Machine Learning for Industry 5.0: A Survey

Industry 5.0 represents a significant advancement from Industry 4.0, emphasizing improved cooperation between humans and machines, eco-friendly practices, and customized solutions in manufacturing. In contrast to earlier research on Industry 4.0, this survey provides a thorough examination of the function of Machine Learning (ML) in Industry 5.0 by examining the ways in which various learning paradigms facilitate sustainable production, human-machine cooperation, and customized industrial solutions. We first examine cutting-edge learning approaches, encompassing supervised, unsupervised, semi-supervised, and reinforcement learning methodologies, and their role in advancing Industry 5.0 objectives. Next, we emphasizes the incorporation of ML techniques to improve manufacturing productivity and optimize decision-making processes. Even with advancements, Industry 5.0 may suffer several challenges and limitations, such as intelligent decision-making, security, data privacy, etc. To address the challenges of Industry 5.0, it is crucial to comprehend its protocols, which this survey examines alongside relevant technologies. Therefore our survey also explores the primary challenges encountered when incorporating ML into Industry 5.0, such as integrating data, ensuring interoperability, and maintaining sustainability. Furthermore, the study elaborates on how these techniques drives substantial progress in various emerging applications by enhancing operational procedures and enabling innovative capabilities. Finally, we discuss potential research routes to overcome these hurdles and achieve the practical implementation of Industry 5.0.