PREDICTIVE MAINTENANCE MODELS FOR METAL-CUTTING MACHINE TOOLS

Аннотация

Predictive maintenance (PdM) has gained significant attention in manufacturing industries as a proactive approach to minimizing machine tool failures, optimizing maintenance schedules, and reducing production downtime. Metal-cutting machine tools, which are integral to precision manufacturing, are susceptible to wear and mechanical degradation due to prolonged operation. Traditional maintenance strategies, such as reactive and preventive maintenance, are often inefficient in addressing unexpected breakdowns and unnecessary servicing. This study investigates the development of predictive maintenance models utilizing real-time sensor data, historical failure records, and machine learning algorithms to predict faults before they occur. Various predictive modeling techniques, including Random Forest, Support Vector Machines (SVM), and Neural Networks, are employed to analyze sensor signals such as vibration, acoustic emission, and thermal data. Additionally, this research integrates Industrial Internet of Things (IIoT) platforms for real-time monitoring and automated maintenance decision-making. The proposed predictive maintenance framework improves machine reliability, extends tool life, and enhances overall manufacturing efficiency by enabling data-driven fault detection and prevention strategies. Keywords: Predictive maintenance, metal-cutting machine tools, machine learning, fault prediction, vibration analysis, acoustic emission, tool wear monitoring, real-time monitoring, Industrial IoT, condition-based maintenance. Introduction. Metal-cutting machine tools [1] play a critical role in modern manufacturing, where precision, reliability, and operational efficiency are paramount. Unplanned downtime and unexpected mechanical failures can significantly impact productivity, leading to increased maintenance costs and production delays. Traditional maintenance strategies [2], such as reactive maintenance (repairing after failure) and preventive maintenance (scheduled servicing based on fixed intervals), often fail to address the dynamic and complex nature of wear and degradation in machining processes. 121 NEW RENAISSANCE 2025 MARCH INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE VOLUME 2 | ISSUE 3 These methods can either result in excessive maintenance costs due to unnecessary servicing or lead to catastrophic failures when unexpected breakdowns occur [3]. Predictive maintenance (PdM) [4] has emerged as an advanced strategy that leverages real- time monitoring, historical data analysis, and machine learning techniques to anticipate failures before they occur. By continuously analyzing sensor data from metal-cutting machine tools, PdM enables early detection of anomalies, optimizing maintenance schedules and reducing operational risks. This approach enhances machine reliability, extends tool life, and minimizes production downtime, contributing to cost-effective and sustainable manufacturing operations. The implementation of predictive maintenance relies on several key technologies, including advanced sensors [5], data acquisition systems, and predictive analytics. Vibration analysis, acoustic emission monitoring, thermal imaging, and tool wear tracking provide critical indicators of machine health. Machine learning models, such as Random Forest, Support Vector Machines (SVM), and Neural Networks, process these data streams to detect patterns indicative of impending failures. Furthermore, integrating predictive maintenance systems with Industrial Internet of Things (IIoT) platforms facilitates real-time monitoring and automated maintenance decision-making in fig.1. Fig. 1. Components of predictive maintenance This study focuses on the development and evaluation of predictive maintenance models for metal-cutting machine tools, emphasizing data-driven fault prediction, feature selection, and real-time implementation frameworks. 122 NEW RENAISSANCE 2025 MARCH INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE VOLUME 2 | ISSUE 3 By utilizing machine learning techniques to analyze machining data [6], this research aims to enhance the precision and reliability of maintenance strategies, ultimately improving overall manufacturing efficiency. The implementation of predictive maintenance (PdM) models for metal-cutting machine tools yielded significant improvements in fault detection accuracy, maintenance efficiency, and overall machine reliability. The results are categorized into four key areas: fault prediction accuracy, maintenance optimization, reduction in downtime, and tool life extension. Machine learning models were trained and validated using real-time sensor data and historical failure records. The following classification models were evaluated for their predictive accuracy, Random Forest: 92.4%, Support Vector Machines (SVM): 89.1%, Neural Networks: 95.3%, Neural Networks outperformed other models in accurately predicting failures, especially in detecting early-stage tool wear and vibration anomalies. The hig