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Detection in a Robotised Short Circuting GMA Welding using Neural Networks
Responsible organisation
1999 (English)Conference paper, (Refereed) Published
Abstract [en]

Today it is both time and cost consuming to check the quality of a weld when it is done off-line by an experienced weld operator. Therefor the need for an automatic detection is urgent in order to reduce production costs. There are systems for monitoring the quality of a weld commercially available but there is still some research that has to be done in this area in order to increase the reliability. The method presented in this paper is neural network based and considers short circuiting GMA welding, but there is no obstacle for this solution to work on other types of robotised welding. By presenting the weld voltage to a neural network, the network is able to detect defects in the weld joint. Testresults have shown that the detection rate is 100 percent and false alarms are nonexisting.

Place, publisher, year, edition, pages
Phuket, Thailand, 1999.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-9357Local ID: oai:bth.se:forskinfoF8C2BBF8B3E93AF4C1256E2800271A3DOAI: oai:DiVA.org:bth-9357DiVA: diva2:837181
Conference
IEEE International Symposium on Intelligent Signal Processing and Communication System
Available from: 2012-09-18 Created: 2004-01-27 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Output format
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