Design and Performance Evaluation of an Intelligent Thermo-Mechanical Control System for Energy-Efficient Industrial Machinery

Energy efficiency has become a critical concern in modern industrial machinery as global manufacturing shifts toward sustainable production. This study presents the design and experimental evaluation of an intelligent thermo-mechanical control system aimed at reducing energy losses in medium-scale industrial equipment. The system integrates adaptive thermal regulation, real-time sensor feedback, and machine-learning-based predictive control to optimize heat transfer, reduce mechanical friction, and improve overall operational stability. Experimental validation was conducted on a 5 kW test rig under variable load conditions. Results demonstrate a reduction of 12–18% in thermal losses and an improvement of 9% in mechanical efficiency compared to conventional PID-based systems. The proposed system also enhances operational reliability by enabling predictive fault detection with an accuracy of 94%. This research highlights the potential of intelligent thermo-mechanical integration in achieving energy-efficient industrial operations and provides a blueprint for the next-generation sustainable machinery design.