corresponding authors at: Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, Kunming 650093, China.Copper alloys, valued for their excellent electrical conductivity and mechanical properties, are widely applied in electronics, power systems, and related fields. However, the extensive diversity and compositional range of alloying elements pose substantial challenges in alloy design. To address this challenge, this study applied a machine learning approach: a Support Vector Regression (SVR) based “composition-conductivity” model was constructed to predict the impact of individual elements on the alloy’s electrical conductivity. According to the prediction results, Zn element was added to Cu-0.4Cr-0.06Ti alloy. Through experimental validation, it was shown that adding 0.05 wt% Zn achieves an ultimate tensile strength of 507 MPa, an electrical conductivity of 79 % IACS, and an elongation of 23 %. Morphology characterization revealed the role of Zn in the alloy: Zn was present in the matrix as a substitutional solid solution, while Cr was present as an interstitial solid solution. The addition of Zn promoted Cr precipitation and accelerated the transformation of Cr-rich phases, altering the interface between the matrix and precipitates from coherent to incoherent, thus reducing lattice distortion. This adjustment in solute elements and interfacial relationships enhanced both electrical conductivity and strength, breaking through the inverted relationship between strength and conductivity of copper alloy Furthermore, this study demonstrated that machine learning-based composition optimization effectively guides experimental design, providing new insights for the development of high-performance copper alloys.http://www.sciencedirect.com/science/article/pii/S0264127525003685Cu-Cr-TiMachine learningAlloy designConductivity |
| spellingShingle | Li Feng Jiangnan Li Qiong Lu Yuanqi You Zunyan Xu Liyuan Liu Li Fu Peng Gao Jianhong Yi Caiju Li Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation Materials & Design Cu-Cr-Ti Machine learning Alloy design Conductivity |
| title | Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation |
| title_full | Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation |
| title_fullStr | Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation |
| title_full_unstemmed | Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation |
| title_short | Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation |
| title_sort | accelerated development of high strength and high conductivity cu cr ti alloys based on data driven design and experimental validation |
| topic | Cu-Cr-Ti Machine learning Alloy design Conductivity |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525003685 |
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