About this Research Topic
In the contemporary landscape of manufacturing, the convergence of digital technologies and physical machinery has given rise to a transformative paradigm known as Intelligent Manufacturing. At its core, this paradigm seeks to enhance manufacturing processes by leveraging the synergy between Digital Twins and Cyber-Physical Systems (CPS) in machine tools. Digital Twins, virtual replicas of physical machines, enable real-time monitoring, simulation, and analysis of manufacturing operations. Simultaneously, Cyber-Physical Machine Tools integrate computational power and physical functionality, facilitating automation and optimization. The symbiotic relationship between Digital Twins and Cyber-Physical Machine Tools holds immense promise for improving efficiency, quality, and sustainability across industries. Despite the significant advancements, challenges such as data integration, real-time processing, and security concerns remain. Addressing these gaps is crucial for the full realization of Intelligent Manufacturing's potential.
This research topic aims to showcase the latest research and applications of digital twin and cyber-physical machine tools in manufacturing. It welcomes original contributions that address the challenges and opportunities of developing, implementing, and evaluating these technologies in various manufacturing domains. Specific questions include how to effectively model and simulate these systems, how to handle vast amounts of data, and how to integrate machine learning and artificial intelligence to optimize performance. Hypotheses to be tested may involve the efficacy of different data processing techniques or the impact of advanced control algorithms on manufacturing efficiency.
To gather further insights in the evolving landscape of Intelligent Manufacturing, we welcome articles addressing, but not limited to, the following themes:
- Modeling and simulation of digital twin and cyber-physical machine tools
- Data acquisition, processing, and analysis for digital twin and cyber-physical machine tools
- Machine learning and artificial intelligence for digital twin and cyber-physical machine tools
- Optimization and control with cyber-physical machine tools
- Fault diagnosis and prognosis with cyber-physical machine tools
- Human-machine interaction and collaboration using digital twin and cyber-physical machine tools
- Security and privacy issues for cyber-physical machine tools
- Case studies and industrial applications of digital twin and cyber-physical machine tools
This research topic aims to showcase the latest research and applications of digital twin and cyber-physical machine tools in manufacturing. It welcomes original contributions that address the challenges and opportunities of developing, implementing, and evaluating these technologies in various manufacturing domains. Specific questions include how to effectively model and simulate these systems, how to handle vast amounts of data, and how to integrate machine learning and artificial intelligence to optimize performance. Hypotheses to be tested may involve the efficacy of different data processing techniques or the impact of advanced control algorithms on manufacturing efficiency.
To gather further insights in the evolving landscape of Intelligent Manufacturing, we welcome articles addressing, but not limited to, the following themes:
- Modeling and simulation of digital twin and cyber-physical machine tools
- Data acquisition, processing, and analysis for digital twin and cyber-physical machine tools
- Machine learning and artificial intelligence for digital twin and cyber-physical machine tools
- Optimization and control with cyber-physical machine tools
- Fault diagnosis and prognosis with cyber-physical machine tools
- Human-machine interaction and collaboration using digital twin and cyber-physical machine tools
- Security and privacy issues for cyber-physical machine tools
- Case studies and industrial applications of digital twin and cyber-physical machine tools
Keywords: Digital Twin, Cyber Physical Machine Tools, Intelligent Manufacturing, Optimization, Fault diagnosis, Security
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
