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LDM process is used for preparing three-dimensional scaffolds for tissue engineering rapid prototyping technologies. Because of its forming process is complex, which influenced by a variety of factors, so the processing environment is not stable, the forming of scaffold pore size can not be guaranteed, therefore the forming precision is poor. However, the scaffold pore size accuracy is mainly decided by the wire filament width. Neural network theory and development provides a powerful tool for the study of nonlinear systems. This article analyzed the influence factors for forming bone scaffold filament width of LDM process, based on improved BP neural network, using MATLAB software programming, then predicted the filament width. The results show that model prediction error was less than 8%, it has high forecasting precision, and it can be used to guide the LDM process parameter selection and forming precision of prediction.

From the perspective of forming bone scaffold, the technologic parameters of LDM which have important influence on the shape of the deposition material are studied. Through building experimental platform and using chitosan solution as experimental material, the influence of the temperature and the layer height on the lap of every fiber layer are firstly discussed in detail. Besides, the effects of nozzle diameter, the velocity of receiving device, the pressure of feeding device are also discussed. Then the above mentioned process parameters are all optimized, which make the forming mechanism of LDM to be clear. Finally, based on the optimal parameters, the scaffold with self-designed pore size and a high degree of pore interconnectivity, which meeting the requirements of the structure of tissue engineering are fabricated.

The scaffold is a key part of the artificial regeneration osseous tissues. The ideal scaffold will have the ability to mimic the fully functional tissue, which can afford the fibrous form and complex function of the native ECM. Although Low-temperature deposition manufacturing is a promising method for fabricate tissue scaffold because scaffold can maintain a good performance of biomaterials in low temperature, the scaffold pore size can not be controlled according to demands. Therefore, the key factors that affect the pore size of bone scaffolds are firstly analyzed. Then the mechanism for on-line control is given. Finally, an automatic control system is proposed and some experimental results are given, which have demonstrated the effects of processing parameters on the Shaping of scaffold material.

Low-temperature deposition manufacturing (LDM) has been proven as an effective bone scaffold preparation process, but its further application has been seriously hindered by the existing material over-accumulation problem. In view of the over-accumulation problem of the traditional pneumatic extrusion material feeding way, designed and developed a feeding system based on pneumatic-extrusion and valve-control, which can achieve rapid pressure/relief, Combined with the inflation / deflation time calculation method of fixed volume container for analysis and calculation. A unified data management method of the material feeding device and a regulation scheme of the controller are given. Experimental results show that, by adjusting parameters, the feeding system based on pneumatic-extrusion and valve-control can achieve rapid gas pressure/relief, the flux has been well controlled, the over-accumulation on deposition path at the end has been eliminated, which lead to effective guarantee of scaffold forming quality.

In this paper, the strength and deformation of an elastic orientating component (sleeve) were analyzed by Finite Element Method (FEM). The highest stress of the part and the relationship between the dimension and deformation were found. The position under the critical situation was obtained. The optimal dimension of the sleeve was given.

SiGe nanoparticles with uniform diameter of about 30 nm in the top silicon of silicon on insulator (SOI) were synthesized by Ge ion implantation combined with subsequent annealing process. The density of those nanoparticles is 6 × 10 7 /cm 2 . These nanoparticles are formed by agglomeration of the recoil atoms and the migrated adatoms. The method presented in this study will allow efficient fabrication of uniform SiGe nanoparticles into SOI.