Interpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behaviour
Metals and Materials International, vol. 30, pp. 1944–1964
Authors 5
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RWTH Aachen University · Southwest Jiaotong University
Affiliation as printed
Institute of Metal Forming, RWTH Aachen University, Aachen, 52072, Germany
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China
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Bing Yang corresponding
Affiliation as printed
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China
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Affiliation as printed
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China
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Affiliation as printed
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China
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Affiliation as printed
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China
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