應用誤差分析與參數擬合提升數位雙生模型逼真度之方法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：10

、訪客IP：3.148.223.53

姓名

許家浩(Jia-Hao,Hsu) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

應用誤差分析與參數擬合提升數位雙生模型逼真度之方法
(A Method for Enhancing Digital Twin Model Fidelity Using Error Analysis and Parameter Fitting)

相關論文

★ 微波化學強化碳化矽表面拋光之研究	★ 智慧製造垂直系統整合之資產管理殼
★ 應用於智慧製造之網宇實體系統訓練資料異常檢知	★ 應用深度學習與物聯網評估CNC加工時間
★ 混合視覺與光達感測的感知融合機器人定位系統	★ 結合遺傳演算法與類神經網路之分散式機械結構最佳化系統之研究
★ 以資料分散式服務發展智慧產品與其系統之研究	★ 精微產品組裝的智能人機協作系統
★ Advancing Human Action Recognition for Precision Assembly Using Vision and Mechanomyography Signals	★ YOLOv7 模型於小物件檢測之改良與應用
★ 應用分治法於刀具壽命預測模型之研究	★ 基於多通道 RSS 的可見光定位系統之設計與其訊號處理方法
★ 自動化工作站排程系統之設計與應用	★ 基於區塊鏈之去中心化製造執行系統
★ 應用於專案排程之混合蟻群演算法

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2025-6-30以後開放)

摘要(中)

本研究改良數位雙生模型建構方法，並提出了一套解決虛實模型逼真度的一致性問題的方法。在此方法中，我們使用參數擬合、定量分析與誤差比較，結合逼真度指標來開發數位雙生技術。此方法能夠給予製造業在數位雙生發展的建議，讓未來在工業製造中面臨數位雙生一致性問題時，能夠參考本研究的建議，進行問題排除。在研究過程中，首先構建了物理實體層，接著建構數位雙生模型，包含虛擬模型、數據模型與知識模型。我們使用商業數位模擬軟體進行虛擬模型的建構，並針對數據模型調整參數結果與知識模型擬合結果進行定量分析、誤差與逼真度指標的比較，以觀察模擬軟體與實體機械手臂在運行加工過程中的工時誤差。最後，透過案例分析與討論，來展示本研究方法的可行性與有效性，並為未來的應用提供實際的參考。

摘要(英)

In this study, we improve the digital twin model construction method and propose a methodology to solve the consistency problem of virtual model simulation. In this approach, we use parameter fitting, quantitative analysis and error comparison, combined with simulation metrics to develop digital twin technology. This methodology can give the manufacturing industry a suggestion for the development of digital twin, so that when facing digital twin consistency problems in industrial manufacturing in the future, they can refer to the suggestions in this study to troubleshoot the problems. In the research process, we first constructed a physical entity layer, and then constructed a digital twin model, including a virtual model, a data model, and a knowledge model. The virtual model was constructed using commercial digital simulation software, and the results of the data model tuning parameters were compared with those of the knowledge model for quantitative analysis, error and simulation metrics, in order to observe the work-time errors during the motion processing between the simulation software and the physical robotic arm. Finally, a case study and discussion are conducted to demonstrate the feasibility and effectiveness of this research methodology and to provide practical references for future applications.

關鍵字(中)

★ 數位雙生
★ 機械手臂
★ 參數擬合
★ 逼真度
★ 模擬軟體
★ 誤差
★ 工業4.0

關鍵字(英)

論文目次

AI工具應用聲明 ........................................................................................................................ i
摘要 ............................................................................................................................................ ii
Abstract ...................................................................................................................................... iii
致謝 ........................................................................................................................................... iv
目錄 ............................................................................................................................................ v
圖目錄 ...................................................................................................................................... vii
表目錄 ..................................................................................................................................... viii
符號表 ........................................................................................................................................ x
第一章、緒論 ............................................................................................................................ 1
1-1研究背景 ...................................................................................................................... 1
1-2研究動機 ...................................................................................................................... 2
1-3主要貢獻 ...................................................................................................................... 2
1-4論文架構 ...................................................................................................................... 3
第二章、相關技術介紹 ............................................................................................................ 4
2-1數位雙生起源與概念 .................................................................................................. 4
2-2製造業數位雙生框架 .................................................................................................. 5
2-3數位雙生應用領域 ...................................................................................................... 6
2-3-1產品設計 ........................................................................................................... 6
2-3-2產品製造 ........................................................................................................... 7
2-3-3產品服務 ........................................................................................................... 8
2-4 數位雙生建模 .............................................................................................................. 9
2-5 目標與差距 ................................................................................................................ 12
第三章、研究方法 .................................................................................................................. 13
3-1 研究方法 .................................................................................................................... 13
3-2 DT模型建構層 .......................................................................................................... 14
3-2-1 虛擬模型 ........................................................................................................ 14
3-2-2 數據模型 ........................................................................................................ 14
3-2-3 知識模型 ........................................................................................................ 15
3-3 誤差評估指標 ............................................................................................................ 16
第四章、實驗設計 .................................................................................................................. 18
4-1 實驗設備 .................................................................................................................... 18
4-2 實驗設計與結果 ........................................................................................................ 20
4-2-1 物理實體層架設 ............................................................................................ 20
4-2-2 DT模型建構 .................................................................................................. 21
第五章、案例分析 .................................................................................................................. 40
5-1 局部最佳參數與工時收集 ........................................................................................ 41
5-2 全局最佳參數與工時收集 ........................................................................................ 42
5-3 知識模型測試 ............................................................................................................ 42
5-4 參數與延遲關係 ........................................................................................................ 44
5-5 案例研究結果 ............................................................................................................ 45
第六章、結論與未來展望 ...................................................................................................... 46
6-1具體貢獻 .................................................................................................................... 46
6-2 限制與範圍 ................................................................................................................ 46
6-3 未來展望 .................................................................................................................... 47
參考文獻 .................................................................................................................................. 48

參考文獻

[1] NVIDIA. (2021). NVIDIA Omniverse: The platform for developing OpenUSD
applications for industrial digitalization and generative physical AI. Accessed:2024 年 8
月22日, [Online.], Available: https://www.nvidia.com/zh-tw/omniverse/。
[2] D. Cearley, B. Burke, S. Searle and M. Walker, "Top 10 strategic technology trends for
2017: A gartner trend insight report", Gartner, vol. 23, Jun. 2017, [online] Available:
https://www.gartner.com/doc/3645332.
[3] Q. Liu, H. Zhang, J. Leng, and X. Chen, “Digital twin-driven rapid individualised
designing of automated flow-shop manufacturing system,” International Journal of
Production Research, vol. 57, no. 12, pp. 3903-3919, 2019.
[4] M. Zhang, F. Tao, and A. Y. C. Nee, “Digital twin enhanced dynamic job-shop scheduling,”
Journal of Manufacturing Systems, vol. 58, pp. 146-156, 2021.
[5] H. Zhang, Q. Liu, X. Chen, D. Zhang, and J. Leng, “A digital twin-based approach for
designing and multi-objective optimization of hollow glass production line,” IEEE Access,
vol. 5, pp. 26901-26911, 2017.
[6] S. Robinson, “Conceptual modelling for simulation Part I: definition and requirements,”
Journal of the Operational Research Society, vol. 59, no. 3, pp. 278-290, 2008.
[7] J. Brynjarsdottir and A. O’Hagan, “Learning about physical parameters: The importance
of model discrepancy,” Inverse Problems, vol. 30, no. 11, p. 114007, 2014.
[8] Grieves, M. Product Lifecycle Management: Driving the Next Generation of Lean
Thinking; McGraw Hill Education: New York, NY, USA, 2005.
[9] E. Negri, L. Fumagalli, and M. Macchi, “A review of the roles of digital twin in CPS-based
production systems,” Procedia Manufacturing, vol. 11, pp. 939-948, 2017.
[10] M. B. Chhetri, S. Krishnaswamy, and S. W. Loke, “Smart virtual counterparts for learning
communities,” in Web Information Systems–WISE 2004 Workshops: WISE 2004
International Workshops, Brisbane, Australia, Nov. 22-24, 2004, pp. 125-134. Springer.
[11] ISO 2021. ISO 23247: Automation Systems and Integration - Digital Twin Framework for
Manufacturing. International Organization for Standardization, Geneva, Switzerland
(2021).
[12] ISO 2021. ISO 23247-1: Automation Systems and Integration - Digital Twin Framework
for Manufacturing – Part 1: Overview and general principles. International Organization
for Standardization, Geneva, Switzerland (2021).
[13] ISO 2021. ISO 23247-2: Automation Systems and Integration - Digital Twin Framework
for Manufacturing – Part 2: Reference architecture. International Organization for
Standardization, Geneva, Switzerland (2021).
[14] ISO 2021. ISO 23247-3: Automation Systems and Integration - Digital Twin Framework
for Manufacturing – Part 3: Digital representation of manufacturing elements.
International Organization for Standardization, Geneva, Switzerland (2021).
[15] ISO 2021. ISO 23247-4: Automation Systems and Integration - Digital Twin Framework
for Manufacturing – Part 4: Information exchange. International Organization for
Standardization, Geneva, Switzerland (2021).
[16] B. He and K.-J. Bai, “Digital twin-based sustainable intelligent manufacturing: A review,”
Advances in Manufacturing, vol. 9, pp. 1-21, 2021.
[17] F. Tao, F. Sui, A. Liu, Q. Qi, M. Zhang, B. Song, Z. Guo, S. C.-Y. Lu, and A. Y. C. Nee,
“Digital twin-driven product design framework,” International Journal of Production
Research, vol. 57, no. 12, pp. 3935-3953, 2019.
[18] L. Hu, N.-T. Nguyen, W. Tao, M. C. Leu, X. F. Liu, M. R. Shahriar, and S. M. N. Al Sunny,
“Modeling of cloud-based digital twins for smart manufacturing with MT connect,”
Procedia Manufacturing, vol. 26, pp. 1193-1203, 2018.
[19] F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui, “Digital twin-driven product
design, manufacturing and service with big data,” The International Journal of Advanced
Manufacturing Technology, vol. 94, pp. 3563-3576, 2018.
[20] S.Meng, S. Tang, Y. Zhu, and C. Chen, “Digital twin-driven control method for robotic
automatic assembly system,” in Proc. IOP Conf. Ser., Mater. Sci. Eng., vol. 493, Mar. 2019,
Art. no. 012128.
[21] D. Botkina, M. Hedlind, B. Olsson, J. Henser, and T. Lundholm, “Digital twin of a cutting
tool,” Procedia CIRP, vol. 72, pp. 215–218, 2018.
[22] M. Schluse, M. Priggemeyer, L. Atorf, and J. Rossmann, “Experimentable digital twins—
Streamlining simulation-based systems engineering for Industry 4.0,” IEEE Trans. Ind.
Informat., vol. 14, no. 4, pp. 1722–1731, 2018.
[23] P. D. U. Coronado, R. Lynn, W. Louhichi, M. Parto, E. Wescoat, and T. Kurfess, “Part data
integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a
manufacturing execution system,” J. Manuf. Syst., vol. 48, pp. 25–33, 2018.
[24] C.-T. Lin and H.-J. Lu, “An intelligent product-driven manufacturing system using data
distribution service” IEEE Access, vol. 12, pp. 16447- 16461, 2024.
[25] Y. Liu, L. Zhang, Y. Yang, L. Zhou, L. Ren, F. Wang, R. Liu, Z. Pang, and M. J. Deen, “A
novel cloud-based framework for the elderly healthcare services using digital twin,” IEEE
Access, vol. 7, pp. 49088-49101, 2019.
[26] Y. Wei, T. Hu, T. Zhou, Y. Ye, and W. Luo, "Consistency retention method for CNC
machine tool digital twin model," Journal of Manufacturing Systems, vol. 58, pp. 313-322,
2021.
[27] C. Zhang, J. Li, G. Zhou, Q. Huang, M. Zhang, Y. Zhi, and Z. Wei, "A multi-level
modelling and fidelity evaluation method of digital twins for creating smart production
equipment in Industry 4.0," International Journal of Production Research, pp. 1-19, 2023.
[28] Mitsubishi Motors Corporation, Japan, MITSUBISHI CR750/CR751 Controller
Instruction Manual Detailed Explanations of Functions and Operations。Accessed: May.
15, 2024. [Online]. Available: https://www.allied-automation.com/wpcontent/uploads/
2015/02/MITSUBISHI_CR750CR751-Controller-Instruction-Manual-Detailed
Explanations-of-Functions-and-Operations.pdf.
[29] X. Zhou, X. Xu, W. Liang, Z. Zeng, S. Shimizu, L. T. Yang, and Q. Jin, "Intelligent small
object detection for digital twin in smart manufacturing with industrial cyber-physical
systems," IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1377-1386, 2021.

指導教授

林錦德

審核日期

2024-11-7

推文