Download file (813KB)
Download as zip (669KB)

コレクション

引用

説明:

Optimizing various fabrication parameters is one of the major challenges in the development of superconducting wires, often resulting in prolonged transition periods from fundamental research to practical applications. Additionally, escalating costs for essential resources such as liquid helium have amplified the difficulty of experimental work, further underscoring the importance of data-driven research approaches. In this study, we focus on magnesium diboride (MgB₂) wires and demonstrate the effectiveness of Bayesian optimization in efficiently searching complex parameter spaces to identify optimal fabrication conditions. Specifically, we investigate the internal magnesium diffusion (IMD) process, employing Bayesian optimization and the BOXVIA visualization tool to explore key heat-treatment parameters—namely, heat-treatment time and temperature—with the aim of maximizing the engineering critical current density (Je). Our results show that, under conventional conditions, the highest Je was achieved at approximately 700 °C with a short holding time of less than one hour. Moreover, our process informatics approach enabled the discovery of optimal conditions even under unconventional parameter settings. This methodology substantially reduces the number of experimental iterations required and enhances the performance of superconducting wires. Overall, our data-driven optimization strategy offers a promising route for faster, more efficient wire fabrication and the accelerated commercialization of superconducting technologies.

権利情報:

• In Copyright
  

  © 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

キーワード: Internal magnesium diffusion, Process informatics, Bayesian optimization, MgB2, Premix-IMD

刊行年月日: 2026-01-14

出版者: Institute of Electrical and Electronics Engineers (IEEE)

掲載誌:

• IEEE Transactions on Applied Superconductivity (ISSN: 10518223) vol. 36 issue. 5 p. 1-4

研究助成金:

• National Institute for Materials Science
• Advanced Research Infrastructure for Materials and Nanotechnology in Japan
• Ministry of Education, Culture, Sports, Science and Technology JPMJCR18J4
• JSPS KAKENHI JP21H01615

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.6169

公開URL: https://doi.org/10.1109/tasc.2026.3652546

関連資料:

その他の識別子:

連絡先:

更新時刻: 2026-01-30 16:30:04 +0900

MDRでの公開時刻: 2026-01-30 13:53:18 +0900