Soldering Data Classification with a Deep Clustering Approach: Case Study of an Academic-Industrial Cooperation

• Published in: Applied sciences Vol. 12; no. 14; p. 6927
• Main Authors: Faragó, Kinga Bettina, Skaf, Joul, Forgács, Szabolcs, Hevesi, Bence, Lőrincz, András
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Artificial intelligence; Automation; Classification; Datasets; deep clustering; Deep learning; Defects; Efficiency; image classification; industrial data; Knowledge; Machine learning; Manufacturing; Neural networks; pre-trained models; Quality control; Software; Subject specialists; visual inspection
• DOI: 10.3390/app12146927
• ISSN: 2076-3417

Modern industries still commonly use traditional methods to visually inspect products, even though automation has many advantages over the skills of human labour. The automation of redundant tasks is one of the greatest successes of Artificial Intelligence (AI). It employs human annotation and finds possible relationships between features within a particular dataset. However, until recently, this has always been the responsibility of AI specialists with a specific type of knowledge that is not available to the industrial domain experts. We documented the joint research of AI and domain experts as a case study on processing a soldering-related industrial dataset. Our image classification approach relies on the latent space representations of neural networks already trained on other databases. We perform dimensionality reduction of the representations of the new data and cluster the outputs in the lower dimension. This method requires little to no knowledge of the underlying architecture of neural networks by the domain experts, meaning it is easily manageable by them, supporting generalization to other use cases that can be investigated in future work. We also suggest a misclassification detecting method. We were able to achieve near-perfect test accuracy with minimal annotation work.