文献
J-GLOBAL ID:202202290351718571
整理番号:22A0323921
機械学習支援確率的クリープ疲労損傷評価【JST・京大機械翻訳】
Machine learning assisted probabilistic creep-fatigue damage assessment
著者 (10件):
Gu Hang-Hang
(Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China)
,
Wang Run-Zi
(Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China)
,
Zhu Shun-Peng
(School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China)
,
Wang Xiao-Wei
(School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, PR China)
,
Wang Dong-Ming
(Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China)
,
Zhang Guo-Dong
(Suzhou Nuclear Power Research Institute, Suzhou 215004, PR China)
,
Fan Zhi-Chao
(Hefei General Machinery Research Institute Co. Ltd., Hefei 230031, PR China)
,
Zhang Xian-Cheng
(Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China)
,
Tu Shan-Tung
(Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China)
,
Wang Run-Zi
(Fracture and Reliability Research Institute, Graduate School of Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan)
資料名:
International Journal of Fatigue
(International Journal of Fatigue)
巻:
156
ページ:
Null
発行年:
2022年
JST資料番号:
D0802B
ISSN:
0142-1123
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
イギリス (GBR)
言語:
英語 (EN)