Industry 4.1. Группа авторов
Чтение книги онлайн.
Читать онлайн книгу Industry 4.1 - Группа авторов страница 25
44 44 Mattern, F. and Floerkemeier, C. (2010). From the internet of computers to the internet of things. From Active Data Management to Event‐Based Systems and More. Lecture Notes in Computer Science 6462: 242–259. https://doi.org/10.1007/978‐3‐642‐17226‐7_15.
45 45 Li, S., Li, D.‐X., and Zhao, S. (2015). The internet of things: a survey. Information Systems Frontiers 17: 243–259. https://doi.org/10.1007/s10796‐014‐9492‐7.
46 46 Brettel, M., Friederichsen, Keller, N.M. et al. (2014). How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4.0 perspective. International Journal of Mechanical, Industrial Science and Engineering 8 (1): 37–44. https://doi.org/10.5281/zenodo.1336426.
47 47 Shen, J., Majid, B.N., Xie, L. et al. (2017). Interactive UHF/UWB RFID tag for mass customization. Information Systems Frontiers 19: 1177–1190. https://doi.org/10.1007/s10796‐016‐9653‐y.
48 48 Nirmala, J. (2016). Japan embracing Industry 4.0 and IoT to leap into next industrial automation. https://bit.ly/2YXoBe4 (accessed 17 Aug 2020).
49 49 The Boston Consulting Group (2015). Industry 4.0 lifts automation and mass customization to new levels. http://goo.gl/ilYMVD (accessed 17 Aug 2020).
50 50 Gross, D. (2016). Siemens CEO Joe Kaeser on the next industrial revolution. http://goo.gl/ZSGgqo (accessed 17 Aug 2020).
51 51 Pollard, D., Chuo, S., and Lee, B. (2016). Strategies for mass customization. Journal of Business & Economics Research 14 (3): 101–110. https://doi.org/10.19030/jber.v14i3.9751.
52 52 Davis, S.M. (1989). From future perfect: mass customizing. Planning Review 17 (2): 16–21. https://doi.org/10.1108/eb054249.
53 53 Gilmore, J.H. and Pine, B.J. 2nd (1997). The four faces of mass customization. Harvard Business Review 75 (1): 91–101.
54 54 Da Silveira, G.J., Borenstein, D., and Fogliatto, F.S. (2001). Mass customization: literature review and research directions. International Journal of Production Economics 72 (1): 1–13. https://doi.org/10.1016/S0925‐5273(00)00079‐7.
55 55 Fogliatto, F.S., Da Silveira, G.J.C., and Borenstein, D. (2012). The mass customization decade: an updated review of the literature. International Journal of Production Economics 138 (1): 14–25. https://doi.org/10.1016/j.ijpe.2012.03.002.
56 56 Peng, D.X., Liu, G., and Heim, G.R. (2011). Impacts of information technology on mass customization capability of manufacturing plants. International Journal of Operations & Production Management 31 (10): 1022–1047. https://doi.org/10.1108/01443571111182173.
57 57 Halpin, J.F. (1966). Zero Defects: A New Dimension in Quality Assurance. New York: McGraw‐Hill.
58 58 Weisenberger, S. (2015). Hannover Messe Day 1 ‐ will Industry 4.0 enable zero defects? how are business models impacted by Industry 4.0. https://bit.ly/3331HDB (accessed 17 Aug 2020).
59 59 Somers, D. (2014). Enter the world of ‘Industrial 4.0’ at Hannover Messe 2014. https://goo.gl/47yfdw (accessed 17 Aug 2020).
60 60 Cheng, F.‐T., Tieng, H., Yang, H.‐C. et al. (2016). Industry 4.1 for wheel machining automation. IEEE Robotics and Automation Letters 1 (1): 332–339. https://doi.org/10.1109/LRA.2016.2517208.
61 61 Cheng, F.‐T., Hsieh, Y.‐S., Zheng, J.‐W. et al. (2017). A scheme of high‐dimensional key‐variable search algorithms for yield improvement. IEEE Robotics and Automation Letters 2 (1): 179–186. https://doi.org/10.1109/LRA.2016.2584143.
2 Data Acquisition and Preprocessing
Hao Tieng1, Haw‐Ching Yang2, and Yu‐Yong Li3
1Associate Research Fellow, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC
2Professor, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan, ROC
3Postdoctoral Research Fellow, Intelligent Manufacturing Research Center, National Cheng Kung University, Tainan, Taiwan, ROC
2.1 Introduction
Various intelligent applications (such as predictive maintenance, virtual metrology, etc.) should be developed for achieving the goals of Intelligent Manufacturing. Taking predictive‐maintenance‐related applications as the illustrative examples, Chen et al. [1] installed one accelerometer, one acoustic emission (AE) sensor and two current sensors on a lathe to estimate the reliability, and remaining useful life (RUL) for cutting tools based on the logistic regression model using vibration signals. Suprock et al. [2] installed one strain gauge and one instrumentation amplifier with the Bluetooth transmitter on a cutting tool to calculate dynamic torque values, which are as accurate as the real measurements by the dynamometer. Ghosh et al. [3] developed an artificial neural network (ANN)‐based sensor fusion model for tool condition monitoring using cutting force, spindle vibration, spindle current, and sound pressure. Abuthakeer et al. [4] analyzed vibration signals based on the full factorial design and utilized ANN to validate the effect of cutting parameters on cutting tools during machining.
The fundamental steps for developing intelligent applications are depicted in Figure 2.1. As shown in Figure 2.1, before developing an intelligent application, the associated process/metrology data source needs to be acquired followed by appropriate data preprocessing. The main purposes of the aforementioned steps are briefly introduced in this subsection and more details can be found in the remaining subsections of Chapter 2.
Figure 2.1 Fundamental steps for developing an intelligent application.
Data Acquisition
Data