Energy Response Prediction for Sheet Metal Forming Processes under Different Lubrication Conditions using Small-Data Surrogate Models

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Key Points

The study aims to develop a surrogate model to predict energy responses in sheet metal forming processes under varying lubrication conditions.
Developed surrogate models using finite element analysis results to predict energy response.
Compared performances of various regression algorithms using small data sets.
Analyzed simulation outcomes based on material properties and lubrication conditions.
Identified optimal lubrication conditions linked to energy response metrics.
Demonstrated the efficacy of surrogate models in predicting forming behavior with reduced computational costs.
Outlined potential applications for new materials through data generation and learning strategies.

Study predicts energy response in sheet metal forming, highlighting optimal lubrication conditions.

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Jhang et al. (2026) studied this question.

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