The problem of tiny and low-contrast surface defect detection is a nontrivial one. To solve the problems, this article proposes an edge and multi-scale reverse attention network (EMRA-Net), of which includes feature extraction and feature fusion. In the process feature extraction, the global dynamic convolution features, global dynamic multi-scale fusion (MSF) feature, and local pyramid edge feature are obtained through the pre-training backbone Resnet 34, the fresh MSF module, and the innovative pyramid edge module, respectively. In the process of feature fusion, these features are blended by a new self-learning scale module and a novel spatial channel domain reverse attention (SCRA) module step by step. The experimental results of five widely used datasets show that the EMRA-Net outperforms the existing methods. In addition, the mean intersection of union (mIoU) ^{1 2} on the printed circuit board (PCB) industry dataset reaches 95.31%. Moreover, the results of EMRA-Net indicated that the local edge feature can improve the performance of the defect detection network. The EMRA-Net has great potential in the application in the detection of tiny and low-contrast defects.

Piezoelectric print-heads (PPHs) are used with a variety of fluid materials with specific functions. Thus, the volume flow rate of the fluid at the nozzle determines the formation process of droplets, which is used to design the drive waveform of the PPH, control the volume flow rate at the nozzle, and effectively improve droplet deposition quality. In this study, based on the iterative learning and the equivalent circuit model of the PPHs, we proposed a waveform design method to control the volume flow rate at the nozzle. Experimental results show that the proposed method can accurately control the volume flow of the fluid at the nozzle. To verify the practical application value of the proposed method, we designed two drive waveforms to suppress residual vibration and produce smaller droplets. The results are exceptional, indicating that the proposed method has good practical application value.

Piezoelectric three-dimensional inkjet printing has been used to manufacture heterogeneous objects due to its high level of flexibility. The materials used are non-Newtonian inks with complex rheological properties, and their behavior in the context of inkjet printing has not been fully understood: for example, the fact that the shear-thinning viscosity affects the droplet generation. Therefore, a control strategy coping with shear-thinning behaviors is needed to ensure printing consistency. In this paper, a novel model-based approach is presented to describe the shear-thinning ink dynamics inside the piezoelectric inkjet printhead, which provides the basis to design the excitation parameters in a systematic way. The dynamic equation is simplified into a quasi-one-dimensional equation through the combination of the boundary layer theory and the constitutive equation of the power-law fluid, of which the viscosity is shear-thinning. Based on this, a nonlinear time-varying equivalent circuit model is presented to simulate the power-law fluid flow rate inside the tube. The feasibility and effectiveness of this model can be evaluated by comparing the results of computational fluid dynamics and the experimental results.

In some applications of piezoelectric three-dimensional inkjet printing, the materials used are power-law fluids as they are shear thinning. Their time-varying viscosities affect the droplet formation, which is determined by the volume flow rate at the nozzle outlet. To obtain a fine printing effect, it is necessary to present a driving waveform design method that considers the shear-thinning viscosities of materials to control the volume flow rate at the nozzle outlet, which lays the foundation for the single and stable droplet generation during the printing process. In this research, we established the relationship between the driving waveform and the volume flow rate at the nozzle outlet by modifying a model that describes the inkjet mechanism of power-law fluid. The modified model was used to present a driving waveform design method based on iterative learning control. The iterative learning law of the method was designed based on the gradient descent algorithm and demonstrated its convergence. The driving waveform design method was verified to be practical and feasible by implementing drop generation experiments.