AI-Driven-IoT (AIIoT)-Based Decision Making in Manufacturing Processes in Mechanical Engineering

Abstract

The rapid evolution of technology has fostered the integration of Artificial Intelligence (AI) and the Internet of Things (IoT), revolutionizing the landscape of manufacturing processes in Mechanical Engineering. This article explores the synergy between AI and IoT, illustrating how their convergence enhances decision-making capabilities within manufacturing environments. Incorporating smart sensors, real-time data analytics, and advanced machine learning algorithms facilitates a proactive approach to production management, ultimately leading to better efficiency, reduced downtime, and improved resource allocation. Manufacturers can anticipate equipment failures, streamline supply chains, and tailor production schedules to meet fluctuating market demands by employing AI-driven predictive analytics. Furthermore, this integrated framework supports enhanced quality control, ensuring products meet stringent quality standards while minimizing waste. This transformation not only elevates operational productivity but also fosters sustainable practices in manufacturing. The findings underscore manufacturers' need to adopt these technologies to remain competitive in an increasingly complex and volatile industry landscape. The pathway towards AI-driven IoT-based decision-making in manufacturing is not merely a technological upgrade but a transformative journey that redefines the operational landscape. By prioritizing integrating these advanced technologies, manufacturers can secure a competitive edge, foster resilience, and shape a sustainable future in an ever-evolving market. As the industry continually adapts to these advancements, ongoing research, and development will be vital in unlocking new possibilities and addressing the future demands of manufacturing in mechanical engineering.