Abstract
High precision manufacturing processes of technical product surfaces have to fulfil demanding requirements regarding functional characteristics like roughness, gloss and colour. Especially the reproducibility and control of aperiodic surface topographies based on grinding processes are influenced by many manufacturing process factors and cleaning methods. This paper outlines a concept for the multivariate analysis of measurement data regarding aperiodic surface topographies (characteristics: roughness and gloss) based on small sample sizes, varied process parameter and different cleaning methods. The concept and analysis are based on non-parametric statistics, due to the particular challenge which is the missing knowledge of the distribution models regarding to the characteristics roughness and gloss. The application of the worked out measurement strategy and the multivariate non-parametric statistical method is shown within a case study “Grinding surfaces of cutlery” comparing four different processes. The compared cleaning compounds are cold degreaser (standard method) and acetone (optional method) in combination with different technical grinding processes (cutting fluid fat respectively water based).
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Acknowledgements
The authors thank Peter Born (Franz Güde GmbH, Solingen Germany) for providing reference samples and thank Pit Fiur (BUW, Wuppertal Germany) supporting the analysing activities. Furthermore the authors thank the project sponsor (Federal Ministry of Education and Research (BMBF, Germany); project organisation by Karsruher Institut für Technologie (KIT), Produktion und Fertigungstechnologien (PTKA-PFT) for supporting the project MuPro2 (multivariate process optimisation 2), which is the base for the shown research work.
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Bracke, S., Radetzky, M. (2019). Aperiodic Surface Topographies Based on High Precision Grinding Processes: Analysis of Cutting Fluid and Cleaning Process Influences Using Non-parametric Statistics. In: Burduk, A., Chlebus, E., Nowakowski, T., Tubis, A. (eds) Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing, vol 835. Springer, Cham. https://doi.org/10.1007/978-3-319-97490-3_5
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DOI: https://doi.org/10.1007/978-3-319-97490-3_5
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