  Hot-rolling is a metal forming process that produces a workpiece with a
desired target cross-section from an input workpiece through a sequence of
plastic deformations; each deformation is generated by a stand composed of
opposing rolls with a specific geometry. In current practice, the rolling
sequence (i.e., the sequence of stands and the geometry of their rolls) needed
to achieve a given final cross-section is designed by experts based on previous
experience, and iteratively refined in a costly trial-and-error process. Finite
Element Method simulations are increasingly adopted to make this process more
efficient and to test potential rolling sequences, achieving good accuracy at
the cost of long simulation times, limiting the practical use of the approach.
We propose a supervised learning approach to predict the deformation of a given
workpiece by a set of rolls with a given geometry; the model is trained on a
large dataset of procedurally-generated FEM simulations, which we publish as
supplementary material. The resulting predictor is four orders of magnitude
faster than simulations, and yields an average Jaccard Similarity Index of
0.972 (against ground truth from simulations) and 0.925 (against real-world
measured deformations); we additionally report preliminary results on using the
predictor for automatic planning of rolling sequences.
