臺灣博碩士論文加值系統

現在許多企業因少子化問題面臨著人才短缺、人力成本提高等問題，使許多傳統產業都須慢慢走向自動化，但在工業4.0中許多自動化產線都需透要人進行測試進而提供一個完整的自動化產線，且在測試失敗的時候成本也相應的提高，使產業將慢慢導向工業5.0的技術，透過虛擬設備先規劃以及測試，再導入實體進行驗證，減少撞機風險、人力成本。本研究將建構虛實整合技術，建立虛擬PLC資訊管理層與台達電子工業股份有限公司(Delta)所提供的虛擬PLC進行串接管理資訊，並與台達電子工業股份有限公司所提供的虛擬模型(DIATwin)進行虛擬模型架設，且與機械手臂追蹤取物系統為例，將虛擬模型架構設計與實體模型架構相同，透過虛擬模型中的控制端(PLC)利用Modbus TCP通訊格式傳輸數值進行輸送帶速度調整、手臂觸發以及感測器接收，並且透過撰寫C#以及C++進行串接、通訊以及資料管理等功能，使模擬在溝通PLC時，能降低所耗費的時間，提升虛擬模型與實體模型的準確性，透過這架構本研究將先使用實體設備進行測試，計算出時間以及速度等資訊，因虛擬模型還未導入完整的物理引擎，所以實體設備會與虛擬設備較不相同，如實體設備是透過辨識系統捕獲物料資訊，而虛擬系統則是透過感測器觸發I/O來讀取資訊，但會將一些參數與實體的資訊使用轉移函數的方式進行轉換，再透過虛擬模型測試新的數值轉換成實體設備參數進行測試，達到自動化追片、虛實整合成果，使操作者能先透過虛擬調機方式進行測試、規劃，再導入實體應用減少撞機風險、人力成本。

Many businesses are currently facing issues such as talent shortages and rising labor costs due to the problem of low birth rates, causing many traditional industries to gradually move towards automation. However, in Industry 4.0, many automated production lines require human testing to provide a complete automated line, and costs also increase correspondingly when tests fail. This is leading industries to gradually adopt Industry 5.0 technologies, utilizing virtual equipment for initial planning and testing before implementation in physical systems, thereby reducing the risk of collisions and labor costs. This research will construct an integrated virtual and physical technology, establishing a virtual PLC information management layer connected with the virtual PLC provided by Delta Electronics, Inc. Additionally, it will set up a virtual model using the DIATwin virtual model also offered by Delta Electronics, Inc. Taking the robotic arm tracking and retrieval system as an example, the virtual model architecture is designed to be the same as the physical model architecture. Through the control terminal (PLC) in the virtual model, values are transmitted using the Modbus TCP communication format to adjust the conveyor belt speed, trigger the arm, and receive sensor inputs. Additionally, by writing C# and C++ for integration, communication, and data management functions, the simulation reduces the time spent communicating with the PLC, enhancing the accuracy between the virtual and physical models. Through this structure, this research will initially use physical equipment for testing, and calculating time and speed information. Since the virtual model has not yet integrated a complete physical engine, there will be discrepancies between the physical and virtual equipment. For example, the physical equipment captures material information through a recognition system. The virtual system retrieves information by triggering I/O through sensors, but some parameters and physical data are transformed using transfer functions. These new values are then tested in the virtual model and converted into parameters for physical equipment testing. Achieving automated tracking and integrated virtual-physical results allows operators to test and plan using virtual tuning methods before implementing physical applications, reducing the risk of collisions and labor costs.

摘要 ......................................................................... i
Abstract .................................................................... ii
誌謝 ........................................................................ iv
目錄 ......................................................................... v
表目錄 ..................................................................... vii
圖目錄 .................................................................... viii
符號說明 ................................................................... xii
第一章 緒論 ................................................................... 1
1.1 研究動機與目的 ............................................................ 1
1.2 文獻探討 ................................................................. 2
1.3 論文架構 ................................................................. 3
第二章 虛擬模型通訊測試 ........................................................ 5
2.1 智慧機械虛擬調試系統(CIROS Studio) ........................................ 5
2.1.1 虛擬三菱PLC與CIROS通訊控制虛擬手臂 ....................................... 5
2.1.2 實體FX3U PLC與CIROS通訊後利用實體I/O觸發控制 ............................. 7
2.1.3 實體Delta AS-PLC連線KEP OPC Server後I/O觸發控制.......................... 9
2.1.4 實體Delta ES-PLC連線至KEP OPC Server後利用I/O觸發控制 ................... 12
2.1.5 實體Delta AS-PLC透過Modbus TCP與KEP OPC Server連線 ..................... 14
2.2 智慧智能家庭訓練系統(HOME I/O) ........................................... 16
2.2.1 實體Delta AS-PLC透過Python與Home I/O連線控制燈具 ........................ 16
2.2.2 實體Delta AS-PLC利用OPC Server與Home I/O連線控制燈具 .................... 18
2.3 虛擬機台開發平台(DIATwin)................................................. 20
2.3.1 虛擬DVP-PLC與C#透過Modbus TCP通訊控制DIATwin ........................... 20
2.3.2 實體AS-PLC與C#通訊控制DIATwin .......................................... 22
2.3.3 虛擬AS-PLC與C#通訊控制DIATwin .......................................... 24
2.3.4 C#使用Thread與實體AS-PLC通訊以及操控DIATwin ............................. 26
2.3.5 C#使用Modbus TCP讀取PLC給MySQL測試速度 ................................. 30
2.4 虛擬模型產品之間的差異性比較 .............................................. 32
第三章 多層級資料存取管理 ..................................................... 33
3.1 開發動機 ................................................................ 33
3.2 整體流程規劃 ............................................................. 34
3.3 資料存取管理規劃 ......................................................... 35
3.4 虛擬PLC操作流程 .......................................................... 37
第四章 實體模型與虛擬模型建構 ................................................. 41
4.1 實體自動化追蹤取物系統 .................................................... 41
4.1.1 主要架構 .............................................................. 41
4.1.2 YOLOv7 ................................................................ 42
4.1.3 座標轉換 .............................................................. 43
4.1.4 通訊整合流程 .......................................................... 46
4.2 虛擬自動化追片系統 ....................................................... 48
4.2.1 主要架構 .............................................................. 48
4.2.2 模型建置 .............................................................. 48
4.2.3 虛擬操作流程 .......................................................... 52
第五章 網頁UI介面顯示 ........................................................ 55
5.1 UI功能劃分 .............................................................. 55
5.1.1 第1區 ................................................................. 55
5.2.1 第2區 ................................................................. 57
5.3.1 第3區 ................................................................. 58
5.4.1 第4區 ................................................................. 59
5.5.1 第5區 ................................................................. 61
第六章 實驗結果與結論 ........................................................ 62
6.1 虛擬PLC通訊方式比較 ...................................................... 62
6.2 實體模型與虛擬模型 ....................................................... 63
6.3 結論 .................................................................... 68
6.4 未來展望 ................................................................ 69
參考文獻 .................................................................... 70
Extended Abstract ........................................................... 72

[1]工業4.0大全：從淺到深一篇搞懂它，工研院產業學習網，可獲取網址URL: https://college.itri.org.tw/Home/InfoData/f6e19f2d-f81c-421c-bc36-ea6409ba0a5d/ca7471e3-8220-494b-aa69-e84f7cfb8ed3,(可訪問日期16,07,2024)
[2]The Digital Twin：Theory vs Reality，FormingWorld，可獲取網址URL: https://formingworld.com/digital-twin-theory-autoform/,(可訪問日期16,07,2024)
[3]數位雙生的工作原理是甚麼，IBM，可獲取網址URL: https://www.ibm.com/cn-zh/topics/what-is-a-digital-twin ,(可訪問日期16,07,2024)
[4]產品技術平析：強化智慧機器人性能的關鍵三技術，DOIT經濟部產業技術司，可獲取網址URL: https://www.moea.gov.tw/MNS/doit/industrytech/IndustryTech.aspx?menu_id=13545&it_id=285 ,(可訪問日期16,07,2024)
[5]台達電虛實整合數位升級亮相「2023台北國際自動化工業大展」，經濟日報，可獲取網址URL: https://money.udn.com/money/story/5612/7389480 , (可訪問日期16,07,2024)
[6]Yadav, M., Vardhan, A., Chauhan, A. S., & Saini, S. (2023). A study on creation of Industry 5.0: New innovations using big data through artificial intelligence, internet of Things and next-origination technology policy. 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), 1-12.
[7]Dimirakopoulos, G., Gutt, T., Ehm, H., Hoess, A., Karathanasopoulou, K., Lackner, A., & Schneider, G. (2023). On the way to realize the 5th industrial revolution: Achievements, challenges and research areas. 2023 IEEE Symposium on Network Operations and Management ,1-6.
[8]Di Leo, P., Del Giudice, M., Fratto, A., & Osello, A. (2024). BIM and digital twin for electrical systems: A case study of an industrial building. 2024 IEEE International Conference on Industrial Technology (ICIT), 1-6.
[9]Stankovic, S., Koprivica, M., Bozilovic, B., Petronijevic, P., & Kostic, S. (2022). Automation of industrial control system management within small-size wind tunnel based on Modbus protocol over serial connection. (2022) International Symposium on INFOTEH-JAHORINA (INFOTEH), 1-6.
[10]Zhang, H., Xiong, A., Lai, L., Chen, C., & Liang, J. (2023). AMME-YOLOv7: Improved YOLOv7 based on attention mechanism and multiscale expansion for electric vehicle driver and passenger helmet wearing detection. (2023) IEEE International Conference on Smart Internet of Things (SmartIoT), 223-227.
[11]Chen, M.-H., Huang, W.-H., & Li, T.-H. S. (2023). A YOLOv7-based method for detecting buttons in service robots during autonomous elevator-taking tasks. (2023) International Conference on Advanced Robotics and Intelligent Systems (ARIS), 1-6.
[12]Chouriya, A., Soni, P., Thomas, E. V., Mahore, V., Patidar, P., & Nagar, H. (2023). Detection of cotton plants using the YOLOv7 deep learning model.(2023) International Conference on Futuristic Technologies (INCOFT) ,. 1-4.
[13]Plakhotnikov, D. P., & Kotova, E. E. (2021). Design and analysis of cyber-physical systems.(2021) IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), 589-593.
[14]Meyer, F., Benisch, M. F., Baueregger, F., & Zimmermann, A. (2023). A cross-company architecture for cyber-physical production systems and its industrial use. (2023) International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 1-6.
[15]Leangarun, T., & Koolpiruck, D. (2022). Supply forecast service in cyber physical production systems: A case study of food business. (2022) SICE Annual Conference, 307-312.
[16]STL container特性介紹，Medium，可獲取網址URL: https://wither23265.medium.com/stl-container%E7%89%B9%E6%80%A7-cc28fd8bc246 ,(可訪問日期16,07,2024)
[17]Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.(2023) Conference on Computer Vision and Pattern Recognition (CVPR), 7464-7475.
[18]Wang, L., Wang, Z., Yu, H.-Y., Ha, H.-M., Kim, Y.-K., & Lee, J.-M. (2015). Vision based robot for recognizing and grasping fast moving conveyor products. (2015) International Conference on Control, Automation and Systems ( ICCAS), 1211-1215.
[19]Lin, C.-J., Shaw, J., Tsou, P.-C., & Liu, C.-C. (2016). Vision servo based Delta robot to pick-and-place moving parts.(2016) IEEE International Conference on Industrial Technology (ICIT), 1626-1631.