Pavel, Daniel (University POLITEHNICA of Bucharest), Stamatescu, Grigore (University Politehnica of Bucharest)

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

Scheduled for presentation during the Invited Session "Intelligent Data processing" (WeBB), Wednesday, June 11, 2025, 14:20−14:40, Room A

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

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.