Earticle

초록

영어

Ridge regression is known to be an effective algorithm for regression problems. However, the algorithm has some drawbacks with highly non-linear datasets. In this research, the hot deformation flow stress of 321 stainless steel was modeled using the kernel ridge regression algorithm. For modeling the flow stress in this research, the tensile test data for 321 stainless steel under temperatures of 700℃, 800℃, and 900℃ at strain rates of 0.0002/s, 0.002/s, and 0.02/s were used. To overcome the drawbacks of the traditional ridge regression algorithm, the algorithm was enhanced by a kernel-type function to handle the non-linear dataset. The predicted data by the kernel ridge regression was accurate. After that, the predicted values were studied in terms of their distribution. The kernel ridge regression algorithm was found to be accurate and stable in predicting the flow stress of hot deformation.

목차

Abstract
1. 서론
2. 릿지 회귀(Ridge regression)
3. 커널 릿지 회귀(Kernel ridge regression)
4. 비교분석
5. 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