Digitization of Manufacturing Processes: From Sensing to Twining
Zero-defect manufacturing and flexibility in production lines is driven from accurate Digital Twins (DT) which monitor, understand, and predict the behavior of a manufacturing process under different conditions while also adapting to them by deciding the right course of action in time intervals rele...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/10/5/98 |
| _version_ | 1797469748069400576 |
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| author | Panagiotis Stavropoulos |
| author_facet | Panagiotis Stavropoulos |
| author_sort | Panagiotis Stavropoulos |
| collection | DOAJ |
| description | Zero-defect manufacturing and flexibility in production lines is driven from accurate Digital Twins (DT) which monitor, understand, and predict the behavior of a manufacturing process under different conditions while also adapting to them by deciding the right course of action in time intervals relevant to the captured phenomenon. During the exploration of the alternative approaches for the development of process twins, significant efforts should be made for the selection of acquisition devices and signal-processing techniques to extract meaningful information from the studied process. As such, in Industry 4.0 era, machine tools are equipped with embedded sensors that give feedback related to the process efficiency and machine health, while additional sensors are installed to capture process-related phenomena, feeding simulation tools and decision-making algorithms. Although the maturity level of some process mechanisms facilitates the representation of the physical world with the aid of physics-based models, data-driven models are proposed for complex phenomena and non-mature processes. This paper introduces the components of Digital Twin and gives emphasis on the steps that are required to transform obtained data into meaningful information that will be used in a Digital Twin. The introduced steps are identified in a case study from the milling process. |
| first_indexed | 2024-03-09T19:25:32Z |
| format | Article |
| id | doaj.art-0e7c911c5678478c9c187d0d8e17dd9a |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-09T19:25:32Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-0e7c911c5678478c9c187d0d8e17dd9a2023-11-24T02:55:51ZengMDPI AGTechnologies2227-70802022-08-011059810.3390/technologies10050098Digitization of Manufacturing Processes: From Sensing to TwiningPanagiotis Stavropoulos0Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rio Patras, GreeceZero-defect manufacturing and flexibility in production lines is driven from accurate Digital Twins (DT) which monitor, understand, and predict the behavior of a manufacturing process under different conditions while also adapting to them by deciding the right course of action in time intervals relevant to the captured phenomenon. During the exploration of the alternative approaches for the development of process twins, significant efforts should be made for the selection of acquisition devices and signal-processing techniques to extract meaningful information from the studied process. As such, in Industry 4.0 era, machine tools are equipped with embedded sensors that give feedback related to the process efficiency and machine health, while additional sensors are installed to capture process-related phenomena, feeding simulation tools and decision-making algorithms. Although the maturity level of some process mechanisms facilitates the representation of the physical world with the aid of physics-based models, data-driven models are proposed for complex phenomena and non-mature processes. This paper introduces the components of Digital Twin and gives emphasis on the steps that are required to transform obtained data into meaningful information that will be used in a Digital Twin. The introduced steps are identified in a case study from the milling process.https://www.mdpi.com/2227-7080/10/5/98digital twinmanufacturing processdata acquisitionprocess related phenomenasignal processing techniques |
| spellingShingle | Panagiotis Stavropoulos Digitization of Manufacturing Processes: From Sensing to Twining Technologies digital twin manufacturing process data acquisition process related phenomena signal processing techniques |
| title | Digitization of Manufacturing Processes: From Sensing to Twining |
| title_full | Digitization of Manufacturing Processes: From Sensing to Twining |
| title_fullStr | Digitization of Manufacturing Processes: From Sensing to Twining |
| title_full_unstemmed | Digitization of Manufacturing Processes: From Sensing to Twining |
| title_short | Digitization of Manufacturing Processes: From Sensing to Twining |
| title_sort | digitization of manufacturing processes from sensing to twining |
| topic | digital twin manufacturing process data acquisition process related phenomena signal processing techniques |
| url | https://www.mdpi.com/2227-7080/10/5/98 |
| work_keys_str_mv | AT panagiotisstavropoulos digitizationofmanufacturingprocessesfromsensingtotwining |