NDLI: The prediction model of CVN for welded joint based on neural network and genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Lige Tong Yunfei Xie Shaowu Yin Li Wang Hongsheng Ding Jiangfeng Yu
Copyright Year	2011
Description	Author affiliation: Chinese People's Liberation Army 96819 Power Specialist Group, Beijing, China (Jiangfeng Yu) \|\| School of Mathematics and Physics, University of Science & Technology Beijing, China (Hongsheng Ding) \|\| School of Mechanical Engineering,University of Science and Technology Beijing, China (Lige Tong; Yunfei Xie; Shaowu Yin; Li Wang)
Abstract	Based on the welding process parameters of high strength pipeline steel, the artificial neural network (ANN) model has been develped to predict Charpy_V notch (CVN) impact toughness of the welded joint. The model with back propagation (BP) algorithm is built in batch mode and optimized using momentum and adaptive learning rate. Futher more, it is also optimized using genetic algorithm (GA). Five process parameters, namely the welding layer, wall thickness, the welding processes, the preheat temperature, the mean energy input, are used as input variables and the CVN of the welded joints is considered as the output variable. The training and testing of the ANN model have been done using 119 datasets which were obtained from practical welding. The number of the testing samples with error less than 20% is about 74% in total testing data. It is found that the CVN of pipeline welded joints can be effectively predicted using the model and the GA outperforms the commonly BP algorithm used as an neural network training technique. Based on this model, the influence of the preheat temperature and the mean energy input on the CVN is also analysed. The results show that the preheat temperature and the mean energy input have little effects on the automatic welding, but large on the semi-automatic welding and the manual welding for root welding. So a reasonable choice of the preheat temperature and the mean energy input is necessary.
Starting Page	865
Ending Page	868
File Size	352260
Page Count	4
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022204
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Welding Artificial neural networks Predictive models prediction genetic algorithm (GA) Joints back propagation (BP) neural network Charpy_V notch (CVN) impact toughness parameter Genetic algorithms Convergence
Content Type	Text
Resource Type	Article

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