dc.description.abstract | Nowadays, the technology of Additive Manufacturing(AM) has been widely applied to the fabrication of biological scaffolds to construct scaffolds with specific porosity. The cells on the scaffold can exchange nutrients and metabolites effectively with the outside environment through the pores between the scaffolds to promote cell growth. In order to make scaffolds with stable porosity, it is necessary to precisely control the linewidth of the scaffolds. If the linewidth of the scaffolds is non-uniform, the pore size will be different, which makes it difficult for the tissue fluid to flow in the pores of the scaffolds and cannot effectively exchange nutrients with cells. Besides, unstable linewidths will reduce the overall mechanical properties of the scaffolds and cause scaffolds collapse.
As one of the mainstream materials for scaffolds, hydrogel is characterized by changes in material viscosity with changes in temperature. Therefore, when using hydrogel as a bio-ink for printing, the ink forming method requires precise control of the deposition surface temperature if a temperature-sensitive forming mechanism is used. Normally, single-point contact temperature sensor is used to measure the temperature of the ink deposition surface, which has the disadvantage of not being able to accurately present the exact temperature distribution of the same layer. In the same layer height, with the different measurement position, the temperature obtained will also change, and in order not to touch scaffolds to cause temperature changes, the sensor will be placed above the deposition surface, resulting in the measured temperature and the actual temperature of the deposition surface deviation.
The purpose of this study is to predict the linewidth of the scaffold at different deposition temperatures and printing parameters. To obtain temperature data, 2D thermal images of the pre-deposited surface of the scaffolds is taken using a thermal imaging camera. The temperature and print parameters (pressure, speed) were used as inputs and the linewidth of the scaffolds is used as output to predict temperature and printing parameters versus linewidth. In this study, a total of 348 data were produced, 298 for the training set and 50 for the validation set, with an R 2 of 98.5%, confirming the reliability of the model learning results. With the predictive capability of the model, the bio-printing can be controlled in the future to regulate the nozzle movement speed at each printing point to obtain a continuous and uniform linewidth to improve the scaffolds quality. | en_US |