Augmented reality application to support the assembly of highly customized products and to adapt to production re-scheduling

  • Dimitris MourtzisEmail author
  • Vasilios Zogopoulos
  • Fotini Xanthi


Despite the high automatization that characterizes modern production, human operators still hold a vital position in manufacturing, which should be reinforced in the transition to the era of Industry 4.0. As human operators may support increased flexibility and adaptability to their tasks, they gain an advantage in highly customized productions, where products’ configuration and tasks allocated per workstation may be dynamically changed. In order to support dynamic knowledge transfer to the human operators in a way that is perceivable and does not limit operators’ capabilities, it is important to exploit novel visualization technologies introduced by Industry 4.0. This paper presents an automated approach for remotely supporting assembly workstations, with human operators using augmented reality technology. The system retrieves the workstation’s schedule and automatically generates assembly instructions, utilizing information from the product’s design, enriched with order-specific annotations based on product customization. Then, the generated augmented reality instructions are transmitted through a cloud environment to the assembly station operator, aiming to support dynamic production re-scheduling. The developed system is validated in a real-life case study provided by the automotive industry.


Assembly Augmented reality Mass customization Re-scheduling 



  1. 1.
    Chryssolouris G (2006) Manufacturing systems: theory and practice. Springer Science & Business MediaGoogle Scholar
  2. 2.
    Mourtzis D, Milas N, Vlachou A (2018) An internet of things-based monitoring system for shop-floor control. J Comput Inf Sci Eng 18:21005. CrossRefGoogle Scholar
  3. 3.
    April WG (2013) 001.Recommendations for implementing the strategic. Acatech:4–7.
  4. 4.
    Nee AY, Ong SK, Chryssolouris G, Mourtzis D (2012) Augmented reality applications in design and manufacturing. CIRP Ann Manuf Technol 61(2):657–679CrossRefGoogle Scholar
  5. 5.
    Efthymiou K, Pagoropoulos A, Mourtzis D (2013) Intelligent scheduling for manufacturing systems: a case study. In: Azevedo A (ed) Advances in sustainable and competitive manufacturing systems. Springer International Publishing, Heidelberg, pp 1153–1164CrossRefGoogle Scholar
  6. 6.
    Mourtzis D, Doukas M (2014) The evolution of manufacturing systems: from craftsmanship to the era of customisation, chap 1. In: handbook of research on design and Management of Lean Production Systems, US, AmericaGoogle Scholar
  7. 7.
    Huffman C, Kahn BE (1998) Variety for sale: mass customization or mass confusion? J Retail 74:491–513. CrossRefGoogle Scholar
  8. 8.
    Faccio M, Gamberi M, Pilati F, Bortolini M (2015) Packaging strategy definition for sales kits within an assembly system. Int J Prod Res 53(11):3288–3305CrossRefGoogle Scholar
  9. 9.
    Huang HH, Pei W, Wu HH, May MD (2013) A research on problems of mixed-line production and the re-scheduling. Robot Comput Integr Manuf 29:64–72. CrossRefGoogle Scholar
  10. 10.
    Mourtzis D, Doukas M, Vlachou A, Xanthopoulos N (2014) Machine availability monitoring for adaptive holistic scheduling: a conceptual framework for mass customization. Procedia CIRP 25:406–413. CrossRefGoogle Scholar
  11. 11.
    Bortolini M, Ferrari E, Gamberi M, Pilati F, Faccio M (2017) Assembly system design in the industry 4.0 era: a general framework. IFAC-PapersOnLine 50:5700–5705. CrossRefGoogle Scholar
  12. 12.
    Yin J-J, Zhang B, Gao D (2018) Research and implementation of customization MES with improved scheduling based on RFID. Int Conf Robot Intell Syst 0:365–369. Google Scholar
  13. 13.
    Zhong RY, Dai QY, Qu T, Hu GJ, Huang GQ (2013) RFID-enabled real-time manufacturing execution system for mass-customization production. Robot Comput Integr Manuf 29:283–292. CrossRefGoogle Scholar
  14. 14.
    Nielsen I, Bocewicz G, Do NAD (2014) Production and resource scheduling in mass customization with dependent setup consideration. In: Brunoe TD, Nielsen K, Joergensen KA, Taps SB (eds) Proceedings of the 7th world conference on mass customization, personalization, and co-creation (MCPC 2014), Aalborg, Denmark, February 4th - 7th, 2014. Springer International Publishing, Cham, pp 461–472CrossRefGoogle Scholar
  15. 15.
    Mourtzis D, Doukas M (2014) Design and planning of manufacturing networks for mass customisation and personalisation: challenges and outlook. Procedia CIRP 19:1–13. CrossRefGoogle Scholar
  16. 16.
    Mourtzis D, Vlachou E, Giannoulis C, Siganakis E, Zogopoulos V (2016) Applications for frugal product customization and Design of Manufacturing Networks. Procedia CIRP 52:228–233. CrossRefGoogle Scholar
  17. 17.
    Qin R, Nembhard DA, Barnes WL (2015) Workforce flexibility in operations management. Surv Oper Res Manag Sci 20:19–33. MathSciNetGoogle Scholar
  18. 18.
    Nelles J, Kuz S, Mertens A, Schlick CM (2016) Human-centered design of assistance systems for production planning and control: the role of the human in industry 4.0. 2016 IEEE Int Conf Ind Technol 2099–2104.
  19. 19.
    Worley JM, Doolen TL (2015) Organizational structure, employee problem solving, and lean implementation. Int J Lean Six Sigma 6:39–58. CrossRefGoogle Scholar
  20. 20.
    Romero D, Bernus P, Noran O, Stahre J, Fast-Berglund Å (2016) The operator 4.0: human cyber-physical systems & adaptive automation towards human-automation Symbiosis work systems. In: Nääs I, Vendrametto O, Mendes Reis J et al (eds) Advances in production management systems. Initiatives for a sustainable world. Springer International Publishing, Cham, pp 677–686CrossRefGoogle Scholar
  21. 21.
    Benešová A, Tupa J (2017) Requirements for education and qualification of people in industry 4.0. Procedia Manuf 11:2195–2202. CrossRefGoogle Scholar
  22. 22.
    Bortolini M, Faccio M, Gamberi M, Pilati F (2018) Motion analysis system (MAS) for production and ergonomics assessment in the manufacturing processes. Comput Ind EngGoogle Scholar
  23. 23.
    Fantini P, Pinzone M, Taisch M (2018) Placing the operator at the Centre of industry 4.0 design: modelling and assessing human activities within cyber-physical systems. Comput Ind Eng:0–1.
  24. 24.
    Makris S, Karagiannis P, Koukas S, Matthaiakis AS (2016) Augmented reality system for operator support in human–robot collaborative assembly. CIRP Ann Manuf Technol 65:61–64. CrossRefGoogle Scholar
  25. 25.
    Kerpen D, Lohrer M, Saggiomo M, et al (2016) Effects of cyber-physical production systems on human factors in a weaving mill: implementation of digital working environments based on augmented reality. Proc IEEE Int Conf Ind Technol 2016–May, 2094–2098.
  26. 26.
    Zhu J, Ong SK, Nee AYC (2014) A context-aware augmented reality system to assist the maintenance operators. Int J Interact Des Manuf 8:293–304. CrossRefGoogle Scholar
  27. 27.
    Palmarini R, Erkoyuncu JA, Roy R (2017) An innovative process to select augmented reality (AR) Technology for Maintenance. Procedia CIRP 59:23–28. CrossRefGoogle Scholar
  28. 28.
    Mourtzis D, Vlachou A, Zogopoulos V (2017) Cloud-based augmented reality remote maintenance through shop-floor monitoring: a product-service system approach. J Manuf Sci Eng 139:61011. CrossRefGoogle Scholar
  29. 29.
    Mourtzis D, Zogopoulos V, Vlachou E (2017) Augmented reality application to support remote maintenance as a Service in the Robotics Industry. Procedia CIRP 63:46–51. CrossRefGoogle Scholar
  30. 30.
    Evans G, Miller J, Iglesias Pena M et al (2017) Evaluating the Microsoft HoloLens through an augmented reality assembly application. 10197:101970V.
  31. 31.
    Wang X, Ong SK, Nee AYC (2016) Multi-modal augmented-reality assembly guidance based on bare-hand interface. Adv Eng Inform 30:406–421. CrossRefGoogle Scholar
  32. 32.
    Radkowski R, Herrema J, Oliver J (2015) Augmented reality-based manual assembly support with visual features for different degrees of difficulty. Int J Hum Comput Interact 31:337–349. CrossRefGoogle Scholar
  33. 33.
    Gavish N, Gutiérrez T, Webel S, Rodríguez J, Peveri M, Bockholt U, Tecchia F (2015) Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks. Interact Learn Environ 23:778–798. CrossRefGoogle Scholar
  34. 34.
    Rentzos L, Papanastasiou S, Papakostas N, Chryssolouris G (2013) Augmented reality for human-based assembly: using product and process semantics. IFACGoogle Scholar
  35. 35.
    Sadaiah M, Yadav DR, Mohanram PV, Radakrishnan P (2002) A generative computer-aided process planning system for prismatic components. Int J Adv Manuf Technol 20(10):709–719CrossRefGoogle Scholar
  36. 36.
    Kardos C, Kovács A, Váncza J (2017) Decomposition approach to optimal feature-based assembly planning. CIRP Ann 66(1):417–420CrossRefGoogle Scholar
  37. 37.
    Dong T, Tong R, Zhang L, Dong J (2005) A collaborative approach to assembly sequence planning. Adv Eng Inform 19(2):155–168CrossRefGoogle Scholar
  38. 38.
    Mourtzis D, Vlachou E, Milas N, Tapoglou N, Mehnen J (2017) A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring. Proc Inst Mech Eng B J Eng Manuf 233:278–292. CrossRefGoogle Scholar
  39. 39.
    Makris S, Pintzos G, Rentzos L, Chryssolouris G (2013) Assembly support using AR technology based on automatic sequence generation. CIRP Ann Manuf Technol 62:9–12. CrossRefGoogle Scholar
  40. 40.
    Unity 3D. Accessed Online: 26/07/2018
  41. 41.
    Siltanen S (2012) Theory and applications of marker-based augmented realityGoogle Scholar
  42. 42.
    Vuforia. Accessed Online: 26/07/2018
  43. 43.
    Hart S G, Staveland L E (1988) Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In: Advances in psychology, 52:139–183). North-HollandGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Dimitris Mourtzis
    • 1
    Email author
  • Vasilios Zogopoulos
    • 1
  • Fotini Xanthi
    • 1
  1. 1.Laboratory for Manufacturing Systems & Automation, Department of Mechanical Engineering & AeronauticsUniversity of PatrasPatrasGreece

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