Earticle

초록

영어

This research presents a GRNN(General regression neural network) approach for modeling the high temperature deformation flow behavior of 316L stainless steel under 800℃, 900℃ and 1000℃ and strain rates of 0.0002/s, 0.002/s and 0.02/s. There are many machine learning approaches of modeling the hot deformation of metallic alloys. Among them, the neural network approach is one of the most popular. However, the neural network approach takes a relatively long time and effort to compose and optimize the final model. In this research, GRNN is applied to study its applicability for modeling the hot deformation flow stress behavior. The prediction results were studied by calculating various types of error and observing the distribution of prediction error. The predicted results by the GRNN were very accurate and the GRNN was found to be highly applicable to modeling the flow stress of the hot deformation of 316L stainless steel.

목차

Abstract
1. 서론
2. 고온 인장시험
3. 응력의 모델링
3.1 인공신경망(neural networks)
3.2 GRNN(General Regression Neural Network)
4. 결과 분석
5. 토의 및 결론
References

키워드

저자

• 송신형 [ Song, Shin-Hyung | Member, Professor, Dept. of Smart Automobile, Soonchunhyang University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620