Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

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

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

Straub J (2016) Automated testing and quality assurance of 3D printing/3D printed hardware: assessment for quality assurance and cybersecurity purposes. In: 2016 IEEE AUTOTESTCON, pp 1–5. https://doi.org/10.1109/AUTEST.2016.7589613

Fang T, Jafari MA, Bakhadyrov I, Safari A, Danforth S, Langrana N (1998) 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. https://doi.org/10.1109/ICSMC.1998.727536

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

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

Szkilnyk G, Hughes K, Surgenor B (2011) Vision based fault detection of automated assembly equipment. In: Proceedings of the ASME/IEEE international conference on mechatronic and embedded systems and applications, parts A and B, Washington, DC, USA, vol 3, pp 691–697. https://doi.org/10.1115/DETC2011-48493

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

Chauhan V, Surgenor B (2017) Fault detection and classification in automated assembly machines using machine vision. Int J Adv Manuf Technol 90(9):2491–2512. https://doi.org/10.1007/s00170-016-9581-5

Straub J (2015) Initial work on the characterization of additive manufacturing (3D printing) using software image analysis. Machines 3(2):55–71. https://doi.org/10.3390/machines3020055

Straub J (2016) Alignment issues, correlation techniques and their assessment for a visible light imaging-based 3D printer quality control system. In: SPIE Proceedings–image sensing technologies: materials, devices, systems, and applications III, vol 9854, pp 9854–9854. https://doi.org/10.1117/12.2228081

Tourloukis G, Stoyanov S, Tilford T, Bailey C (2015) Data driven approach to quality assessment of 3D printed electronic products. In: Proceedings of the 38th international spring seminar on electronics technology (ISSE), Eger, Hungary, pp 300–305. https://doi.org/10.1109/ISSE.2015.7248010

Makagonov NG, Blinova EM, Bezukladnikov II (2017) 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. https://doi.org/10.1109/EIConRus.2017.7910849

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

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

Sitthi-Amorn P, Ramos JE, Wangy Y, Kwan J, Lan J, Wang W, Matusik W (2015) MultiFab: a machine vision assisted platform for multi-material 3D printing. ACM Trans Graph 34(4):129:1–129:11. https://doi.org/10.1145/2766962

Gardner MR, Lewis A, Park J, McElroy AB, Estrada AD, Fish S, Beaman JJ, Milner TE (2018) In situ process monitoring in selective laser sintering using optical coherence tomography. Opt Eng 57:55–57. https://doi.org/10.1117/1.OE.57.4.041407

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

Fastowicz J, Okarma K (2016) Texture based quality assessment of 3D prints for different lighting conditions. In: Chmielewski LJ, Datta A, Kozera R, Wojciechowski K (eds) Computer vision and graphics: international conference, ICCVG 2016, Warsaw, Poland, Proceedings, Springer International Publishing, LNCS, vol 9972, pp 17–28. https://doi.org/10.1007/978-3-319-46418-3_2

Okarma K, Fastowicz J (2016) 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. https://doi.org/10.1109/MMAR.2016.7575237

Okarma K, Fastowicz J (2017) Quality assessment of 3D prints based on feature similarity metrics. In: Choraś RS (ed) Image processing and communications challenges 8: 8th international conference, IP&C 2016 Bydgoszcz, Poland, Proceedings, Springer International Publishing, AISC, vol 525, pp 104–111. https://doi.org/10.1007/978-3-319-47274-4_12

Okarma K, Fastowicz J, Tecław M (2016) Application of structural similarity based metrics for quality assessment of 3D prints. In: Chmielewski LJ, Datta A, Kozera R, Wojciechowski K (eds) Computer vision and graphics: international conference, ICCVG 2016, Warsaw, Poland, Proceedings, Springer International Publishing, LNCS, vol 9972, pp 244–252. https://doi.org/10.1007/978-3-319-46418-3_22

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 RS (eds) Image processing and communications challenges 9: 9th international conference, IP&C 2017 Bydgoszcz, Poland, September 2017 Proceedings, Springer International Publishing, AISC, vol 681, pp 67–74. https://doi.org/10.1007/978-3-319-68720-9_9

Fastowicz J, Okarma K (2017) Entropy based surface quality assessment of 3D prints. In: Silhavy R, Senkerik R, Kominkova Oplatkova Z, Prokopova Z, Silhavy P (eds) Artificial intelligence trends in intelligent systems: proceedings of the 6th computer science on-line conference 2017 (CSOC2017), Vol 1, Springer International Publishing, AISC, vol 573, pp 404–413. https://doi.org/10.1007/978-3-319-57261-1_40

International Telecommunication Union (2011) Recommendation ITU-R BT.601-7–Studio encoding parameters of digital television for standard 4:3 and wide-screen 16:9 aspect ratios. https://www.itu.int/rec/R-REC-BT.601/

Okarma K, Fastowicz J (2018) Color independent quality assessment of 3D printed surfaces based on image entropy. In: Kurzynski M, Wozniak M, Burduk R (eds) Proceedings of the 10th international conference on computer recognition systems CORES 2017, Springer International Publishing, Cham, pp 308–315. https://doi.org/10.1007/978-3-319-59162-9_32

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

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

Liu TJ, Lin W, Kuo CCJ (2013) Image quality assessment using multi-method fusion. IEEE Trans Image Process 22(5):1793–1807. https://doi.org/10.1109/TIP.2012.2236343

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

Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, Kuo CCJ (2015) Image database TID2013: peculiarities, results and perspectives. Sig Process Image Commun 30:57–77. https://doi.org/10.1016/j.image.2014.10.009