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Updated: Sep 16, 2026

A Novel Biaxial Testing Apparatus for the Determination of Forming Limit under Hot Stamping Conditions
Published on: April 4, 2017
Fusion Method of Experiment and Finite Element for Constructing Process Performance Dataset of 22MnB5 Steel in
Fangfang Li1, Liang Wang1, Run Wu1
1School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China.
Abstract:
Performance prediction, process parameter optimization, and various data-driven research for low-temperature hot stamping (LTHS) processes all rely on abundant, continuous, and reliable process performance sample data. Collecting data merely through physical experiments leads to high costs, long test cycles, and limited coverage of working conditions. This paper focused on the LTHS process of 22MnB5 high-strength steel and proposed a dataset construction method that integrates experiments with finite element simulation. Firstly, LTHS experiments of 22MnB5 steel V-shaped parts were conducted under different combinations of forming temperature, in-die holding time, and stamping speed. Key performance parameters such as temperature field, forming springback angle, and Vickers hardness were measured. Secondly, a thermo-mechanical-phase transformation multi-field coupled finite element model (FEM) was established and validated using the experimental data. The results revealed that the simulation results agree well with the experimentally measured springback angle and Vickers hardness, and the FEM could accurately characterize the forming features and material property evolution throughout the whole LTHS process. On this basis, an experiment-simulation data integration framework was constructed: validated FEMs were adopted to supplement missing working conditions within the parameter space based on physical test samples. An LTHS integrated dataset with wide coverage, high data continuity, and strong usability was built by unifying variable definitions, sample organization modes, and standardized data formats. The dataset established in this paper can provide solid data support for the development of performance prediction models, process parameter optimization, and other data-driven studies of LTHS processes. Moreover, this dataset construction strategy integrating experiments and simulations can be extended to other metal plastic forming fields.
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