Defect engineering of fatigue-resistant steels by data-driven models
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-09
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Mcode
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Language
en
Pages
12
Series
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, Volume 124
Abstract
As inclusions are inevitable from the material-producing processes, an engineering concept regarding multiple features of them is needed for material design. In this study, a unique approach integrating physical-meaningful microstructure-sensitive models with the machine-learning-based data-driven model is proposed to reveal the complex relationship between the fatigue life of materials with intrinsic features of inclusions including size, stiffness, thermal properties, and extrinsic stress amplitudes. This high-fidelity presentation of the relation of these variables enables a detailed and systematic analysis of the effects of inclusions on fatigue life. The data-based phase map provides a designing envelope of inclusion features for fatigue-resistant steels.Description
Funding Information: This work is supported by the Fundamental Research Funds for the Central Universities, China (No. FRF-TP- 20-026A1 ) and the special grade of China Postdoctoral Science Foundation (No. 2021T140050 ). The authors also acknowledge the Aalto Science Institute (AScI) at Aalto University, Finland for funding the research program. Publisher Copyright: © 2023 The Author(s)
Keywords
Inclusion, Machine learning, Microstructure-sensitive modeling, Thermal expansion coefficient, Young's modulus
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Citation
Gu, C, Bao, Y, Prasad, S, Lyu, Z & Lian, J 2023, ' Defect engineering of fatigue-resistant steels by data-driven models ', Engineering Applications of Artificial Intelligence, vol. 124, 106517 . https://doi.org/10.1016/j.engappai.2023.106517