Guest Editor(s)
Assoc. Prof. Mihai Crenganis
Email: mihai.crenganis@ulbsibiu.ro
Affiliation: Research, Innovation and Internationalization, Faculty of Engineering, Lucian Blaga University of Sibiu, Sibiu, Romania
Homepage:
Research Interests: industrial and mobile robotics, digital twins, robot-based manufacturing, embedded control, intelligent decision methods
Assoc. Prof. Cristina Maria Biris
Email: cristina.biris@ulbsibiu.ro
Affiliation: Research, Innovation and Internationalization, Faculty of Engineering, Lucian Blaga University of Sibiu, Sibiu, Romania
Homepage:
Research Interests: CAD/CAM/CAE, manufacturing technologies, polymer processing, injection moulding, CNC programming, production-system development
Assoc. Prof. Claudia Emilia Girjob
Email: claudia.girjob@ulbsibiu.ro
Affiliation: Department of Machines and Industrial Equipments, Faculty of Engineering, Lucian Blaga University of Sibiu, Sibiu, Romania
Homepage:
Research Interests: mobile robotics, digital twins, collaborative robot safety, sensor-based data acquisition, industrial communication
Summary
Artificial intelligence, digital twins, advanced robotics, and cyber-physical production systems are reshaping manufacturing by connecting physical equipment with virtual models, real-time data, and autonomous decision-making. This Special Issue aims to present theoretical, methodological, and application-oriented advances in intelligent automation for smart manufacturing, with emphasis on solutions that can be validated experimentally and transferred to industrial environments.
The issue will address the design, modelling, control, monitoring, and optimization of machines, robots, and production systems. Particular attention will be given to digital twin architectures for CNC machine tools, industrial and mobile robots, collaborative robotic systems, flexible manufacturing cells, and smart production lines. Contributions may investigate artificial intelligence and machine learning for process monitoring, quality assessment, fault diagnosis, predictive maintenance, adaptive control, production planning, and data-driven optimization. Research integrating machine vision, sensor fusion, industrial communication networks, the Industrial Internet of Things, and edge or cloud computing is also encouraged.
The Special Issue welcomes original research articles, review papers, and industrial case studies from robotics, mechatronics, control engineering, computer science, and manufacturing engineering. Submissions should clearly identify their scientific contribution and provide appropriate validation through experiments, benchmark datasets, comparative analyses, simulations linked to physical systems, or documented industrial implementation. Studies addressing interoperability, virtual commissioning, cybersecurity, human-robot collaboration, explainable and trustworthy AI, scalability, energy efficiency, and sustainability are particularly relevant. Contributions considering uncertainty, data quality, lifecycle management, real-time constraints, and the economic feasibility of intelligent automation solutions are also within scope.
The objective is to assemble a focused collection that advances intelligent and trustworthy manufacturing systems while demonstrating measurable improvements in productivity, flexibility, reliability, safety, and resource efficiency. Priority will be given to reproducible methods, transparent evaluation, and solutions capable of progressing beyond laboratory-scale demonstrations toward robust industrial deployment.
Suggested topics include, but are not limited to:
· Digital twin architectures and cyber-physical production systems for machines, robots, and smart production lines.
· Artificial intelligence and machine learning for process monitoring, quality assessment, fault diagnosis, and predictive maintenance.
· Intelligent control, optimization, and real-time automation of industrial, mobile, and collaborative robots.
· Machine vision, sensor fusion, Industrial Internet of Things, and edge/cloud integration for smart manufacturing.
· Virtual commissioning, simulation, interoperability, cybersecurity, and trustworthy intelligent automation.
· Sustainable, energy-efficient, human-centered, and scalable manufacturing systems.
Graphic Abstract
Keywords
digital twins, intelligent automation, industrial robotics, mechatronics, artificial intelligence, smart manufacturing, CNC machine tools, cyber-physical systems, machine learning, predictive maintenance, flexible manufacturing systems, industrial internet of things
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