Human-Centric Smart Manufacturing in Industry 5.0

We reviewed AI, digital twins, and collaborative robots as enabling technologies for human-centered, sustainable, and resilient manufacturing. Each is well developed at the component level, with ML-based inspection and prognostics, standards-based DT frameworks, and defined collaborative operation types. The main finding is that human-centered outcomes depend on the links between the technologies, and those links have the thinnest evidence. The literature has more algorithms and architectures than human-subject validation, lifecycle cost analysis, or measurement of effects on wellbeing and sustainability. The layered architecture and its split into safety, tactical, and strategic loops give a way to decide where AI and DTs can act on their own and where certified deterministic logic and human authority must remain. We identified seven gaps: integrated evaluation with human participants, validated human models, specification of DT fidelity, assurance of adaptive behavior, outcome measurement, evaluation of explanations, and security and privacy. This review has limits. Its selection is narrative and incomplete, the published work it draws on leans toward laboratory and pilot evidence, and the proposed architecture is conceptual. The next step is to test it in controlled studies with people in the loop.

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