Research on parameter identification of fracture model for titanium alloy under wide stress triaxiality based on machine learning

Rui Feng; Ming-He Chen; Ning Wang; Lan-Sheng Xie

doi:10.1007/s40436-024-00487-z

Advances in Manufacturing ›› :1 -21. DOI: 10.1007/s40436-024-00487-z

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Research on parameter identification of fracture model for titanium alloy under wide stress triaxiality based on machine learning

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¹ College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, People’s Republic of China

² Engineering Technology Training Center, Nanjing Vocational University of Industry Technology, 210023, Nanjing, People’s Republic of China

^b meemhchen@nuaa.edu.cn

Rui Feng is a Ph.D. candidate at the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics. He received M.S. degree in Aeronautical Engineering from the Nanchang Hangkong University of China, in 2020. The main research interests focus on forming of difficult-to-machine materials.

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Ming-He Chen Doctor, Professor, Doctoral Supervisor, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics. He received Ph.D. degree in Aeronautical and Astronautics Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics in 2008. The main research interests include sheet metal forming CAE technology, integrated Computational Materials Engineering (ICME), material superplasticity form and diffusion bonding technology.

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Ning Wang Doctor, Lecturer, Engineering Technology Training Center of Nanjing Vocational University of Industry Technology. He received Ph.D. degree in Aeronautical and Astronautics Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics in 2018. The main research interests focus on sheet metal forming CAE technology and plastic forming simulation technology.

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Lan-Sheng Xie Doctor, Professor, Doctoral Supervisor, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics. He received Ph.D. degree from Harbin Institute of Technology of China. The main research interests including sheet metal forming CAE technology and plastic forming simulation technology.

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Abstract

The abilities to describe the fracture behavior and calibrate the relevant parameters are essential factors in evaluating ductile fracture criteria of titanium alloys. In this study, 14 different shapes and notched specimens were designed for uniaxial tensile and compression experiments to characterize their ductile fracture behaviors. Based on the analysis of plastic behavior and fracture mechanism, a mixed hardening model, the Von Mises yield criterion and DF2016 fracture criterion were established, respectively. A parameter-identification method based on machine learning was proposed to improve the parameter calibration of the ductile fracture model. The results showed that the DF2016 fracture model accurately predicted the damage initiation and fracture process of the forged TC4 titanium alloy during the forming process. The machine-learning method avoided extracting different stress state evolution processes and large amounts of data from the numerical model of the calibrated specimens. The combination of the semi-coupled fracture model and parameter-identification method provides a new method that alleviates the difficulty of balancing parameter calibration and the ability to characterize the ductile fracture criteria.

Keywords

Titanium alloy / Fracture criterion / Parameter identification / Machine learning / Ductile fracture prediction

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Rui Feng, Ming-He Chen, Ning Wang, Lan-Sheng Xie. Research on parameter identification of fracture model for titanium alloy under wide stress triaxiality based on machine learning. Advances in Manufacturing 1-21 DOI:10.1007/s40436-024-00487-z

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