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Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network
Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003179/ https://www.ncbi.nlm.nih.gov/pubmed/33803767 http://dx.doi.org/10.3390/ma14061494 |
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author | Li, Ran Dong, Manshu Gao, Hongming |
author_facet | Li, Ran Dong, Manshu Gao, Hongming |
author_sort | Li, Ran |
collection | PubMed |
description | Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively. |
format | Online Article Text |
id | pubmed-8003179 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80031792021-03-28 Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network Li, Ran Dong, Manshu Gao, Hongming Materials (Basel) Article Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively. MDPI 2021-03-18 /pmc/articles/PMC8003179/ /pubmed/33803767 http://dx.doi.org/10.3390/ma14061494 Text en © 2021 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/). |
spellingShingle | Article Li, Ran Dong, Manshu Gao, Hongming Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network |
title | Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network |
title_full | Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network |
title_fullStr | Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network |
title_full_unstemmed | Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network |
title_short | Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network |
title_sort | prediction of bead geometry with changing welding speed using artificial neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003179/ https://www.ncbi.nlm.nih.gov/pubmed/33803767 http://dx.doi.org/10.3390/ma14061494 |
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