DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control
<|reference_start|>DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control: This study presents a novel method for microstructure control in closed die hot forging that combines Model Predictive Control (MPC) with a developed machine learning model called DeepForge. DeepForge uses an architecture that combines 1D convolutional neural networks and gated recurrent units. It uses surface temperature measurements of a workpiece as input to predict microstructure changes during forging. The paper also details DeepForge's architecture and the finite element simulation model used to generate the data set, using a three-stroke forging process. The results demonstrate DeepForge's ability to predict microstructure with a mean absolute error of 0.4$\pm$0.3%. In addition, the study explores the use of MPC to adjust inter-stroke wait times, effectively counteracting temperature disturbances to achieve a target grain size of less than 35 microns within a specific 2D region of the workpiece. These results are then verified experimentally, demonstrating a significant step towards improved control and quality in forging processes where temperature can be used as an additional degree of freedom in the process.<|reference_end|>
@article{petrik2024deepforge:,
    title={DeepForge: Leveraging AI for Microstructural Control in Metal Forming
  via Model Predictive Control},
    author={Jan Petrik and Markus Bambach},
    journal={Journal of Manufacturing Processes 121 (2024) 193-204},
    year={2024},
    doi={10.1016/j.jmapro.2024.05.023},
    number={S1526612524004869},
    archivePrefix={arXiv},
    eprint={2402.16119},
    primaryClass={cs.LG cs.SY eess.SY}
}