Time Series Display for Knowledge Discovery on Selective Laser Melting Machines
This paper presents a method for displaying industrial time series. It aims to support data and process engineers on the data analytics tasks, specially in the area of Industry 4.0 where data and process joins. The method is entitled SCG, from Splitting, Clustering and Graph making which are its main pillars. It brings two innovations: Samples making and Visualizations. The first one is in charge of build well-suited samples fostered to reach the data exploring objectives, whereas the second one is in charge showing a graph-based view and a time-based view. The final objective of this method is the detection of stable working states on a working machine, which is key for process understanding, while at the same time it enlightens on knowledge discovery and monitoring. The use case in which this work is grounded is the Selective Laser Melting (SLM) industrial process, though the introduced SCG procedure could be applied to any time series collection.
KeywordsTime series Clustering Knowledge discovery Graph Process monitoring Selective laser melting Additive manufacturing
Authors and Acknowledgments
The work done on this paper is focused in one of the hybrid production Cells that use SLM as AM process, developed in an European Project of Factories of the Future (FoF) which is a public-private partnership (PPP) for advanced manufacturing research and innovation initiative. IK4-LORTEK is a Spanish research center specialized in additive manufacturing, joining processes, and industrial digitization, which is the coordinator of the project and in charge of implementing the self-learning system within the H2020 EU project named HyProCell (Development and validation of integrated multiprocess Hybrid Production Cells for rapid individualized laser-based production), in collaboration with SmartFactory who is in charge of the middleware and OPC-UA adapters, and Adira who is the machine manufacturer.
The part of data generation has been funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 723538 (HYPROCELL project). The project is framed in the initiative for advanced manufacturing research and innovation of the Photonics and Factories of the Future Public Private Partnership.
The part of the data engineering has been made under the financial support of the project KK-2018/00104 (Departamento de Desarrollo Económico e Infraestructuras del Gobierno Vasco, Programa ELKARTEK Convovatoria 2018).
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