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In recent years, inkjet technology has played an important role in industrial materials printing and various sensors fabrication, but the mechanisms of the inkjet print head should be researched more elaborately. The steady state deformation analysis of a tubular piezoelectric print head, which can be classified as a plane strain problem because the radii of the tubes are considerably smaller than the lengths, is discussed in this paper. The geometric structure and the boundary conditions are all axisymmetric, so a one-dimensional mathematical model is constructed. By solving the model, the deformation field and stress field, as well as the electric potential distribution of the piezoelectric tube and glass tube, are obtained. The results show that the deformations are on the nanometer scale, the hoop stress is larger than the radial stress on the whole, and the potential is not linearly distributed along the radial direction. An experiment is designed to validate these computations. A discussion of the effect of the tubes’ thicknesses on the system deformation status is provided.

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In this paper, a new swarm optimization improved BP (Back Propagation) algorithm, combination of PSE (Particle Swarm Evolution) and BP, called PSE-BP algorithm, is introduced to train ANN (Artificial Neural Network) for the purpose of microchananel resistance factor prediction. The PSE algorithm was firstly proposed by comprehensively learning the principle of gradient descent, genetic algorithm and particle swarm optimization. Then, the search capability of PSE was analyzed by a standard cost function. Its appropriate control parameters were also determined by the same time. By utilizing the global search ability and high search efficiency of PSE algorithm, the improved BP (PSE-BP) algorithm was proposed. The training efficiency of PSE-BP algorithm was tested and compared with BP algorithm using Iris dataset. Finally, the resistance factor of rectangular cross-section microchannel was established using ANN, and trained with PSE-BP and BP algorithm, respectively. The results show that the PSE-BP algorithm can greatly improve the training efficiency of ANN, compare with BP algorithm. And the microchananel resistance factors, which predicted by ANN model and trained with PSE-BP algorithm, are in good agreement with the simulation samples.

The serpentine microchannel or bent microchannel has been identified as one of the essential elements for inertial microfluidic devices. Understanding the transient flow of liquid is important in predicting flow time and evaluating device performance. In this paper, based on the unsteady Bernoulli equation, the inertial transient flow model was derived by analyzing the microchannel resistance factor using back propagation (BP) artificial neural network (ANN). In order to train the ANN, an improved BP algorithm was used to make the resistance model more accurate. A centrifugal test platform was employed to measure flow time in the microchannel with different structure sizes. The results show that the transient flow model has a good agreement with the experiment values, the results also show that the method using BP neural network can also be used to solve the problem of nonlinear inertial flow.