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Manufacturing automation has evolved significantly from conventional mechanical production systems to intelligent and interconnected digital manufacturing environments. Recent advancements integrate technologies such as artificial intelligence (AI), machine learning (ML), robotics, the Internet of Things (IoT) and digital twins to improve efficiency, productivity and sustainability. This paper presents a comprehensive review of research developments in manufacturing automation between 2020 and 2026. The review focuses on key domains including smart manufacturing systems, adaptive automation, human–robot collaboration, predictive maintenance and sustainable manufacturing practices. Studies indicate that AI-driven predictive maintenance can reduce unplanned downtime by up to 50%, while hybrid machine learning optimization approaches can decrease energy consumption by 20–30% in advanced manufacturing processes (Gao et al., 2024; Mahajan, 2025). Collaborative robots have further improved productivity and workplace safety in modern production systems. Despite these benefits, challenges such as cybersecurity risks, data interoperability issues, workforce reskilling requirements and high implementation costs remain significant barriers to adoption. The study also highlights emerging trends including explainable AI, physics-informed machine learning and the integration of sustainable manufacturing technologies. The findings provide valuable insights for researchers, industry practitioners and policymakers seeking to implement intelligent and sustainable manufacturing automation systems.
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"Recent Advances in Manufacturing Automation: A Review of AI-Driven Smart and Sustainable Production Systems", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.a134-a138, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606015.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator