Sustainable Manufacturing Application of Embedded Learning Algorithms for Vision-based Defect Detection under the Industry 5.0 Paradigm

The improvement of flexible manufacturing systems towards sustainable use of raw materials and increased resource efficiency represents a core tenant of Industry 5.0 competitiveness. This can be currently achieved through the adoption and accelerated implementation of state-of-the-art artificial intelligence models in forecasting, anomaly detection and classification applications. Human-centric approaches balance the deployment and implementation models for control and cognition with socially relevant goals for increased resilience. The paper presents and embedded learning application for vision-based defect detection on a five-station connected laboratory flexible manufacturing line. Quantitative results are illustrated and discussed that comparatively benchmark multiple generations of the YOLO real-time object detection model family along with implementation considerations and integration aspects with industrial automation technology.