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Title:

An attempt to detect anomalies in CT-data of car body parts using machine learning algorithms

Document type:
Konferenzbeitrag
Contribution type:
Elektronisches Dokument
Author(s):
Schromm, T.; Diewald, F.; Große, C.
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...     »
Keywords:
Computed tomography, non-destructive testing, neural networks, automation, self-piercing half-hollow rivets
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Book / Congress title:
9th Conference on Industrial Computed Tomography (iCT) 2019
Date of congress:
13-15 February 2019
Year:
2019
Quarter:
1. Quartal
Year / month:
2019-03
Pages:
5
Reviewed:
nein
Language:
en
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