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Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Elektronisches Dokument
Autor(en):
Thomas Schromm, Fabian Diewald, Christian Große
Titel:
An attempt to detect anomalies in CT-data of car body parts using machine learning algorithms
Abstract:
Industries, which produce hundreds of terabyte of CT data per year, demand automated evaluation approaches. This work provides a first glance of an attempt to automatically detect and characterize possible defects and/or anomalies which formed during common joining processes. We investigated a standard riveting process with respect to the resulting final head height of steel selfpiercing half-hollow rivets. The methods include conventional image processing algorithms, like edge-detection, threshol...    »
Stichworte:
Computed tomography, non-destructive testing, neural networks, automation, self-piercing half-hollow rivets
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
9th Conference on Industrial Computed Tomography (iCT) 2019, 13-15 Feb, Padova, Italy (iCT 2019)
Jahr:
2019
Quartal:
1. Quartal
Jahr / Monat:
2019-03
Seiten:
5
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