A Learning Method for Automated Disassembly

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Wolff, J.; Kolditz, T.; Raatz, A.: A Learning Method for Automated Disassembly. In: Ratchev, S. (Ed.): Precision Assembly in the Digital Age : 8th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2018, Chamonix, France, January 14—16, 2018, Revised Selected Papers. Cham : Springer, 2018 (IFIP Advances in Information and Communication Technology ; 530), S. 63-71. DOI: https://doi.org/10.1007/978-3-030-05931-6_6

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/13322

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Sum total of downloads: 115




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Abstract: 
While joining tolerances, and therefore forces, are known in the assembly process, the determination of disassembly forces is not possible. This is caused by changes in the product properties during the product operation, which has multiple reasons such as thermal or mechanical stress on the product. Regarding the planning of disassembly tasks, disassembly times and tools cannot be planned properly. They have to be determined in the process or stay undefined, which can result in damaging of the product.This article shows an approach to describe the necessary disassembly forces without having to investigate the complex physical influences caused by the usage of the product. To do so, a Learning Method is developed, which is sustained by a Lookup-Table for the estimation of disassembly forces based on basic input data such as hours of operation and operating characteristics. Missing values will be interpolated by using multiple linear regression. The concept will be illustrated in the example of a turbine blade connection.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: BookPart
Publishing status: acceptedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Maschinenbau

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 68 59.13%
2 image of flag of Russian Federation Russian Federation 15 13.04%
3 image of flag of Czech Republic Czech Republic 15 13.04%
4 image of flag of United States United States 8 6.96%
5 image of flag of Ireland Ireland 2 1.74%
6 image of flag of Sweden Sweden 1 0.87%
7 image of flag of Indonesia Indonesia 1 0.87%
8 image of flag of United Kingdom United Kingdom 1 0.87%
9 image of flag of China China 1 0.87%
10 image of flag of Switzerland Switzerland 1 0.87%
    other countries 2 1.74%

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