In production environments such as coating lines, maintaining consistent quality while maximizing efficiency is a constant challenge. By leveraging machine learning, manufacturers can analyze vast amounts of real-time data to predict quality issues, optimize parameters, and minimize defects. This session will explore a use case where machine learning is applied to improve quality control in coating lines, showcasing how AI-driven insights can lead to enhanced production consistency, reduced waste, and better resource utilization.

• How can machine learning be integrated into coating line operations to enhance quality control?
• What machine learning models are most effective for predicting and preventing quality issues in production?
• How can real-time data analysis from coating lines help optimize production parameters and reduce defects?

• Optimising information provision – What strategies can be used to provide machine-specific information to maintenance technicians in real time and how does this affect the efficiency of maintenance processes?
• Sharing knowledge within the team – How can the knowledge from the minds of the technicians be shared within the team, and what contribution can AI make here?
• Learning organisation – How could improved documentation of machine failures help to drive continuous improvement in maintenance?
• Network collaboration – How could an improved exchange between machine operators, but also with service partners and machine manufacturers, help to make maintenance more efficient and minimise machine downtimes?
• Minimising downtime – How can the use of AI help to minimise machine downtime and what impact does this have on the total cost of ownership?