Ontology-Based Context-Aware Data Analytics in Additive Manufacturing

Abstract Recent advances in Additive Manufacturing (AM), particularly in production scenarios, have been largely driven by insights achieved through data analytics. AM has greatly benefited from the increasingly large amounts of data generated during the design to product transformation. Despite the large amounts of data that can be generated from each build, the variations that can occur between builds create challenges in data reuse. Advances in data analytics are coming in the form of advanced machine learning algorithms, and are often associated with deep learning, where large amounts of data are analyzed, and interpretations are made. While such algorithms are increasingly adept at solving complex problems, solutions are highly dependent on the data used to train the algorithms and thus often subject to unwanted bias. Contextualization of data can help limit unintended bias and improve the probability of attaining viable results. Data contextualization often comes through domain context. This work describes the development of an AM ontology to support context-aware data analytics. The Additive Manufacturing Data Analytics Ontology, or AMDA Ontology, is developed to facilitate the contextualization of AM data through the representation of explicit AM concepts throughout the design to product transformation and the encoding of inhering relationships within the concepts. The AM concepts are accompanied by a suite of concepts representative of the necessary modeling, simulation, and analytics terms to create links between AM data, AM data analytics opportunities, and appropriate machine learning algorithms. The early results indicate that AMDA ontology has the ability to facilitate key correlations between AM data and the analytic opportunities to enhance the design to product transformation of AM parts.