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Material kitting in selective assembly: a manual order picking system based on augmented reality

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Abstract

In the process of selective assembly for precision and complex mechanical products, it is necessary to perform the selective material kitting one by one and conduct check repeatedly according to the optimal matching result to ensure quality. The traditional manual picking and checking method by Bill of Material is inefficient and laborious. Currently, there is still no consensus reached on how to better transmit information and implement the material kitting in the course of selective assembly. In this paper, we pioneer in testing the performance between mainstream methods (Pick-by-Voice, Pick-by-Light, Pick-by-AR) and traditional methods (Pick-by-Paper) in terms of task time, errors, workload, and information perception for the selective material kitting. We developed an AR (augmented reality) on the level of material individual information level and verified its applicability. Meanwhile, we enhanced the effect of material individual information expression from different visual perspectives. We found that Pick-by-AR can outperform others in terms of selective assembly, which means expanding the dimension of information expression and enhancing users’ perception of information can help users quickly and accurately select parts with the same appearance but different quality characteristics fast and accurately, thus providing a viable option for material kitting in the selective assembly process.

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Availability of data and material

All the data are obtained by experiments and are authentic.

Code availability

The code we wrote can support the normal operation of the system.

Notes

  1. Vuforia: https://developer.vuforia.com/

References

  1. Coullard CR, Gamble AB, Jones PC (1998) Matching problems in selective assembly operations. Ann Oper Res 76:95–107

    Article  MATH  Google Scholar 

  2. Yamada Y, Komura Y, Mizutani J, Tanabe I (2000) Development of a precision assembly system using selective assembly and micro machining. In: Computer Aided Systems Theory - EUROCAST’99, pp 407–413

  3. Li LL, Chen K, Gao JM, Gao ZY, Liu JK (2021) Research on optimizing selection and optimizing matching technologies of aeroengine fan rotor blades. Shock Vib 2021(125):1–17

    Google Scholar 

  4. Selvaraj AS, Anusha S (2021) RFID enabled smart data analysis in a smart warehouse monitoring system using IoT. J Phys: Conf Ser 1717:012–022

    Google Scholar 

  5. Karaagac A, Haxhibeqiri J, Joseph W, Moerman I, Hoebeke J (2017) Wireless industrial communication for connected shuttle systems in warehouses. In: IEEE International Workshop on Factory Communication Systems, pp 1–4

  6. Fang W, An ZW (2020) A scalable wearable AR system for manual order picking based on warehouse floor-related navigation. Int J Adv Manuf Technol 109(7–8):2023–2037

    Article  Google Scholar 

  7. Battini D, Calzavara M, Persona A, Sgarbossa F (2015) A comparative analysis of different paperless picking systems. Ind Manag Data Syst 115(3):483–503

    Article  Google Scholar 

  8. Schwerdtfeger B, Klinker G (2008) Supporting order picking with augmented reality. In: 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, pp 91–94

  9. Fager P, Hanson R, Medbo L, Johansson MI (2019) Kit preparation for mixed model assembly - efficiency impact of the picking information system. Comput Ind Eng 129:169–178

    Article  Google Scholar 

  10. Grosse EH, Glock CH, Neumann WP (2015) Human factors in order picking system design: a content analysis. IFAC-PapersOnLine 48(3):320–325

    Article  Google Scholar 

  11. Doshi A, Smith RT, Thomas BH, Bouras C (2016) Use of projector based augmented reality to improve manual spot-welding precision and accuracy for automotive manufacturing. The International Journal of Advanced Manufacturing Technology 89(5–8):1279–1293

    Google Scholar 

  12. de Koster R, Le-Duc T, Roodbergen KJ (2007) Design and control of warehouse order picking: a literature review. Eur J Oper Res 182(2):481–501

    Article  MATH  Google Scholar 

  13. Kumar S, Narkhede BE, Jain K (2021) Revisiting the warehouse research through an evolutionary lens: a review from 1990 to 2019. Int J Prod Res 59(11):3470–3492

    Article  Google Scholar 

  14. Finnsgaerd C, Wanstroem C (2013) Factors impacting manual picking on assembly lines: an experiment in the automotive industry. Int J Prod Res 51(5–6):1789–1798

    Article  Google Scholar 

  15. Neumann WP, Medbo L (2010) Ergonomic and technical aspects in the redesign of material supply systems: big boxes vs. narrow bins. Int J Ind Ergon 40(5):541–548

  16. Carotenuto R, Merenda M, Iero D, Corte FGD (2020) Indoor object positioning using smartphone and RFID or QRCode. In: 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), pp 1–6

  17. Grosse EH, Glock CH, Jaber MY, Neumann WP (2015) Incorporating human factors in order picking planning models: framework and research opportunities. Int J Prod Res 53(3):695–717

    Article  Google Scholar 

  18. Fu T, Sun B (2018) Application of speech recognition technology in logistics selection system. In: Human Centered Computing, pp 654–659

  19. Hanson R, Medbo L, Berlin C, Hansson J (2018) Manual picking from flat and tilted pallet containers. Int J Ind Ergon 64(1):199–212

    Article  Google Scholar 

  20. Hanson R, Medbo L, Johansson MI (2015) Order batching and time efficiency in kit preparation. Assem Autom 35(1):143–148

    Article  Google Scholar 

  21. Wu X, Haynes M, Zhang Y, Jiang Z, Shen Z, Guo A, Starner T, Gilliland S (2015) Comparing order picking assisted by head-up display versus pick-by-light with explicit pick confirmation. In: Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp 133–136

  22. Wang Z, Bai XL, Zhang SS, Billinghurst M, He WP, Wang Y, Han D, Chen G, Li JH (2021) The role of user-centered AR instruction in improving novice spatial cognition in a high-precision procedural task. Adv Eng Inform 47:101250

    Article  Google Scholar 

  23. Gül LF (2018) Studying gesture-based interaction on a mobile augmented reality application for co-design activity. Journal on multimodal user interfaces 12(2):109–124

    Article  Google Scholar 

  24. Balasubramaniyan C, Manivannan D (2016) IoT enabled air quality monitoring system (AQMS) using Raspberry Pi. Indian J Sci Technol 9(39):1–6

    Article  Google Scholar 

  25. Guo A, Wu X, Shen Z, Starner T, Baumann H, Gilliland S (2015) Order picking with head-up displays. Computer 48(6):16–24

    Article  Google Scholar 

  26. Schwerdtfeger B, Reif R, Gunthner WA, Klinker GG (2011) Pick-by-vision: there is something to pick at the end of the augmented tunnel. Virtual Reality 15(2):213–223

    Article  Google Scholar 

  27. Hanson R, Falkenström W, Miettinen M (2017) Augmented reality as a means of conveying picking information in kit preparation for mixed-model assembly. Comput Ind Eng 113:570–575

    Article  Google Scholar 

  28. Gharbi S, Zgaya H, Zoghlami N, Hammadi S, De Barbarin C, Vinatier L, Coupier C (2014) Proposition of an intelligent system based on the augmented reality for warehouse logistics. In: 16th International Conference on Logistics and Maritime Systems (ICLMS 2014)

  29. Sidiropoulos V, Bechtsis D, Vlachos D (2021) An augmented reality symbiosis software tool for sustainable logistics activities. Sustainability 13(19):10929

    Article  Google Scholar 

  30. Fang W, Zheng S, Liu Z (2019) A scalable and long-term wearable augmented reality system for order picking. In: 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp 4–7

  31. Ponis ST, Plakas G, Agalianos K, Aretoulaki E, Gayialis SP, Andrianopoulos A (2020) Augmented reality and gamification to increase productivity and job satisfaction in the warehouse of the future. Procedia Manufacturing 51:1621–1628

    Article  Google Scholar 

  32. Bräuer P, Mazarakis A (2020) Visualization of turnover rate in a warehouse using augmented reality: a demo with the Microsoft Hololens. In: Proceedings of the Conference on Mensch und Computer, pp 519–522

  33. Bräuer P, Mazarakis A (2018) AR in order-picking–experimental evidence with Microsoft HoloLens. Mensch und Computer 2018-Workshopband

  34. Kim S, Nussbaum MA, Gabbard JL (2019) Influences of augmented reality head-worn display type and user interface design on performance and usability in simulated warehouse order picking. Appl Ergon 74:186–193

    Article  Google Scholar 

  35. Renner P, Pfeiffer T (2017) [POSTER] Augmented reality assistance in the central field-of-view outperforms peripheral displays for order picking: results from a virtual reality simulation study. In: 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct), pp 176–181

  36. Baechler A, Baechler L, Autenrieth S, Kurtz P, Hoerz T, Heidenreich T, Kruell G (2016) A comparative study of an assistance system for manual order picking -- called Pick-by-Projection -- with the guiding systems Pick-by-Paper, Pick-by-Light and Pick-by-Display. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp 523–531

  37. Lorenz M, Pfeiffer F, Klimant P (2020) Augmented reality for pack optimization using video and depth data. In: 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp 21–23

  38. Yeow PHP, Goomas DT (2014) Ergonomics improvement in order selection in a refrigerated environment. Human Factors and Ergonomics in Manufacturing & Service Industries 24(3):262–274

    Article  Google Scholar 

  39. Mourtzis D, Samothrakis V, Zogopoulos V, Vlachou E (2019) Warehouse design and operation using augmented reality technology: a papermaking industry case study. Procedia CIRP 79:574–579

    Article  Google Scholar 

  40. Wang W, Wang F, Song W, Su S (2020) Application of augmented reality (AR) technologies in inhouse logistics. In: E3S Web of Conferences, pp 02018

  41. Cragg T, Loske D (2019) Perceived work autonomy in order picking systems: an empirical analysis. IFAC-PapersOnLine 52(13):1872–1877

    Article  Google Scholar 

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Acknowledgements

We thank Shuxia Wang and Weiping He for adjusting the experimental design and revising the grammar of the paper and Jianghong Li, ZhiweiCao, and Bingzhao Wei for their help in our experiments. We also thank Manxian Wang of Aecc Xi’an Aero-Engine Ltd. for providing us with the source of ideas for this paper and accompanying us with on-site research and discussion.

Funding

This work is partly supported by the National Key R&D Program of China (Grant No. 2019YFB1703800, 2021YFB1714900, 2021YFB1716200, 2020YFB1712503), the Programme of Introducing Talents of Discipline to Universities (111 Project), China (Grant No. B13044), and the Fundamental Research Funds for the Central Universities, NPU (Grant No. 3102020gxb003).

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Authors and Affiliations

  1. Laboratory of Cyber-Physical Interaction, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Jie Zhang, Shuxia Wang, Weiping He, Jianghong Li, Zhiwei Cao & Bingzhao Wei

  2. Aecc Xi’an Aero-Engine Ltd., Xi’an 710021, China

    Manxian Wang

Authors
  1. Jie Zhang
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  2. Shuxia Wang
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  3. Weiping He
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  7. Manxian Wang
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Contributions

All authors contributed to the study. The system conception was proposed by Shuxia Wang, Weiping He, Jie Zhang, and Manxian Wang. Material preparation and system development were performed by Jie Zhang, Jianghong Li, ZhiweiCao, and Bingzhao Wei. Data collection and analysis were performed by Jie Zhang. The first draft of the manuscript was written by Jie Zhang and Shuxia Wang. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shuxia Wang or Weiping He.

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Zhang, J., Wang, S., He, W. et al. Material kitting in selective assembly: a manual order picking system based on augmented reality. Int J Adv Manuf Technol 123, 675–686 (2022). https://doi.org/10.1007/s00170-022-10188-1

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