Abstract
Manual assembly operations are still essential in modern manufacturing industries because of their flexibility and adaptability for mass customized production. However, as the product variety increases, the complexity of the assembly operations and workstations increases. Thus, several investigations have focused on improving manual assembly operations. One of the main challenges is the capture, digitalization and transferring of the assembly knowledge and skills of qualified assembly operators. In this paper, a novel approach to digitalize and evaluate the performance of manual assembly operators based on a marker-less motion tracking strategy is proposed. The aim is twofold: firstly, to digitalize manual assembly operations in order to log the experience and knowledge of qualified operators; and secondly, to objectively evaluate the assembly performance of new operators or trainees in order to qualify them. In addition, several new assembly metrics are proposed to quantitatively evaluate and compare the operator’s assembly performance with the performance of a qualified operator. The proposed marker-less motion approach uses a leap motion sensor to track and capture the hand motions and execution times of an operator during the realization of manual assembly processes. A set of experimental tests was conducted to prove and validate the functionality of the proposed method. The results have demonstrated that the proposed strategy allows the digitalization of manual assembly operations and the immediate evaluation of the operator’s performance.
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Relji´c VR, Milenkovi´c IM, Dudi´cdudi´c S, Šulc J, Bajči B (2021) Augmented reality applications in Industry 4.0 environment, Appl Sci 11(12):5592. https://doi.org/10.3390/APP11125592
Santi GM, Ceruti A, Liverani A, Osti F (2021) Augmented reality in Industry 4.0 and future innovation programs. Technologies 9(2):33. https://doi.org/10.3390/technologies9020033
Nee AYC, Ong SK, Chryssolouris G, Mourtzis D (2012) Augmented reality applications in design and manufacturing. CIRP Ann Manuf Technol 61:657–679. https://doi.org/10.1016/j.cirp.2012.05.010
Rodriguez L, Quint F, Gorecky D, Romero D, Siller HR (2015) Developing a mixed reality assistance system based on projection mapping technology for manual operations at assembly workstations. Procedia Comput Sci 75:327–333. https://doi.org/10.1016/J.PROCS.2015.12.254
Müller BC, Nguyen TD, Dang QV, Duc BM, Seliger G, Krüger J et al (2016) Motion tracking applied in assembly for worker training in different locations. Procedia CIRP 48:460–465. https://doi.org/10.1016/j.procir.2016.04.117
Liu L, Liu Y, Zhang J (2019) Learning-based hand motion capture and understanding in assembly process. IEEE Trans Ind Electron 66(12):9703–9712. https://doi.org/10.1109/TIE.2018.2884206
Pilati F, Faccio M, Gamberi M, Regattieri A (2020) Learning manual assembly through real-time motion capture for operator training with augmented reality. Procedia Manuf 45:189–195. https://doi.org/10.1016/j.promfg.2020.04.093
Mitzner K, Doe B, Akulin A, Suponin A, Müller D (2019) Introduction to design for manufacturing. In: Mitzner IK, Doe B, Akulin A, Suponin A, Müller D (eds) Complete PCB Design Using OrCAD® Capture and PCB, 2nd edn. Academic Press, Amsterdam, pp 83–109. https://doi.org/10.1016/B978-0-12-817684-9.00005-9
Miqueo A, Torralba M, Yagüe-Fabra JA (2020) Lean Manual Assembly 4.0: a systematic review. Appl Sci 10:8555. https://doi.org/10.3390/APP10238555.
Johansson PEC, Malmsköld L, Fast-Berglund Å, Moestam L (2018) Enhancing future assembly information systems – putting theory into practice. Procedia Manuf 17:491–498. https://doi.org/10.1016/J.PROMFG.2018.10.088
Turk M, Šimic M, Pipan M, Herakovič N (2022) Multi-criterial algorithm for the efficient and ergonomic manual assembly process. Int J Environ Res Public Health 19:3496. https://doi.org/10.3390/IJERPH19063496
Cohen Y, Faccio M, Pilati F, Yao X (2019) Design and management of digital manufacturing and assembly systems in the Industry 4.0 era. Int J Adv Manuf Technol 105:3565–3577. https://doi.org/10.1007/S00170-019-04595-0/FIGURES/3
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. https://doi.org/10.1016/j.procir.2016.04.125
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. https://doi.org/10.1016/j.ifacol.2018.08.329
Bortolini M, Gamberi M, Pilati F, Regattieri A (2018) Automatic assessment of the ergonomic risk for manual manufacturing and assembly activities through optical motion capture technology. Procedia CIRP 72:81–86. https://doi.org/10.1016/j.procir.2018.03.198
Wang P, Liu H, Wang L, Gao RX (2018) Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Annals 67:17–20. https://doi.org/10.1016/j.cirp.2018.04.066
Bortolini M, Faccio M, Gamberi M, Pilati F (2020) Motion analysis system (MAS) for production and ergonomics assessment in the manufacturing processes. Comput Ind Eng 139:105485. https://doi.org/10.1016/j.cie.2018.10.046
Hu H, Cao Z, Yang X, Xiong H, Lou Y (2021) Performance evaluation of optical motion capture sensors for assembly motion capturing. IEEE Access 9:61444–61454. https://doi.org/10.1109/ACCESS.2021.3074260
Ja YC, Jong MK, Chang OK, Yoon SK, Seung JL (2007) Process start/end event detection and dynamic time warping algorithms for run-by-run process fault detection. In: IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings, Santa Clara, CA, pp 1–4. https://doi.org/10.1109/ISSM.2007.4446846
West N, Schlegl T, Deuse J (2021) Feature extraction for time series classification using univariate descriptive statistics and dynamic time warping in a manufacturing environment. In: IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China, pp 762–768. https://doi.org/10.1109/ICBAIE52039.2021.9389954
Volke J, Heim HP (2023) Evaluation of the injection molding process behavior during start-up and after parameter changes using dynamic time warping correspondences. J Manuf Process 95:183–203. https://doi.org/10.1016/J.JMAPRO.2023.03.076
Weichert F, Bachmann D, Rudak B, Fisseler D (2013) Analysis of the accuracy and robustness of the leap motion controller. Sensors 13:6380–6393. https://doi.org/10.3390/s130506380
Tao Y, Both A, Silveira RI, Buchin K, Sijben S, Purves RS et al (2021) A comparative analysis of trajectory similarity measures. GIsci Remote Sens 58:643–669. https://doi.org/10.1080/15481603.2021.1908927
Su H, Liu S, Zheng B, Zhou X, Zheng K (2020) A survey of trajectory distance measures and performance evaluation. VLDB Journal 29:3–32. https://doi.org/10.1007/s00778-019-00574-9
Gong S, Cartlidge J, Bai R, Yue Y, Li Q, Qiu G (2020) Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation. Int J Geogr Inf Sci 34:1210–1234. https://doi.org/10.1080/13658816.2019.1641715
Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D et al (2008) A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci USA 105:19052–19059. https://doi.org/10.1073/pnas.0800375105
Andersson M, Gudmundsson J, Laube P, Wolle T, Andersson M, Gudmundsson J et al (2008) Reporting leaders and followers among trajectories of moving point objects. Geoinformatica 12:497–528. https://doi.org/10.1007/s10707-007-0037-9
Wozniak P, Vauderwange O, Mandal A, Javahiraly N, Curticapean D (2016) Possible applications of the LEAP motion controller for more interactive simulated experiments in augmented or virtual reality. In: Proc. SPIE 9946, Optics Education and Outreach IV 9946:99460P. https://doi.org/10.1117/12.2237673
Vaughan N, Gabrys B (2016) Comparing and combining time series trajectories using dynamic time warping. Procedia Comput Sci 96:465–474. https://doi.org/10.1016/j.procs.2016.08.106
Funding
The authors would like to thank the financial support from the National Science and Technology Council (CONACYT) of Mexico, grant number CB-2010-01-154430. The first author also acknowledges CONACYT for the scholarship provided for his PhD studies.
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All authors contributed to the design, development and evaluation of the proposed system, and in the analysis of the results and preparation of the manuscript. The first draft of the manuscript was written by Rodrigo Delgadillo-Gaytan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Delgadillo-Gaytan, R., Medellin-Castillo, H.I. Automatic digitalization and performance assessment of manual assembly processes using a marker-less motion tracking approach. Int J Adv Manuf Technol 129, 5101–5115 (2023). https://doi.org/10.1007/s00170-023-12496-6
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DOI: https://doi.org/10.1007/s00170-023-12496-6