Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data

TLDR

Manufacturing equipment is often operated without planned maintenance, causing unplanned downtime and high costs, while Industry 4.0’s predictive maintenance promises to reduce failures, increase availability, and extend component life. The study presents AI‑based predictive maintenance algorithms applied to monitor cutting tool wear and spindle motor bearing health. A data‑driven modeling approach is used to analyze tool wear and bearing failures.

Abstract

Often, manufacturing equipment is utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost. The emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In this paper, the AI-based algorithms for predictive maintenance are presented, and are applied to monitor two critical machine tool system elements: the cutting tool and the spindle motor. A data-driven modeling approach will be described, and it will be utilized to investigate the tool wear and the bearing failures.