CrystalMind: A surrogate model for predicting 3D models with recrystallization in open-die hot forging including an optimization framework

This work introduces CrystalMind, a surrogate model developed to accurately reconstruct 3D models along with recrystallization induced by the open-die hot forging process. CrystalMind employs two types of data inputs, a 3D Model depicting pre-stroke workpiece geometry, and a forging vector outlining the proposed forging strategy. This forging strategy consists of bite infeed, bite offset, number of offsets and initial position. Moreover, a machine learning architecture with two different units, MLP-based (Multi-Layer Perceptron) and PointNET++-based, was implemented and compared, demonstrating similar performance in terms of recrystallization and deformation errors. However, MLP proved 36 times faster in computational time reaching an average computational time of 5 ms pro run. Furthermore, CrystalMind adheres to the volume conservation condition and limits recrystallization error to less than 2% and deformation error to less than 0.1 mm or 0.9% for the test data. Finally, CrystalMind is employed in conjunction with an optimization algorithm, leading to remarkable enhancements in time efficiency. The optimization framework can effectively optimize a forging vector(s) for one, two, or three strokes. For instance, in the case of three strokes, CrystalMind enables the optimization process to be completed within an average of 7 min, a stark contrast to the approximately 2.3 years required when utilizing FEM simulation. Overall, CrystalMind provides fast and accurate predictions of deformed 3D models with corresponding recrystallization per point caused by a forging process, something not to be found in the state of the art so far.