An Ontology for Defect Detection in Metal Additive Manufacturing

In this context, additive manufacturing (AM), and specifically metal additive manufacturing (MAM), is particularly suited to industrial paradigms based on automation, flexibility, and efficiency. Indeed, MAM can be considered as a native digital technology, providing a seamless workflow from the digital design environment to the final product, which can be potentially completed without any human intervention [30]. However, a broader adoption of MAM technologies in industry is still hindered by such factors as: (i) lack of widely adopted standardisations and specifications of material properties, machines, and processes [40]; (ii) lack of adequate digital infrastructures, and interoperability issues between different production environments [7]; (iii) lack of accessible interfaces providing process information that is easily interpretable by non-experts [47]; (iv) lack of advanced control systems capable of automatically adjusting, at run-time, the production parameters [54]; (v) challenges in quality assurance due part accuracy and variability [48]. Thus, achieving semantically transparent and interoperable data sets and systems, to address Points (i), (ii) and (iii) above, is arguably of paramount importance. In this direction, several approaches based on ontology engineering and knowledge representation techniques have been proposed [29, 10, 66, 67, 60]. Broadly conceived as formal specifications of conceptualisations over a domain of interest, computational ontologies (cf.