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Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product...

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Autores principales: Bazarbaev, Manas, Chuluunsaikhan, Tserenpurev, Oh, Hyoseok, Ryu, Ga-Ae, Nasridinov, Aziz, Yoo, Kwan-Hee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747414/
https://www.ncbi.nlm.nih.gov/pubmed/35009572
http://dx.doi.org/10.3390/s22010029
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author Bazarbaev, Manas
Chuluunsaikhan, Tserenpurev
Oh, Hyoseok
Ryu, Ga-Ae
Nasridinov, Aziz
Yoo, Kwan-Hee
author_facet Bazarbaev, Manas
Chuluunsaikhan, Tserenpurev
Oh, Hyoseok
Ryu, Ga-Ae
Nasridinov, Aziz
Yoo, Kwan-Hee
author_sort Bazarbaev, Manas
collection PubMed
description Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data.
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spelling pubmed-87474142022-01-11 Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network Bazarbaev, Manas Chuluunsaikhan, Tserenpurev Oh, Hyoseok Ryu, Ga-Ae Nasridinov, Aziz Yoo, Kwan-Hee Sensors (Basel) Article Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data. MDPI 2021-12-22 /pmc/articles/PMC8747414/ /pubmed/35009572 http://dx.doi.org/10.3390/s22010029 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bazarbaev, Manas
Chuluunsaikhan, Tserenpurev
Oh, Hyoseok
Ryu, Ga-Ae
Nasridinov, Aziz
Yoo, Kwan-Hee
Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network
title Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network
title_full Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network
title_fullStr Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network
title_full_unstemmed Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network
title_short Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network
title_sort generation of time-series working patterns for manufacturing high-quality products through auxiliary classifier generative adversarial network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747414/
https://www.ncbi.nlm.nih.gov/pubmed/35009572
http://dx.doi.org/10.3390/s22010029
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