Towards the Digitalization of Additive Manufacturing

Additive manufacturing (AM) is a trending technology that is being adopted by many companies around the globe. The high level of product customization that this technology can provide, added to its link with key green targets such as the reduction of emissions or materials waste, makes AM a very attractive vehicle towards the transition to more adaptive and sustainable manufacturing. However, such a level of customization and this fast acceptance, raise new needs and challenges on how to monitor and digitalize the AM product life cycles and processes, which are essential features of a flexible factory that address adaptive and first-time-right manufacturing through the exploitation of knowledge gathered with the deep analysis of large amounts of data. How to organize and transfer such amounts of information becomes particularly complex in AM given not just its volume but also its level of heterogeneity. This work proposes a common methodology matching with specific data formats to solve the integration of all the information from AM processes in industrial digital frameworks. The scenario proposed in this work deals with the AM of metallic parts as a specially complex process due to the thermal properties of metals and the difficulties of predicting defects within their manipulation, making metal AM particularly challenging for stability and repeatability reasons but at the same time, a hot topic within AM research in general due to the large impact of such customized production in sectors like aeronautical, automotive, or medical. Also, in this work, we present a dataset developed following the proposed methodology that constitutes the first public available one of multi-process Metal AM components.