Fracture prediction using damage models for incremental sheet forming process: A brief review

Recently, a team led by Kai ZHANG from Shandong University of Science and Technology, China made a literature review for simulation of incremental sheet forming process using macroscopic models，including (1) uncoupled damage models; (2) GTN damage models; (3) Continuum Damage Mechanics(CDM) models . This work provides a brief review on fracture prediction of incremental forming process based on uncoupled and coupled damage models, the predictive capability the of damage models in ISF process is compared via forming depth, final shape and crack paths.

The team published their work in Journal of Advanced Manufacturing Science and Technology (2025011, 5(2), 2025).

“A review of fracture prediction of incremental forming process for manufacturing complex-shaped components was conducted. The paper has discussed research studies for various damage models and addressing their advantages and limitations, simulations based on these models were carefully analyzed and compared with experiments of ISF processes.” said Kai ZHANG, professor at College of Mechanical and Electronic Engineering at Shandong University of Science and Technology (China), a senior expert whose research interests focus on the field of High-performance lightweight metal forming manufacturing.

“It is found that Artificial Intelligence (AI)-based computational methods e.g., artificial neural networks, deep learning technique, shows great potential in solving sheet metal forming relevant problems. To the authors’ knowledge, the combination of physical constitutive models and AI-based computational models seems promising in future simulation of ISF process.” said Kai ZHANG.

Other contributors include Bing LIU, Chunjian SU, Yuqing HU from the College of Mechanical and Electronic Engineering at Shandong University of Science and Technology in Qingdao, China; Mingchuan WANG from the Sino-French Engineer School at Nanjing University of Science and Technology in Nanjing, China; Zhenming YUE from the School of Mechanical and Electrical Engineering at Shandong University in Weihai, China.

This work was supported by the National Natural Science Foundation of China (No. 52205392) and Shandong Provincial Natural Science Foundation (No. ZR2022ME032). The authors would like to acknowledge the support by the Project of Shandong Province Higher Educational Young Innovative Talent Introduction and Cultivation Team (Intelligent Transportation Team of Offshore Products).

About Author

Kai ZHANG holds Bachelor's degree in mechanical design, manufacturing and automation from China University of Petroleum (East China), Master's degree in mechanical engineering from China University of Petroleum (East China), and Ph.D. from the University of Technology of Troyes, France.

He is mainly engaged in the research of high-performance lightweight metal forming technology, multi-scale constitutive modelling, intelligent manufacturing. As the person in charge, he has presided over 1 National Natural Science Foundation of China, 1 China Postdoctoral Science Foundation, 1 Qingdao Postdoctoral Applied Research Project.

In recent years, he has published more than 20 scientific research papers in the field of mechanical engineering and mechanics, published more than 10 SCI papers in famous journals such as IJSS, JMPT, IJDM and other famous journals as the first author, applied for 3 invention patents, and registered 1 software copyright. Served as a reviewer for IJDM, IJSS and other journals.

About Journal of Advanced Manufacturing Science and Technology (JAMST)

Journal of Advanced Manufacturing Science and Technology (JAMST) is an open-access and peer-reviewed journal that was launched by Dalian University of Technology and Engineering Research Center of Advanced Manufacturing Technology for Aero Engine, Ministry of Education, Northwestern Polytechnical University in 2021. The journal is published by Tsinghua University Press.