A miniature precision glass encapsulated electrical connectors introduced by glass powder and metal wires through a special complicated process. Aiming at the porosity defects on the surface, a defect detection algorithm propose based on threshold segmentation and feature extraction. Pre-operation, global threshold segmentation processing and feature extraction (based on area, circularity aspect ratio, compactness, and contour length) are preformed to detect the defects. Experimental results show that the algorithm can accurately identify porosities defects.
Micro-precision Glass Insulated Terminals (referred to as glass terminals) are the core components used in precision electronic equipment and are often used for electrical connections between modules. As a glass terminal, its quality has a great influence on the performance of precision electronic equipment. Due to the limitations of materials and production processes, some of the glass terminals produced have defects, such as missing blocks, pores and cracks. At present, most of the defect detection of glass terminals is done by manual inspection, and rapid detection easily causes eye fatigue, so it is difficult to ensure product quality and production efficiency. The traditional defect detection technology is difficult to effectively detect the very different defects of the glass terminal. Therefore, this paper proposes to use deep learning technology to detect missing blocks. First, preprocess the sample pictures of the missing block defects of the glass terminal, and then train the improved Faster Region-CNN deep learning network for defect detection. According to the test results, the accuracy of the algorithm in detecting missing defects in the glass terminal is as high as 93.52%.
It is essential for the diversity of operation targets during the underwater manipulator’s task. The joint friction, the time-varying characteristics of physical parameters, and inaccurate measurement of the dynamic bring trouble of model uncertainty. For these problems, a model reference adaptive impedance controller is proposed to achieve the manipulator flexible operation. The desired impedance model is designed for the outer loop force tracking, and the model reference adaptive impedance controller is employed for the inner loop. At the same time, the adaptive law is designed based on the operation space position and the desired position of the desired impedance model output. To eliminate the time-varying characteristics of physical parameters, a new bounded-gain-forgetting adaptive law is designed to compensate the uncertain error between the manipulator model and estimate model. It is ensured that the closed-loop manipulator dynamic is consistent with the desired impedance model, correspondingly the manipulator end operation force tracking the standard force signal is realized, and the desired position error of the end position of the manipulator to the desired impedance model output asymptotically converges to zero. The simulation experiment of a two-degree-of-freedom manipulator is implemented on the Matlab/Simulink platform. The results show that the designed controller has good force–position tracking asymptotical convergence ability under the uncertain dynamic, and the controller has robustness and stability performance.
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