Pretraining Techniques for Steel Surface Roughness Prediction with Long Thin Spatial Industrial Data

Machine learning offers promising advancements in industrial processes, yet collecting labeled samples during production remains challenging. In steel production, the surface roughness $$R_a$$ parameter of steel coils is crucial, but on-line labeled data collection, with our apparatus, is infeasible, while off-line methods are time-consuming and imperfect. However, unlabeled samples are readily available from on-line production. This paper examines pretraining on a large, unlabeled dataset and its impacts on performance after fine-tuning on a smaller labeled dataset. We use three techniques: (1) contrastive learning, (2) Autoencoder, and (3) Classification of coil ID. We address the challenges posed by the unique structure of the data, comprising 2-dimensional, long and thin arrays. Our results show that our classification pretraining approach improves regression performance and outperforms the baseline.