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Erschienen in:
Introduction to the Smart Machining System Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

12. The Cyber-Physical Production System of Smart Machining System

verfasst von : Kunpeng Zhu

Erschienen in: Smart Machining Systems

Verlag: Springer International Publishing

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Abstract

In the year 2013, the German scientists introduced the concept of “Industry 4.0” (Kagermann et al. in Securing the future of German manufacturing industry recommendations for implementing the strategic initiative INDUSTRIE 4.0. Germany: Federal Ministry of education and research. Final Report of the Industrial 4.0 Working Group, 2012). They believed that in the next 10 years, the industrialization based on the cyber-physical system (CPS) will make the society enter the fourth revolution dominated by intelligent manufacturing. “Industry 4.0” will make the manufacturing process more flexible and strong, develop new business models, and promote the formation of a new cyber-physical system platform. The core of the “Industry 4.0” strategy is to realize the real-time connection, mutual recognition, and effective communication between people, equipment, and products through CPS network, to build a highly flexible personalized and digital intelligent manufacturing mode.

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Vorheriges Kapitel Big Data Oriented Smart Tool Condition Monitoring System
Literatur
1.
Kagermann H, Wahlster W, Helbig J (2012) Securing the future of German manufacturing industry recommendations for implementing the strategic initiative INDUSTRIE 4.0. Federal Ministry of education and research Final report of the Industrial 4.0 Working Group, Germany
2.
Lee EA (2006) Cyber-physical systems—are computing foundations adequate? Position paper for NSF workshop on cyber physical systems: research motivation, techniques and roadmap
3.
Rajkumar R, Lee I (2010) Cyber-physical systems: the next computing revolution. Proceedings of the design automation conference. pp 731–736
4.
Harrison R, Vera D, Ahmad B (2016) Engineering methods and tools for cyber–physical automation systems. IEEE Proc 104(5):973–985
5.
Sztipanovits J, Karsai G, et al (2012) Toward a science of cyber–physical system integration. Proc IEEE 100(1)
6.
Leitao P, Colombo AW, Karnouskos S (2016) Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput Ind 81:11–25
7.
Groover MP (2015) Automation, production systems, and computer-integrated manufacturing, 4th edn. Pearson
8.
Liu C, Xu X (2017) Cyber-physical machine tool—the era of machine tool 4.0. Procedia CIRP 63:70–75
9.
Ghimire S, Luis-Ferreira F, Nodehi T, et al (2017) IoT based situational awareness framework for real-time project management. Int J Comput Integr Manuf 30(1):74–83
10.
Zhong RY, Wang L, Xu X (2017) An IoT-enabled real-time machine status monitoring approach for cloud manufacturing. Procedia CIRP 63:709–714
11.
Zuo Y, Tao F, Nee AYC (2017) An internet of things and cloud-based approach for energy consumption evaluation and analysis for a product. Int J Comput Integr Manuf. 1–12
12.
Cai Y, Starly B, Cohen P, et al (2017) Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. 10:1031–1042
13.
Zheng M, Ming X (2017) Construction of cyber-physical system–integrated smart manufacturing workshops: a case study in automobile industry. Adv Mech Eng 9(10):168781401773324
14.
Zhang J, Deng C, et al (2021) Development of an edge computing-based cyber-physical machine tool. Robot Comput Integr Manuf 67:102042
15.
Luo W, Hu T, Ye Y, Zhang C, Wei Y (2020) A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robot Comput Integr Manuf 65:101974
16.
Xie Y, Lian K (2021) Digital twin for cutting tool: modeling, application and service strategy. J Manuf Syst 58(B):305–312
17.
Morgan J, O’Donnell GE (2015) Cyber physical process monitoring systems. J Int Manuf 26(6):1–12
18.
Caggiano A, Segreto T, Teti R (2016) Cloud manufacturing framework for smart monitoring of machining. Procedia CIRP 55:248–253
19.
Herwan J, Kano S, et al (2018) Cyber-physical system architecture for machining production line. In: IEEE industrial cyber-physical systems (ICPS). St. Petersburg, Russia
20.
Zhu KP, Zhang Y (2018) A cyber-physical production system framework of smart CNC machining monitoring system. IEEE/ASME Trans Mechatron 23(6):2579–2586
21.
Teti R, Jemielniak K, O’Donnel G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739
22.
Deshayes L, Welsch L, Donmez A, Ivester R, Gilsinn D, Rhorer R, Whitenton E, Potra F (2005) Smart machining systems: issues and research trends. The 12th CIRP life cycle engineering seminar, Grenoble, France, pp 3–5
23.
Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, Helu M (2015) Cloud-enabled prognosis for manufacturing. CIRP Ann Manuf Technol 64(2):749–772
24.
Altintas Y, Kersting P, Biermann D, Budak E, Denkena B, Lazoglu I (2014) Virtual process systems for part machining operations. CIRP Ann Manuf Technol 63(2):585–605
25.
Lee J, Lapira E, Bagheri B, Kao HA (2012) Recent advances and trends in predictive manufacturing systems in big data environment. Manuf Lett 1(1):38–41
26.
Monostori L, Kadar B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, Sauer O, Schuh G, Sihn W, Ueda K (2016) Cyber-physical systems in manufacturing. CIRP Ann Manuf Technol 65(2):621–641
27.
Foundations for Innovation in Cyber-Physical Systems (2014) National Institute of Standards and Technology (NIST), Gaithersburg
28.
Lv C, Liu Y, Hu X, et al (2018) Simultaneous observation of hybrid states for cyber-physical systems: a case study of electric vehicle powertrain. IEEE Trans Cybern 48(8):2357–2367
29.
Lv C, Xing Y, Zhang J, Na X, et al (2018) Levenberg-Marquardt backpropagation training of multilayer neural networks for state estimation of a safety critical cyber-physical system. IEEE Trans Industr Inform 14(8):3436–3446
30.
Mourtzis D, Vlachou E, Milas N, Xanthopoulos N (2016) A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring. Procedia CIRP 41:655–660
31.
Tapoglou N, Mehnen J, Vlachou A, Doukas M, Milas N, Mourtzis D (2015) Cloud-based platform for optimal machining parameter selection based on function blocks and real-time monitoring. J Manuf Sci Eng 137(4):040909
32.
Li X, Djordjevich A, Venuvinod PK (2000) Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring. IEEE Trans Ind Electron 47(3):697–702
33.
Hung CW, Lu MC (2012) Model development for tool wear effect on AE signal generation in micro-milling. Int J Adv Manuf Technol 66(9–12):1845–1858
34.
Szydłowski M, Powałka B, Matuszak M, et al (2016) Machine vision micro-milling tool wear inspection by image reconstruction and light reflectance. Prec Eng 44:236–244
35.
Zhu KP, Yu XL (2017) The monitoring of micro milling tool wear conditions by wear area estimation. Mech Syst Signal Process 93:80–91
36.
Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4–5):672–693
37.
Wang W, Jianu OA (2010) A smart sensing unit for vibration measurement and monitoring. IEEE/ASME Trans Mechatron 15(1):70–78
38.
Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2015) Development and testing of an integrated rotating dynamometer on tool holder for milling process. Mech Syst Signal Process 52–53:559–576
39.
Mahajan A, Wang K, Ray PK (2001) Multisensor integration and fusion model that uses a fuzzy inference system. IEEE/ASME Trans Mech 6(2):188–196
40.
Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mat Process Technol 209(10):4903–4914
41.
Wang J, Xie J, Zhao R, Mao K, Zhang L (2016) A new probabilistic kernel factor analysis for multisensory data fusion: application to tool condition monitoring. IEEE Trans Instrum Meas 65(11):2527–2537
42.
Ompusunggu AP, Papy JM, Vandenplas S (2015) Kalman-filtering-based prognostics for automatic transmission clutches. IEEE/ASME Trans Mech 21(1):419–430
43.
Zhu KP, Liu T (2018) Online tool wear monitoring via hidden Semi-Markov model with dependent durations. IEEE Trans Ind Inform 14(1):69–78
44.
Geramifard O, Xu JX, Zhou JH, Li X (2012) Multimodal hidden Markov model-based approach for tool wear monitoring. IEEE Trans Ind Electron 61(6):2900–2911
45.
Xia M, Li T, Xu L, Liu L, Silva CWD (2018) Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans Mech 23(1):101–110
46.
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
47.
Wang P, Yan AR, Gao R (2017) Visualization and deep recognition for system fault classification. J Manuf Syst 44:310–316
48.
Droniou A, Ivaldi S, Sigaud O (2015) Deep unsupervised network for multimodal perception, representation and classification. Robot Auton Syst 71:83–98
49.
Sun W, Shao S, Zhao R, Yan R, Zhang X, Chen X (2016) A sparse auto-encoder-based deep network approach for induction motor faults classification. Measurement 89:171–178
50.
Monostori L (2014) Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17:9–13
51.
Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23
52.
Zhu KP, Zhang Y (2017) Modeling of the instantaneous milling force per tooth with tool run-out effect in high speed ball-end milling. Int J Mach Tools Manuf 118–119:37–48
53.
Morgan J, O’Donnell GE (2017) Multi-sensor process analysis and performance characterization in CNC turning-a cyber physical system approach. Int J Adv Manuf Technol 92(1–4):855–868
Metadaten
Titel
The Cyber-Physical Production System of Smart Machining System
verfasst von
Kunpeng Zhu
Copyright-Jahr
2022
Buch
Smart Machining Systems

Print ISBN: 978-3-030-87877-1

Electronic ISBN: 978-3-030-87878-8

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-87878-8_12

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