Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines
With the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/12/1147 |
| _version_ | 1797456658602917888 |
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| author | Donggun Lee Chong-Keun Kim Jinho Yang Kang-Yeon Cho Jonghwan Choi Sang-Do Noh Seunghoon Nam |
| author_facet | Donggun Lee Chong-Keun Kim Jinho Yang Kang-Yeon Cho Jonghwan Choi Sang-Do Noh Seunghoon Nam |
| author_sort | Donggun Lee |
| collection | DOAJ |
| description | With the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the operation phase. Therefore, systems that can pre-validate and allow accurate and reliable analysis in the manufacturing design phase, as well as apply and optimize variations in production lines in real time, are required. Previously, digital twin (DT) has been studied a lot in product design and facility prognostics and management fields. Research on the system framework leading to DT utilization and optimization and analysis through DT in complex manufacturing systems with continuous processes such as production lines is insufficient. In this study, a system based on a DT and simulation results is developed; this system can reflect, analyze, and optimize dynamic changes in the design of processes and production lines in real time. First, the framework and application of the proposed system are designed. Subsequently, optimization methodologies based on heuristics and reinforcement learning (RL) are developed. Finally, the effectiveness and applicability of the proposed system are verified by implementing an actual DT application at a real manufacturing site. |
| first_indexed | 2024-03-09T16:10:00Z |
| format | Article |
| id | doaj.art-6bce3c6c572046b5b8e82f6143173486 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T16:10:00Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-6bce3c6c572046b5b8e82f61431734862023-11-24T16:16:25ZengMDPI AGMachines2075-17022022-12-011012114710.3390/machines10121147Digital Twin-Based Analysis and Optimization for Design and Planning of Production LinesDonggun Lee0Chong-Keun Kim1Jinho Yang2Kang-Yeon Cho3Jonghwan Choi4Sang-Do Noh5Seunghoon Nam6Department of Industrial Engineering, Sungkyunkwan University, Suwon-si 16419, Republic of KoreaGlobal Technology Research, Samsung Electronics, Suwon-si 16677, Republic of KoreaDepartment of Industrial Engineering, Sungkyunkwan University, Suwon-si 16419, Republic of KoreaDepartment of Industrial Engineering, Sungkyunkwan University, Suwon-si 16419, Republic of KoreaDepartment of Industrial Engineering, Sungkyunkwan University, Suwon-si 16419, Republic of KoreaDepartment of Industrial Engineering, Sungkyunkwan University, Suwon-si 16419, Republic of KoreaGlobal Technology Research, Samsung Electronics, Suwon-si 16677, Republic of KoreaWith the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the operation phase. Therefore, systems that can pre-validate and allow accurate and reliable analysis in the manufacturing design phase, as well as apply and optimize variations in production lines in real time, are required. Previously, digital twin (DT) has been studied a lot in product design and facility prognostics and management fields. Research on the system framework leading to DT utilization and optimization and analysis through DT in complex manufacturing systems with continuous processes such as production lines is insufficient. In this study, a system based on a DT and simulation results is developed; this system can reflect, analyze, and optimize dynamic changes in the design of processes and production lines in real time. First, the framework and application of the proposed system are designed. Subsequently, optimization methodologies based on heuristics and reinforcement learning (RL) are developed. Finally, the effectiveness and applicability of the proposed system are verified by implementing an actual DT application at a real manufacturing site.https://www.mdpi.com/2075-1702/10/12/1147digital twindigital twin applicationdesign analysis and optimizationreinforcement learning |
| spellingShingle | Donggun Lee Chong-Keun Kim Jinho Yang Kang-Yeon Cho Jonghwan Choi Sang-Do Noh Seunghoon Nam Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines Machines digital twin digital twin application design analysis and optimization reinforcement learning |
| title | Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines |
| title_full | Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines |
| title_fullStr | Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines |
| title_full_unstemmed | Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines |
| title_short | Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines |
| title_sort | digital twin based analysis and optimization for design and planning of production lines |
| topic | digital twin digital twin application design analysis and optimization reinforcement learning |
| url | https://www.mdpi.com/2075-1702/10/12/1147 |
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