Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Human Factor Analyser for work measurement of manual manufacturing and assembly processes

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The novel generation of production facilities fostered by the fourth industrial revolution widely adopts different technologies to digitalise the manufacturing and assembly processes. In this context, work measurement techniques are one of the main candidates for the application of these new technologies because of the time, cost, and competences required to analyse manual production activities and considering the limited precision of the traditional approaches. This paper proposes a new hardware/software architecture devoted to the motion and time analysis of the activities performed by human operators within whatsoever industrial workplace. This architecture, called Human Factor Analyser (HFA), is constituted by a network of ad hoc depth cameras able to track the worker movements during the task execution without any interference with the monitored process. The data provided by these cameras are then elaborated in a post-process phase by the HFA to automatically and quantitatively measure the work content of the considered activities through an accurate motion and time analysis. The developed architecture evaluates the worker in a 3D environment considering his interaction with the industrial workplace through the definition of appropriate control volumes within the layout. To test the accuracy of HFA, an extensive experimental campaign is performed at the Bologna University Laboratory for Industrial Production adopting several realistic industrial configurations (different workplaces, operators and tasks). Finally, the HFA is applied to a real manufacturing case study of an Italian company producing refrigerator metal grates. A wide and deep analysis of the obtained key results is presented and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Yao X, Zhou J, Lin Y, Li Y, Yu H & Liu Y (2017). Smart manufacturing based on cyber-physical systems and beyond. J Intell Manuf. https://doi.org/10.1007/s10845-017-1384-5

  2. 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(1):5700–5705

    Article  Google Scholar 

  3. Jardim-Goncalves R, Romero D, Grilo A (2017) Factories of the future: challenges and leading innovations in intelligent manufacturing. Int J Comput Integr Manuf 30(1):4–14

    Google Scholar 

  4. Gorecky D, Schmitt M, Loskyll M & Zühlke D (2014). Human-machine-interaction in the industry 4.0 era. In 2th IEEE International Conference on Industrial Informatics. https://doi.org/10.1109/INDIN.2014.6945523

  5. Longo F, Nicoletti L, Padovano A (2017) Smart operators in industry 4.0: a human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput Ind Eng 113:144–159

    Article  Google Scholar 

  6. Jamoussi A, Heragu SS (2017). Facilities design and planning. In: Starr M, Gupta S (ed) The Routledge companion to production and operations management, 1st edn. Routledge, New York, pp 147–168

  7. Qiu S, Fan X, Wu D, He Q, Zhou D (2013) Virtual human modeling for interactive assembly and disassembly operation in virtual reality environment. Int J Adv Manuf Technol 69(9-12):2355–2372

    Article  Google Scholar 

  8. Klumpp M (2018) Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements. Int J Log Res Appl 21(3):224–242

    Article  Google Scholar 

  9. Mandolini M, Favi C, Germani M, Marconi M (2018) Time-based disassembly method: how to assess the best disassembly sequence and time of target components in complex products. Int J Adv Manuf Technol 95(1-4):409–430

    Article  Google Scholar 

  10. Kong XT, Luo H, Huang GQ, Yang X (2018). Industrial wearable system: the human-centric empowering technology in Industry 4.0. J Intell Manuf. https://doi.org/10.1007/s10845-018-1416-9

  11. Sim ES, Lee HG, Lee JC, Park JW (2006) Efficient work measurement system of manufacturing cells using speech recognition and digital image processing technology. Int J Adv Manuf Technol 29(7-8):772–785

    Article  Google Scholar 

  12. Bortolini M, Faccio M, Gamberi M, Pilati F (2017) Multi-objective assembly line balancing considering component picking and ergonomic risk. Comput Ind Eng 112:348–367

    Article  Google Scholar 

  13. Bortolini M, Faccio M, Gamberi M, Pilati F (2018) Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes. Comput Ind Eng, [in press]. https://doi.org/10.1016/j.cie.2018.10.046

  14. Cohen Y (2015) A technique for integrated modelling of manual and automatic assembly. J Manuf Technol Manag 26(2):164–181

    Article  Google Scholar 

  15. 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: IFIP International Conference on Advances in Production Management Systems. Springer, Cham, pp 677–686

    Google Scholar 

  16. Romero D, Stahre J, Wuest T, Noran O, Bernus P, Fast-Berglund Å & Gorecky D (2016). Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In: Dessouky M (ed) CIE46Proceedings, 1st edn. Caie, Tianjin, pp 1–11

  17. Małachowski B, Korytkowski P (2016) Competence-based performance model of multi-skilled workers. Comput Ind Eng 91:165–177

    Article  Google Scholar 

  18. Etgar R, Gelbard R, Cohen Y (2018) Feature assignment in multi-release work plan: accelerating optimization using gene clustering. Comput Ind Eng 118:123–137

    Article  Google Scholar 

  19. Production process improvement based on work study. Int J simul model 17(8):7–12.

  20. Dağdeviren M, Eraslan E, Çelebi FV (2011) An alternative work measurement method and its application to a manufacturing industry. J Loss Prev Process Ind 24(5):563–567

    Article  Google Scholar 

  21. Taylor FW (1914). The principles of scientific management. Dover Pubns, London

  22. Gilbreth FB, Gilbreth LM (1919). Applied motion study: a collection of papers on the efficient method to industrial preparedness. Nabu Press, New York

  23. Bedaux CE (1921) The Bedaux Unit principle of industrial measurement. J Appl Psychol 5(2):119

    Article  Google Scholar 

  24. Robinson MA (2010) Work sampling: methodological advances and new applications. Human factors and ergonomics in manufacturing & service industries 20(1):42–60

    Google Scholar 

  25. Maynard HB, Stegemerten GJ, Schwab JL (1948). Methods-time measurement. McGraw-Hill Book Co, London.

  26. Zandin KB (2002). MOST work measurement systems. CRC press, Boca Raton

  27. Ferrari E, Gamberi M, Pilati F, Regattieri A (2018) Motion Analysis System for the digitalization and assessment of manual manufacturing and assembly processes. IFAC-PapersOnLine 51(11):411–416

    Article  Google Scholar 

  28. 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):3288–3305

    Article  Google Scholar 

  29. Gao W, Shao XD, Liu HL (2016) Enhancing fidelity of virtual assembly by considering human factors. Int J Adv Manuf Technol 83(5-8):873–886

    Article  Google Scholar 

  30. Oyekan J, Prabhu V, Tiwari A, Baskaran V, Burgess M, Mcnally R (2017) Remote real-time collaboration through synchronous exchange of digitised human–workpiece interactions. Futur Gener Comput Syst 67:83–93

    Article  Google Scholar 

  31. Filippeschi A, Schmitz N, Miezal M, Bleser G, Ruffaldi E, Stricker D (2017) Survey of motion tracking methods based on inertial sensors: a focus on upper limb human motion. Sensors 17(6):12–57

    Article  Google Scholar 

  32. Kim S, Nussbaum MA (2013) Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks. Ergonomics 56(2):314–326

    Article  Google Scholar 

  33. Lv N, Jiang Z, Huang Y, Meng X, Meenakshisundaram G, Peng J (2018) Generic content-based retrieval of marker-based motion capture data. IEEE Trans Vis Comput Graph 24(6):1969–1982

    Article  Google Scholar 

  34. Aggarwal JK, Xia L (2014) Human activity recognition from 3d data: a review. Pattern Recogn Lett 48:70–80

    Article  Google Scholar 

  35. Cohen Y, Golan M, Singer G, Faccio M (2018) Workstation–Operator Interaction in 4.0 Era: WOI 4.0. IFAC-PapersOnLine 51(11):399–404

    Article  Google Scholar 

  36. Prabhu VA, Song B, Thrower J, Tiwari A, Webb P (2016) Digitisation of a moving assembly operation using multiple depth imaging sensors. Int J Adv Manuf Technol 85(1-4):163–184

    Article  Google Scholar 

  37. Jayaram U, Jayaram S, Shaikh I, Kim Y, Palmer C (2006) Introducing quantitative analysis methods into virtual environments for real-time and continuous ergonomic evaluations. Comput Ind 57(3):283–296

    Article  Google Scholar 

  38. Du JC, Duffy VG (2007) A methodology for assessing industrial workstations using optical motion capture integrated with digital human models. Occup Ergon 7(1):11–25

    Google Scholar 

  39. Nguyen TD, Kleinsorge M, Postawa A, Wolf K, Scheumann R, Krüger J, Seliger G (2013). Human centric automation: using marker-less motion capturing for ergonomics analysis and work assistance in manufacturing processes. In: Seliger G (ed) GSCM11Proceedings, 1st edn. Cirp, Berlin, pp 586–592

  40. Agethen P, Otto M, Mengel S, Rukzio E (2016) Using marker-less motion capture systems for walk path analysis in paced assembly flow lines. Procedia CIRP 54:152–157

    Article  Google Scholar 

  41. Geiselhart F, Otto M, Rukzio E (2016) On the use of multi-depth-camera based motion tracking systems in production planning environments. Procedia CIRP 41:759–764

    Article  Google Scholar 

  42. Bin Che Ani MN, Hamid A, Binti SA (2014). Analysis and reduction of the waste in the work process using time study analysis: a case study. Appl mech mater 660:971–975

  43. Thomas LM, Meller RD (2015) Developing design guidelines for a case-picking warehouse. Int J Prod Econ 170:741–762

    Article  Google Scholar 

  44. Fang W, Zheng L, Xu J (2017) Self-contained optical-inertial motion capturing for assembly planning in digital factory. Int J Adv Manuf Technol 93(1-4):1243–1256

    Article  Google Scholar 

  45. Meredith M, Maddock S (2001) Motion capture file formats explained. Department of Computer Science, University of Sheffield 211:241–244

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management and Engineering, University of Padova, Stradella San Nicola 3, 36100, Vicenza, Italy

    Maurizio Faccio

  2. Department of Industrial Engineering, University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy

    Emilio Ferrari, Mauro Gamberi & Francesco Pilati

Authors
  1. Maurizio Faccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emilio Ferrari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauro Gamberi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francesco Pilati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Pilati.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(MP4 36,687 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Faccio, M., Ferrari, E., Gamberi, M. et al. Human Factor Analyser for work measurement of manual manufacturing and assembly processes. Int J Adv Manuf Technol 103, 861–877 (2019). https://doi.org/10.1007/s00170-019-03570-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-03570-z

Keywords

Search

Navigation