臺灣博碩士論文加值系統

摘要 i
Abstract ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 ix
一、緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究架構 4
1.5 研究流程 5
二、文獻探討 6
2.1 金屬成形製程 6
2.1.1 開發預測金屬成形變形量之方法 6
2.1.2 感測器收集數據與運用數據驅動 7
2.1.3 開發即時監控之演算法彙總至應用系統 8
2.2 物聯網類型 9
2.2.1 七層式模型架構 9
2.2.2 四層式模型架構 11
2.2.3 三層式美國國家技術標準局雲端運算架構 11
2.2.4 三層式歐洲電信標準協會架構 12
2.3 工業物聯網應用 14
2.3.1 數據驅動 14
2.3.2 分析預測 15
2.3.3 應用決策 16
2.4 工業物聯網嵌入預防保養作法 17
2.4.1 設備故障預測與健康管理 17
2.4.2 開發預測監控系統 18
2.4.3 預防性保養系統整合工業物聯網 19
2.5 預防保養之相關演算法 21
2.5.1 機器學習與深度學習 21
2.5.2 監督式學習之相關演算法比較 22
2.6 工業物聯網整合應用系統 28
2.6.1 應用系統的功能 28
2.6.2 工業物聯網整合相關應用系統 29
三、個案研究設計與方法 31
3.1工業物聯網架構的智慧扣件工廠 31
3.1.1 扣件製造流程 31
3.1.2 工業物聯網嵌入式扣件成形製程之架構 31
3.2採用工業物聯網之實施過程 34
3.2.1 安裝感知組件 34
3.2.2 評估感測器安裝方案 35
3.2.3 安裝感測器的方式 37
3.2.4 數據可視化 41
3.2.5 虛實代理人 42
3.2.6 網路層技術 43
3.2.7 應用層整合 44
四、分析結果與討論 45
4.1 工業物聯網嵌入扣件成形製程的步驟 45
4.2 感測器部署、數據預處理和特徵工程 46
4.3 分類模型選擇與機台狀態管理 52
4.4 管理系統整合 58
五、結論與建議 67
5.1 結論 67
5.1.1 研究成果與貢獻 67
5.1.2 研究創新性與優點 69
5.1.3 研究限制 70
5.2 後續研究建議 71
5.2.1 品質追溯與異常發現的根源 71
5.2.2 鑑往知來與掌握趨勢 72
參考文獻 74

一、英文部分
1.Abdillah, T., Haris, I. and Lahinta, A., 2017. Optimizing libraries' content findability using Simple Object Access Protocol (SOAP) with multi-tier architecture. IOP Conference Series: Materials Science and Engineering, 180(1), 12–61.
2.Aberle, L., 2015. A comprehensive guide to enterprise IoT project success. New York: IoT Agenda.
3.Al-Hashimi, H.T. and Basuhail, A.A., 2021. A proposed data partitioning approach on heterogeneous HPC platforms: Data locality perspective. IEEE Access, 9, 81432–81442.
4.Almeida, C., Huisingh, D., Liu, Y., Ren, S., Sakao, T. and Zhang, Y., 2019. A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. Journal of Cleaner Production, 210, 1343–1365.
5.Alpay, K. and Ayvaz, S., 2021. Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 4598–4598.
6.Alvares, A.J. and Silva, P.J., 2020. Investigation of tool wear in single point incremental sheet forming. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(1-2), 170–188.
7.Ardakani, H.D., Jin, C., Lee, J. and Liu, Z., 2017. Introduction to data-driven methodologies for prognostics and health management. Probabilistic Prognostics and Health Management of Energy Systems, Springer International Publishing, 9–32.
8.Ashton, K., 2009. That 'internet of things' thing in the real world, things matter more than ideas. RFID Journal, 1.
9.Avila, M., Kratz, F. and Vrignat, P., 2022. Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, Part A, 218, 108140.
10.Avvaru, V.S., Bruno, G., Chiabert, P. and Traini, E., 2020. Integration of PLM, MES and ERP systems to optimize the engineering, production and business. IFIP Advances in Information and Communication Technology, 594, 70–82.
11.Azamfar, M., Feng, J., Jiang, B., Lee, J., Ni, J. and Singh, J., 2020. Intelligent maintenance systems and predictive manufacturing. Journal of Manufacturing Science and Engineering, 142(11), 110805–110827.
12.Babu, M.C.L. and Takalkar, A.S., 2019. A review on effect of thinning, wrinkling and spring-back on deep drawing process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(4), 1011–1036.
13.Bagchi, J., Basak, S., Ganesh, Y.S., Mehto, A., Mohanty, S., Panda, S.K., Prasad, K.S. and Sidpara, A.M., 2020. Parameter optimization and texture evolution in single point incremental sheet forming process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(1-2), 126–139.
14.Baudry, D., Bettayeb, B., Duval, F., Havard, V. and Sahnoun, M., 2021. Data architecture and model design for Industry 4.0 components integration in cyber-physical production systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235 (14), 2338-2349.
15.Beese, A.M., De, A., DebRoy, T., Elmer, J.W., Milewski, J.O., Mukherjee, T., Wei, H.L., Wilson-Heid, A., Zhang, W. and Zuback, J.S., 2018. Additive manufacturing of metallic components–Process, structure and properties. Progress in Materials Science, 92, 112–224.
16.Behera, S., Choubey A., Kanani, C.S., Misra, R., Patel, Y.S. and Sillitti, A., 2019. Ensemble trees learning based improved predictive maintenance using IIoT for turbofan engines. SAC'19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 842–850.
17.Berhili, K., Berrehili, Y. and Koulali, M.A., 2020. IIoT-Based prognostic health management using a markov decision process approach. Springer International Publishing, 146–157.
18.Boad, C., 2017. Fully automated off-line cartridge filling station. SAE Technical Paper 2017-01-2100, 1–6.
19.Boyes, H., Cunningham, J., Hallaq, B. and Watson, T., 2018. The industrial internet of things (IIoT): An analysis framework. ScienceDirect- Computers in Industry (Elsevier), 101, 1–12.
20.Brosius, A., Ghiotti, A., Groche, P., Kinsey, B. L., Liewald, M., Madej, L., Min, J., Volk, W. and Yanagimoto, J., 2019. Models and modelling for process limits in metal forming. CIRP Annals, 68(2), 775–798.
21.Casbas-Gimenez, J., Gil-Hernandez, D., Muñoz-Navascues, O., Murillo, N., Perez-Alfaro, I. and Sanchez-Catalan J.C., 2020. Low-cost piezoelectric sensors for time domain load monitoring of metallic structures during operational and maintenance processes. Sensors, 20(5), 1471.
22.Chan, S.T., 2021. Digital transformation of family small-to-medium-sized enterprises. Succession and Innovation in Asia's Small-and-Medium-Sized Enterprises, 289–305.
23.Chang, S.Y., Chen, T.L., Li, D.C. and Liu, C.W., 2011. Steps for the advanced product quality planning approach to improve product quality: a case of fastener manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(9), 1621–1635.
24.Chatterjee, A.G., Hadric, B., Khurram, R., Kumar, A., Samtaney, R. and Verma, M.K., 2018. Scaling of a fast Fourier transform and a pseudo-spectral fluid solver up to 196608 cores. Journal of Parallel and Distributed Computing, 113, 77–91.
25.Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M. and Yin, B., 2018a. Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access, 6, 6505–6519.
26.Chen, C.F., Cheng, F.T., Kao, C.A. and Tsai, W.H., 2015. Tutorial on applying the VM technology for TFT-LCD manufacturing. IEEE Transactions on Semiconductor Manufacturing, 28(1), 55–69.
27.Chen, J.G., Chen, S., Choy, C.M. and Qin, Y., 2018b. A forging method for reducing process steps in the forming of automotive fasteners. International Journal of Mechanical Sciences, 137, 1–14.
28.Chen, K., Chen, W., Cheng, J. and Wang, Q., 2020. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 112, 103087.
29.Chen, S.L., Li, I.C. and Yeh, C.S., 2021. Implementation of MQTT protocol based network architecture for smart factory. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(13), 2132–2142.
30.Cheng, F.T., 2022. Industry 4.1: Intelligent manufacturing with zero defects. Piscataway, NJ: Wiley-IEEE Press.
31.Cheng, F.T., Hung, M.H., Lin, Y.C., Shieh, Z.Y., Tieng, H., Wei, C.F. and Yang, H.C., 2016. Industry 4.1 for wheel machining automation. IEEE Robotics and Automation Letters, 1(1), 332–339.
32.Cheng, H., Yang, S. and Zheng, Y., 2019. An application framework of digital twin and its case study. Journal of Ambient Intelligence and Humanized Computing, 10, 1141–1153.
33.Choi, J.H., Jang, H.S., Jung, K., Kwon, D., Lee, J., Lee, J.Y., Shin, I. and Youn, B.D., 2018. A framework for prognostics and health management applications toward smart manufacturing systems. International Journal of Precision Engineering and Manufacturing-Green Technology, 5, 535–554.
34.Chorbev, I., Garcia, N., Goleva, R., Kulakov, A., Lameski, P., Pombo, N., Trajkovik, V. and Zdravevski, E., 2017. Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering. IEEE Access, 5, 5262–5280.
35.Clemons, J., 2022. The advantages of the IIoT: industrial Internet of Things adds benefits to automation, human-machine interface, MES, ERP, enterprise manufacturing intelligence, and analytics. Control Engineering, 68(4), 24.
36.Deen, M.J., Liu, Y., Lü, J., Ren, L. and Wang, X., 2021. Cloud–Edge-Based lightweight temporal convolutional networks for remaining useful life prediction in IIoT. IEEE Internet of Things Journal, 8(16), 12578–12587.
37.Dey, A. and Yodo, N., 2021. A dropout-based neural network framework for tool wear prediction under uncertainty. IIE Annual Conference. Proceedings, 902–907.
38.Dillon, T., Li, M., Liu, Y., Rahayu, W. and Yu, W., 2022. Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring. IEEE Transactions on Industrial Informatics, 18(2), 1345–1354.
39.Dourado, A., Viana, F. and Yucesan, Y.A., 2021. A survey of modeling for prognosis and health management of industrial equipment. Advanced Engineering Informatics, 50, 101404.
40.Du, X., 2019. Fault detection using bispectral features and one-class classifiers. Journal of Process Control, 83, 1–10.
41.Ecker, M. and Hellfeier, M., 2019. Forecast model for optimization of the massive forming machine OEE. Lecture Notes in Mechanical Engineering, 147–156.
42.Epifancev, K. and Mishura, T., 2020. Problems and advantages of SCADA systems when performing measurements at hazardous production technologies. Journal of Physics: Conference Series, 1515(3), 032071.
43.ETSI, V., 2011. Machine-to-machine communications (M2M): Functional architecture. International Telecommunication Union, Geneva, Switzerland, Technical report TS, 102, 690.
44.European Commission, 2008. Internet of things in 2020 road map for the future. Working Group RFID of the ETP EPOSS, Technology Report, 1–27.
45.Friedman, J., Hastie, T. and Tibshirani, R., 2008. The Elements of Statistical Learning 2nd ed. Springer, New York USA.
46.Friess, P. and Guillemin, P., 2009. Internet of things strategic research roadmap. The Cluster of European Research Projects, Technology Report, 1–50.
47.Gangoiti, U., Iriondo, N. and Priego, R., 2017. Agent-based middleware architecture for reconfigurable manufacturing systems. The International Journal of Advanced Manufacturing Technology, 92, 1579–1590.
48.Gashi, M., Huemer, C., Jekic, N., Kappel, G., Kittl, C., Lindstaedt, S., Mangler, J., Pauker, F., Pollak, C., Rinderle-Ma, S., Schreck, T., Schulte, S., Streit, M., Suschnigg, J., Thalmann, S., Vukovic, M. and Weichhart, G., 2018. Data analytics for industrial process improvement a vision paper. IEEE 20th Conference on Business Informatics (CBI), Vienna: IEEE, 92–96.
49.Gayathri, R. and Vasudevan, S.K., 2018. Internet of Things based smart health monitoring of industrial standard motors. Indonesian Journal of Electrical Engineering and Informatics, 6(4), 361–367.
50.Giampiccolo, S., Mairot, N., Masry, Z.A., Omri, N. and Zerhouni, N., 2020. Industrial data management strategy towards an SME-oriented PHM. Journal of Manufacturing Systems, 56, 23–36.
51.Glaeser, A., Hwang, Y., Lee, K., Lee, N., Lee, S.C., Lee, S.Y., Min, S. and Selvaraj, V., 2020. Remote machine mode detection in cold forging using vibration signal. Procedia Manufacturing, 48, 908–914.
52.Golmie, N., Griffith D., Xu, H. and Yu, W., 2018. A survey on Industrial Internet of Things: A Cyber-Physical Systems perspective. IEEE Access, 6, 78238–78259.
53.Gong, Y., Guan, Z., Luo, D., Yue, L. and Zhang, Z., 2022. Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor. International Journal of Production Research, 60(3), 1016-1035.
54.Goriushkina, A., Kolisnyk, M. and Tkachenko, V., 2018. Communication Messaging Models in IoT/WoT: Survey and Application. 2018 International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), 417–422.
55.Govender, E., Sishi, M.N. and Telukdarie A., 2019. Approach for implementing Industry 4.0 framework in the steel industry. 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 1314–1318.
56.Grance, T. and Mell, P., 2011. The NIST definition of cloud computing. National Institute of Standards and Technology (NIST), COMPUTER SECURITY RESOURCE CENTER, Special Publication 800-145, 1–7.
57.Guo, D., Huang, G.G.Q., Rong, Y. and Zhong, R.Y., 2021. Synchronization of shop-floor logistics and manufacturing under IIoT and digital twin-enabled graduation intelligent manufacturing system. IEEE Transactions on Cybernetics, 1–12.
58.Guo, Q., Li, X., Song, L., Yang, H., Yang, Y. and Zhu, X., 2018. Detection of micro-defects on metal screw surfaces based on deep convolutional neural networks. Sensors, 18(11), 3709.
59.Haldimann, R., 2016. High speed fastener inspection. SAE Technical Paper 2016-01-2145, 1–4.
60.Halili, F. and Ramadani, E., 2018. Web services: A comparison of SOAP and REST services. Modern Applied Science, 12(3), 175–183.
61.Han, J., Jia, Z., Liu, Z., Pecht, M., Vong, C.M. and Yan C., 2018. A patent analysis of Prognostics and Health Management (PHM) innovations for electrical systems. IEEE Access, 6, 18088–18107.
62.He, K., Jia, M. and Liu, C., 2018. A review of optimal sensor deployment to diagnose manufacturing systems. IEEE Access, 6, 27418–27432.
63.Heier, H., 2019. Uncertainty propagation in a PHM enhanced dynamic reliability model. 2019 IEEE Aerospace Conference, 1–11.
64.Helmiö, P., 2018. Open Source in Industrial Internet of Things: A Systematic Literature Review Master's Thesis. School of Business and Management, Lappeenranta University of Technology, 21.
65.Hoover, J., 2020. Factors that determine the adaptation of a leadership style: A case study in the 21st century manufacturing industry. Doctoral dissertation, Baker College (Michigan).
66.Hou , Z.T., Liu, Z.W., Ming, X.G., Qu, Y.J. and Zhang , X.Y., 2019. Smart manufacturing systems: state of the art and future trends. The International Journal of Advanced Manufacturing Technology, 103, 3751–3768.
67.Hsu, B.M., Shu, M.H. and Wang, T.C., 2020. Lot-dependent sampling plans for qualifying long-term production capability with a one-sided specification. Computers & Industrial Engineering, 146, 106583.
68.Huisingh, D., Liu, Y., Ren, S., Sakao, T. and Zhang, Y., 2017. A framework for Big Data driven product lifecycle management. Journal of Cleaner Production, 159, 229–240.
69.Hwang, Y.C. and Lin, H.I., 2019. Integration of robot and IIoT over the OPC unified architecture. 2019 International Automatic Control Conference (CACS), 1–6.
70.Imran, M., Li, D., Shu, Z., Tang, S., Vasilakos, A.V., Wan, J. and Wang, S., 2016. Software-defined Industrial Internet of Things in the context of Industry 4.0. IEEE Sensors Journal, 16(20), 7373–7380.
71.Jalayer, M., Orsenigo, C. and Vercellis, C., 2021. Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, fast fourier and continuous wavelet transforms. Computers in Industry, 125, 103378.
72.Jazdi, N., Jung, T., Lindemann, B., Sahlab, N., Schloegl, W., Talkhestani, B.A. and Weyrich, M., 2019. An architecture of an intelligent digital twin in a Cyber-Physical production system. Journal of Automatisierungstechnik, 67(9), 762–782.
73.Kamphake, G.A., 2020. Digitalization in Controlling. Digitization in Controlling, 1549, 3–25.
74.Kannel, K. and Moghbel, F., 2022. How can Data-Driven Decision-Making support performance improvements in Production, Maintenance, and Sustainability in SMEs?. KTH ROYAL INSTITUTE OF TECHNOLOGY, Degree Project in Mechanical Engineering, Stockholm, Sweden, 1–92.
75.Kauppi, J., 2019. The benefits and feasibility of IoT in mining equipment: tracking of consumable components in industrial filters. LUT University, School of Energy Systems, Master's thesis, 1–68.
76.Khan, M.A., Mittal, S., Romero, D. and Wuest, T., 2019. Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1342–1361.
77.Krishnan, S. and Wu, E., 2019. Alphaclean: Automatic generation of data cleaning pipelines. arXiv:1904.11827.
78.Landgrebe, D. and Safavian, S.R., 1991.A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674.
79.Lapira, E.R., Lee, J. and Siegel, D., 2013. Development of a predictive and preventive maintenance demonstration system for a semiconductor etching tool. The Electrochemical Society, 52(1), 913–927.
80.Lei, Z., Sun, H. and Wu, C., 2022. Uncertainty Measurement of the Prediction of the Remaining Useful Life of Rolling Bearings. ASME, Journal of Nondestructive Evaluation, 5(3), 031007–031015.
81.LI, C., Mantravadi, S., Møller, C. and Schnyder, R., 2022. Design choices for next-generation IIoT-connected MES/MOM: An empirical study on smart factories. Robotics and Computer-Integrated Manufacturing, 73, 102225.
82.Lu, T. and Neng, W., 2010. Future internet: The internet of things. The 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 5, 376–380.
83.Mangano, F.E., 2018. Modernization of manufacturing with cybersecurity at the forefront. PhD Thesis, Master of Science, University of Michigan-Dearborn.
84.Manickam, S., Praptodiyono, S., Rehman, S. U. and Singh, P., 2020. Towards Sustainable IoT Ecosystem. 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE), Lombok: IEEE, 135–138.
85.Manoj, R.J., Praveena, M.D.A. and Vijayakumar, K., 2019. An ACO-ANN based feature selection algorithm for big data. Cluster Computing, 22, 3953–3960.
86.Matt, D.T., Modrak, V. and Zsifkovits, H., 2021. Implementing Industry 4.0 in SMEs: Concepts, Examples and Applications. Springer Nature, 429.
87.Milas, N., Mourtzis, D. and Vlachou, A., 2018. An Internet of Things-based monitoring system for shop-floor control. Journal of Computing and Information Science in Engineering, 18(2), 021005.
88.Pech, M. and Vrchota, J., 2020. Classification of small and medium-sized enterprises based on the level of Industry 4.0 implementation. Applied Sciences, 10(15), 5150.
89.Quinlan, J.R., 2014. C4. 5: programs for machine learning. Elsevier.
90.Ralph, B. and Stockinger, M., 2020. Digitalization and digital transformation in metal forming: key technologies, challenges and current developments of industry 4.0 applications. In: XXXIX Colloquium on Metal Forming, 13–23.
91.Ralph, B.J., Schwarz, A. and Stockinger, M., 2020. An implementation approach for an academic learning factory for the metal forming industry with special focus on digital twins and finite element analysis. Procedia Manufacturing, 45, 253–258.
92.Ravindran, D. and Rex, F.M.T., 2017. An integrated approach for optimal fixture layout design. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(7), 1217–1228.
93.Swan, M., 2012. Sensor Mania! The Internet of Things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1(3), 217–253.
94.Yang, L., 2017. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10.
95.Zion Market Research, 2018. Global pharmaceutical continuous manufacturing market report. New York: Market Research Store.

二、中文部分
1.丁慶榮、王立志、林春成、范書愷、郭財吉與許嘉裕，2018，生產系統於先進智慧製造之展望，管理與系統，第廿五卷第三期，381–412頁。
2.王文楷與蕭炎泉，2020，物聯網在物業管理應用的研究，中華大學土木工程學系博士論文。
3.王伶文、林式庭、林志修、林志哲、郭秀怡、張培毓、葉建南、蔡孟樵、簡良如、蘇中源，2021，應用於成型製程線上檢測之動態力智慧感測模組，財團法人工業技術研究院智慧微系統科技中心，第 19 屆精密機械與製造科技研討會論文集－PMMT 2021。
4.比爾‧蓋茲 (Bill Gates)，譯者：王美音，1996，擁抱未來，原文名稱：The Road Ahead，遠流出版社。
5.李文興與陳柏淵，2018，冰水主機系統應用廣義迴歸類神經網路及隨機森林之節能最佳化研究，台灣台北科技大學能源與冷凍空調工程系碩士論文。
6.周珮婷與趙蘭益，2017，監督式學習與R語言，台灣政治大學統計學系碩士論文。
7.周碩彥，2015，「物聯網發展趨勢展示內容」研究報告，國立臺灣科技大學 / 國立科學工藝博物館。
8.張馨云與蔡佩璇，2021，具螺絲模具使用壽命預測功能之智慧倉儲管理系統，台灣成功大學製造資訊與系統研究所碩士論文。

三、網路參考文獻
1.Wikipedia, 2022, OSI 模型；
(https://zh.wikipedia.org/zh-tw/OSI%E6%A8%A1%E5%9E%8B)。
2.Wikipedia, 2022, 支持向量機；(https://zh.wikipedia.org/zh-tw/支持向量機)。
3.Wikipedia, 2022, 協議族；
(https://zh.wikipedia.org/zh-tw/TCP/IP%E5%8D%8F%E8%AE%AE%E6%97%8F)。
4.黃啟賢，2022，AI專欄機器學習演演算法-K近鄰，台灣政治大學人工智慧與數位教育中心，(https://aiec.nccu.edu.tw › ai-column)。
5.諶怡如，2021，英國、歐盟扣件市場趨勢，台灣經貿網。(https://info.taiwantrade.com/biznews/%E8%8B%B1%E5%9C%8B-%E6%AD%90%E7%9B%9F%E6%89%A3%E4%BB%B6%E5%B8%82%E5%A0%B4%E8%B6%A8%E5%8B%A2-2394483.html)。