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The dynamic and static characteristics of hydrostatic thrust bearings are significantly affected by the bearing surface topography. Previous studies on hydrostatic thrust bearings have focused on Gaussian distribution models of bearing surface topography. However, based on actual measurements, the non-Gaussianity of the distribution characteristics of bearing surface topography is clear. To accurately characterize the non-Gaussian distribution of bearing surface topography, the traditional probability density function of Gaussian distribution was modified by introducing Edgeworth expansion. The non-Gaussian surface was then reflected by two parameters: kurtosis and skewness. This had an effect on the static characteristics of hydrostatic thrust bearings with both circumferential and radial surface topographies. The comparison between the Gaussian distribution results and those of the non-Gaussian model showed that errors between the two models could reach more than 10%. Therefore, it is important to take into account the non-Gaussianity of bearing surface when discussing static characteristics of hydrostatic thrust bearings considering the surface topography. [ABSTRACT FROM AUTHOR]

In the manufacturing process of LED chips, the accuracy of the LED chip visual localization system affects the quality of LED chip production directly. There are many errors that have impacts on positioning system accuracy. Therefore, the identification and compensation of the critical errors is key to efficiently improving the precision of the system. Based on this fact, an error analysis method and an error compensation strategy are proposed in this paper. The first step was to measure the relevant error sources that may affect the localization system. Then error model of localization system was established, and the validity of this error model was verified by comparing simulated and actual positioning results. In addition, the impact factors of each error source on localization system accuracy were obtained using the error transfer theory. According to the error analysis results, an efficient error compensation strategy was proposed, which could compensate the errors in order of impact factors, and judge whether the error compensation method is optimal. Finally, the experimental results proved that the proposed error analysis method was valid and the error compensation strategy could efficiently enhance the positioning accuracy to meet the industrial application requirements. [ABSTRACT FROM AUTHOR]

The noise in an LED chip image directly affects its visual positioning accuracy and efficiency. The existing denoising method can improve the image visual quality after removing the noise, but the improvement of the recognition rate and the positioning accuracy of the chip is not apparent. To solve this problem, this paper proposes a convolutional neural network combined with generative adversarial networks framework, the (CNN-GAN-GAN)-based blind denoiser, comprising three subnetworks. First, a noise extraction subnetwork with the proposed adaptive residual dense block extracts noise information from noisy images. Then, the noise modeling subnetwork with a dual-discriminator learns the noise distribution and constructs a paired training dataset. Finally, the blind denoising subnetwork with a hybrid loss function is trained for denoising. Contrast experiments show the proposed method can effectively suppress image noise and artifacts and meet the requirements of LED chip visual positioning accuracy while ensuring chip recognition rate. [ABSTRACT FROM AUTHOR]

Positioning accuracy of chip directly impacts on the quality and efficiency of LED chip production. The purpose of this paper is to improve the chip positioning accuracy by improving the camera calibration algorithm of LED chip visual positioning system. Firstly, by making the error analysis for the visual positioning system, the systematic errors of each parts of the system are obtained, and the relationship between chip positioning error and chip position distribution in image is found. Then, according to the result of error analysis and the characteristics of the chip positioning process, an improved calibration algorithm is proposed to improve the chip positioning accuracy. This improved algorithm solves the calibration parameters in two steps, which highlights the main cause of errors in calibration process and meets the requirements of chip positioning. Finally, the experiment results show that the proposed algorithm can improve the chip positioning accuracy obviously, and has good stability and robustness. [ABSTRACT FROM AUTHOR]