Adeyi, T., Adebayo, J., Oresegun, O., Ademokoya, S., Jegede, S., Cheok, A. (2025). A review study on ai-driven robotics and automation in smart manufacturing: applications, challenges, and economic impacts. , 43(7), 546-560. doi: 10.30684/etj.2025.158872.1934
Timothy A. Adeyi; Jacob K. Adebayo; Olakunle I. Oresegun; Shade O. Ademokoya; Sunday E. Jegede; Adrian D. Cheok. "A review study on ai-driven robotics and automation in smart manufacturing: applications, challenges, and economic impacts". , 43, 7, 2025, 546-560. doi: 10.30684/etj.2025.158872.1934
Adeyi, T., Adebayo, J., Oresegun, O., Ademokoya, S., Jegede, S., Cheok, A. (2025). 'A review study on ai-driven robotics and automation in smart manufacturing: applications, challenges, and economic impacts', , 43(7), pp. 546-560. doi: 10.30684/etj.2025.158872.1934
Adeyi, T., Adebayo, J., Oresegun, O., Ademokoya, S., Jegede, S., Cheok, A. A review study on ai-driven robotics and automation in smart manufacturing: applications, challenges, and economic impacts. , 2025; 43(7): 546-560. doi: 10.30684/etj.2025.158872.1934

A review study on ai-driven robotics and automation in smart manufacturing: applications, challenges, and economic impacts

Engineering and Technology Journal
Volume 43, Issue 7, July 2025, Pages 546-560    PDF (712.67 K)
Document Type: Review Paper
DOI: 10.30684/etj.2025.158872.1934
Authors
Timothy A. Adeyi* 1; Jacob K. Adebayo2; Olakunle I. Oresegun3; Shade O. Ademokoya4; Sunday E. Jegede5; Adrian D. Cheok6
1School of Automation, Nanjing University of Information Science and Technology, Ningliu Road, Nanjing, Jiangsu 210044, China. Department of Mechanical Engineering, Lead City University, Off Oba Otudeko Avenue, Lagos-Ibadan Express Way, Toll Gate Area, Oyo, Ibadan, 200255 Nigeria.
2Department of Mechanical Engineering, Olabisi Onabanjo University. PMB 2002. Ago-Iwoye, Ogun State, Nigeria.
3School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang, China. Ningbo Research & Innovation Center, Zhejiang University, Ningbo, China.
4Department of Mechanical Engineering, Babcock University, PMB 4003, Ilishan Remo, Ogun State, Nigeria.
5Mechanical Engineering Department, University of Ibadan, Ibadan, Oyo State, Nigeria. Omotosho Generation Company Limited, Ore, Ondo State, asubsidiary of Niger Delta Power Holding Company Limited Abuja Nigeria. Plot 1490 Samuel Ademulegun Street, CBD, FCT, Abuja, Nigeria.
6School of Automation, Nanjing University of Information Science and Technology, Ningliu Road, Nanjing, Jiangsu 210044, China.
Abstract
Integrating AI-driven robotics and automation revolutionizes smart manufacturing by enhancing operational efficiency, productivity, and system flexibility across automotive, aerospace, and general equipment manufacturing industries. This review synthesizes findings from 84 peer-reviewed publications to evaluate the transformative potential of key AI technologies—including machine learning, digital twins, edge AI, and human-machine collaboration—in optimizing production lines and enabling predictive maintenance, real-time monitoring, and adaptive decision-making. While these innovations offer significant benefits in quality control, cost reduction, and sustainability, challenges remain in integrating AI with legacy systems, addressing workforce skill gaps, and ensuring cybersecurity and ethical compliance. Emerging trends such as 5G-enabled edge computing and collaborative robots (cobots) pave the way for low-latency communication and safer, more adaptable production environments aligned with Industry 5.0 principles. Real-world case studies demonstrate measurable economic impacts, including a 30% reduction in downtime at KONE’s elevator manufacturing facility and scalable ROI for SMEs adopting AI-driven solutions. Furthermore, regulatory frameworks and ethical AI guidelines are increasingly essential for ensuring transparency, safety, and responsible deployment. Looking ahead, the convergence of immersive technologies (AR/VR/MR), digital twins, and ethical AI will further enhance virtual simulation, reduce material waste, and support sustainable industrial ecosystems. As manufacturers adopt these cutting-edge innovations, resilient, agile, and human-centric systems will become the new standard, balancing dynamic market demands with environmental and social responsibility. Ultimately, AI-driven automation promises to reshape global manufacturing ecosystems, driving economic growth and sustainable industrial transformation.

Highlights
  • AI-driven robotics transforms smart manufacturing by improving efficiency, flexibility, and productivity.
  • Emerging tools like edge AI and digital twins enable real-time decisions through human-machine collaboration.
  • AI-based automation reduces energy use, emissions, and waste, aligning with global sustainability objectives.
  • The transition to Industry 5.0 shifts toward more human-focused and environmentally sustainable production.
Keywords
Smart Manufacturing AI; Driven Robotics Machine Learning Automation Predictive Maintenance
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