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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/1/29 |
| _version_ | 1797497714177474560 |
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| author | Manas Bazarbaev Tserenpurev Chuluunsaikhan Hyoseok Oh Ga-Ae Ryu Aziz Nasridinov Kwan-Hee Yoo |
| author_facet | Manas Bazarbaev Tserenpurev Chuluunsaikhan Hyoseok Oh Ga-Ae Ryu Aziz Nasridinov Kwan-Hee Yoo |
| author_sort | Manas Bazarbaev |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T03:23:11Z |
| format | Article |
| id | doaj.art-7c3d2e98006d4442901ff17527205d81 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:23:11Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7c3d2e98006d4442901ff17527205d812023-11-23T12:15:47ZengMDPI AGSensors1424-82202021-12-012212910.3390/s22010029Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial NetworkManas Bazarbaev0Tserenpurev Chuluunsaikhan1Hyoseok Oh2Ga-Ae Ryu3Aziz Nasridinov4Kwan-Hee Yoo5Elektrolitnyy Proyezd, 115230 Moscow, RussiaDepartment of Computer Science, Chungbuk National University, Cheongju 28644, KoreaDepartment of Big Data, Chungbuk National University, Cheongju 28644, KoreaDepartment of Computer Science, Chungbuk National University, Cheongju 28644, KoreaDepartment of Computer Science, Chungbuk National University, Cheongju 28644, KoreaDepartment of Computer Science, Chungbuk National University, Cheongju 28644, KoreaProduct 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.https://www.mdpi.com/1424-8220/22/1/29continuous casting machinedeep learninginduction furnacetime-series working patternsauxiliary classifier generative adversarial network |
| spellingShingle | Manas Bazarbaev Tserenpurev Chuluunsaikhan Hyoseok Oh Ga-Ae Ryu Aziz Nasridinov Kwan-Hee Yoo Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network Sensors continuous casting machine deep learning induction furnace time-series working patterns 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 | continuous casting machine deep learning induction furnace time-series working patterns auxiliary classifier generative adversarial network |
| url | https://www.mdpi.com/1424-8220/22/1/29 |
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