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Optical inline monitoring of the burnish surface in the punching process

  • Punching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediatePunching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.show moreshow less

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Metadaten
Author:Maximilian LorenzORCiDGND, Matthias Menzl, Christian DonhauserGND, Michael Layh, Bernd R. PinzerORCiD
DOI:https://doi.org/10.1007/s00170-021-07922-6
Identifier:1433-3015 OPAC HS OPAC extern
Identifier:0268-3768 OPAC HS OPAC extern
Parent Title (English):The International Journal of Advanced Manufacturing Technology
Publisher:Springer
Place of publication:London
Document Type:Article
Language:English
Date of Publication (online):2021/09/25
Year of first Publication:2021
Issue:118
Number of pages:16 Seiten
First Page:3585
Last Page:3600
Institutes:EPT – Institut für Effiziente Produktionstechnik
IMS - Institut für Maschinelles Sehen
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP2: Mobilität
FSP3: Produktion
Publication Lists:Donhauser, Christian
Layh, Michael
Lorenz, Maximilian
Pinzer, Bernd
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2021/10/06
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