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A next-generation IoT-based collaborative framework for electronics assembly

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Abstract

In today’s dynamic manufacturing environments, the adoption of virtual reality (VR)-based simulation technologies to help in product and process design activities is becoming more widespread. With the onset of the next IT-oriented revolution involving global cyber manufacturing practices, the recent emergence of Internet of things (IoT)-related technologies holds significant promise in ushering an era of seamless information exchange which will provide a robust foundation for the next generation of smart manufacturing frameworks dependent on cyber physical system (CPS)-based principles, approaches, and technologies. In this paper, we present a broad framework for IoT-based collaborations involving the adoption of VR-based analysis environments networked with other cyber physical components. The process context for this VR-centered approach is electronics assembly, which involves the assembly of printed circuit boards. In such manufacturing contexts, it is essential to have a seamless flow of data/information among the various cyber physical components to ensure an agile collaborative strategy which can accommodate changing customer needs. VR-based simulation environments play a key role in this framework which supports multiple users collaborating using haptic interfaces and next-generation network technologies. The simulation outcomes and production data from physical shop floors can be compared and analyzed using this IoT framework and approach.

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References

  1. Carlsson I, Aronsson H (2017) Investing in lean to improve basic capabilities: a strategy for system supply? J Ind Eng Manag 10(1):28–48. https://doi.org/10.3926/jiem.2163

    Google Scholar 

  2. Lu Y, Cecil J (2016) An Internet of Things (IoT)-based collaborative framework for advanced manufacturing. Int J Adv Manuf Technol 84(5):1141–1152

    Google Scholar 

  3. Cecil, J (2017) Internet of things (IoT) based cyber physical frameworks for advanced manufacturing and medicine, Internet of things (IoT) & data analytics handbook, John Wiley & Sons, pp. 545–559

  4. Cecil J, Chandler D (2014) In: Aung W et al (eds) Cyber physical systems and technologies for next generation e-learning activities, innovations 2014. iNEER, Potomac

    Google Scholar 

  5. Da Xu L, Wu H, Li S (2014) Internet of things in industries: a survey. IEEE Trans Ind Inf 10(4):2233–2243. https://doi.org/10.1109/TII.2014.2300753

    Article  Google Scholar 

  6. Liu Y, Peng Y, Wang B, Yao S, Liu Z (2017) Review on cyber-physical systems. IEEE/CAA J Autom Sin 4(1):27–38. https://doi.org/10.1109/JAS.2017.7510349

    Article  Google Scholar 

  7. Uckelmann D, Harrison M, Michahelles F (2011) An architectural approach towards the future internet of things. In: Architecting the internet of things. Springer, Berlin Heidelberg, pp 1–24. https://doi.org/10.1007/978-3-642-19157-2

    Chapter  Google Scholar 

  8. Martinez B, Monton M, Vilajosana I, Prades JD (2015) The power of models: modeling power consumption for IoT devices. IEEE Sensors J 15(10):5777–5789. https://doi.org/10.1109/JSEN.2015.2445094

    Article  Google Scholar 

  9. Pan J, Jain R, Paul S, Vu T, Saifullah A, Sha M (2015) An internet of things framework for smart energy in buildings: designs, prototype, and experiments. IEEE Internet Things J 2(6):527–537. https://doi.org/10.1109/JIOT.2015.2413397

    Article  Google Scholar 

  10. Catarinucci L, De Donno D, Mainetti L, Palano L, Stefanizzi M, Tarricone L (2015) An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J 4662(c):1–1

    Google Scholar 

  11. Kelly SDT, Suryadevara NK, Mukhopadhyay SC (2013) Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sensors J 13(10):3846–3853. https://doi.org/10.1109/JSEN.2013.2263379

    Article  Google Scholar 

  12. Maksimovic, M., Vujovic, V., Perisic, B (2015) A custom internet of things healthcare system. 2015 10th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE

  13. Amendola S, Lodato R, Manzari S, Occhiuzzi C, Marrocco G (2014) RFID technology for IoT-based personal healthcare in SmartSpaces. IEEE Internet Things J PP(99):1–1

    Google Scholar 

  14. Alam KM, Saini M, El Saddik A (2015) Toward social internet of vehicles: concept, architecture, and applications. IEEE Access 3:343–357. https://doi.org/10.1109/ACCESS.2015.2416657

    Article  Google Scholar 

  15. Kantarci B, Mouftah HT (2014) Trustworthy sensing for public safety in cloud-centric internet of things. IEEE Internet Things J 1(4):360–368

    Article  Google Scholar 

  16. Xiang-li, Z., Jin, Y., Kun, Y., Jian, L (2012) A remote manufacturing monitoring system based on the Internet of Things, Proc. 2012 2nd Int Conf Comput Sci Netw Technol, pp. 221–224

  17. Zhu Q, Zou F, Deng Y (2015) Intelligent data analysis and applications. Adv Intell Syst Comput 370:439–448

    Google Scholar 

  18. Sundmaeker, H., Guillemin, P., Friess, P., Woelfflé, S (2010) Vision and challenges for realizing the Internet of Things, cluster of European research projects on the Internet of Things—CERP IoT

  19. Buckley J (ed) (2006) The internet of things: from RFID to the next-generation pervasive networked systems. Auerbach Publications, New York

    Google Scholar 

  20. Ashton, K (2009) That “Internet of Things” thing, RFiD J

  21. M. Hermann, T. Pentek and B. Otto (2016) Design principles for industrie 4.0 scenarios, 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, pp. 3928–3937

  22. Lee J, Kao HA, Yang S (2014) Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16:3–8. https://doi.org/10.1016/j.procir.2014.02.001

    Article  Google Scholar 

  23. Brettel M, Friederichsen N, Keller M, Rosenberg M (2014) How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective. Int J Sci Eng Technol 8(1):37–44

    Google Scholar 

  24. Pérez, F., Irisarri, E., Orive, D., Marcos, M., & Estevez, E (2015) A CPPS architecture approach for industry 4.0. In Emerging technologies & factory automation (ETFA), 2015 I.E. 20th Conference on (pp. 1-4). IEEE

  25. Yen, C. T., Liu, Y. C., Lin, C. C., Kao, C. C., Wang, W. B., & Hsu, Y. R. (2014) Advanced manufacturing solution to industry 4.0 trend through sensing network and cloud computing technologies. In Automation Science and Engineering (CASE), 2014 I.E. International Conference on (pp. 1150-1152). IEEE

  26. Posada J, Toro C, Barandiaran I, Oyarzun D, Stricker D, de Amicis R, Vallarino I (2015) Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Comput Graph Appl 35(2):26–40. https://doi.org/10.1109/MCG.2015.45

    Article  Google Scholar 

  27. Gorecky, D., Schmitt, M., Loskyll, M., & Zühlke, D (2014) Human-machine-interaction in the industry 4.0 era. In Industrial Informatics (INDIN), 2014 12th IEEE International Conference on (pp. 289-294). IEEE

  28. Berman M, Demeester P, Lee J, Nagaraja K, Zink M, Colle D et al (2015) Future internets escape the simulator. Commun ACM 58(6):78–89

    Article  Google Scholar 

  29. Cecil J, Kanchanapiboon A (2007) Virtual engineering approaches in product and process design. Int J Adv Manuf Technol 31(9-10):846–856. https://doi.org/10.1007/s00170-005-0267-7

    Article  Google Scholar 

  30. Son S, Na S, Kim K, Lee S (2014) Collaborative design environment between ECAD and MCAD engineers in high-tech products development. Int J Prod Res 52:1–14

    Article  Google Scholar 

  31. Zhang C, Wu. (2014) Investigating the impact of operational variables on manufacturing cost by simulation optimization. Int J Prod Econ 147:634–646. https://doi.org/10.1016/j.ijpe.2013.04.018

    Article  Google Scholar 

  32. Bensmaine A, Dahane M, Benyoucef L (2014) A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems. Int J Prod Res 52(12):1–12

    Article  Google Scholar 

  33. Girard, Auvray, & Ammi (2012) Haptic designation strategy for collaborative molecular modelling. Haptic Audio-Visual Environments and Games (HAVE), 2012 I.E. International Workshop on, 119–123

  34. Qin J, Choi K, Xu R, Pang W, Heng P (2013) Effect of packet loss on collaborative haptic interactions in networked virtual environments: An experimental study. Presence 22(1):36–53

    Article  Google Scholar 

  35. Son S, Na S, Kim K, Lee S (2014) Collaborative design environment between ECAD and MCAD engineers in high-tech products development. Int J Prod Res 25:1–14

    Google Scholar 

  36. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107. https://doi.org/10.1109/TSSC.1968.300136

    Article  Google Scholar 

  37. Chellali A, Dumas C, Milleville-Pennel I (2011) Influence of haptic communication on a shared manual task in a collaborative virtual environment. Interact Comput 23:317–328

    Article  Google Scholar 

  38. Cecil, J, Cecil-Xavier, A, Gupta, A Foundational elements of next generation cyber physical and IoT frameworks for distributed collaboration, Proceedings of the 2017 13th IEEE Conference on Automation Science and Engineering (CASE 2017), Xian, China, Aug 20–23

  39. Wu, Sarma (2005) A framework for fast 3D solid model exchange in integrated design environment. Comput Ind 56(3):289–304. https://doi.org/10.1016/j.compind.2004.11.003

    Article  Google Scholar 

  40. Tian, Alregib (2006) On-demand transmission of 3D models over lossy networks. Signal Process Image Commun 21(5):396–415. https://doi.org/10.1016/j.image.2006.01.003

    Article  Google Scholar 

  41. Ahmad S, Hamzaoui R, Al-Akaidi M (2009) Optimal packet loss protection of progressively compressed 3-D meshes. IEEE Trans Multimedia 11(7):1381–1387. https://doi.org/10.1109/TMM.2009.2030546

    Article  Google Scholar 

  42. Liukkonen M, Tsai TN (2016) Toward decentralized intelligence in manufacturing: recent trends in automatic identification of things. Int J Adv Manuf Technol 87(9-12):2509–2531. https://doi.org/10.1007/s00170-016-8628-y

    Article  Google Scholar 

  43. Marquez J, Villanueva J, Solarte Z, Garcia A (2013) Advances in information systems and technologies. Adv Intell Syst Comput AISC 206(115):201–212

    Google Scholar 

  44. Li S, Da Xu L, Zhao S (2015) The internet of things: a survey. Inf Syst Front 17(2):243–259. https://doi.org/10.1007/s10796-014-9492-7

    Article  Google Scholar 

  45. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010

    Article  MATH  Google Scholar 

  46. Liu, J, Yu, J (2013) Research on the framework of internet of things in manufacturing for aircraft large components assembly site. In Green computing and communications (GreenCom), 2013 I.E. and Internet of Things (iThings/CPSCom), IEEE International Conference on Cyber, Physical and Social Computing (pp. 1192-1196). IEEE

  47. Bi Z, Da Xu L, Wang C (2014) Internet of Things for enterprise systems of modern manufacturing. IEEE Trans Ind Inform 10(2):1537–1546

    Article  Google Scholar 

  48. Xu X (2012) From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 28(1):75–86. https://doi.org/10.1016/j.rcim.2011.07.002

    Article  Google Scholar 

  49. Mujber S, Hashmi (2004) Virtual reality applications in manufacturing process simulation. J Mater Process Tech 155-156:1834–1838. https://doi.org/10.1016/j.jmatprotec.2004.04.401

    Article  Google Scholar 

  50. Yao, J, Lin, C, Xie, X, Wang, A, & Hung, C (2010). Path planning for virtual human motion using improved A* star algorithm. Information Technology: New Generations (ITNG), 2010 Seventh International Conference on, 1154–1158

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Acknowledgements

The authors would like to acknowledge the assistance received from the GENI program technical staff in providing access to the GENI networking resources.

Funding

This material is based upon work supported by the National Science Foundation under Grant Numbers CISE 1447237 and CMMI 1547156.

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Authors and Affiliations

  1. Center for Cyber Physical Systems, Oklahoma Manufacturing Alliance, Oklahoma State University, Stillwater, OK, USA

    Rajesh Krishnamurthy

  2. Center for Cyber Physical Systems, Oklahoma State University, Stillwater, OK, USA

    J. Cecil

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  1. Rajesh Krishnamurthy
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  2. J. Cecil
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Correspondence to J. Cecil.

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Krishnamurthy, R., Cecil, J. A next-generation IoT-based collaborative framework for electronics assembly. Int J Adv Manuf Technol 96, 39–52 (2018). https://doi.org/10.1007/s00170-017-1561-x

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  • DOI: https://doi.org/10.1007/s00170-017-1561-x

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