Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

1. Introduction
2. Experimental
    2.1 데이터베이스 구축
    2.2 상관분석
    2.3 기계학습
3. Results and Discussion
    3.1 상관분석
    3.2 기계학습
4. Conclusion
Acknowledgement

• 마은호(서울과학기술대학교 신소재공학과) | Eunho Ma (Department of Materials Science & Engineering, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea)
• 박수원(국민대학교 신소재공학부) | Suwon Park (School of Materials Science & Engineering, Kookmin University, Seoul 02707, Republic of Korea)
• 최현주(국민대학교 신소재공학부) | Hyunjoo Choi (School of Materials Science & Engineering, Kookmin University, Seoul 02707, Republic of Korea)
• 황병철(서울과학기술대학교 신소재공학과서, 서울과학기술대학교 분말기술연구소) | Byoungchul Hwang (Department of Materials Science & Engineering, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea, The Institute of Powder Technology, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea) Corresponding Author
• 변종민(서울과학기술대학교 신소재공학과서, 서울과학기술대학교 분말기술연구소) | Jongmin Byun (Department of Materials Science & Engineering, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea, The Institute of Powder Technology, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea) Corresponding Author