AI-driven optimization techniques for smart sustainable manufacturing in Industry 5.0 ecosystem: A comprehensive review
Sita Rani, Ramesh Karnati, Vivek Patel, M.K. Ranganathaswamy, Prakhar Tomar, Aman Kataria, Amrindra Pal
Abstract
The integration of Artificial Intelligence (AI) driven optimization techniques is transforming smart manufacturing in the industry 5.0 landscape leading to sustainable industrial processes. This review comprehensively explores AI-driven optimization methods that enhance efficiency, resilience, and sustainability in modern manufacturing ecosystems. It highlights the role of various AI - based algorithms in optimizing production processes, energy consumption, and supply chains. Along with this, it also presents the significance of AI-driven manufacturing in improving secure production by facilitating real-time monitoring, anomaly detection, and predictive maintenance. In this work, the authors also examine how AI contributes to human-centric manufacturing, addressing challenges such as resource utilization, waste reduction, and adaptive decision-making. Key advancements, limitations, and future research directions are analyzed to provide a holistic view of AI’s transformative potential. The findings underscore the necessity of AI-driven optimization for achieving sustainable, efficient, and flexible manufacturing processes in Industry 5.0. This work serves as a significant reference for researchers, industry professionals, and policymakers seeking to leverage AI for sustainable industrial advancements. This paper presents the comprehensive synthesis of AI-driven optimization techniques represented for the emerging Industry 5.0 model, prioritizing smart sustainable manufacturing. Unlike prior reviews, it systematically compares traditional and AI-based approaches, highlights the transformative synergy of advanced technologies like AI, IoT, digital twins, and blockchain for real-time, human-centric manufacturing, and details hybrid optimization methods integrating AI algorithms. This review uniquely maps the integration of these innovations with sustainability, adaptability, and mass personalization, presenting a roadmap to help industries employ intelligent, data-driven, and eco-friendly optimization solutions for future-ready manufacturing.