High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: a domain knowledge-guided symbolic regression approach
Huan Yu(Ningbo University), Shengchuan Wu(Southwest Jiaotong University), Yanan Hu(Southwest Jiaotong University), Bingqing Chen(Beijing Institute of Aeronautical Materials), Xin Peng(East China University of Science and Technology), Guozheng Kang(Southwest Jiaotong University)
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
November 20, 2023
Cited by 20
Related Papers
Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method
|IEEE Transactions on Industrial Electronics|2017|148
Modeling of competition between shear yielding and crazing in amorphous polymers’ scratch
|International Journal of Solids and Structures|2017|48
The causal relationship between immune cells and ankylosing spondylitis: a bidirectional Mendelian randomization study
|Arthritis Research & Therapy|2024|39
Black tea withering moisture detection method based on convolution neural network confidence
|Journal of Food Process Engineering|2020|37
Critical physics-informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects
|International Journal of Mechanical Sciences|2024|37