Development of machine tool predictive maintenance models using machine learning algorithms

Abstract

Predictive maintenance (PdM) has emerged as a transformative solution for optimizing machine tool performance and minimizing unplanned downtime in manufacturing environments. This paper investigates the development of PdM models utilizing machine learning (ML) algorithms to predict tool failures before they occur, thereby enabling proactive maintenance and operational efficiency. The research focuses on several ML models, such as support vector machines (SVM), decision trees, and random forests, applied to real-time sensor data from industrial machines, including parameters like temperature, vibration, and pressure. The methodology integrates historical data with real-time inputs to train and validate the models, assessing their effectiveness in predicting failures. The findings highlight that ML-based PdM approaches significantly outperform traditional maintenance methods, offering substantial improvements in reliability, reduced downtime, and cost savings. The results underscore the potential of machine learning to revolutionize machine tool maintenance by providing accurate, data-driven predictions. This study contributes to the field by offering practical insights into the use of predictive analytics for machine tool reliability and lays the groundwork for further advancements in this area.