Developing a semi-supervised learning and ordinal classification framework for quality level prediction in manufacturing

The authors of this work propose a novel semi-supervised learning framework for quality prediction in manufacturing. Semi-supervised learning is a promising method in neural network applications, where label generation incurs significant time and cost. However, a semi-supervised learning mechanism in the manufacturing industry has not been as popular as a supervised learning method, especially in quality prediction tasks. The proposed framework trains a deep neural network-based model in a self-training scheme that uses filtered unlabeled data based on prediction confidence. In particular, the framework successfully handles ordinal classification tasks by using ordinal label rendering based on a state-of-the-art technique called the soft ordinal vector (SORD) that reflects ordinality in multiple labels. Furthermore, a newly proposed information measure named ordinal entropy, which takes ordinality into account, is used to selectively utilize confident labels among generated pseudo-labels. The proposed framework's efficacy is validated through a case study using real-world data from the ultraviolet (UV) lamp manufacturing process. The proposed framework has shown better performance in quality prediction than the learned model with only supervision. In addition, various configurations of the proposed framework have been validated with extensive experiments.