nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Combined Metrics for Quality Assessment of 3D Printed Surfaces for Aesthetic Purposes: Towards Higher Accordance with Subjective Evaluations

verfasst von : Jarosław Fastowicz, Piotr Lech, Krzysztof Okarma

Erschienen in: Computational Science – ICCS 2020

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Objective quality assessment for 3D printing purposes may be considered as one of the most useful applications of machine vision in smart monitoring related to the development of the Industry 4.0 solutions. During recent years several approaches have been proposed, assuming observing the side surfaces, mainly based on the analysis of the regularity of visible patterns, which represent the consecutive printed layers. These methods, based on the use of general purpose image quality assessment (IQA) metrics, Hough transform, entropy and texture analysis, make it possible to classify the printed samples, independently of the filament’s colour, into low and high quality classes, with the use of photos or 3D scans of the side surfaces.

The next step of research, investigated in this paper, is the combination of various proposed approaches to develop a combined metric, possibly highly correlated with subjective opinions. Since the correlation of single metrics developed mainly for classification is relatively low, their combination makes it possible to achieve much better results, verified using an original, newly developed database containing 107 captured images and 3D scans of the 3D printed surfaces with various colours and local distortions caused by external factors, together with Mean Opinion Scores (MOS) gathered from independent observers. Obtained results are promising and may be a starting point for further research towards the optimisation of the newly developed metrics for the automatic assessment of the 3D printed surfaces, mainly for aesthetic purposes.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Ontology-Driven Edge Computing

Nächstes Kapitel Path Markup Language for Indoor Navigation

Azimi, P., Zhao, D., Pouzet, C., Crain, N.E., Stephens, B.: Emissions of ultrafine particles and volatile organic compounds from commercially available desktop three-dimensional printers with multiple filaments. Environ. Sci. Technol. 50(3), 1260–1268 (2016)CrossRef

Busch, S.F., Weidenbach, M., Fey, M., Schäfer, F., Probst, T., Koch, M.: Optical properties of 3D printable plastics in the THz regime and their application for 3D printed THz optics. J. Infrared Millimeter Terahertz Waves 35(12), 993–997 (2014). https://doi.org/10.1007/s10762-014-0113-9CrossRef

Chauhan, V., Surgenor, B.: A comparative study of machine vision based methods for fault detection in an automated assembly machine. Proc. Manuf. 1, 416–428 (2015). https://doi.org/10.1016/j.promfg.2015.09.051CrossRef

Chauhan, V., Surgenor, B.: Fault detection and classification in automated assembly machines using machine vision. Int. J. Adv. Manuf. Technol. 90(9), 2491–2512 (2016). https://doi.org/10.1007/s00170-016-9581-5CrossRef

Cheng, Y., Jafari, M.A.: Vision-based online process control in manufacturing applications. IEEE Trans. Autom. Sci. Eng. 5(1), 140–153 (2008). https://doi.org/10.1109/TASE.2007.912058CrossRef

Delli, U., Chang, S.: Automated process monitoring in 3D printing using supervised machine learning. Proc. Manuf. 26, 865–870 (2018). https://doi.org/10.1016/j.promfg.2018.07.111CrossRef

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, CA, USA, vol. 5, pp. 4373–4378 (1998). https://doi.org/10.1109/ICSMC.1998.727536

Fang, T., Jafari, M.A., Danforth, S.C., Safari, A.: Signature analysis and defect detection in layered manufacturing of ceramic sensors and actuators. Mach. Vis. Appl. 15(2), 63–75 (2003). https://doi.org/10.1007/s00138-002-0074-1CrossRef

Fastowicz, J., Grudziński, M., Tecław, M., Okarma, K.: Objective 3D printed surface quality assessment based on entropy of depth maps. Entropy 21(1), 97 (2019). https://doi.org/10.3390/e21010097CrossRef

10.

Fastowicz, J., Okarma, K.: Texture based quality assessment of 3D prints for different lighting conditions. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 17–28. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46418-3_2CrossRef

11.

Fastowicz, J., Okarma, K.: Quality assessment of photographed 3D printed flat surfaces using Hough transform and histogram equalization. J. Univ. Compu. Sci. 25(6), 701–717 (2019). http://www.jucs.org/jucs_25_6/quality_assessment_of_photographed

12.

Gardner, M.R., et al.: In situ process monitoring in selective laser sintering using optical coherence tomography. Opt. Eng. 57, 041407 (2018). https://doi.org/10.1117/1.OE.57.4.041407CrossRef

13.

Hirsch, M., et al.: Assessing the capability of in-situ nondestructive analysis during layer based additive manufacture. Addit. Manuf. 13, 135–142 (2017). https://doi.org/10.1016/j.addma.2016.10.004CrossRef

14.

Holzmond, O., Li, X.: In situ real time defect detection of 3D printed parts. Addit. Manuf. 17, 135–142 (2017). https://doi.org/10.1016/j.addma.2017.08.003CrossRef

15.

Ieremeiev, O., Lukin, V., Ponomarenko, N., Egiazarian, K.: Combined no-reference IQA metric and its performance analysis. Electron. Imaging 2019(11), 260-1–260-7 (2019). https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-260CrossRef

16.

Kim, H., Lin, Y., Tseng, T.L.B.: A review on quality control in additive manufacturing. Rapid Prototyping J. 24(3), 645–669 (2018). https://doi.org/10.1108/RPJ-03-2017-0048CrossRef

17.

Lech, P., Fastowicz, J., Okarma, K.: Quality evaluation of 3D printed surfaces based on HOG features. In: Chmielewski, L.J., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A.M., Petkov, N. (eds.) ICCVG 2018. LNCS, vol. 11114, pp. 199–208. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00692-1_18CrossRef

18.

Makagonov, N.G., Blinova, E.M., Bezukladnikov, I.I.: Development of visual inspection systems for 3D printing. In: 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus, pp. 1463–1465, February 2017. https://doi.org/10.1109/EIConRus.2017.7910849

19.

Okarma, K., Fastowicz, J.: No-reference quality assessment of 3D prints based on the GLCM analysis. In: Proceedings of the 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR, pp. 788–793 (2016). https://doi.org/10.1109/MMAR.2016.7575237

20.

Okarma, K.: Combined full-reference image quality metric linearly correlated with subjective assessment. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 539–546. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13208-7_67CrossRef

21.

Okarma, K.: Combined image similarity index. Opt. Rev. 19(5), 349–354 (2012). https://doi.org/10.1007/s10043-012-0055-1CrossRef

22.

Okarma, K.: Quality assessment of images with multiple distortions using combined metrics. Elektronika Ir Elektrotechnika 20(6), 128–131 (2014). https://doi.org/10.5755/j01.eee.20.6.7284CrossRef

23.

Okarma, K., Fastowicz, J.: Color independent quality assessment of 3D printed surfaces based on image entropy. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds.) CORES 2017. AISC, vol. 578, pp. 308–315. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59162-9_32CrossRef

24.

Okarma, K., Fastowicz, J.: Adaptation of full-reference image quality assessment methods for automatic visual evaluation of the surface quality of 3D prints. Elektronika Ir Elektrotechnika 25(5), 57–62 (2019). https://doi.org/10.5755/j01.eie.25.5.24357CrossRef

25.

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.) ICCS 2008. LNCS, vol. 5101, pp. 790–799. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69384-0_84CrossRef

26.

Oszust, M.: Decision fusion for image quality assessment using an optimization approach. IEEE Signal Process. Lett. 23(1), 65–69 (2016). https://doi.org/10.1109/LSP.2015.2500819CrossRef

27.

Scime, L., Beuth, J.: Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit. Manuf. 19, 114–126 (2018). https://doi.org/10.1016/j.addma.2017.11.009CrossRef

28.

Sitthi-Amorn, P., et al.: MultiFab: a machine vision assisted platform for multi-material 3D printing. ACM Trans. Graph. 34(4), 129:1–129:11 (2015). https://doi.org/10.1145/2766962CrossRef

29.

Stephens, B., Azimi, P., Orch, Z.E., Ramos, T.: Ultrafine particle emissions from desktop 3D printers. Atmos. Environ. 79, 334–339 (2013)CrossRef

30.

Straub, J.: Initial work on the characterization of additive manufacturing (3D printing) using software image analysis. Machines 3(2), 55–71 (2015). https://doi.org/10.3390/machines3020055CrossRef

31.

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

32.

Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861CrossRef

33.

Wu, H., Chen, T.: Quality control issues in 3D-printing manufacturing: a review. Rapid Prototyping J. 24(3), 607–614 (2018). https://doi.org/10.1108/RPJ-02-2017-0031CrossRef

34.

Zeltmann, S.E., Gupta, N., Tsoutsos, N.G., Maniatakos, M., Rajendran, J., Karri, R.: Manufacturing and security challenges in 3D printing. JOM 68(7), 1872–1881 (2016). https://doi.org/10.1007/s11837-016-1937-7CrossRef

35.

Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011). https://doi.org/10.1109/TIP.2011.2109730MathSciNetCrossRefMATH

36.

Zhao, X., Lian, Q., He, Z., Zhang, S.: Region-based online flaw detection of 3D printing via fringe projection. Meas. Sci. Technol. 31(3), 035011 (2019). https://doi.org/10.1088/1361-6501/ab524bCrossRef

Titel: Combined Metrics for Quality Assessment of 3D Printed Surfaces for Aesthetic Purposes: Towards Higher Accordance with Subjective Evaluations
verfasst von: Jarosław Fastowicz
Piotr Lech
Krzysztof Okarma
Verlag: Springer International Publishing
Buch: Computational Science – ICCS 2020
Print ISBN: 978-3-030-50435-9

Electronic ISBN: 978-3-030-50436-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-50436-6_24

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner