Automated quality characterization of 3D printed bone scaffolds

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.

[1]  D. Hutmacher,et al.  Scaffolds in tissue engineering bone and cartilage. , 2000, Biomaterials.

[2]  Ho Gi Jung,et al.  A neural network approach to target classification for active safety system using microwave radar , 2010, Expert Syst. Appl..

[3]  Liang Chen,et al.  Classification of 2-dimensional array patterns: Assembling many small neural networks is better than using a large one , 2010, Neural Networks.

[4]  Abdesselam Bouzerdoum,et al.  A generalized feedforward neural network architecture for classification and regression , 2003, Neural Networks.

[5]  P. Ma,et al.  Polymeric Scaffolds for Bone Tissue Engineering , 2004, Annals of Biomedical Engineering.

[6]  L. Grover,et al.  Preparation of tricalcium phosphate/calcium pyrophosphate structures via rapid prototyping , 2008, Journal of materials science. Materials in medicine.

[7]  Witold Pedrycz,et al.  Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error , 2007, Pattern Recognit..

[8]  Peter X. Ma,et al.  Scaffolding In Tissue Engineering , 2005 .

[9]  I. Zein,et al.  Fused deposition modeling of novel scaffold architectures for tissue engineering applications. , 2002, Biomaterials.

[10]  Lance Chun Che Fung,et al.  Binary classification using ensemble neural networks and interval neutrosophic sets , 2009, Neurocomputing.

[11]  Gabriele Grimm,et al.  Development of a new calcium phosphate powder-binder system for the 3D printing of patient specific implants , 2007, Journal of materials science. Materials in medicine.

[12]  Yongjin Kwon,et al.  Integrated remote control of the process capability and the accuracy of vision calibration , 2014 .

[13]  Raphaël Féraud,et al.  A methodology to explain neural network classification , 2002, Neural Networks.

[14]  Mehmet Korürek,et al.  ECG beat classification using particle swarm optimization and radial basis function neural network , 2010, Expert Syst. Appl..

[15]  David Casasent,et al.  Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification , 2003, Neural Networks.

[16]  Yongjin Kwon,et al.  Improvement of vision guided robotic accuracy using Kalman filter , 2013, Comput. Ind. Eng..

[17]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[18]  S. Milz,et al.  Hydroxyapatite scaffolds for bone tissue engineering made by 3D printing , 2005, Journal of materials science. Materials in medicine.

[19]  Scott C. Brown,et al.  A three-dimensional osteochondral composite scaffold for articular cartilage repair. , 2002, Biomaterials.

[20]  Taskin Kavzoglu,et al.  Increasing the accuracy of neural network classification using refined training data , 2009, Environ. Model. Softw..

[21]  Carlos Roberto de Souza Filho,et al.  TEXTNN - A MATLAB program for textural classification using neural networks , 2009, Comput. Geosci..