Automatic Fault Detection in CNC Machines Using Real-Time Sensor Data: Leveraging the Power of Machine Learning

In modern manufacturing environments, Computer Numerical Control (CNC) machines play a pivotal role in ensuring precision and productivity. However, unexpected faults in CNC systems can lead to significant production losses, equipment damage, and safety risks. This research presents an automated fault detection framework that utilizes real-time sensor data in conjunction with a Support Vector Machine (SVM) machine learning algorithm to identify and classify machine faults effectively. Multiple sensor inputs, including vibration, temperature, spindle speed, and acoustic signals, are continuously monitored and processed to detect anomalies indicative of mechanical or operational issues. The collected data is pre-processed, normalized, and used to train the SVM model, which distinguishes between normal and faulty states based on learned patterns. The system is evaluated on various performance metrics such as accuracy, precision, loss demonstrating high reliability and robustness in fault detection. The proposed approach not only enhances predictive maintenance capabilities but also contributes to reduced downtime, increased equipment lifespan, and improved overall manufacturing efficiency. This study underscores the potential of integrating machine learning with industrial sensor networks for smart and adaptive manufacturing systems.