Journal of Computational Design and Engineering

Society for Computational Design and Engineering (한국CDE학회)
권호 표지
DOI QR코드

DOI QR Code

Automated quality characterization of 3D printed bone scaffolds

  • Tseng, Tzu-Liang Bill (Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso) ;
  • Chilukuri, Aditya (Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso) ;
  • Park, Sang C. (Department of Industrial Engineering, Ajou University) ;
  • Kwon, Yongjin James (Department of Industrial Engineering, Ajou University)
  • Received : 2014.02.04
  • Accepted : 2014.05.12
  • Published : 2014.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Optimization of design is an important step in obtaining tissue engineering scaffolds with appropriate shapes and inner micro-structures. Different shapes and sizes of scaffolds are modeled using UGS NX 6.0 software with variable pore sizes. The quality issue we are concerned is the scaffold porosity, which is mainly caused by the fabrication inaccuracies. Bone scaffolds are usually characterized using a scanning electron microscope, but this study presents a new automated inspection and classification technique. Due to many numbers and size variations for the pores, the manual inspection of the fabricated scaffolds tends to be error-prone and costly. Manual inspection also raises the chance of contamination. Thus, non-contact, precise inspection is preferred. In this study, the critical dimensions are automatically measured by the vision camera. The measured data are analyzed to classify the quality characteristics. The automated inspection and classification techniques developed in this study are expected to improve the quality of the fabricated scaffolds and reduce the overall cost of manufacturing.

Keywords

References

  1. Ma PX, Elisseeff J. Scaffolding in tissue engineering. Boca Raton(FL): CRC Press; 2006. 656 p.
  2. Dietmar H. Scaffolds in tissue engineering bone and cartilage. Biomaterials. 2000; 21(24): 2529-2543. https://doi.org/10.1016/S0142-9612(00)00121-6
  3. Zein I, Hutmacher DW, Tan KC, Teoh SH. Fused deposition modeling of novel scaffold archtectures for tissue engineering applications. Biomaterials. 2002; 23(4): 1169-1185. https://doi.org/10.1016/S0142-9612(01)00232-0
  4. Liu X, Ma PX. Polymeric scaffolds for bone tissue engineering. Annals of Biomedical Engineering. 2004; 32(3): 477-486. https://doi.org/10.1023/B:ABME.0000017544.36001.8e
  5. Gbureck U, Holzel T, Biermann I, Barralet JE, Grover LM. Preparation of tricalcium phosphate/calcium pyrophosphate structures via rapid prototyping. Journal of Material Science: Materials in Medicine. 2008; 19(4): 1559-1563. https://doi.org/10.1007/s10856-008-3373-x
  6. Leukers B. Hydroxyapatite scaffolds for bone tissue engineering made by 3D printing. Journal of Materials Science: Materials in Medicine. 2005; 16(12): 1121-1124. https://doi.org/10.1007/s10856-005-4716-5
  7. Khalyfa A, Vogt S, Weisser J, Grimm G, Rechtenbach A, Meyer W, Schnabelrauch M. Development of a new calcium phosphate powder- binder system for 3D printing of patient specific implants. Journal of Materials Science: Materials in Medicine. 2007; 18(5): 909-916. https://doi.org/10.1007/s10856-006-0073-2
  8. Sherwood JK, Rileyb SL, Palazzoloa R, Browna SC, Monkhousea DC, Coatesc M, Griffithc LG, Landeenb LK, Ratcliffe A. A three dimensional osteochondral composite scaffold for articular cartilage repair. Biomaterials. 2002; 23(24): 4739-4751. https://doi.org/10.1016/S0142-9612(02)00223-5
  9. Haykin S. Neural Networks: A Comprehensive Foundation. New Jersey (NJ), Prentice Hall PTR; 1994. 716 p.
  10. Feraud R, Fabrice C. A methodology to explain neural network classification. Neural Networks. 2001; 15(2): 237-246.
  11. Leite E, Carlos R. TEXTNN-a matlab program for textural classification using neural networks. Computers and Geosciences. 2009; 35(10): 2084-2094. https://doi.org/10.1016/j.cageo.2008.10.009
  12. Chen L, Wei X, Naoyuki T. Classification of 2 dimensional array patterns: assembling many small neural networks is better than using a large one. Neural Networks. 2010; 23(6): 770-781. https://doi.org/10.1016/j.neunet.2010.03.006
  13. Arulampalam G, Abdesselam B. A generalized feed forward neural network architecture for classification and regression. Neural networks. 2003; 16(5): 561-568. https://doi.org/10.1016/S0893-6080(03)00116-3
  14. Kraipeerapun P, Chun C. Binary classification using ensemble neural networks and interval neutrosophic sets. Neurocomputing. 2009; 72(13): 2845-2856. https://doi.org/10.1016/j.neucom.2008.07.017
  15. Kavzoglu T. Increasing the accuracy of neural network classification using refined training data. Environmental Modelling & Software. 2009; 24(7): 850-858. https://doi.org/10.1016/j.envsoft.2008.11.012
  16. Casasent D, Chen XW. Radial basis function neural network for non-linear Fisher discrimination and Neyman-Pearson classification. Neural Networks. 2003; 16(5-6): 529-535. https://doi.org/10.1016/S0893-6080(03)00086-8
  17. Korurek M, Berat D. ECG beat classification using particle swarm optimization and radial basis function neural network. Expert System with Applications. 2010; 37(12): 7563-7569. https://doi.org/10.1016/j.eswa.2010.04.087
  18. Ng WWY, Dorado A, Yeung DS, Pedrycz W, Izquierdo E. Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error. Pattern Recognition. 2007; 40(1): 19-32. https://doi.org/10.1016/j.patcog.2006.07.002
  19. Park S, Hwang JP, Kim ET, Lee HJ, Jung HG. A neural network approach to target classification for active safety system using microwave radar. Expert System with Applications. 2010; 37(3): 2340-3246. https://doi.org/10.1016/j.eswa.2009.07.070
  20. Kwon Y, Park Y. Improvement of vision guided robotic accuracy using Kalman filter. Journal of Computers & Industrial Engineering. 2013; 65(1): 148-155. https://doi.org/10.1016/j.cie.2011.11.018
  21. Kwon Y, Hong J. Integrated remote control of the process capability and the accuracy of vision calibration. Journal of Robotics & Computer Integrated Manufacturing. 2014; 30(5): 451-459. https://doi.org/10.1016/j.rcim.2014.02.004

Cited by

  1. A method to fabricate Low-Cost and large area vitreous carbon mold for glass molded microstructures vol.16, pp.2, 2015, https://doi.org/10.1007/s12541-015-0038-9
  2. Software to generate 3-D continuous printing paths for the fabrication of tissue engineering scaffolds pp.1433-3015, 2015, https://doi.org/10.1007/s00170-015-7866-8
  3. Characterization of shape memory polymer parts fabricated using material extrusion 3D printing technique pp.1355-2546, 2019, https://doi.org/10.1108/RPJ-08-2017-0157