AI in Manufacturing: Review of Architectures, Applications, and Challenges in Industry 4.0
Akib Zaved *
School of Electronic Information Engineering, China West Normal University, Nanchong, Sichuan, P.R. China.
*Author to whom correspondence should be addressed.
Abstract
This review examines Artificial Intelligence (AI) in manufacturing, a key driver of Industry 4.0, synthesizing literature from 2017 – July, 2025 using peer-reviewed journals, industry reports, and case studies selected for relevance. It explores AI architectures, integrating IoT data acquisition, machine learning algorithms, and industrial system deployment to enable real-time optimization. Applications include smart factories, predictive maintenance (e.g., reducing downtime by 20–30% in automotive cases), supply chain optimization, and process control, improving efficiency and quality. Challenges involve ethics, data security, legacy system integration, and scalability. Future directions include AI integration with 6G networks and quantum computing to enhance analytics and eco-friendly practices to reduce environmental impact. Interdisciplinary collaboration is essential to realize AI’s potential in manufacturing.
Keywords: Artificial Intelligence, manufacturing, industry 4.0, predictive maintenance, smart factories