Texture Based Quality Assessment of 3D Prints for Different Lighting Conditions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9972)

Abstract

In the paper the method of “blind” quality assessment of 3D prints based on texture analysis using the GLCM and chosen Haralick features is discussed. As the proposed approach has been verified using the images obtained by scanning the 3D printed plates, some dependencies related to the transparency of filaments may be noticed. Furthermore, considering the influence of lighting conditions, some other experiments have been made using the images acquired by a camera mounted on a 3D printer. Due to the influence of lighting conditions on the obtained images in comparison to the results of scanning, some modifications of the method have also been proposed leading to promising results allowing further extensions of our approach to no-reference quality assessment of 3D prints. Achieved results confirm the usefulness of the proposed approach for live monitoring of the progress of 3D printing process and the quality of 3D prints.

Keywords

3D prints Image analysis GLCM Image quality assessment 

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Cheng, Y., Jafari, M.A.: Vision-based online process control in manufacturing applications. IEEE Trans. Autom. Sci. Eng. 5(1), 140–153 (2008)CrossRefGoogle Scholar
  3. 3.
    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, San Diego, California, USA, vol. 5, pp. 4373–4378, October 1998Google Scholar
  4. 4.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  5. 5.
    ITU-T: Recommendation BT.601-7 - Studio encoding parameters of digital television for standard 4: 3 and wide-screen 16: 9 aspect ratios (2011)Google Scholar
  6. 6.
    Okarma, K., Grudziński, M.: The 3D scanning system for the machine vision based positioning of workpieces on the CNC machine tools. In: Proceedings of 17th International Conference Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, pp. 85–90, August 2012Google Scholar
  7. 7.
    Straub, J.: Initial work on the characterization of additive manufacturing (3D printing) using software image analysis. Machines 3(2), 55–71 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    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, Washington, DC, USA, vol. 3, pp. 691–697, August 2011Google Scholar
  9. 9.
    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), Eger, Hungary, pp. 300–305, May 2015Google Scholar
  10. 10.
    Žujović, J., Pappas, T.N., Neuhoff, D.L.: Structural similarity metrics for texture analysis and retrieval. In: Proceedings of 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, pp. 2225–2228, November 2009Google Scholar
  11. 11.
    Žujović, J., Pappas, T.N., Neuhoff, D.L.: Structural texture similarity metrics for image analysis and retrieval. IEEE Trans. Image Process. 22(7), 2545–2558 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Signal Processing and Multimedia Engineering, Faculty of Electrical EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

Personalised recommendations