Rockwell Adhesion Test - Approach to Standard Modernization

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dc.contributor.authorHatic, Damjanen_US
dc.contributor.authorCheng, Xiaoyinen_US
dc.contributor.authorWeibel, Thomasen_US
dc.contributor.authorRauhut, Markusen_US
dc.contributor.authorHagen, Hansen_US
dc.contributor.editorByška, Jan and Jänicke, Stefanen_US
dc.date.accessioned2020-05-24T13:49:38Z
dc.date.available2020-05-24T13:49:38Z
dc.date.issued2020
dc.description.abstractAutomatization of industry processes and analyses has been successfully applied in many different areas using varying methods. The basis for these industrial analyses is defined by global or country specific standards and often development of automated solutions works towards streamlining processes currently done heuristically. Lately, image classification, as one of the automatization development areas, has turned to machine learning in search for solutions. Though approaches that involve neural networks often result in high accuracy predictions, their complexity makes feature hard to understand and ultimately reproduce. To this end, we introduce a pipeline for the design, implementation and evaluation of a hand-crafted feature set used for the parameterization of two thin film coating adhesion classification standards. The method mimics the current expert classification process, and is developed in collaboration with domain experts. Implementation of an automated classification process was used for verification and integration testing.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2020 - Posters
dc.identifier.doi10.2312/eurp.20201121
dc.identifier.isbn978-3-03868-105-2
dc.identifier.pages29-31
dc.identifier.urihttps://doi.org/10.2312/eurp.20201121
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurp20201121
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.titleRockwell Adhesion Test - Approach to Standard Modernizationen_US
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