고강도 Nb기 초내열 합금 설계를 위한 기계학습 기반 데이터 분석
(주)코리아스칼라
- 최초 등록일
- 2023.07.31
- 최종 저작일
- 2023.06
- 6페이지/ 어도비 PDF
- 가격 4,000원
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서지정보
ㆍ발행기관 : 한국분말야금학회
ㆍ수록지정보 : 한국분말야금학회지 / 30권 / 3호
ㆍ저자명 : 마은호, 박수원, 최현주, 황병철, 변종민
목차
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
영어 초록
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.
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