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?