• Journal of the Korean Society for Precision Engineering
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• v.33 no.12
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• pp.979-984
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• 2016

Recently, ball screws have been used in machine tools, robot parts, and medical instruments. The demand for ball screws of high precision and reduced size is increasing because of the growth of high value-added industries. Three types of ball screws are typically used: deflector type, end-cap type, and tube type. They are also classified from C0 to C9 according to the precision level. A deflector type ball screw can reduce the variation of rotational torque and the size of the nut of the ball screw is minimized. To ensure the reliable design of ball screws, it is important to perform a structural analysis. The purpose of this study is to perform a stability evaluation through analysis of a deflector type miniature ball screw for weapon systems. The analysis is performed through Finite Elements Method (FEM) simulation to predict characteristics such as deformation, stress, and thermal effects. The interference between the shaft and the deflector for smooth rotation are also studied. Based on the results of the analysis, the development of the deflector type miniature ball screw for weapon systems is performed.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.120-130
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• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.