The recent advancements in the field of digital twin technology are significantly enhancing various industrial applications, particularly in metrology, condition monitoring, and cleanliness management. Innovations are being driven by the integration of advanced machine learning methods, thermal imaging, and vision systems, which are enabling more precise and proactive management of industrial processes. The use of digital twins is not only improving the accuracy of measurements and predictive maintenance but also facilitating the simulation and optimization of cleanliness strategies in public spaces. Additionally, the field is witnessing breakthroughs in autonomous manipulation tasks, such as the handling of transparent materials, through the application of convolutional neural networks and depth sensing technologies. These developments are poised to revolutionize traditional industrial practices by introducing higher levels of automation, efficiency, and safety.

Noteworthy papers include one that introduces a Metrology and Manufacturing-Integrated Digital Twin, significantly improving measurement accuracy through ensemble machine learning methods, and another that demonstrates a predictive digital twin for condition monitoring using thermal imaging, enhancing proactive asset management.