Predicting Surface Texture in Steel Manufacturing at Speed
This work addresses a domain-specific problem in steel manufacturing for optimizing product quality through real-time adjustments, but it is incremental as it builds on existing methods with minor modifications.
The paper tackles the problem of predicting steel surface texture from inline laser measurements by using a machine learning model to improve accuracy and speed for real-time control, achieving faster GPU-accelerated inference with a modified ROCKET model.
Control of the surface texture of steel strip during the galvanizing and temper rolling processes is essential to satisfy customer requirements and is conventionally measured post-production using a stylus. In-production laser reflection measurement is less consistent than physical measurement but enables real time adjustment of processing parameters to optimize product surface characteristics. We propose the use of machine learning to improve accuracy of the transformation from inline laser reflection measurements to a prediction of surface properties. In addition to accuracy, model evaluation speed is important for fast feedback control. The ROCKET model is one of the fastest state of the art models, however it can be sped up by utilizing a GPU. Our contribution is to implement the model in PyTorch for fast GPU kernel transforms and provide a soft version of the Proportion of Positive Values (PPV) nonlinear pooling function, allowing gradient flow. We perform timing and performance experiments comparing the implementations