  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.
