Optimizing Predictive Maintenance in Mechanical Engineering: AI and ML for Lathe Machines

Predictive maintenance (PdM) of machines using Artificial Intelligence (AI) and Machine Learning (ML) is an emerging and rapidly growing field within Mechanical Engineering. By leveraging AI and ML algorithms, engineers can analyse vast amounts of data collected from sensors and other monitoring devices to predict when machinery components will likely fail. In continuous flow production systems, the combination of AI and machine learning ML for PdM is presently reaching a peak., yet its penetration into machines within small and medium-scale enterprises remains comparatively subdued. The lathe machine is one of the important machine in small and medium-scale companies, PdM of lathe machines using AI & ML techniques could be the development of more accurate and robust models for early fault detection. While existing ML approaches failure prediction is based on historical data patterns, Regarding the precision and dependability of these forecasts, there might be space for improvement. In the present work, the loT based low cost data acquisition system is applied for condition monitoring, and data obtained is analyzed using AI & ML algorithms. This predictive approach enables maintenance to be performed before breakdowns occur, reducing downtime, minimizing costs, and improving overall efficiency and reliability of mechanical systems. This intersection of AI, ML, and mechanical engineering holds significant promise for optimizing maintenance practices and enhancing the performance of industrial machinery across various sectors.