Intelligent Manufacturing and Production
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 1604
Special Issue Editors
2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
Interests: machine tool; robots; intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals
2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
Interests: production scheduling optimization and dynamic scheduling; digital twin technology of manufacturing workshops; assembly sequence optimization of complex machine tool components
Special Issue Information
Dear Colleagues,
The integration of deep learning, machine learning, swarm intelligence, big data analytics, generative artificial intelligence, and large language models (LLM) has significantly advanced intelligent decision-making processes in smart production and services across diverse industrial sectors. As customer needs evolve, the paradigm is shifting from mass production and mass customization to a more personalized and flexible approach to smart production and service delivery. This shift not only addresses the challenges present in real-world settings but also opens up new opportunities. Cutting-edge soft computing and AI technologies now enable novel applications that enhance supply chain resilience and reshape the business ecosystem.
Prof. Dr. Qiang Cheng
Dr. Jun Yan
Guest Editors
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Keywords
- advanced equipment/process control (AEC/APC)
- intelligent decision technologies for real-time decision making
- equipment diagnosis, predictive maintenance, and tool health monitoring
- factory modeling, analysis, and performance evaluation
- flexible production planning and scheduling
- Industry 4.0 and manufacturing strategy
- manufacturing intelligence and informatics
- mass personalization and customization
- predictive maintenance
- smart decision-making for corporate resource planning and allocation
- sustainability and circular economy
- smart CNC machines and automated machining systems
- AI-driven machine tool condition monitoring and performance optimization
- intelligent manufacturing systems for precision engineering
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