Aug 2026· Cureus Journal of Computer Science· Vol 3· 0 citations· 42 references
TL;DR
The findings show that machine learning, deep learning, computer vision, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making.
Abstract
Artificial intelligence (AI) is transforming smart manufacturing by enabling intelligent automation, data-driven decisions, and stronger collaboration between humans and manufacturing systems. The widespread adoption of collaborative robots, the industrial internet of things, and cyber-physical systems is driving demand for manufacturing environments that are safer, more flexible, and more efficient. Despite AI’s broad application in manufacturing, few studies have combined adaptive safety and intelligent task allocation within a single human-centered framework. This review offers a comprehensive look at AI applications that support these two complementary functions. Literature from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library was systematically reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and analysed thematically. The findings show that machine learning, deep learning, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making. That said, challenges remain - such as interoperability, explainability, limited access to high-quality manufacturing data, industrial validation, and integrating multiple AI technologies. This review gives researchers and practitioners a holistic perspective and highlights integrated, human-centered AI frameworks as key enablers of resilient, efficient, and sustainable Industry 5.0 manufacturing systems.
This review examines recent advances in the integration of Artificial Intelligence with Autonomous Mobile Robots (AMRs) for real-time material flow optimization in EV manufacturing ecosystems to identify key performance improvements in throughput, operational efficiency, and cost reduction attributed to AI-AMR integration.
Olasubomi Akanbi· International Journal of Eng...· 0 citations
Industrial Artificial Intelligence (IAI) has emerged as a transformative paradigm that integrates advanced computational intelligence with industrial systems, enabling unprecedented levels of automation, optimization, and decision support across manufacturing and process industries. This review provides a comprehensive synthesis of IAI technologies, spanning machine learning, deep learning, computer vision, natural language processing, reinforcement learning, digital twins, and explainable AI, and examines their deployment across diverse industrial sectors including automotive, aerospace, energy, logistics, and semiconductor manufacturing. Key application domains, including predictive maintenance, quality control, process optimization, and supply chain management, are critically analyzed in terms of methodology, performance benchmarks, and deployment maturity. Unlike prior reviews that focus primarily on a single technology family or a single application domain, this review integrates technology, application, and challenge perspectives within one evidence-based framework and benchmarks technology maturity against deployment readiness across eight industrial sectors. The review further identifies persistent challenges such as data scarcity, black-box opacity, legacy system integration, cybersecurity vulnerabilities, and workforce readiness, and proposes evidence-based mitigation strategies for each. A forward-looking perspective is offered, highlighting the convergence of large language models, federated learning, and Industry 5.0 human-centric frameworks as defining trajectories for the next decade. Drawing on 40 peer-reviewed journal articles identified through a systematic literature search, this article serves as a structured foundation for researchers, engineers, and policymakers navigating the complex IAI landscape.
Rajiv Kumar Verma, Priya Nair, Amit Sharma· Data Intelligence and Inform...· 0 citations
The findings indicate that effective Decision Intelligence requires the seamless integration of artificial intelligence, optimization, digital twins, knowledge graphs, explainable AI, and Human-in-the-Loop AI within a unified decision ecosystem that combines computational intelligence with human judgment.
H. Mural, Mahbub E. Khoda, Abdul Halim et al.· Journal of Sustainable Smart...· 0 citations
: The rapid adoption of artificial intelligence (AI) in the production sector has triggered a revolutionary change in the design, manufacturing, monitoring, and optimization. The paper is a critical analysis of AI-based additive manufacturing (AM) and smart manufacturing systems with an emphasis on design intelligence and process optimization, real-time quality control, and sustainable production. Based on recent literature, this paper considers the major AI methods, such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and physics-informed neural networks (PINNs) throughout the manufacturing lifecycle. The results show that AI can greatly decrease the time spent on design iterations, increase the accuracy of predictions of process parameters, and allow in-situ defect detection with high accuracy. Moreover, AI
Flexible manufacturing, characterized by high-mix, low-volume, and highly variable production, demands robotic systems with strong adaptability, dexterity, and intelligence that conventional offline-programmed industrial robots cannot provide. This paper presents a systematic review of key technologies for robot embodied intelligence oriented toward flexible manufacturing, organized around the closed loop of perception, decision-making, and execution. The purpose is to clarify the current research landscape, identify core technical bottlenecks, and outline future directions for embodied-intelligent manufacturing. Adopting a literature-analysis and comparative-review method, the study examines representative advances at three levels: multimodal environmental perception and real-time modeling, flexible adaptive precision manipulation, and intelligent decision-making for process planning and scheduling. The review finds that multimodal fusion and semantic SLAM are overcoming perception bottlenecks, that deep learning and force/position hybrid control are balancing flexible adaptability with high-precision operation, and that deep reinforcement learning and large models are advancing intelligent process planning. It concludes that data scarcity, model reliability, software-hardware integration, and ethical-legal standards remain the principal challenges to large-scale industrial deployment.
Zheng-Yang Chen· Advances in Engineering Inno...· 0 citations
This review examines recent progress in large AI models for intelligent manufacturing, covering model architectures, adaptation strategies, system integration, and applications across product development, production processes, equipment maintenance, and manufacturing services.
Baotong Chen, Lu Dai, Chuangjian Wang et al.· IEEE Access· 0 citations
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