Revista UIS Ingenierías

Vol. 24 No. 1 (2025): Revista UIS Ingenierías
Articles

Generation of Numerical Models of Extension Springs Based on Images

PDF (Español (España))
  • Marco Ciaccia-Sortino,
  • David Ojeda-Peña
Marco Ciaccia-Sortino
Universidad Técnica del Norte
David Ojeda-Peña
Universidad Técnica del Norte
DOI: https://doi.org/10.18273/revuin.v24n1-2025010

Published 2025-03-21

Keywords

  • calibration,
  • extreme flows,
  • hydrological basins,
  • probability distribution functions,
  • torrentiality index,
  • spatial interpolation,
  • modified rational method,
  • precipitation,
  • Andean region
    • ...More
    Less

How to Cite

Ciaccia-Sortino, M. ., & Ojeda-Peña, D. . (2025). Generation of Numerical Models of Extension Springs Based on Images. Revista UIS Ingenierías, 24(1), 113–122. https://doi.org/10.18273/revuin.v24n1-2025010

Copyright (c) 2025 Revista UIS Ingenierías

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Abstract

Non-standard springs manufactured in Ecuador show geometric deviations from design assumptions, affecting their performance and reliability. As an alternative to testing with specialized machines to assess their mechanical behavior, a procedure based on computer vision and numerical modeling of the spring is suggested. This work presents a step forward in this direction through a methodology to construct a numerical model of the real geometry of the spring using photographs and image processing algorithms. A photographic setup is created that makes it easier to take images in orthogonal directions, and the images are processed using the OpenCV library in Python to define points that describe the path of the spring wire. These points are converted into a finite element mesh to simulate the mechanical behavior of the spring. To validate the methodology, a geometric compliance check of the model with the spring is performed, and the actual and simulated force-displacement curves are compared. The results show good geometric compliance and an excellent match between the experimentally obtained and simulated stiffness constants.

 

PDF (Español (España))

Downloads

Download data is not yet available.

References

  1. S. E. Umbaugh, Digital Image Processing and Analysis: Computer Vision and Image Analysis. Boca Raton: CRC Press, 2023, doi: https://doi.org/10.1201/9781003221135
  2. M. Perani, S. Baraldo, M. Decker, A. Vandone, A. Valente, B. Paoli, “Track geometry prediction for Laser Metal Deposition based on on-line artificial vision and deep neural networks”, Robot. Comput.-Integr. Manuf., vol. 79, p. 102445, 2023, doi: https://doi.org/10.1016/j.rcim.2022.102445
  3. Q. Li, W. Ge, H. Shi, W. Zhao, S. Zhang, “Research on Coaxiality Measurement Method for Automobile Brake Piston Components Based on Machine Vision”, Appl. Sci., vol. 14, no. 6, 2024, doi: https://doi.org/10.3390/app14062371
  4. A. Martini, E. M. Tronci, M. Q. Feng, R. Y. Leung, “A computer vision-based method for bridge model updating using displacement influence lines”, Eng. Struct., vol. 259, p. 114129, 2022, doi: https://doi.org/10.1016/j.engstruct.2022.114129
  5. C. Sun, D. Gu, X. Lu, “Three-dimensional structural displacement measurement using monocular vision and deep learning-based pose estimation”, Mech. Syst. Signal Process., vol. 190, p. 110141, 2023, doi: https://doi.org/10.1016/j.ymssp.2023.110141
  6. W. He, Z. Jiang, W. Ming, G. Zhang, J. Yuan, L. Yin, “A critical review for machining positioning based on computer vision”, Measurement, vol. 184, p. 109973, 2021, doi: https://doi.org/10.1016/j.measurement.2021.109973
  7. B. McGuinness, M. Duke, C. K. Au, S. H. Lim, “Measuring radiata pine seedling morphological features using a machine vision system”, Comput. Electron. Agric., vol. 189, p. 106355, 2021, doi: https://doi.org/10.1016/j.compag.2021.106355
  8. C. Xi, J. Ruan, A. Liu, “Machine vision-based measurement of air compressor crankshaft journal dimensions”, en Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), L. Shen y G. Zhong, Eds., Hangzhou, p. 114, 2023, doi: https://doi.org/10.1117/12.2684335
  9. Y. Saif et al., “Roundness Holes’ Measurement for milled workpiece using machine vision inspection system based on IoT structure: A case study”, Measurement, vol. 195, p. 111072, 2022, doi: https://doi.org/10.1016/j.measurement.2022.111072
  10. H. Hu, J. Wang, C.-Z. Dong, J. Chen, T. Wang, “A hybrid method for damage detection and condition assessment of hinge joints in hollow slab bridges using physical models and vision-based measurements”, Mech. Syst. Signal Process., vol. 183, p. 109631, 2023, doi: https://doi.org/10.1016/j.ymssp.2022.109631
  11. A. Ugural, Mechanical Design of Machine Components. Boca Raton: CRC Press, 2015, doi: https://doi.org/10.1201/b18000
  12. D. G. Ullman, The Mechanical Design Process. Independence, Oregon: David Ullman LLC, 2017.
  13. R. G. Budynas, J. K. Nisbett, Diseño en ingeniería mecánica de Shigley, México: McGraw-Hill, 2021.
  14. R. L. Norton, Diseño de máquinas: un enfoque integrado. México, México: Pearson Educación, 2011.
  15. A. Spring, Design Handbook: Engineering Guide to Spring Design. Associated Spring, Barnes Group Inc., 1987.
  16. ISO, ISO 22705-1 (en), Springs - Measurement and test parameters - Part 1: Cold formed cylindrical helical compression springs, 2021. [En línea]. Disponible en: https://www.iso.org/es/contents/data/standard/07/37/73723.html
  17. ISO, ISO 22705-2 (en), Springs - Measurement and test parameters - Part 2: Cold formed cylindrical helical extension springs, 2023. [En línea]. Disponible en: https://www.iso.org/es/contents/data/standard/07/89/78984.html
  18. Tecniresortes, “Entrevista Gerente General”, 2022.
  19. D. Almeida, M. Ciaccia, D. Ojeda, “Mechanical behavior characterization of helical extension springs manufactured in Ecuador”, Applied Engineering and Innovative Technologies, 2023, doi: https://doi.org/10.1007/978-3-031-70760-5_20
  20. “Samsung to Bring Industry’s Highest Resolution for Mobile Cameras with New 64Mp ISOCELL Image Sensor”. [En línea]. Disponible en: https://news.samsung.com/global/samsung-to-bring-industrys-highest-resolution-for-mobile-cameras-with-new-64mp-isocell-image-sensor
  21. G. Bradski, “The OpenCV Library”, Dr Dobbs J. Softw. Tools, 2000.
  22. Ansys Inc, “Ansys Parametric Design Language guide, version 18.2”. 2018.
  23. Ansys Inc, “Ansys Mechanical APDL command reference, version 18.2”. 2018.
  24. Ansys Inc, “Ansys Mechanical APDL element reference, version 18.2”. 2018.
  25. J. Miao, Q. Tan, B. Sun, J. Zhao, S. Liu, Y. Zhang, “Online measurement method for dimensions of disk parts based on machine vision”, PLOS ONE, vol. 19, n.o 7, p. e0307525, 2024, doi: https://doi.org/10.1371/journal.pone.0307525