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ABSTRACT
The integration of Artificial Intelligence (AI) into industrial automation represents a transformative shift in manufacturing and production processes. This review examines the current landscape of AI applications within industrial automation, highlighting key technologies such as machine learning, computer vision, and natural language processing. By analyzing recent advancements and case studies, this paper discusses how AI enhances operational efficiency, predictive maintenance, quality control, and decision-making in manufacturing environments. Furthermore, we explore the challenges and limitations associated with implementing AI systems, including data privacy concerns, the need for skilled labor, and the complexities of system integration. Through a comprehensive evaluation of existing literature and emerging trends, this review underscores the potential of AI to revolutionize industrial practices, drive innovation, and foster sustainable development in the sector. The findings emphasize the necessity for continued research and collaboration among stakeholders to fully realize the benefits of AI in industrial automation.
Introduction
1.1 Background
Artificial intelligence (AI) has revolutionized various sectors, with industrial automation being one of the most impacted fields. AI technologies are increasingly being integrated into industrial processes, enhancing efficiency, productivity, and operational flexibility (Shahbaz et al., 2023). As industries face the challenge of meeting rising demand while maintaining quality and reducing costs, AI presents innovative solutions that streamline operations and optimize resource utilization (Zhou et al., 2023). The convergence of AI and automation is thus critical for the modernization of manufacturing and production systems.
The concept of industrial automation has evolved significantly over the past few decades, moving from basic mechanization to advanced automated systems powered by AI. Traditionally, automation relied on fixed programming and human oversight, which limited responsiveness to dynamic changes in production environments (Kumar & Sahu, 2023). However, with the advent of AI, systems can now learn from data, adapt to new situations, and make real-time decisions without human intervention, leading to greater operational resilience (García et al., 2024). AI’s impact on industrial automation extends beyond mere operational enhancements; it also influences strategic decision-making. For instance, predictive analytics, a subset of AI, enables manufacturers to forecast equipment failures and maintenance needs, thereby minimizing downtime and associated costs (Saha et al., 2023). This proactive approach to maintenance exemplifies how AI can transform traditional reactive practices into predictive and preventative strategies, ultimately enhancing overall productivity.