Enhancing Critical Industrial Processes with Artificial Intelligence Models

This review explores the deployment of Artificial Intelligence (AI) technologies to augment key industry processes in the new paradigm of Industry 5.0. Based on a handpicked collection of 35 peer-reviewed articles and leading resources, the study integrates the latest breakthroughs in Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL) with their applications in predictive maintenance, process planning, real-time monitoring, and operational excellence. The results emphasize AI’s central role in making manufacturing smarter, minimizing system downtime, and facilitating decision-making based on information in various industries like aerospace, energy, and intelligent manufacturing. Yet, the review also highlights significant challenges, ranging from data heterogeneity to model interpretability, security risks, and the ethics of automation. Solutions in the making, including Explainable AI (XAI), privacy-enhancing collaborative models, and enhanced cybersecurity protocols, are postulated to be the key drivers for the development of dependable and resilient industrial AI systems. The study concludes by postulating directions for further research and practice to secure the safe, transparent, and human-centered deployment of AI in industrial settings.