Research on cutting constitutive model considering the coupling of ultra-low temperature and high strain rate
Year: 2026
Type: article
Abstract: Constitutive models form the foundation of cutting simulations, where their accuracy is crucial for reliable numerical predictions. To overcome the limitation of existing models in accurately capturing the dynamic mechanical response of materials under cryogenic conditions, this study develops a constitutive model for the aerospace alloy Inconel 718 that couples the effects of cryogenic temperature and strain rate. First, Split Hopkinson Pressure Bar (SHPB) tests were conducted to investigate the coupled influence of cryogenic temperatures and high strain rates on the material's mechanical properties. Subsequently, a constitutive model for cryogenic machining was developed based on the characterized mechanical behavior. Finally, the model's accuracy was validated through cryogenic orthogonal cutting experiments. The results indicate that increasing strain rates induce significant strain-rate hardening and enhance plasticity. Simultaneously, decreasing temperatures intensify cryogenic strengthening. The proposed model achieves high predictive accuracy in cryogenic cutting simulations, with errors for cutting force, cutting temperature, and chip segmentation degree remaining within 10%, 6%, and 20%, respectively. This model effectively addresses the persistent challenge of characterizing cryogenic mechanical behavior in cryogenic cutting processes. It provides a theoretical framework for the digital development of cryogenic machining techniques for aerospace difficult-to-machine materials, thereby helping to reduce the associated process validation costs.
Authors: Jianming Li, Yishun Cheng, Pengfei Wang, Yuqi Feng, Xiaoxia Gao, Shaowei Jiang, Yang Wang, Haibo Liu, Kuo Liu, Yongqing Wang
Institution: Dalian University of Technology
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,520
Awards: G731635159, G7751872502
OpenAlex ID: W7130357249
