PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models

We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam's ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics.

Xenakis: Experimenting with Data, Cities, and Sounds

In this work, we report on the results and lessons learned from different disciplines while researching the loosely-defined problem of hearing a city. We present Xenakis, a tool for the musification of urban data, which is able to capture some features of a city's topology through the distribution of street orientations, and turn it into a (very) small piece of music, a loop, which can be used as building block for compositions. Besides providing complementary visual and auditory channels to interface with this data, we also allow the piping of \textit{midi} signals to other applications. This concept was developed by visualization researchers collaborating with musicians using design study methodologies in an open-ended way. Our results include musical tracks, and we take advantage of the scope of alt.VIS to communicate our research in a sincere, humorous, and engaging format.