New Data Science for Human Operational Analysis in Smart Manufacturing

This award will contribute to national prosperity and economic welfare by advancing data science methods for improving manufacturing systems combining automated machines and human workers using data from in-situ sensors. This will allow the operational uncertainties arising from human operations to be quantified and integrated into models for performance evaluation and operations planning. The award will also prepare the next generation of scientists by providing multidisciplinary research, training, and international collaboration opportunities for K-12, undergraduate, and graduate students. Our team will broadly disseminate their research findings and share data and the resulting software packages to the data science and operations engineering community. This project will make significant scientific advances in data science and smart manufacturing, going beyond current methods focused on human action recognition by incorporating a contextual understanding of human motions analysis in operational analysis. The use of wrapped Gaussian distributions will introduce new mathematical and probability spaces related to human operations and novel computational approaches to related inference problems. These methods will complement current smart manufacturing research by supporting digital twins of manufacturing systems with human operational data, contributing to the body of scientific and engineering knowledge and improved industrial productivity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.