An AI-based Multi-objective Optimization Approach for Monitoring Manufacturing Processes

Recently, considerable effort has been devoted to applying new techniques such as Artificial Intelligence (AI) and machine learning in manufacturing systems. Implementing effi-cient fault detection and diagnosis procedure for manufacturing systems can provide manufacturers with significant advantages, e.g., enhancing product quality and yield while reducing cost. Maximizing efficiency and controlling costs is the goal of every operation. Optimization methods like Evolutionary Algorithms can be considered for modeling manufacturing operational procedures using datasets whose contents are populated by various sensors and other data sources. Embracing AI to empower organizations to analyze data can lead to efficient and intelligent automation. In this paper, we propose a hybrid model for monitoring manufacturing operations based on a multi-objective approach. This model considers different conflicting objectives that should be minimized simultaneously. Our goal is to provide an advanced methodology for exploring manufacturing processes and to gain perspective on production status. It enables manufacturers to access the effectiveness of predictive technologies and respond well to any disruptive trends.