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arXiv · 2402.16119

DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control

Jan Petrik·Markus Bambach
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Abstract

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.

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Jan Petrik, Markus Bambach. 2024-02-25. DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control. https://doi.org/10.1016/j.jmapro.2024.05.023

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