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.

本研究介绍了一种结合模型预测控制（MPC）和深度锻造（DeepForge）机器学习模型的封闭模具热锻微结构控制的新方法。DeepForge利用包含1D卷积神经网络和门控循环单元的体系结构，利用工件表面温度测量值作为输入来预测锻造过程中的微结构变化。此外，研究还探讨了利用MPC调整间锤等待时间以对抗温度干扰以在工件的特定2D区域实现目标晶粒尺寸小于35微米。实验结果验证了方法的有效性，取得了锻造过程控制和质量改进的重要进展，其中温度作为额外的自由度被利用。

DeepForge：通过模型预测控制在金属成形中利用人工智能进行微结构控制