簡易檢索 / 詳目顯示

回結果列表
研究生： 丁顥
Tieng, Hao
論文名稱： 運用全自動虛擬量測於工具機產業之加工精度全檢機制
Total Inspection Scheme using AVM for Machining Precisions of Machine Tools
指導教授： 鄭芳田
Cheng, Fan-Tien
共同指導教授: 楊浩青
Yang, Haw-Ching
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 製造資訊與系統研究所
Institute of Manufacturing Information and Systems
論文出版年： 2015
畢業學年度： 104
語文別： 英文
論文頁數： 85
中文關鍵詞： 加工精度 、全檢 、全自動虛擬量測 、鋁圈加工自動化 、持續精進 、智能與彈性製造 、工廠自動化 、工業4.1 、零缺陷
外文關鍵詞： Machining Precision, Total Inspection, Automatic Virtual Metrology (AVM), Wheel Machining Automation (WMA), Continuous Improvement, Intelligent and Flexible Manufacturing, Factory Automation, Industry 4.1, Zero Defects
相關次數： 點閱：270  下載：21 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 全自動虛擬量測已成功被運用在半導體廠之產品檢測，將離線且具有延遲特性之品質抽檢方式改為即時線上之品質全檢。近年來，隨著產品加工量產時對於安全性與持續精進能力的需求提高，全自動且即時之全檢方法已逐漸成為全球工具機廠共同追求的目標。本博士論文旨在將全自動虛擬量測技術導入至工具機產業。然而，在工具機台的加工過程中，固有的大量雜訊造成資料收集、資料淨化與特徵萃取的進行更加棘手；因此，論文首先嚴謹明確地定義將遭遇的挑戰，並詳細說明如何克服。最重要的是，透過實際加工的案例研究，包括標準件與手機背板的精度預測準確率，顯示全自動虛擬量測技術可以成功針對工具機產業的加工進行即時精度預測；接著，以實際的鋁圈加工自動化線，驗證即使在大量生產的環境下，重要精度即時全檢之目標依舊能實現。最後，基於全自動虛擬量測的特性，本論文提出一先進製造物聯雲平台，不僅滿足當前產業界對於工業4.0的需求，還能同時達成在加工生產階段與交貨階段中零缺陷的目標。透過實際的機台販售商與鋁圈加工廠，驗證先進製造物聯雲平台的擴散性與應用效能，正式宣告工業4.0時代的結束，取而代之的是工業4.1時代的來臨。

    The technology of Automatic Virtual Metrology (AVM) has been applied in the semiconductor industry to convert sampling inspection with metrology delay into real-time and online total inspection. Nowadays, as the requirements of secure mass-production and continuous improvement increase, means of real-time automated total inspection in the machine-tool industry have gradually become a global trend. Thus, the purpose of this paper is trying to apply AVM into the machine-tool industry. However, machining processes will cause severe vibrations that make process data collection, data cleaning, and feature extraction difficult to handle. These challenges are judiciously addressed and successfully resolved in this paper. Practical testing-results of machining standard workpieces and cellphone shells show that the proposed AVM-based approach to accomplish total inspection of machine tools is promising. Then, a wheel machining automation (WMA) cell is also utilized to evaluate the performance for achieving the goal of total inspection under the mass-production environment. Finally, based on the merits of AVM, this paper proposes a platform denoted Advanced Manufacturing Cloud of Things (AMCoT) to not only achieve the objectives of Industry 4.0 but also accomplish the goal of Zero Defects. As such, by applying Industry 4.0 together with AVM to achieve the goal of Zero Defects, the era of Industry 4.1 is taking place. The application of WMA is adopted to illustrate how AMCoT and Industry 4.1 work.

    摘 要 I ABSTRACT II 誌 謝 III ACKNOWLEDGEMENTS IV TABLE OF CONTENTS V FIGURES VIII TABLES I CHAPTER 1 OVERVIEW 1 1.1 Background and Motivation 1 1.2 Organization 3 1.3 Abbreviation 4 CHAPTER 2 DEVELOPMENT OF A GED-PLUS AUTOMATIC VIRTUAL METROLOGY SYSTEM FOR THE MACHINE-TOOL INDUSTRY 6 2.1 Introduction 6 2.1.1 AVM Server 7 2.1.2 Challenges of Applying AVM to Machine Tools 11 2.2 The Proposed GAVM System for Machine Tools 12 2.2.1 Generic Embedded Device 13 2.2.2 Challenge 1: Segmentation 15 2.2.3 Challenge 2: Data Cleaning 18 2.2.4 Challenge 3: Feature Extraction 21 2.3 Illustrative Examples 24 2.3.1 Example 1: Standard Workpiece 25 2.3.2 Example 2: Cellphone Shell 29 2.4 Summary 32 CHAPTER 3 AUTOMATIC VIRTUAL METROLOGY FOR WHEEL MACHINING AUTOMATION 33 3.1 Introduction 33 3.1.1 Introducing the WMA Cell 34 3.1.2 AVM Server 36 3.1.3 The Model Refreshing Algorithm of GAVM 39 3.1.4 GAVM Scheme for Machine Tools 41 3.2 Integrating GAVM into WMA 43 3.3 Considerations for Mass Production 44 3.3.1 Case 1: Identical Precision Item in Different Machines of the Same Type 44 3.3.2 Case 2: Different yet Similar Precision Item in Machines of the Same Type 45 3.4 Illustrative Examples 46 3.4.1 Example 1: Pitch Circle Diameter Model Refreshing of Case 1 47 3.4.2 Example 2: Hubcap Diameter Model Refreshing of Case 2 49 3.5 Summary 50 CHAPTER 4 INDUSTRY 4.1 FOR WHEEL MACHINING AUTOMATION 51 4.1 INTRODUCTION 51 4.2 DEFINITION AND KEY COMPONENTS OF INDUSTRY 4.0 51 4.2.1 Zero Defects 52 4.2.2 Internet of Things 54 4.2.3 Cyber Physical Systems 54 4.2.4 Cloud-Based Manufacturing 55 4.3 Integrating GAVM into WMA for Total Inspection 56 4.4 Requirements of Cyber Physical Agent 58 4.4.1 Functions of CPA 59 4.4.2 Implementation of CPA 61 4.5 Advanced Manufacturing Cloud of Things 64 4.6 Applying AMCoT to WMA 65 4.7 Illustrative Example: AVM Models Refreshing between the Vender and Customers 66 4.8 Two Stages of Achieving Zero Defects 71 4.9 Summary 72 CHAPTER 5 CONCLUSIONS AND FUTURE WORK 73 5.1 Conclusions 73 5.2 Future Work 74 REFERENCES 78 BIOGRAPHY 83 PUBLICATION LISTS 84

    [1] Cheng, F.-T., C.-A. Kao, C.-F. Chen, and W.-H. Tsai. 2015a. “Tutorial on Applying the VM Technology for TFT-LCD Manufacturing.” IEEE Transactions on Semiconductor Manufacturing 28 (1): 55-69. doi:10.1109/TSM.2014.2380433.
    [2] Valiño, G., C. M. Suárez, J. C. Rico, B. J. Álvarez, and, D. Blanco. 2012. “Comparison between a Laser Micrometer and a Touch Trigger Probe for Workpiece Measurement on a CNC Lathe.” Advanced Materials Research 498: 49-54. doi: 10.4028/www.scientific.net/AMR.498.49.
    [3] Lu, C. 2008. “Study on Prediction of Surface Quality in Machining Process.” Journal of materials processing technology 205 (1-3): 439-450. doi:10.1016/j.jmatprotec.2007.11.270.
    [4] Teti, R., K. Jemielniak, G. O’Donnell, and D. Dornfeld. 2010. “Advanced Monitoring of Machining Operations.” CIRP Annals-Manufacturing Technology 59 (2). 717-739. doi:10.1016/j.cirp.2010.05.010.
    [5] Abellan-Nebot, J.V., and F. R., Subirón. 2010. “A Review of Machining Monitoring Systems based on Artificial Intelligence Process Models.” The International Journal of Advanced Manufacturing Technology 47 (1-4): 237-257. doi:10.1007/s00170-009-2191-8.
    [6] Chandrasekaran, M., M. Muralidhar, C. M. Krishna, and U. S. Dixit. 2010 “Application of Soft Computing Techniques in Machining Performance Prediction and Optimization: a Literature Review.” The International Journal of Advanced Manufacturing Technology 46 (5-8): 445-464. doi:10.1007/s00170-009-2104-x.
    [7] Cheng, F.-T., C.-F. Chen, Y.-S. Hsieh, H.-H. Huang, and C.-C. Wu. 2015b “Intelligent Sampling Decision Scheme Based on the AVM System.” International Journal of Production Research 53 (7): 2073-2088. doi:10.1080/00207543.2014.955924.
    [8] Cheng, F.-T., and Y.-C. Chiu. 2013 “Applying the Automatic Virtual Metrology System to Obtain Tube-to-Tube Control in a PECVD Tool.” IIE Transactions 45 (6): 670-681. doi:10.1080/0740817X.2012.725507.
    [9] Cheng, F.-T. H.-C. Huang, and C.-A. Kao. 2012. “Developing an Automatic Virtual Metrology System.” IEEE Transactions on Automation Science and Engineering 9 (1): 181-188. doi:10.1109/TASE.2011.2169405.
    [10] Huang, Y.-T., and F.-T. Cheng. 2011. “Automatic Data Quality Evaluation for the AVM System.” IEEE Transactions on Semiconductor Manufacturing 24 (3): 445-454. doi:10.1109/TSM.2011.2154910.
    [11] Cheng, F.-T., Y.-T. Chen, Y.-C. Su, and D.-L. Zeng. 2008. “Evaluating Reliance Level of a Virtual Metrology System.” IEEE Transactions on Semiconductor Manufacturing 21 (1): 92-103. doi:10.1109/TSM.2007.914373.
    [12] Cheng, F.-T., H.-C. Huang, and C.-A. Kao. 2007. “Dual-Phase Virtual Metrology Scheme.” IEEE Transactions on Semiconductor Manufacturing 20 (4): 566-571. doi:10.1109/TSM.2007.907633.
    [13] Huang, Y.-T., F.-T. Cheng, Y.-H. Shih, and Y.-L. Chen. 2014. “Advanced ART2 Scheme for Enhancing Metrology-Data-Quality Evaluation.” Journal of the Chinese Institute of Engineers 37 (8): 1064-1079. doi:10.1080/02533839.2014.912773.
    [14] Witten, I. H. and E. Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, CA: Morgan Kaufman.
    [15] Tukey, J. W. 1977. Exploratory Data Analysis. Addison-Wesley, Reading, Massachusetts.
    [16] Walpolem, R. E., R. H. Myers, S. L. Myers, and K. Yee. 2002. Probability and Statistics for Engineers and Scientists (7th Edition), Prentice Hall.
    [17] Su, Y.-C., F.-T. Cheng, M.-H. Hung, and H.-C. Huang. 2006 “Intelligent Prognostics System Design and Implementation.” IEEE Transactions on Semiconductor Manufacturing 19 (2): 195-207. doi:10.1109/TSM.2006.873512.
    [18] Daubechies, I. 1992. “Ten Lectures on Wavelets.” Philadelphia: Society for industrial and applied mathematics 61: 198-202.
    [19] Donoho, D. L. 1995. “De-Noising by Soft-Thresholding.” IEEE Transactions on Information Theory 41 (3): 613-627. doi:10.1109/18.382009.
    [20] Mallat, S. G. 1989. “A Theory for Multiresolution Signal Decomposition: the Wavelet Representation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (7): 674-693. doi:10.1109/34.192463.
    [21] Mou, J., and C. R. Liu. 1992. “A Method for Enhancing the Accuracy of CNC Machine Tools for On-Machine Inspection.” Journal of Manufacturing Systems 11 (4): 229-237. doi:10.1016/0278-6125(92)90023-9.
    [22] Ghosh, N., Y. B. Ravi, A. Patra, S. Mukhopadhyay, S. Paul, A. R. Mohanty, and A. B. Chattopadhyay. 2007. “Estimation of Tool Wear during CNC Milling Using Neural Network-Based Sensor Fusion.” Mechanical Systems and Signal Processing 21 (1): 466-479. doi:10.1016/j.ymssp.2005.10.010.
    [23] Lin, T.-H., F.-T. Cheng, W.-M. Wu, C.-A. Kao, A.-J. Ye, and F.-C. Chang. 2009. “NN-Based Key-Variable Selection Method for Enhancing Virtual Metrology Accuracy.” IEEE Transactions on Semiconductor Manufacturing 22 (1): 204-211. doi:10.1109/TSM.2008.2011185.
    [24] Haber, R. E., J. E. Jiménez, C. R. Peres, and J. R. Alique. 2004. “An Investigation of Tool-Wear Monitoring in a High-Speed Machining Process.” Sensors and Actuators A: Physical 116 (3): 539-545. doi:10.1016/j.sna.2004.05.017.
    [25] Jemielniak, K., T. Urbański, J. Kossakowska, and S. Bombiński. 2012. “Tool Condition Monitoring based on Numerous Signal Features.” The International Journal of Advanced Manufacturing Technology 59 (1-4): 73-81. doi:10.1007/s00170-011-3504-2.
    [26] Wang, H., and P. Chen. 2009. “Fault Diagnosis Method based on Kurtosis Wave and Information Divergence for Rolling Element Bearings.” WSEAS Transactions Systems 8 (10): 1155-1165.
    [27] Lahdelma, S., and E. Juuso. 2011. “Signal Processing and Feature Extraction by Using Real Order Derivatives and Generalised Norms. Part 1: Methodology.” International Journal of Condition Monitoring 1 (2): 46-53. doi:10.1784/204764211798303805.
    [28] Lei, Y., Z. He, Y. Zi, and Q. Hu. 2008. “Fault Diagnosis of Rotating Machinery based on a New Hybrid Clustering Algorithm.” The International Journal of Advanced Manufacturing Technology 35 (9-10): 968-977. doi:10.1007/s00170-006-0780-3.
    [29] Kim, G. D., and C. N. Chu. 2001. “In-Process Tool Fracture Monitoring in Face Milling Using Spindle Motor Current and Tool Fracture Index.” The International Journal of Advanced Manufacturing Technology 18 (6): 383-389. doi:10.1007/s001700170047.
    [30] Kaiser, W., R. P. Marques, and A. F. Correa. 2006. “Impact of Current Crest Factor at High and Low Frequency Operation on Fluorescent Lamp Electrodes.” Industry Applications Conference, 2006. 41st IAS Annual Meeting. Conference Record of the 2006 IEEE, Tampa, Florida, United States, 8-12 Oct, 2006: 236-241. New York: IEEE.
    [31] Li, X. 2001. “Detection of Tool Flute Breakage in End Milling Using Feed-Motor Current Signatures.” IEEE/ASME Transactions on Mechatronics 6 (4): 491-498. doi:10.1109/3516.974863. doi:10.1109/3516.974863.
    [32] Yang, H.-C., H. Tieng, and F.-T. Cheng. 2015. “Total Precision Inspection of Machine Tools with Virtual Metrology,” to appear in Journal of the Chinese Institute of Engineers, 2016. DOI: 10.1080/02533839.2015.1091279.
    [33] Halpin, James F. Zero Defects: A New Dimension in Quality Assurance, New York: McGraw-Hill, 1966.
    [34] S. Ferber. “Industry 4.0 – Germany Takes First Steps toward the Next Industrial revolution.” Internet: http://blog.bosch-si.com/categories/manufacturing/2012/10/industry-4-0-germany-takes-first-steps-toward-the-next-industrial-revolution/, October 16 2012.
    [35] N. Jazdi, “Cyber Physical Systems in the Context of Industry 4.0,” in IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, Romania, pp. 1-4, May 22-24, 2014.
    [36] S. Weisenberger. “Hannover Messe Day 1 - Will Industry 4.0 Enable Zero Defects? How are Business Models Impacted by Industry 4.0?” Internet: http://scn.sap.com/community/mining-and-mill-products/blog/2015/04/14/hannover-messe-day-1--will-industry-40-enable-zero-defects-how-are-business-models-impacted-by-industry-40, April 14, 2015.
    [37] S. Chou. “Enter the World of ‘Industrial 4.0’ at Hannover Messe 2014. ”Internet: https://blogs.saphana.com/2014/04/17/enter-the-world-of-industrial-40-at-hannover-messe-2014/, April 17, 2014.
    [38] P. Werr. “How Industry 4.0 and the Internet of Things are Connected” Internet: http://www.iotevolutionworld.com/m2m/articles/401292-how-industry-40-the-internet-things-connected.htm. April 9, 2015
    [39] C. Perera, C. H. Liu, S. Jayawardena, and M. Chen, “A Survey on Internet of Things from Industrial Market Perspective,” Access, IEEE, vol. 2, pp. 1660-1679, January 2015.
    [40] J. Wan, M. Chen, F. Xia, L. Di, and K. Zhou, “From Machine-to-Machine Communications towards Cyber-Physical Systems,” Computer Science and Information Systems, vol. 10, no. 3, pp. 1105-1128, 2013.
    [41] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, September 2013.
    [42] A. W.Colombo, T. Bangemann, S. Karnouskos, J. Delsing, P. Stluka, R. Harrison, and J. L. Lastra, Industrial cloud-based cyber-physical systems, The IMC-AESOP Approach, Springer International Publishing Switzerland, 2014.
    [43] C.-W. Tsai, C.-F. Lai, M.-C. Chiang, and L.-T., Yang, “Data Mining for Internet of Things: A survey,” Communications Surveys & Tutorials, IEEE, vol. 16 no. 1, pp. 77-97, February 2014.
    [44] Crosby, Philip B, Quality Improvement Program, Quality Is Free: The Art of Making Quality Certain, New York: McGraw-Hill. pp. 127–139, 1979.
    [45] C. Perera, A.Zaslavsky, C.-H. Liu, M. Compton, P. Christen, and D. Georgakopoulos, “Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things,” Sensors Journal, IEEE, vol. 14, no. 2, pp. 406-420, February 2014.
    [46] F. Xia, A. Vinel, R. Gao, L. Wang, and T. Qiu, “Evaluating IEEE 802.15. 4 for Cyber-Physical Systems,” arXiv preprint arXiv:1312.6837, December 2013.
    [47] T. S´anchez L´opez, D. C. Ranasinghe, M. Harrison, and D. Mcfarlane, “Adding Sense to the Internet of Things,” Personal and Ubiquitous Computing, vol. 16, no. 3, pp. 291-308, March 2012.
    [48] D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of Things: Vision, Applications and Research Challenges,” Ad Hoc Networks, vol. 10, no. 7, pp. 1497-1516, September 2012.
    [49] H.-C. Huang, Y.-C. Lin, M.-H. Hung, C.-C. Tu, and F.-T. Cheng, “Development of Cloud-based Automatic Virtual Metrology System for Semiconductor Industry,” Robotics and Computer-Integrated Manufacturing, Vol. 34, pp. 30-43, February 2015.
    [50] Z. Bi, L. Da Xu, and C. Wang, “Internet of Things for Enterprise Systems of Modern Manufacturing,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1537-1546, January 2014.
    [51] F. Mattern, and C. Floerkemeier, “From the Internet of Computers to the Internet of Things,” Lecture Notes in Computer Science, Berlin Heidelberg Springer, vol. 6462, pp. 242-259, 2010.
    [52] H. Tieng, H.-C. Yang, M.-H. Hung, and F.-T. Cheng, “A Novel Virtual Metrology Scheme for Predicting Machining Precision of Machine Tools,” in Proc. of The 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), Karlsruhe, Germany, pp. 264-269, May 6-10, 2013.
    [53] Microsoft ASP.Net MVC. Available: http://www.asp.net/mvc.
    [54] A. Flammini, P. Ferrari, D. Marioli, E. Sisinni, and A. Taroni, “Wired and Wireless Sensor Networks for Industrial Applications,” Microelectronics Journal, vol. 40, no. 9, pp. 1322-1336, September 2009.
    [55] Fadlullah, Z. M., Fouda, M. M., Kato, N., Takeuchi, A., Iwasaki, N., and Nozaki, Y., “Toward Intelligent Machine-to-Machine Communications in Smart Grid,” IEEE Communications Magazine, vol. 49, no. 4, pp. 60-65, April 2011.
    [56] F.-T. Cheng, Y.-S. Hsieh, C.-F. Chen, and J.-R. Lyu, “Automated Sampling Decision Scheme Based on the AVM System,” International Journal of Production Research, published online: Aug. 2015. DOI: 10.1080/00207543.2015.1072649.

    下載圖示 校內：2016-12-09公開
    校外：2016-12-09公開
    QR CODE