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1,052 results on '"Kang, Guozheng"'

227. High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: a domain knowledge-guided symbolic regression approach.

Author

Yu, Huan, Hu, Yanan, Kang, Guozheng, Peng, Xin, Chen, Bingqing, and Wu, Shengchuan

Subjects
MACHINE learning, FATIGUE life, MANUFACTURING defects, ALLOYS, FORECASTING
Abstract

The large scatter in high-cycle fatigue (HCF) life poses significant challenges to safe and reliable in-service assessment of additively manufactured metal components. Previous investigations have indicated that inherent manufacturing defects are a critical factor affecting the fatigue performance of the components, and the HCF life is significantly influenced by the geometric parameters of the critical defects inducing crack nucleation. Therefore, it is highly important to elucidate the correlation of the HCF life with the geometric parameters of critical defects. This study proposes a new fatigue life prediction model for laser additively manufactured AlSi10Mg alloys by including the combined effects of loading stress and defect geometries (size, location and morphology) in terms of domain knowledge-guided symbolic regression (SR). Domain knowledge is extracted from the semi-empirical Murakami, Z-parameter and X-parameter fatigue life models to establish the variable subtrees. The results show that compared with these semi-empirical models, the domain knowledge integration-based SR model has higher prediction accuracy and generalization ability. Moreover, compared with traditional 'black box' machine learning models, SR excels at balancing prediction accuracy and model interpretability, which provides useful insights into the relationship between fatigue life and defect geometries. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'. [ABSTRACT FROM AUTHOR]

Published
2024
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231. Experimental and Numerical Investigations of Evaluation Criteria and Material Parameters' Coupling Effect on Polypropylene Scratch

Author

Zhang, Jianwei, Jiang, Han, Jiang, Chengkai, Kang, Guozheng, Kan, Qianhua, and Li, Yonghua

Subjects
Starch -- Mechanical properties -- Models -- Usage, Mathematical models -- Usage -- Models -- Mechanical properties, Polypropylene -- Mechanical properties -- Models -- Usage, Strength of materials -- Models -- Usage -- Mechanical properties, Finite element method -- Usage -- Mechanical properties -- Models, Elasticity -- Models -- Usage -- Mechanical properties, Engineering and manufacturing industries, Science and technology
Abstract

Nine PP systems were synthesized to investigate the coupling effect of elastic modulus and yield strength on its scratch behavior. Adopting an integrated approach of scratch test and finite element simulation, the tangential force, the residual scratch depth and the groove shoulder area are identified as the evaluation criteria of the scratch resistance of PP. With those identified criteria, the coupling effect of elastic modulus and yield strength on PP scratch is revealed that the PP with large yield strength and small elastic modulus owns a remarkable scratch resistance. The implication of the present work on designing the PP with good scratch resistant is also discussed., INTRODUCTION Polypropylene (PP) has been widely used in automotive industry. The scratch behavior of PP has drawn much attention since the scratch can significantly affect its surface aesthetics [1-5]. It [...]

Published
2018
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237. Additively Manufactured Dual‐Faced Structured Fabric for Shape‐Adaptive Protection

Author

Tian, Yuanyuan, primary, Chen, Kaijuan, additional, Zheng, Han, additional, Kripalani, Devesh R., additional, Zeng, Zhuohong, additional, Jarlöv, Asker, additional, Chen, Jiayao, additional, Bai, Lichun, additional, Ong, Adrian, additional, Du, Hejun, additional, Kang, Guozheng, additional, Fang, Qihong, additional, Zhao, Lihua, additional, Qi, H. Jerry, additional, Wang, Yifan, additional, and Zhou, Kun, additional

Published
2023
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