| 摘要(英) |
The materials of bio-scaffolds can be roughly divided into two categories: natural and synthetic materials. The most widely used natural materials are collagen, gelatin and chitosan. As these natural materials are obtained from flora and fauna, there is always a slight difference between the bio-inks in each batch. Most of the fabrication parameters have dependence with each other, it is difficult to derive effective predictive formulas for manufacturing scaffolds. Often can only use the trial and error method to adjust the fabrication parameters for achieving the preset target. Multilayer perceptron is one kind of Deep learning, and it can train a model through a large amount of historical data, and predictable results obtain after inputting new data into the model. The capability can help to improve the appearance quality of the bio-scaffold.
The purpose of this study is to improve the appearance quality of the bio-scaffold by utilizing deep learning. The multilayer perceptron is used to investigate the material properties and the fabrication parameters in order to achieve optimal quality of the scaffold. The fabrication parameters are based on kinematic viscosity, print pressure, nozzle temperature, nozzle speed, platform temperature, and so on. According to these models, it can be seen that the kinematic viscosity is the most relevant parameter in the printing process. 75 scaffolds were actually used as the training dataset and 8 scaffolds were used as the test set. Six parameters including dynamic viscosity, nozzle temperature, nozzle speed, platform temperature, print pressure and dew point temperature are used as important parameters for the model prediction. The model uses two hidden layers, and The minimum mean square error of predicted score and average line width is 0.0246. |
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