Cognitive Automation and AI-Driven Approaches for Smart Manufacturing

Dear Colleagues,

As manufacturing systems evolve toward higher levels of digitalization, autonomy, and interconnectedness, it has become a defining priority that these advancements remain human-centric within the Industry 5.0 paradigm. In this context, cognitive automation and AI-driven methods represent two complementary pathways toward building manufacturing environments where humans and intelligent systems collaborate effectively. Cognitive automation enhances human performance by providing context-aware guidance, adaptive support, and transparent decision augmentation, allowing operators to manage complexity, variability, and cognitive load. In parallel, AI-driven approaches (e.g., machine learning, generative models, intelligent robotics, and digital twins) enable systems to learn from data, predict future states, and optimize manufacturing operations. To this end, emerging AI-driven approaches leverage cognition in two complementary ways: they emulate key aspects of human reasoning to transform raw industrial data into actions, and they strengthen human cognitive performance by improving diagnostics, situational awareness, and disruption prediction. The objective is to support and improve operator performance in tasks such as learning, planning, operational work, and problem-solving.

Despite this growing technological potential, the rapid expansion and commercialization of AI, particularly generative models and human–AI collaboration, has created a landscape where expectations often outpace evidence. Smart factories are increasingly experimenting with AI-based solutions, yet many implementations remain fragmented, poorly validated, or insufficiently grounded in rigorous engineering principles. Therefore, there is a pressing need for high-quality scientific investigations that critically examine the implementation pathways, methodological foundations, and state-of-the-art developments of cognitive automation and AI-driven manufacturing. In addition, the implementations must be human-centric, i.e., they should abide by ISO 9241-210:2019 or other suitable established human-centered frameworks, and be supported by quantitative and qualitative research approaches.

Topics include, but are not limited to:

• Cognitive automation strategies for operator support and adaptive decision-making AI-driven process optimization, predictive quality, and predictive maintenance.
• Digital twins, cognitive digital twins, and knowledge-driven manufacturing intelligence focused on human–automation collaboration, Operator 4.0/5.0, cognitive ergonomics, and human-in-the-loop design.
• Machine learning, deep learning, reinforcement learning, and transfer learning for smart factory applications with human–AI-centered design processes or ergonomic methods with iterative tests.
• Explainable, trustworthy, and safe AI for industrial decision support for operators working with cognitively complex decision-making.
• Generative AI, LLM-based assistants, and human–AI teaming in manufacturing contexts studying collaborative aspects, e.g., team personas, computer-supported collaborative work, and collaborative learning.
• Intelligent robotics, co-bots, and adaptive human–robot interaction with real-time analytics and edge/cloud intelligence supporting resilient manufacturing system architectures that enhance human performance and awareness.
• Hybrid simulation–AI models, cyber–physical systems, and autonomous decision-making in industrial case studies demonstrating measurable impacts of cognitive or AI-driven systems that ensure human wellbeing, e.g., cognitive and organizational.
• Generation and utilization of synthetic data for training and validating AI models in smart manufacturing environments that enable ergonomic problem-solving for operators.

Dr. Sandra Mattsson
Dr. Peter Andersson
Dr. Mohaiad Elbasheer
Guest Editors

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• cognitive automation
• AI-driven manufacturing
• human–AI collaboration
• smart factories
• digital twins
• machine learning
• Industry 5.0