A large barrier to the adaptation of additive manufacturing (AM) as replacement for conventional manufacturing is underdeveloped quality assurance practices. To address this and leverage the layer-by-layer fabrication in AM, this work presents the development and implementation of a digital thread pipeline for a Wire Arc Directed Energy Deposition (Wire-Arc DED) system. The pipeline is an open-source and free software stack, co-developed between the University of Maine and U.S. Army Corps of Engineers ERDC. The process agnostic framework is comprised of customizable Robot Operating System in-situ monitoring support and a MongoDB process-structure-property database architecture. For the Wire-Arc DED cell, the in-situ monitoring consists of temporally fused machine data and sensors, including weld properties, thermal camera footage, and interpass temperature measurements, to capture dense datasets during fabrication. This in-situ data is visualized real-time using Foxglove to inform operators of process anomalies. Subsequently, the heterogeneous data is packaged into rosbags and pushed automatically to the database, ensuring data integrity and accessibility for post-analysis. The novel functionality of this framework is its ability to directly link the captured process data to resultant structure and property data. This is fundamental for accelerating quality assurance of additively manufactured parts, as it allows for dense inspection of process data throughout the part’s fabrication process, as well as provenance connectivity to relevant historical destructive testing. The pipeline also provides the data necessary for developing and validating traditional and machine learning based process models and performance predictions. Through the correlation of specific processing conditions to microstructural and mechanical performance, there is a clear pathway to enable real-time sensor-based structure and property prediction during manufacturing. By integrating an open-source framework into a Wire-Arc DED cell, this research demonstrates a viable pathway to produce digital threads and shows a blueprint for creating more reliable and repeatable AM components and processes.