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Estimation of Geometrical Deformations of 3D Prints Using Local Cross-Correlation and Monte Carlo Sampling

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 681))

Abstract

This paper presents a novel method for estimating the degree of deformations in 3D prints. The increased popularity of three-dimensional printing techniques introduces a necessity to create methods for quality assessment of printed materials. One of the key problems of 3D printing are strains and deformations of printed objects. This problem is determined by many factors like: printing material (filament), object geometry or temperature. The conducted research is focused on a method of automatic analysis of deformations in 3D prints based on surface scans of objects. In our research some surface scans containing varying degrees of deformations have been used for verification of obtained results. In order to evaluate the degree of deformations of materials a local cross-correlation with Monte Carlo sampling have been used. Tests carried on multiple samples have shown that the local cross-correlation technique works well when assessing the degree of deformations of printed objects on the basis of surface scans. The obtained results show that our method can be further applied for improvement of the quality of the objects received from 3D printers.

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References

  1. Chan, S.H., Zickler, T., Lu, Y.M.: Monte carlo non-local means: random sampling for large-scale image filtering. IEEE Trans. Image Process. 23(8), 3711–3725 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  2. Chauhan, V., Surgenor, B.: A comparative study of machine vision based methods for fault detection in an automated assembly machine. Procedia Manufact. 1, 416–428 (2015)

    Article  Google Scholar 

  3. Cheng, Y., Jafari, M.A.: Vision-based online process control in manufacturing applications. IEEE Trans. Autom. Sci. Eng. 5(1), 140–153 (2008)

    Article  Google Scholar 

  4. Fang, T., Jafari, M.A., Bakhadyrov, I., Safari, A., Danforth, S., Langrana, N.: Online defect detection in layered manufacturing using process signature. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 5, pp. 4373–4378. San Diego, California, USA (1998)

    Google Scholar 

  5. Gallea, R., Ardizzone, E., Pirrone, R.: Monte-Carlo image retargeting. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 402–408 (2014)

    Google Scholar 

  6. Gu, J., Peng, S., Wang, X.: Digital image inpainting using Monte Carlo method. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 2, pp. 961–964 (2004)

    Google Scholar 

  7. International Telecommunication Union: Recommendation BT.601-7 - studio encoding parameters of digital television for standard 4: 3 and wide-screen 16: 9 aspect ratios (2011)

    Google Scholar 

  8. Marciniak, T., Lutowski, Z., Marciniak, B., Maszewski, M.: The use of texture analysis in optical inspection of manufacturing processes. In: Advances in Manufacturing Engineering II, Solid State Phenomena, vol. 237, pp. 95–100. TransTech Publications Ltd. (2015)

    Google Scholar 

  9. Okarma, K., Fastowicz, J.: No-reference quality assessment of 3D prints based on the glcm analysis. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 788–793 (2016)

    Google Scholar 

  10. Okarma, K., Fastowicz, J.: Quality assessment of 3D prints based on feature similarity metrics. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 8: 8th International Conference, IP&C 2016 Bydgoszcz, Poland, September 2016 Proceedings, pp. 104–111. Springer, Cham (2017)

    Google Scholar 

  11. Okarma, K., Fastowicz, J., Tecław, M.: Application of structural similarity based metrics for quality assessment of 3D prints. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) Computer Vision and Graphics: International Conference, ICCVG 2016, Warsaw, Poland, 19–21 September 2016, Proceedings. Lecture Notes in Computer Science, vol. 9972, pp. 244–252. Springer, cham (2016)

    Google Scholar 

  12. Okarma, K., Lech, P.: Monte Carlo based algorithm for fast preliminary video analysis. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) Computational Science - ICCS 2008, Lecture Notes in Computer Science, vol. 5101, pp. 790–799. Springer, Heidelberg (2008)

    Google Scholar 

  13. Straub, J.: Initial work on the characterization of additive manufacturing (3D printing) using software image analysis. Machines 3(2), 55–71 (2015)

    Article  Google Scholar 

  14. Szkilnyk, G., Hughes, K., Surgenor, B.: Vision based fault detection of automated assembly equipment. In: Proceedings of ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B. vol.3, pp. 691–697. Washington, DC, USA (2011)

    Google Scholar 

  15. Tourloukis, G., Stoyanov, S., Tilford, T., Bailey, C.: Data driven approach to quality assessment of 3D printed electronic products. In: Proceedings of 38th International Spring Seminar on Electronics Technology (ISSE), pp. 300–305. Eger, Hungary, May 2015

    Google Scholar 

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Correspondence to Jarosław Fastowicz .

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Fastowicz, J., Bąk, D., Mazurek, P., Okarma, K. (2018). Estimation of Geometrical Deformations of 3D Prints Using Local Cross-Correlation and Monte Carlo Sampling. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications Challenges 9. IP&C 2017. Advances in Intelligent Systems and Computing, vol 681. Springer, Cham. https://doi.org/10.1007/978-3-319-68720-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-68720-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68719-3

  • Online ISBN: 978-3-319-68720-9

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