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Digitalization of a standard robot arm toward 4th industrial revolution

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Abstract

Increasing the productivity while maintaining the quality of manufactured products is essential in the present industrial context. In this sense, the use of robotic devices in manufacturing facilities is increasing due to the advantages related to flexibility, repeatability, and low-cost, when compared to machining centers. However, a lack of digital connectivity between machines within the manufacturing system is a fact. Thus, in this paper, a low-cost instrumentation, sensing, and cloud technologies are proposed to monitor robotized manufacturing processes by digitalization of traditional robot arms. As a case study, a drilling process of different aircraft materials is performed to prove the digital integration. So, the results showed an interesting potential of the proposed methodology, especially in the case of material removal processes performed by robotized cells that are still challenging for conventional robots due to the lack of rigidity of their components.

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  1. https://thingspeak.com/

  2. https://developer.android.com/things

References

  1. Stäubli robotics documentation - Recorder addon. https://secure.staubli.com/Intranet_Applications/Robotics/Group/RobDoc.nsf/webcategory/980264EE028DE11DC1257A320034A51A?opendocument

  2. Industrial robot sales increase worldwide by 31 percent (2018). https://ifr.org/ifr-press-releases/news/industrial-robot-sales-increase-worldwide-by-29-percent

  3. de Aguiar PR, de Paula WCF, Bianchi EC, Ulson JAC, Cruz CED (2010) Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks. J Braz Soc Mech Sci Eng 32:146–153

    Google Scholar 

  4. Aguiar PR, Silva RBD, Gerônimo TM, Franchin MN, Bianchi EC (2017) Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques. Int J Brazilian Soc Mech Sci Eng 39:127–153. https://doi.org/10.1007/s40430-016-0525-7

    Article  Google Scholar 

  5. Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press. https://books.google.com.br/books?id=St38a25qLa0C

  6. Altintas Y, Aslan D (2017) Integration of virtual and on-line machining process control and monitoring. CIRP Ann 66(1):349–352

    Article  Google Scholar 

  7. Andò B, Baglio S, Pistorio A (2014) A low cost multi-sensor approach for early warning in structural monitoring of buildings and structures. In: 2014 IEEE International instrumentation and measurement technology conference (I2MTC) proceedings, pp 742–746. https://doi.org/10.1109/I2MTC.2014.6860841

  8. Backer JD, Christiansson A, Oqueka J, Bolmsjö G (2012) Investigation of path compensation methods for robotic friction stir welding. Industr Robot: Int J 39:601–608. https://doi.org/10.1108/01439911211268813

    Article  Google Scholar 

  9. Barton D, Gönnheimer P, Schade F, Ehrmann C, Becker J, Fleischer J (2019) Modular smart controller for industry 4.0 functions in machine tools. Procedia CIRP 81:1331–1336

    Article  Google Scholar 

  10. Belchior J, Guillo M, Courteille E, Maurine P, Leotoing L, Guines D (2013) Off-line compensation of the tool path deviations on robotic machining: application to incremental sheet forming. Robot Comput Integr Manuf 29:58–69. https://doi.org/10.1016/j.rcim.2012.10.008

    Article  Google Scholar 

  11. Caro S, Dumas C, Garnier S, Furet B (2013) Workpiece placement optimization for machining operations with a KUKA KR270-2 robot. https://doi.org/10.1109/icra.2013.6630982

  12. Chang WY, Wu SJ (2018) Big data analysis of a mini three-axis CNC machine tool based on the tuning operation of controller parameters. Int J Adv Manuf Technol 99:1077–1083. https://doi.org/10.1007/s00170-016-9846-z

    Article  Google Scholar 

  13. Cordes M, Hintze W, Altintas Y (2019) Chatter stability in robotic milling. Robot Comput Integr Manuf 55:11–18. https://doi.org/10.1016/j.rcim.2018.07.004

    Article  Google Scholar 

  14. Drath R, Horch A (2014) Industrie 4.0: hit or hype? [Industry Forum]. IEEE Ind Electron Mag 8:56–58. https://doi.org/10.1109/mie.2014.2312079

    Article  Google Scholar 

  15. García MV, Irisarri E, Pérez F, Estévez E, Marcos M (2016) OPC-UA communications integration using a CPPS architecture. In: 2016 IEEE Ecuador technical chapters meeting (ETCM), pp 1–6, DOI https://doi.org/10.1109/ETCM.2016.7750838, (to appear in print)

  16. Ge M, Xu Y, Du R (2008) An intelligent online monitoring and diagnostic system for manufacturing automation. IEEE Trans Autom Sci Eng 5(1):127–139

    Article  Google Scholar 

  17. Ghomi EJ, Rahmani AM, Qader NN (2019) Cloud manufacturing: challenges, recent advances, open research issues, and future trends. Int J Adv Manuf Technol 102:3613–3639. https://doi.org/10.1007/s00170-019-03398-7

    Article  Google Scholar 

  18. Godoy AC, Pérez IG (2018) Integration of sensor and actuator networks and the SCADA system to promote the migration of the legacy flexible manufacturing system towards the industry 4.0 concept. J Sensor Actuat Netw 7:23. https://doi.org/10.3390/jsan7020023

    Article  Google Scholar 

  19. Guillo M, Dubourg L (2016) Impact & improvement of tool deviation in friction stir welding: weld quality & real-time compensation on an industrial robot. Robot Comput Integr Manuf 39:22–31. https://doi.org/10.1016/j.rcim.2015.11.001

    Article  Google Scholar 

  20. Guo Y, Dong H, Wang G, Ke Y (2016) Vibration analysis and suppression in robotic boring process. Int J Mach Tools Manuf 101:102–110. https://doi.org/10.1016/j.ijmachtools.2015.11.011

    Article  Google Scholar 

  21. Hao X, Li Y, Li M, Liu C (2019) A part deformation control method via active pre-deformation based on online monitoring data. Int J Adv Manuf Technol, 1–12

  22. Jazdi N (2014) Cyber physical systems in the context of Industry 4.0. https://doi.org/10.1109/aqtr.2014.6857843

  23. Ji W, Wang L (2017) Big data analytics based fault prediction for shop floor scheduling. J Manuf Syst 43:187–194

    Article  Google Scholar 

  24. Jiang JR (2017) An improved cyber-physical systems architecture for Industry 4.0 smart factories. https://doi.org/10.1109/icasi.2017.7988589

  25. Kang HS, Lee J, Choi S, Kim H, Park JH, Son JY, Kim BH, Noh SD (2016) Smart manufacturing: past research, present findings, and future directions. Int J Precis Eng Manuf-Green Technol 3:111–128. https://doi.org/10.1007/s40684-016-0015-5

    Article  Google Scholar 

  26. Kiangala KS, Wang Z (2018) Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts. Int J Adv Manuf Technol 97:3251–3271. https://doi.org/10.1007/s00170-018-2093-8

    Article  Google Scholar 

  27. Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23

    Article  Google Scholar 

  28. Lin Y, Zhao H, Ding H (2017) Posture optimization methodology of 6R industrial robots for machining using performance evaluation indexes. Robot Comput Integr Manuf 48:59–72. https://doi.org/10.1016/j.rcim.2017.02.002

    Article  Google Scholar 

  29. Lockwood AJ, Hill G, Moldoveanu M, Coles R, Scott R (2018) Digitalisation of legacy machine tools. Tech. rep., Advanced Manufacturing Research Centre (AMRC) University of Sheffield

  30. Lu Y (2017) Industry 4.0: a survey on technologies, applications and open research issues. J Indus Inf Integr 6:1–10. https://doi.org/10.1016/j.jii.2017.04.005

    Google Scholar 

  31. Martin C, Snabe JH, Nanterme P (2017) Digital transformation initiative in collaboration with accenture. Tech. rep., World Economic Forum. http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/dti-executive-summary-20180510.pdf

  32. Matsushima M, Kawai N, Fujie H, Yasuda K, Fujimoto K (2010) Visual inspection of soldering joints by neural network with multi-angle view and principal component analysis. In: Shirase K, Aoyagi S (eds) Service robotics and mechatronics. Springer, London, pp 329–334

    Google Scholar 

  33. Mejri S, Gagnol V, Le TP, Sabourin L, Ray P, Paultre P (2016) Dynamic characterization of machining robot and stability analysis. Int J Adv Manuf Technol 82:351–359. https://doi.org/10.1007/s00170-015-7336-3

    Article  Google Scholar 

  34. Mourtzis D, Milas N, Athinaios N (2018) Towards machine shop 4.0: a general machine model for CNC machine-tools through OPC-UA. Procedia CIRP 78:301–306

    Article  Google Scholar 

  35. Munoa J, Beudaert X, Dombovari Z, Altintas Y, Budak E, Brecher C, Stepan G (2016) Chatter suppression techniques in metal cutting. CIRP Ann 65(2):785–808

    Article  Google Scholar 

  36. Pan Z, Zhang H, Zhu Z, Wang J (2006) Chatter analysis of robotic machining process. J Mater Process Technol 173:301–309. https://doi.org/10.1016/j.jmatprotec.2005.11.033

    Article  Google Scholar 

  37. Perez F, Irisarri E, Orive D, Marcos M, Estevez E (2015) A CPPS Architecture approach for Industry 4.0. https://doi.org/10.1109/etfa.2015.7301606

  38. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51 (5):363– 376. https://doi.org/10.1016/j.ijmachtools.2011.01.001. http://www.sciencedirect.com/science/article/pii/S0890695511000022

    Article  Google Scholar 

  39. Rivin EI (1996) Machine-tool vibration, chap. 40. McGraw-Hill, pp 1–22

  40. Sabato A, Niezrecki C, Fortino G (2017) Wireless MEMS-based accelerometer sensor boards for structural vibration monitoring: a review. IEEE Sensors J 17(2):226–235. https://doi.org/10.1109/JSEN.2016.2630008

    Article  Google Scholar 

  41. Schwab K, Miranda DM (2015) A quarta revoluçao industrial. EDIPRO. https://books.google.com.br/books?id=0wgcvgAACAAJ

  42. Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 61:27–47. https://doi.org/10.1016/j.ijmachtools.2012.05.007

    Article  Google Scholar 

  43. Sisinni E, Saifullah A, Han S, Jennehag U, Gidlund M (2018) Industrial internet of things: challenges, opportunities, and directions. IEEE Trans Industr Inf, 1–1. https://doi.org/10.1109/TII.2018.2852491

    Article  Google Scholar 

  44. Tang D, Zheng K, Zhang H, Sang Z, Zhang Z, Xu C, Espinosa-Oviedo JA, Vargas-Solar G, Zechinelli-Martini JL (2016) Using autonomous intelligence to build a smart shop floor. Procedia CIRP 56:354–359. https://doi.org/10.1016/j.procir.2016.10.039

    Article  Google Scholar 

  45. Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94:3563–3576. https://doi.org/10.1007/s00170-017-0233-1

    Article  Google Scholar 

  46. Trotta D, Garengo P (2018) Industry 4.0 key research topics: a bibliometric review. In: 2018 7th international conference on industrial technology and management (ICITM), pp 113–117, https://doi.org/10.1109/ICITM.2018.8333930

  47. Varanis M, Silva A, Mereles A, Pederiva R (2018) MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications. J Braz Soc Mech Sci Eng 40(11):527. https://doi.org/10.1007/s40430-018-1445-5

    Article  Google Scholar 

  48. Vogel-Heuser B, Wildermann S, Teich J (2017) Towards the co-evolution of industrial products and its production systems by combining models from development and hardware/software deployment in cyber-physical systems. Prod Eng 11:687–694. https://doi.org/10.1007/s11740-017-0765-0

    Article  Google Scholar 

  49. Vosniakos GC, Matsas E (2010) Improving feasibility of robotic milling through robot placement optimisation. Robot Comput Integr Manuf 26:517–525. https://doi.org/10.1016/j.rcim.2010.04.001

    Article  Google Scholar 

  50. Wang G, Dong H, Guo Y, Ke Y (2016) Dynamic cutting force modeling and experimental study of industrial robotic boring. Int J Adv Manuf Technol 86(1–4):179–190

    Article  Google Scholar 

  51. Xiong G, Ding Y, Zhu L (2019) Stiffness-based pose optimization of an industrial robot for five-axis milling. Robot Comput Integr Manuf 55:19–28. https://doi.org/10.1016/j.rcim.2018.07.001

    Article  Google Scholar 

  52. Yixu S, Hongbo L, Zehong Y (2012) An adaptive modeling method for a robot belt grinding process. IEEE/ASME Trans Mechatron 17:309–317. https://doi.org/10.1109/tmech.2010.2102047

    Article  Google Scholar 

  53. Yuan L, Pan Z, Ding D, Sun S, Li WMCPS (2018) A review on chatter in robotic machining process regarding both regenerative and mode coupling mechanism. IEEE/ASME Trans Mechatron, 1–1. https://doi.org/10.1109/tmech.2018.2864652

    Article  Google Scholar 

  54. Zaeh MF, Roesch O (2014) Improvement of the machining accuracy of milling robots. Product Eng Res Develop 8:737–744. https://doi.org/10.1007/s11740-014-0558-7

    Article  Google Scholar 

  55. Zarte M, Pechmann A, Wermann J, Gosewehr F, Colombo AW (2016) Building an Industry 4.0-compliant lab environment to demonstrate connectivity between shop floor and IT levels of an enterprise. In: IECON 2016 - 42nd annual conference of the IEEE industrial electronics society, pp 6590–6595. https://doi.org/10.1109/IECON.2016.7792956

  56. Zivanovic S, Slavkovic N, Milutinovic D (2018) An approach for applying STEP-NC in robot machining. Robot Comput Integr Manuf 49:361–373. https://doi.org/10.1016/j.rcim.2017.08.009

    Article  Google Scholar 

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Acknowledgements

The first author would like to thank the National Council for Scientific and Technological Development (CNPq) for his technological productivity fellowship (process 314516/2018-2). Also, the authors would like to thank Nova Tecnologia, OSG Sulamerica and Latam MRO companies for providing supports to the current research, and the Metrology Laboratory of Engineering School of Sao Carlos (EESC – USP) for the assistance in the measurements of geometric tolerances.

Funding

This research project received financial support from CNPq Universal (grant 432002/2018-9).

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Correspondence to Gustavo Franco Barbosa.

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Barbosa, G.F., Shiki, S.B. & Savazzi, J.O. Digitalization of a standard robot arm toward 4th industrial revolution. Int J Adv Manuf Technol 105, 2707–2720 (2019). https://doi.org/10.1007/s00170-019-04523-2

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