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Inspection Data to Support a Digital Twin for Geometry Assurance

Kristina Wärmefjord (Institutionen för industri- och materialvetenskap, Produktutveckling) ; Rikard Söderberg (Institutionen för industri- och materialvetenskap, Produktutveckling) ; Lars Lindkvist (Institutionen för industri- och materialvetenskap, Produktutveckling) ; Björn Lindau (Institutionen för industri- och materialvetenskap, Produktutveckling) ; Johan S Carlson (Institutionen för industri- och materialvetenskap, Produktutveckling)
Proc. ASME IMECE Vol. 2 (2017),
[Konferensbidrag, refereegranskat]

Geometrical variation is a problem in all complex, assembled products. Recently, the Digital Twin concept was launched as a tool for improving geometrical quality and reduce costs by using real time control and optimization of products and production systems. The Digital Twin for geometry assurance is created together with the product and the production systems in early design phases. When full production starts, the purpose of the Digital Twin turns towards optimization of the geometrical quality by small changes in the assembly process. To reach its full potential, the Digital Twin concept is depending on high quality input data. In line with Internet of Things and Big Data, the problem is rather to extract appropriate data than to find data. In this paper, an inspection strategy serving the Digital Twin is given. Necessary input data describing form and shape of individual parts, and how this data should be collected, stored and utilized is described.

Nyckelord: Digital Twin, variation, tolerances, geometrical variation, inspection, Big Data, point cloud



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Denna post skapades 2017-10-16.
CPL Pubid: 252542