A Robust Intelligent Hyper-Automation Structure for Autonomous Smart Manufacturing Systems

Abstract

The revolution driven by Industry 4.0 has fast-tracked the integration of automation approaches in manufacturing environments; But, a completely autonomous production system is still a work in progress due to the communication issues between distributed manufacturing equipment, heterogeneity of Industry 4.0 components, reactive-only maintenance strategies, and heterogeneous Industry 4.0 systems. Although the artificial intelligence (AI) tools, plus others like machine learning (ML), robotics automation, and cyber-physical systems have demonstrated their ability to improve the manufacturing performance, their isolated implementation is limited for out-of-the-box decision-making applications and autonomous process management. This research work defines a Robust Intelligent Hyper-automation System (RIHF) to establish an autonomous, open, and intelligent smart manufacturing environment by combining adaptive sensors, data analytics prognostics automation, and equipment control technologies to create an autonomous Industry 4.0 system. The setup constantly gathers data from multiple interconnected facilities, preprocesses data at the edge, and applies hybrid AI models trained using deep learning, ensemble learning, and outlier detection algorithms to identify equipment's prognosis, optimize scheduling, and execute autonomous maintenance actions at optimal operating conditions. In addition, the hybrid system supports real-time digital twin synchronization, open industrial communication protocols, and digital manufacturing standard systems to maximize the equipment cooperation among the Industry 4.0 components, automated workstations, robotic agents, connected sensors, and enterprise systems. Implementation of a continuous feedback mechanism improves the autonomous system decision models in tune with the condition changes of the automated manufacturing system. From the experimentation, the autonomous structure has achieved to improve the predictive maintenance accuracy above 95%, minimized unplanned equipment downtime by about 40%, enhanced Overall Equipment Effectiveness (OEE) at about 22%, increased the production throughput over 18%, and minimized the maintenance costs and energy consumption at least 65%. The main benefit for this research work is the design of an AI-enabled decision automation structure which can improve the autonomous system efficiency, guaranteed the responsiveness, and reach the resilient manufacturing system.