VMLC: Statistical Process Control for Image Classification in Manufacturing

Through ground-breaking advances in Machine Learning its real-world applications have become commonplace in many areas over the past decade. Deep and complex models are able to solve difficult tasks with super-human precision. But for manufacturing quality control, in theory a ideal match for these methods, the step from proof-of-concept towards live deployment is often not feasible. One major obstacle is the unreliability of Machine Learning predictions when confronted with data diverging from the known characteristics. While overall accuracy is high, wrong results may be returned with no indication of their uncertainty. In manufacturing, where scarce errors mean great damages, additional safety measures are required. In this work, I present Visual Machine Learning Control (VMLC), an approach developed upon a real world visual quality control system that operates in a high throughput manufacturing line. Instead of applying sole classification or anomaly detection, both is done in combination. A scalar metric derived from an Auto-Encoder reconstruction error measures the compliance of captured images with the training data the system is trained on. This metric is integrated into the widely used framework of industrial Statistical Process Control (SPC), significantly increasing robustness through meaningful control limits and enabling active learning. The system is evaluated on a large dataset of real-world industrial welding images.