Use of Machine Learning Algorithms for Weld Quality Monitoring using Acoustic Signature-Reference-Cited by-同舟云学术

Use of Machine Learning Algorithms for Weld Quality Monitoring using Acoustic Signature

Published:2015 Issue: Volume:50 Page:316-322
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Sumesh A.,Rameshkumar K.,Mohandas K.,Babu R. Shyam

Publisher

Elsevier BV

Subject

General Engineering

Reference12 articles.

1. fast and robust decision support system for in-line quality assessment of resistance seam welds in the steelmaking industry;Julio Molleda;J. Computers in Industry,2012

2. Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks;Mirapeix;J. NDT&E International.,2007

3. Detection of GTA welding quality and disturbance factors with spectral signal of arc light;Li Zhiyong;Journal of Materials Processing Technology,2009

4. Samantaray, Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals;Sukhomay Pal;J. Materials processing technology.,2008

5. Application of artificial neural network for predicting weld quality in laser transmission welding of thermoplastics;Bappa Acherjee;J. Applied Soft Computing.,2011

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