A Bibliometric Approach to Characterizing Technology Readiness Levels Using Machine Learning

Abstract

Presented at the AIAA SCITECH 2023 Forum in National Harbor, Maryland.As cislunar space becomes more accessible to national space agencies and commercial entities, there is a constant need to improve the way in which space missions are planned, and development progress is tracked. A technologies stage of development, which is related to mission budget and schedule, is typically quantified using technology readiness levels (TRL). The process of determining TRL is often long and laborious, and requires the use of subject matter experts. As a part of the Georgia Institute of Technology Cislunar Architecture Initiative, this work serves to develop the early stages of an environmental scanning approach to maturity assessment that allows for the automatic determination of a technologies TRL using machine learning ordinal regression techniques with bibliometric factors. The bibliometric factors considered were: scientific publications, National Science Foundation awards, patents, and NASA Spinoff articles. Annual data on these factors was collected for 31 technologies between 1995-2015 using public APIs, and S-curves fit to the data to estimate each technologies point in the development cycle. The final model performed with an R² of 0.817, 0.812, and 0.567 on the training, validation, and test data, respectively. Additionally, a better performing model to classify a technologies technology life cycle phase was created and drawbacks to this approach discussed