Open Access
Issue |
MATEC Web Conf.
Volume 269, 2019
IIW 2018 - International Conference on Advanced Welding and Smart Fabrication Technologies
|
|
---|---|---|
Article Number | 04004 | |
Number of page(s) | 6 | |
Section | Welding Design, Automation, and Simulation | |
DOI | https://doi.org/10.1051/matecconf/201926904004 | |
Published online | 22 February 2019 |
- YAMANE, et. al., “Estimation of welding voltage using neural network in GMA Welding”. P. 27s-31s. (2009) [Google Scholar]
- Yoshiro, et. al. “Design of neural network based FRIT PID controller and its application”. 11th IFAC international workshop on adaptation and learning in control and signal processing. (2013) [Google Scholar]
- Weman, K,. “MIG welding guide”. Boca Raton. CRC Press. [Google Scholar]
- Scotti, et. al., “A scientific application oriented classification for metal transfer modes in GMA Welding”. Journal of materials processing technology. 6, 1406-1413. (2012) [CrossRef] [Google Scholar]
- Naso D & Turchiano B., “A fuzzy logic based optical sensor for online weld defect detection”. IEEE transaction on industrial informatics, 1. 259-273. (2005) [CrossRef] [Google Scholar]
- Wand, J.F., “Feature extraction in welding penetration monitoring with arc sound signals”. Journal of engineering manufacture, 225. 1683-1691. (2011) [CrossRef] [Google Scholar]
- G.-Q. Zeng et al,. “Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control system”. Swarm and evolutionary computation. (2018) [Google Scholar]
- Siriparapu et. al,. Development of low-cost plotter for educational; purposes using Arduino”. IOP Conf. Series. Material science and engineering. (2017) [Google Scholar]
- M. ahmadzadeh, et. al., “Prediction of residual stresses in gas arc welding by back-propagation neural network”. NDT&E International 52, 136-143. (2012) [CrossRef] [Google Scholar]
- C.S WI et. al,. “A neural network for weld penetration control in gas tungsten arc welding”. ACTA Metal. Sin. (Engl. Lett). Vol. 19. No. 1 pp27-33. (2006) [CrossRef] [Google Scholar]
- H. Taghavifar & A. Mardani,. “Application of artificial neural network for prediction of traction performance parameters”. Journal of the Saudi society of agriculture sciences, 13, 35-43. (2014) [CrossRef] [Google Scholar]
- Luiz, I et. al,. “Low cost surface Electromyography signal amplifier based on Arduino microcontroller”. International journal of electrical. Robotic, electronic, and communication, 8. 310-314. (2014) [Google Scholar]
- D.A Patel & K.N. Jha,. “Neural network model for the prediction of safe work behavior in construction projects”. J. Constr. Eng. Manage. 141.04014966. (2015) [Google Scholar]
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