Wear-adaptive optimization of in-process conditioning parameters during face plunge grinding of PcBN

Download statistics - Document (COUNTER):

Denkena, B.; Krödel-Worbes, A.; Müller-Cramm, D.: Wear-adaptive optimization of in-process conditioning parameters during face plunge grinding of PcBN. In: Scientific reports 12 (2022), 1012. DOI: https://doi.org/10.1038/s41598-022-05066-5

Repository version

To cite the version in the repository, please use this identifier: https://doi.org/10.15488/13091

Selected time period:

year: 
month: 

Sum total of downloads: 67




Thumbnail
Abstract: 
Polycrystalline cubic boron nitride is a very hard material. Machining of this material is performed by grinding with diamond tools. Due to its high hardness, grinding tools are subjected to severe microscopic and macroscopic tool wear. This wear leads to short tool life and results in high effort in conditioning the abrasive layer. Contrary to the usual conditioning of diamond grinding wheels with diamond dressing tools, this study investigates a conditioning process based entirely on the use of white corundum cup rolls. These conditioning tools allow the in-process face plunge conditioning of vitrified bond diamond grinding tools. The circumferential speed of the conditioning tool and the average grain diameter of the corundum are identified as the main factors influencing the topography of the generated grinding layer. To describe the performance of the conditioning process, a specific conditioning removal rate Q′sd is derived. This parameter represents a cumulated variable that allows a comparison of different conditioning strategies. It is shown that an increase in Q′sd significantly counteracts microscopic wear on the abrasive layer. Therefore, optimized process parameters enable the process of in-process conditioning to significantly reduce wear on the grinding tool without increasing the process time or the non-productive time.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Fakultät für Maschinenbau

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 30 44.78%
2 image of flag of United States United States 23 34.33%
3 image of flag of China China 6 8.96%
4 image of flag of Czech Republic Czech Republic 2 2.99%
5 image of flag of Taiwan Taiwan 1 1.49%
6 image of flag of France France 1 1.49%
7 image of flag of Finland Finland 1 1.49%
8 image of flag of Denmark Denmark 1 1.49%
9 image of flag of Belgium Belgium 1 1.49%
10 image of flag of Australia Australia 1 1.49%

Further download figures and rankings:


Hinweis

Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository


Browse