Kshirsagar, R., Jones, S., Lawrence, J. et al. (1 more author) (2019) Prediction of bead geometry using a two-stage SVM–ANN algorithm for automated tungsten inert gas (TIG) welds. Journal of Manufacturing and Materials Processing, 3 (2). 39. ISSN 2504-4494
Abstract
Prediction of weld bead geometry is critical for any welding process, since several mechanical properties of the weldment depend on this. Researchers have used artificial neural networks (ANNs) to predict the bead geometry based on the input parameters for a welding process; however, the number of hidden layers used in these ANNs are limited to one due to the small amount of data usually available through experiments. This results in a reduction in the accuracy of prediction. Such ANNs are also incapable of capturing sudden changes in the input–output trends; for example, where a wide range of heat inputs results in flat crown (zero crown height), but any further reduction in the current sharply increases the crown height. In this study, it was found that above mentioned issues can be resolved on using a two-stage algorithm consisting of support vector machine (SVM) and an ANN. The two-stage SVM–ANN algorithm significantly improved the accuracy of prediction and could be used as a replacement for the multiple hidden layer ANN, without requiring additional data for training. The improvement in prediction was evident near regions of sudden changes in the input–output correlation and can lead to a better prediction of mechanical properties.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | bead geometry prediction; support vector machines; artificial neural networks; data classification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > Nuclear Advanced Manufacturing Research Centre |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 May 2020 11:33 |
Last Modified: | 04 May 2020 11:33 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/jmmp3020039 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160161 |