Bead Geometry Prediction in Pulsed GMAW Welding: A Comparative Study on the Performance of Artificial Neural Network and Regression Models
Author:
Affiliation:
1. Universidade Federal do Rio Grande do Sul, Brasil
Publisher
FapUNIFESP (SciELO)
Subject
Metals and Alloys,Mechanical Engineering,Mechanics of Materials
Link
http://www.scielo.br/scielo.php?script=sci_pdf&pid=S0104-92242022000100702&tlng=en
Reference19 articles.
1. Optimization of different welding processes using statistical and numerical approaches - a reference guide;Benyounis KY;Advances in Engineering Software,2008
2. Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis;Xiong J;Journal of Intelligent Manufacturing,2014
3. Comparison of multiple regression and back propagation neural network approaches in modelling top bead height of multipass gas metal arc welds;Kim IS;Science and Techonology of Welding and Joining,2003
4. Comparing the regression analysis and artificial neural network in modelling the Submerged Arc Welding (SAW) process;Towsyfyan H;Research Journal of Applied Sciences, Engineering and Technology,2013
5. Training feed forward networks with the Marquardt algorithm;Hagan MT;IEEE Transactions on Neural Networks,1994
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