Search

Micro lens arrays (MLAs) are widely applied in different areas to fulfill different functions. Ultra-precision diamond cutting technology can generate MLA with sub-micrometer form accuracy and nanometer surface roughness but mostly for nonferrous material. In this paper, a three-axis micro punching machine tool is developed to use diamond tool to produce MLAs on steel mold. The developed micro punching machine realizes high-feed and high-precision motions by a macro-micro composite structure. An on-site measurement system based on machine vision is designed to provide evaluation results for MLAs and compensation data for the PZT. The fabrication methodologies for MLAs are studied under the consideration of the output voltage of PZT, tool path generation, and machining parameters. Experiments are conducted to testify the machine tool and the on-site measurement system by machining of MLAs on mold steel. The experimental results show that the micro punching machine can produce MLAs on hard materials with sub-micrometer form accuracy, and the evaluation results from the on-site measurement are close to those from the white-interference profiler.

Microstructure functional surface is widely used in an optical system because of its special micro-topological structure and particular physical properties. This paper presents a context-aware autonomously controlling method of micro-dots punching machine tool via establishing the digital twin-driven cyber-physical system. Key enabling techniques on twinning of cyberspace and physical equipment are discussed. A dynamic adjustment model of piezoelectric ceramics for micro-dots punching is presented based on the high-precision online detection and control system. A novel staggered punching approach is proposed for improving the punching speed. A joint optimization model is proposed for coordinating micro-punching system and staggered process. Context-aware autonomous adjusting of the system with errors analysis and compensations in the punching process is realized. Finally, a positioning accuracy of $2~\mu \text{m}$ and a high punching speed of 20-65 dots/s are achieved. This paper is expected to provide a new approach for incorporating smart-enabling techniques in the ultra-precision machining of microstructure arrays.

As the medium of human-computer interaction, it is crucial to correctly and quickly interpret the motion information of surface electromyography (sEMG). Deep learning can recognize a variety of sEMG actions by end-to-end training. However, most of the existing deep learning approaches have complex structures and numerous parameters, which make the network optimization problem difficult to realize. In this paper, a novel PSO-based optimized lightweight convolution neural network (PLCNN) is designed to improve the accuracy and optimize the model with applications in sEMG signal movement recognition. With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. Meanwhile, the particle swarm optimization (PSO) is used to optimize hyperparameters and improve the autoadaptive ability of the designed sEMG pattern recognition model. To further indicate the potential application, three experiments are designed according to the progressive process of body movements with respect to the Ninapro standard data set. Experiment results demonstrate that the proposed PLCNN recognition method is superior to the four other popular classification methods.

Microfluidics offer microenvironments for reagent delivery, handling, mixing, reaction, and detection, but often demand the affiliated equipment for liquid control for these functions. As a helpful tool, the capillary pressure control valve (CPCV) has become popular to avoid using affiliated equipment. Liquid can be handled in a controlled manner by using the bubble pressure effects. In this paper, we analyze and categorize the CPCVs via three determining parameters: surface tension, contact angle, and microchannel shape. Finally, a few application scenarios and impacts of CPCV are listed, which includes how CPVC simplify automation of microfluidic networks, work with other driving modes; make extensive use of microfluidics by open channel, and sampling and delivery with controlled manners. The authors hope this review will help the development and use of the CPCV in microfluidic fields in both research and industry.