Process-related characteristic–based topography evaluation of wear conditions on grinding wheels

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Strug, M.; Denkena, B.; Breidenstein, B.; Krödel-Worbes, A.: Process-related characteristic–based topography evaluation of wear conditions on grinding wheels. In: The International Journal of Advanced Manufacturing Technology 120 (2022), Nr. 11-12, S. 7707-7720. DOI: https://doi.org/10.1007/s00170-022-09274-1

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/14676

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Sum total of downloads: 22




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Abstract: 
Non-productive auxiliary processes affect the single part and small badge production of milling tools. The key production process grinding is inevitably linked to the auxiliary conditioning process. The time demand of those process steps decreases the overall productivity of the manufacturing process. However, today the machine operator decides on conditioning cycles individually by the use of experience. Until today, there is no objective data based approach available that supports the initiation of these conditioning processes or the adaption of the grinding process itself in order to improve its process efficiency. For this purpose, a process-related topography evaluation method of the grinding wheel surface is developed within this study. For the measurement, an optical method based on laser triangulation is used. The measurement system is implemented into a common tool grinding machine tool. In addition, characteristic topography values are defined that show the wear conditions of the grinding tool. Moreover, the data is summarized in a database of wear conditions. The developed measurement method can save grinding and dressing tool resources, process times and minimizes scrap parts. In addition, an adaptation of the process and a targeted launch of auxiliary processes can be enabled. The novel characteristic-based topography measurement creates the opportunity to enhance the tool life of the grinding wheels up to 30% without losing productivity.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of United States United States 9 40.91%
2 image of flag of Germany Germany 6 27.27%
3 image of flag of Belgium Belgium 4 18.18%
4 image of flag of Israel Israel 2 9.09%
5 image of flag of Indonesia Indonesia 1 4.55%

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