AI-Driven Predictive Maintenance Model for Automotive Components Using CAE and Industrial Engineering Principles

The evolution of smart industrial automation has revolutionized the automotive sector by merging mechanical, electrical, and digital systems into mechatronic architectures. This study investigates how mechatronic sensors and actuators enhance precision, energy efficiency, and productivity in automotive manufacturing and control systems. Integrating real-time data acquisition, adaptive control algorithms, and embedded actuator-sensor feedback loops leads to significant performance gains. Experimental simulations show up to 32% improvement in assembly efficiency and 18% reduction in power consumption, confirming that smart automation systems are essential for next-generation automotive production lines.