We present on automated data capture and storage for machine learning-aided digital twins, with a focus on mechanical test data streams. Our pipeline connects mechanical test measurement data using a web interface, test hardware API, and a database, allowing for efficient and accurate data collection for our digital twin platform. These digital twins can then be used for process and part evaluation, which can help optimize the design and performance of parts. We present the benefits of using automated data capture and storage, including improved data quality, reduced time and resources required for data collection, and improved accuracy and consistency of data. Though rooted in Direct Ink Write, this work is extendable to other additive manufacturing techniques in which data science can establish a link between the fabrication process and part performance.