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.