AI-Driven Integrated Process Optimization and Predictive Maintenance for Industrial Manufacturing Systems

Abstract The rapid advancement of Industry 4.0 technologies has significantly transformed industrial manufacturing systems by enabling intelligent, data-driven decision-making. This paper proposes an AI-driven integrated framework that combines predictive maintenance and process optimization to enhance operational efficiency and system reliability. The proposed methodology leverages machine learning techniques to analyze real-time sensor data, perform fault detection, and estimate Remaining Useful Life (RUL) of industrial equipment. These predictive insights are further utilized by an optimization module to dynamically adjust operational parameters, thereby minimizing downtime, maintenance costs, and energy consumption. The system architecture incorporates data acquisition, preprocessing, predictive modeling, and feedback-driven optimization within a unified framework. Experimental results demonstrate high model accuracy and significant improvements in system performance compared to traditional maintenance strategies. The findings highlight the effectiveness of integrating predictive analytics with process optimization, offering a scalable and efficient solution for modern manufacturing environments.