Dear Colleagues,

The manufacturing industry faces several challenges related to the volatility of demand, the speed of emergence of new products and mass customization. However, enhancing productivity and product quality with off-line optimization techniques using time-consuming and costly machining experiments does not help manufacturers respond to new market requirements and therefore weakens their competitiveness. In the era of Industry 4.0, driven by digitalization developments and ever-increasing global competitiveness, the manufacturing industry needs smart and agile machining systems and processes. Over the last few years, and taking advantage of the development of new communication technologies (Internet of Things—IoT, Communication Protocols), non-invasive sensors, big data analytics and cloud computing, several research works have been carried out on the development of cyber-physical systems, including CPSs and Digital Twins (DTs), for monitoring and controlling CNC Machine Tools (CNMTs) and optimizing machining conditions. Nevertheless, a significant amount of work is still needed to validate these technologies under an industrial environment. Efforts should focus on the development of robust predictive models and enhancing their computational speed, the virtualization of physical systems (CNCMTs, cutting tools, lubrication systems, etc.), real-time data acquisition and decision making, fog–edge–cloud computing and the interoperability of CPSs and DTs.  

Topics within the scope of this Special Issue include, but are not limited to:

• Machine diagnostics and prognostics (condition monitoring);
• Systems and control engineering;
• Applications of automation;
• Mechanical engineering;
• Computer engineering;
• Mechatronics;
• Mechanical systems, machines and related components;
• Machine vision.

Dr. Walid Jomaa
Dr. Christophe Danjou
Guest Editors

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• CNC Machines Tools
• cyber-physical systems
• Digital Twins
• big data management and analytics
• automation
• interoperability
• digital thread
• multisensor data fusion
• data acquisition
• smart monitoring and control
• precision machining
• intelligent process planning