Improve Quality Control with Predictive Analytics: Using Machine Learning and IO-Link Sensors for Early Detection of Defects in Modern Production

Predictive analytics is becoming increasingly important in modern manufacturing. This study addresses the challenges of assembly error prevention by exploring the implementation of machine learning (ML) models in an IO-Link sensor-equipped demonstration cell, focusing on the early detection of potential quality issues in production processes. The precise measurements and reliable data transmission of IO-Link technology are critical to the effective application of ML. Our approach integrates predictive analytics with optical quality control based on a profile sensor. We can show improvements in predictive quality, fostering a proactive quality culture and providing insights for data-driven decision making in various industrial sectors.