User: Guest  Login
Title:

An attempt to detect anomalies in car body parts using machine learning algorithms.

Document type:
Konferenzbeitrag
Author(s):
Schromm, T.; Diewald, F.; Grosse, C.U.
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 self- piercing half-hollow rivets. The methods include conventional image processing algorithms, like edge-detection, t...     »
Keywords:
Computed tomography, non-destructive testing, neural networks, automation, self-piercing half-hollow rivets
Book / Congress title:
9th Conference on Industrial Computed Tomography (iCT 2019)
Volume:
24 (3)
Date of congress:
13-15 February 2019
Year:
2019
Quarter:
1. Quartal
Year / month:
2019-02
Month:
Feb
E-ISBN:
1435-4934
Reviewed:
nein
Language:
en
Fulltext / DOI:
doi:10.58286/23651
WWW:
https://www.ndt.net/search/docs.php3?id=23651
 BibTeX