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Towards an autonomous maintenance, repair and overhaul process
Author(s) -
Berend Denkena,
Peter Nyhuis,
Benjamin Bergmann,
Nicolas Nübel,
Torben Lucht
Publication year - 2019
Publication title -
procedia manufacturing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.504
H-Index - 43
ISSN - 2351-9789
DOI - 10.1016/j.promfg.2020.02.014
Subject(s) - process (computing) , inefficiency , scrap , function (biology) , workload , risk analysis (engineering) , engineering , work (physics) , computer science , production (economics) , reliability engineering , systems engineering , causal chain , process management , manufacturing engineering , mechanical engineering , business , philosophy , epistemology , macroeconomics , evolutionary biology , economics , biology , microeconomics , operating system
The maintenance, repair and overhaul (MRO) processes of aircraft engines are dominated by a high proportion of manual work and subjective condition assessment of used parts. This leads to inefficiency due to additional, partially not required workload and high scrap rates. Further, there is a lack of knowledge about the effects of the respective repair measures on the performance of the parts. So far, there are no autonomous repair solutions that allow an optimal and individually tailored regeneration. In order to realize such a process, it is necessary to bring together the manufacturing, function-simulating and logistics-oriented disciplines in an integrated system. For this, data management along the process chain is an important success factor. In particular, the provision and linking of the data and data formats required for simulation and the production environment is of fundamental importance. This paper presents a data architecture that can serve as a framework for data integration within a representative process chain for regeneration.

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