The term "Industry 4.0" was initially used to refer to the computerization of manufacturing and the skills that the future workforce will need to develop to compete for high-paying professions. Numerous advances, including the internet of things (IoT), digital manufacturing, and cyber-physical systems, are now viewed as important components in conceptualizing and adopting Industry 4.0. At its core lies a technical revolution in collecting, transferring, and analyzing big data in real-time. Industry 4.0, which is deeply intertwined with smart manufacturing, is a concept that aims to build smart factories that combine these new technologies to quickly adapt and respond to changes in market demand for high-quality items. In reality, smart factories are at the Centre of Industry 4.0 and smart manufacturing.

The following topics are of interest:

(1) Machine learning approaches for process management and component quality improvement

(1) Reinforcement learning-based production scheduling

(1) AI technology and robotics in smart manufacturing

(1) IoT-enabled smart manufacturing

(1) Intelligent modelling of factory floors and manufacturing processes that incorporate sensor data

(1) New technologies for human-machine interfaces and communication

(1) Innovative sensing methods for process monitoring and product history tracking

(1) Smart tooling and in-line part inspection

(1) Manufacturing data analysis and diagnostics for real-time reporting via intranet and capabilities

(1) Case studies in the design and deployment of networked-distributed digital manufacturing paradigms

(1) Cyber-physical systems for smart manufacturing facility design and operation

(1) Application of cloud, distributed, and digital manufacturing paradigms in automation and control systems

(1) Dynamic modelling of material machining processes

(1) Manufacturing system efficiency analysis

(1) Collaboration in digital manufacturing

(1) Computational methods in cloud-based material machining and process quality control

(1) Artificial intelligence for predictive maintenance