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An overall framework and subsystems for smart manufacturing integrated system (SMIS) from multi-layers based on multi-perspectives

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Abstract

In the tide of smart manufacturing in the world, many countries have put forward their own reference frameworks for smart manufacturing system. Based on the framework of smart manufacturing system (SMS) proposed by China, a reference smart manufacturing integration system (SMIS) from multi-level and multi-perspective was proposed. The reference model of SMIS was analyzed by means of system engineering. Based on this model, the overall architecture of SMIS was proposed. Then, the physics subsystem, information integration subsystem, network integration subsystem, data integration subsystem, and visualization integration subsystem for SMIS were proposed through the overall architecture of SMIS. Finally, the above integrated subsystems were collected together to deduce the implementation path of SMIS. Four reference subsystems for SMIS proposed in this paper have achieved good effects in the projects we participated in, which were organized by the state. The research results of this paper can be used as a reference for industry and government to design, set, and carry out SMIS. At the same time, it has a certain reference value for the improvement and supplement of national smart manufacturing system architecture.

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References

  1. Moeuf A, Pellerin R, Lamouri S, Tamayogiraldo S, Barbaray R (2018) The industrial management of SMEs in the era of Industry 4.0. International. J Prod Res:1–19 https://www.researchgate.net/publication/319612802

  2. Cheng H, Li F, Mao Q (2015) The empirical analysis on the influence of CO2 emission regulation on the export transformation of Chinese manufacturing industries. J Coast Res 73:209–215 https://www.researchgate.net/publication/277579825

    Article  Google Scholar 

  3. Gentner S (2016) Industry 4.0: reality, future or just science fiction? How to convince today’s management to invest in tomorrow’s future! Successful strategies for Industry 4.0 and manufacturing IT. Chimia 70(9):628–633 https://www.researchgate.net/publication/308272980

    Article  Google Scholar 

  4. Wang Q, Sun X, Cobb S, Lawson G, Sharples S (2018) 3D printing system: an innovation for small-scale manufacturing in home settings? – early adopters of 3D printing systems in China. Int J Prod Res 54(20):1–16 https://www.researchgate.net/publication/296620706

    Article  Google Scholar 

  5. Flatt H, Schriegel S, Jasperneite J, Trsek H 2016 Adamczyk H Analysis of the cyber-security of Industry 4.0 technologies based on RAMI 4.0 and identification of requirements. In: IEEE International Conference on Emerging Technologies and Factory Automation

  6. Lei Y (2018) Discussion of current international standards landscape for smart manufacturing reference models. Standard Science. http://www.cnki.com.cn/Article/CJFDTotal-SJBZ201805010.htm

  7. Weyrich M, Ebert C (2015) Reference architectures for the Internet of things. IEEE Softw 33(1):112–116 https://www.researchgate.net/publication/288855901

    Article  Google Scholar 

  8. Lv C, Liu Y, Hu X, Guo H, Cao D, Wang FY (2017) Simultaneous observation of hybrid states for cyber-physical systems: a case study of electric vehicle powertrain. IEEE Trans Cybern 48(8):1–11 https://www.researchgate.net/publication/319240485

    Google Scholar 

  9. Li L (2017) China’s manufacturing locus in 2025: with a comparison of “Made-in-China 2025” and “Industry 4.0”. Technol Forecast Associal Chang https://www.sciencedirect.com/science/article/pii/S0040162517307254

  10. Wei S, Hu J, Cheng Y, Ma Y, Yu Y 2018 The essential elements of intelligent manufacturing system architecture. In: Automation science and engineering

  11. Chien C-F, Gen M, Shi Y, Hsu C-Y (2014) Manufacturing intelligence and innovation for digital manufacturing and operational excellence. J Intell Manuf 25(5):845–847. https://doi.org/10.1007/s10845-014-0896-5 https://link.springer.com/article/10.1007/s10845-014-0896-5

    Article  Google Scholar 

  12. Lee C, Leem CS, Hwang I (2011) PDM and ERP integration methodology using digital manufacturing to support global manufacturing. Int J Adv Manuf Technol 53(1–4):399–409. https://doi.org/10.1007/s00170-010-2833-x https://link.springer.com/article/10.1007/s00170-010-2833-x

    Article  Google Scholar 

  13. Hwang G, Lee J, Park J, Chang TW (2017) Developing performance measurement system for Internet of things and smart factory environment. Int J Prod Res 55(9):2590–2602 https://www.researchgate.net/publication/309217530

    Article  Google Scholar 

  14. Ying B, Sayed AH (2016) Information exchange and learning dynamics over weakly connected adaptive networks. IEEE Trans Inf Theory 62(3):1396–1414 http://www.researchgate.net/profile/Bicheng_Ying/publication/269116930

    Article  MathSciNet  MATH  Google Scholar 

  15. Baxter G, Sommerville I (2011) Socio-technical systems: from design methods to systems engineering. Interact Comput 23(1):4–17 https://www.researchgate.net/publication/220054725

    Article  Google Scholar 

  16. Daza L, Misra S (2018) Beyond the internet of things: everything interconnected: technology, communications and computing. IEEE Wirel Commun 24(6):10–11 https://ieeexplore.ieee.org/document/8246819

    Article  Google Scholar 

  17. Zhong RY, Xu C, Chen C, Huang GQ (2017) Big data analytics for physical Internet-based intelligent manufacturing shop floors. Int J Prod Res 55(9):2610–2621 https://www.researchgate.net/publication/281649322

    Article  Google Scholar 

  18. Yang X, Zhao N, Bian Z, Chai J, Mi C (2015) An intelligent storage determining method for inbound containers in container terminals. J Coast Res 73:197–204 https://www.researchgate.net/publication/277579827

    Article  Google Scholar 

  19. Kim S, D’Anjou M, Lanz KJ, Evans CE, Gibson ER, Olesberg JT, Mallem M, Shandil I, Nylen A, Koerperick EJ (2015) Real-time monitoring of glycerol and methanol to enhance antibody production in industrial Pichia pastoris bioprocesses. Biochem Eng J 94:115–124 https://www.researchgate.net/publication/270339589

    Article  Google Scholar 

  20. Leng J, Jiang P (2017) Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information. J Intell Manuf. https://doi.org/10.1007/s10845-017-1301-y https://doi.org/10.1007/s10845-017-1301-y

  21. Ahlswede R, Cai N, Li SYR, Yeung RW (2000) Network information flow. IEEE Trans Inf Theory 46(4):1204–1216 https://www.researchgate.net/publication/220443954

    Article  MathSciNet  MATH  Google Scholar 

  22. Chen X, Irie K, Banks D, Haslinger R, Thomas J, West M (2016) Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data. J Am Stat Assoc https://www.researchgate.net/publication/305186220

  23. Zhang Y, Zhang G, Liu Y, Hu D (2017) Research on services encapsulation and virtualization access model of machine for cloud manufacturing. J Intell Manuf 28(5):1109–1123. https://doi.org/10.1007/s10845-015-1064-2 https://doi.org/10.1007/s10845-015-1064-2

    Article  Google Scholar 

  24. Strozzi F, Colicchia C, Creazza A, Noè C (2017) Literature review on the ‘Smart Factory’ concept using bibliometric tools. Int J Prod Res 55(22):1–20 https://www.researchgate.net/publication/316872924

    Article  Google Scholar 

  25. Wan N, Wang Z, Mo R (2013) An intelligent fixture design method based on smart modular fixture unit. Int J Adv Manuf Technol 69(9–12):2629–2649. https://doi.org/10.1007/s00170-013-5134-3 https://link.springer.com/article/10.1007/s00170-013-5134-3

    Article  Google Scholar 

  26. Koren Y, Bollinger JG (2008) Design parameters for sampled-data drives for CNC machine tools. IEEE Trans Ind Appl IA-14(3):255–264 https://www.researchgate.net/publication/3173589

    Article  Google Scholar 

  27. Vasiliev VA, Chernov PS (2013) Smart sensors, sensor networks, and digital interfaces general problems;of metrology and measurement technique. Meas Tech 55(10):1115–1119 https://www.researchgate.net/publication/263719688

    Article  Google Scholar 

  28. Németh E, Bartha T, Fazekas C, Hangos KM (2009) Verification of a primary-to-secondary leaking safety procedure in a nuclear power plant using coloured Petri nets. Reliab Eng Syst Saf 94(5):942–953 https://www.researchgate.net/publication/222339737

    Article  Google Scholar 

  29. Zhou JH, Wee L, Zhong ZW (2010) A knowledge base system for rotary equipment fault detection and diagnosis. IEEE:1335–1340 https://www.researchgate.net/publication/221144152

  30. Kusiak A (2017) Smart manufacturing must embrace big data. Nature 544(7648):23–25 https://www.researchgate.net/publication/315792168

    Article  Google Scholar 

  31. Iyama T, Mizuno M, Mckay KN, Nishikawa N, Kawakita T (2011) A corrective assembly method using a buffer in a high-precision machining-assembly production system. Int J Prod Res 49(10):2745–2758 https://www.researchgate.net/publication/233370610

    Article  Google Scholar 

  32. Schwake DO, Alum A, Abbaszadegan M (2015) Automobile windshield washer fluid: a potential source of transmission for Legionella. Sci Total Environ 526:271–277 https://www.researchgate.net/publication/276066144

    Article  Google Scholar 

  33. Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 96(1–4):1149–1163. https://doi.org/10.1007/s00170-018-1617-6 http://link.springer.com/10.1007/s00170-018-1617-6

    Article  Google Scholar 

  34. Singh P, Rajesh B, Bishnoi S, Swati G, Jaiswal VV, Shanker V, Haranath D (2016) Optimization of processing parameters for designing an efficient AC driven powder electroluminescent device. Ceram Int 42(15):17016–17022 https://www.researchgate.net/publication/305750800

    Article  Google Scholar 

  35. Renfroe M, Mcdonald E, Bradshaw K (2013) Integrated tracking of components by engineering and logistics utilizing logistics asset tracking system. AIAA J https://core.ac.uk/display/10412917

  36. White TS, Alexander R, Callow G, Cooke A, Harris S, Sargent J (2005) A mobile climbing robot for high precision manufacture and inspection of aerostructures. Int J Robot Res 24(7):589–598 https://www.researchgate.net/publication/220122274

    Article  Google Scholar 

  37. Zhou K (2009) Application of on-line monitoring and fault diagnosis technology of power transmission equipment. Electr Eng http://lib.cqvip.com/qk/81668X/200001/1001044254.html

  38. Pocovi G, Kolding T, Lauridsen M, Mogensen R, Markmoller C, Jess-Williams R (2018) Measurement framework for assessing reliable real-time capabilities of wireless networks. IEEE Commun Mag PP 99:1–8 https://ieeexplore.ieee.org/document/8450876

    Google Scholar 

  39. Wu Y, Jin Y, Li Y, Sun D, Liu X, Chen Y (2012) NIR spectroscopy as a process analytical technology (PAT) tool for on-line and real-time monitoring of an extraction process. Vib Spectrosc 58(1):109–118 http://www.researchgate.net/publication/241100211

    Article  Google Scholar 

  40. Wang S, Wan J, Imran M, Li D, Zhang C (2016) Cloud-based smart manufacturing for personalized candy packing application. J Supercomput 1:1–19

    Google Scholar 

  41. Zhuang YT, Wu F, Chen C, Pan YH (2017) Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inform Technol Electron Eng 18(1):3–14 https://link.springer.com/article/10.1631/FITEE.1601883

    Article  Google Scholar 

  42. Li W, Wu WJ, Wang HM, Cheng XQ, Chen HJ, Zhou ZH, Ding R (2017) Crowd intelligence in AI 2.0 era. Front Inform Technol Electron Eng 18(1):15–43. https://doi.org/10.1631/FITEE.1601859 https://link.springer.com/article/10.1631%2FFITEE.1601859

    Article  Google Scholar 

  43. Zheng NN, Liu ZY, Ren PJ, Ma YQ, Chen ST, Yu SY, Xue JR, Chen BD, Wang FY (2017) Hybrid-augmented intelligence: collaboration and cognition. Front Inform Technol Electron Eng 18(2):153–179. https://doi.org/10.1631/FITEE.1700053 https://link.springer.com/article/10.1631/FITEE.1700053

    Article  Google Scholar 

  44. Peng YX, Zhu WW, Zhao Y, Chang-Sheng XU, Huang QM, Han-Qing LU, Zheng QH, Huang TJ, Gao W (2017) Cross-media analysis and reasoning: advances and directions. Front Inform Technol Electron Engin 18(1):44–57 https://link.springer.com/article/10.1631/FITEE.1601787

    Article  Google Scholar 

  45. Zhang T, Qing LI, Zhang CS, Liang HW, Ping LI, Wang TM, Shuo LI, Zhu YL, Cheng WU, Automation DO (2017) Current trends in the development of intelligent unmanned autonomous systems. Front Inform Technol Electron Eng 18(1):68–85 https://link.springer.com/article/10.1631%2FFITEE.1601650

    Article  Google Scholar 

  46. Caprihan R, Kumar A, Stecke KE (2013) Evaluation of the impact of information delays on flexible manufacturing systems performance in dynamic scheduling environments. Int J Adv Manuf Technol 67(1–4):311–338. https://doi.org/10.1007/s00170-013-4755-x https://www.researchgate.net/publication/257337363

    Article  Google Scholar 

  47. Srai JS, Kumar M, Graham G, Phillips W, Tooze J, Ford S, Beecher P, Raj B, Gregory M, Tiwari MK (2016) Distributed manufacturing: scope, challenges and opportunities. Int J Prod Res 54(23):6917–6935 https://www.researchgate.net/publication/290445246

    Article  Google Scholar 

  48. Liu Q, Dong M, Lv W, Ye C (2017) Manufacturing system maintenance based on dynamic programming model with prognostics information. J Intell Manuf. https://doi.org/10.1007/s10845-017-1314-6 https://doi.org/10.1007/s10845-017-1314-6

  49. Wan J, Chen B, Imran M, Tao F, Li D, Liu C, Ahmad S (2018) Toward dynamic resources management for IoT-based manufacturing. IEEE Commun Mag 56(2):52–59 https://www.researchgate.net/publication/323155753

    Article  Google Scholar 

  50. Papazoglou M, Heuvel WJVD, Mascolo J (2015) Reference architecture and knowledge-based structures for smart manufacturing networks. IEEE Softw 32(3):61–69 https://www.researchgate.net/publication/276139822

    Article  Google Scholar 

  51. Pu Z, Jiang Q, Yue H, Tsaptsinos M (2018) Agent-based supply chain allocation model and its application in smart manufacturing enterprises. J Supercomput 11:1–11

    Google Scholar 

  52. Lade P, Ghosh R, Srinivasan S (2017) Manufacturing analytics and industrial Internet of things. IEEE Intell Syst 32(3):74–79 https://www.researchgate.net/publication/317161415

    Article  Google Scholar 

  53. Li X, Li D, Wan J, Vasilakos AV, Lai CF, Wang S (2017) A review of industrial wireless networks in the context of Industry 4.0. Wirel Netw 23(1):1–19 http://link.springer.com/10.1007/s11276-015-1133-7

    Article  Google Scholar 

  54. Li BH, Hou BC, Yu WT, Lu XB, Yang CW (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Front Inform Technol Electron Eng 18(1):86–96 https://link.springer.com/article/10.1631%2FFITEE.1601885

    Article  Google Scholar 

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Acknowledgment

The authors would like to thank Shanghai Key Laboratory of Advanced Manufacturing Environment, Shanghai Research Center for Industrial Informatics (SRCI2), and SJTU Innovation Center of Producer Service Development (SICPSD) for the funding support to this research.

Funding

This work was supported by the National Natural Science Foundation of China (grant number 71632008); Transformation and Upgrading of Industry in 2017 (China Manufacturing 2025) (grant number ZL35060009002); and Innovation and Development of Industrial Internet in Shanghai of China (grant number 2017-GYHLW-01009).

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Authors and Affiliations

  1. Shanghai Research Center for Industrial Informatics, Shanghai Key Lab of Advanced Manufacturing Environment, Producer Service Development Innovation Center of Shanghai Jiao Tong University, Institute of Smart Manufacturing, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai City, People’s Republic of China

    Xianyu Zhang, Xinguo Ming, Zhiwen Liu, Yuanju Qu & Dao Yin

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  1. Xianyu Zhang
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Zhang, X., Ming, X., Liu, Z. et al. An overall framework and subsystems for smart manufacturing integrated system (SMIS) from multi-layers based on multi-perspectives. Int J Adv Manuf Technol 103, 703–722 (2019). https://doi.org/10.1007/s00170-019-03593-6

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