Inherent Load Measurement and Component Identification by multi-dimensional Coded Data in the Component's Subsurface Region

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Barton, S.; Mroz, G.; Reimche, W.; Maier, H.J.: Inherent Load Measurement and Component Identification by multi-dimensional Coded Data in the Component's Subsurface Region. In: Procedia Technology 26 (2016), S. 537-543. DOI: https://doi.org/10.1016/j.protcy.2016.08.067

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/3237

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Sum total of downloads: 108




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Abstract: 
In industrial production, the absence of component markings and unrecognized component failure can result in a lack of protection against product piracy and malfunctions of machinery and installations. A technique for storing data inherently in the subsurface region of the component was developed. Inherently stored data is highly resistant to external stresses and inseparably linked to the component. To evaluate the integrity of highly stressed components, material inherent sensors are induced in the subsurface region of the component to store the loading history. These techniques offer the potential for securely identifying and evaluating the status of components, and thus reducing failure costs.
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2016
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 65 60.19%
2 image of flag of United States United States 17 15.74%
3 image of flag of China China 8 7.41%
4 image of flag of Russian Federation Russian Federation 4 3.70%
5 image of flag of France France 2 1.85%
6 image of flag of New Zealand New Zealand 1 0.93%
7 image of flag of Korea, Republic of Korea, Republic of 1 0.93%
8 image of flag of Kenya Kenya 1 0.93%
9 image of flag of Japan Japan 1 0.93%
10 image of flag of Australia Australia 1 0.93%
    other countries 7 6.48%

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