Efficient integration process of production data into Augmented Reality based maintenance of machine tools

With the increasing requirements on machine tools such as higher complexity, individuality and digitization, their maintenance becomes also more complex, which increases time consumption and demands special knowledge. The maintenance engineer can be supported in this process using the Augmented Real...

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Bibliographic Details
Published inProduction engineering (Berlin, Germany) Vol. 15; no. 3-4; pp. 311 - 319
Main Authors Kollatsch, Christian, Klimant, Philipp
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021
Springer Nature B.V
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Summary:With the increasing requirements on machine tools such as higher complexity, individuality and digitization, their maintenance becomes also more complex, which increases time consumption and demands special knowledge. The maintenance engineer can be supported in this process using the Augmented Reality (AR) technology, mobile devices and suitable production data. This requires the integration and adaptation of the maintenance documentation as well as further production systems and their data such as the CAD system, product data management (PDM) system, manufacturing execution system (MES) and CNC. For this purpose, a method is developed that allows an efficient integration process of various production data into an AR supported maintenance documentation. This includes concepts for the integration process of different production systems and data into an AR maintenance system as well as the design of the AR maintenance system architecture. Basis is the analysis of the required production and AR systems, data and processes. The aim of the method is to reduce the high expenditure of the overall planning process and the necessary expertise of the maintenance planner of the AR technology. The developed method is verified with different maintenance scenarios, production systems and data as well as machine tools and AR devices.
ISSN:0944-6524
1863-7353
DOI:10.1007/s11740-021-01026-6