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Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check
Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945458/ https://www.ncbi.nlm.nih.gov/pubmed/36810419 http://dx.doi.org/10.1038/s41598-023-29906-0 |
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author | Abd Halim, Suhaila Manurung, Yupiter H. P. Raziq, Muhamad Aiman Low, Cheng Yee Rohmad, Muhammad Saufy Dizon, John R. C. Kachinskyi, Vladimir S. |
author_facet | Abd Halim, Suhaila Manurung, Yupiter H. P. Raziq, Muhamad Aiman Low, Cheng Yee Rohmad, Muhammad Saufy Dizon, John R. C. Kachinskyi, Vladimir S. |
author_sort | Abd Halim, Suhaila |
collection | PubMed |
description | Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain. |
format | Online Article Text |
id | pubmed-9945458 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99454582023-02-23 Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check Abd Halim, Suhaila Manurung, Yupiter H. P. Raziq, Muhamad Aiman Low, Cheng Yee Rohmad, Muhammad Saufy Dizon, John R. C. Kachinskyi, Vladimir S. Sci Rep Article Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain. Nature Publishing Group UK 2023-02-21 /pmc/articles/PMC9945458/ /pubmed/36810419 http://dx.doi.org/10.1038/s41598-023-29906-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Abd Halim, Suhaila Manurung, Yupiter H. P. Raziq, Muhamad Aiman Low, Cheng Yee Rohmad, Muhammad Saufy Dizon, John R. C. Kachinskyi, Vladimir S. Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check |
title | Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check |
title_full | Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check |
title_fullStr | Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check |
title_full_unstemmed | Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check |
title_short | Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check |
title_sort | quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and gui-based application tool q-check |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945458/ https://www.ncbi.nlm.nih.gov/pubmed/36810419 http://dx.doi.org/10.1038/s41598-023-29906-0 |
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