Combined Artificial Neural Network and Least Squares Method for Exploring Relationships between Welding Conditions and Weld Characteristics-Reference-Cited by-同舟云学术

Combined Artificial Neural Network and Least Squares Method for Exploring Relationships between Welding Conditions and Weld Characteristics

Published:2018 Issue:4 Volume:36 Page:5WL-8WL
ISSN:0288-4771
Container-title:QUARTERLY JOURNAL OF THE JAPAN WELDING SOCIETY
language:en
Short-container-title:QUARTERLY JOURNAL OF THE JAPAN WELDING SOCIETY

Author:

Kitano Houichi¹,Nakamura Terumi¹

Affiliation:

1. Welding and Joining Technology Group, Research Center for Structural Materials, National Institute for Materials Science, Ibaraki, Japan

Publisher

Japan Welding Society

Subject

Metals and Alloys,Surfaces, Coatings and Films,Mechanical Engineering,Mechanics of Materials

Link

https://www.jstage.jst.go.jp/article/qjjws/36/4/36_5WL/_pdf

Reference8 articles.

1. 1) H. Fujii and K. Ichikawa: Estimation of Weld Properties by Bayesian Neural Network Part 1, Journal of the Japan Welding Society, 69-3 (2000), 44-47.

2. 2) H. Fujii and K. Ichikawa: Estimation of Weld Properties by Bayesian Neural Network Part 2, Journal of the Japan Welding Society, 70-3 (2001), 23-27.

3. 3) M. P. Lightfoot, G. J. Bruce, N. A. McPherson and K. Woods: The application of artificial neural networks to weld-induced deformation in ship plate, WELDING JOURNAL-NEW YORK-, 84-2 (2005), 23-30.

4. 4) R. Dhas and S. Kumanan: Weld residual stress prediction using artificial neural network and. Fuzzy logic modeling, Indian Jounarl of Engineering & Materials Sciences, 18 (2011), 351-360.

5. 5) A. Iqbal, S. M Khan and H. S. Mukhtar: ANN assisted prediction of weld bead geometry in gas tungsten arc welding of HSLA steels, Proceedings of the World congress on engineering, 1 (2011), 6-8.

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