M
Ming-June Tsai
Researcher at National Cheng Kung University
Publications - 34
Citations - 1044
Ming-June Tsai is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Polishing & Feature (computer vision). The author has an hindex of 17, co-authored 34 publications receiving 974 citations. Previous affiliations of Ming-June Tsai include Ohio State University & June.
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Geometric optimization of serial chain manipulator structures for working volume
TL;DR: In this paper, the regional structure of a manipulator, which consists of the three inboard joints and their associated members, determines the workspace shape and volume, and the orientation structu...
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Kinematic sensitivity analysis of linkage with joint clearance based on transmission quality
Ming-June Tsai,Tien-Hsing Lai +1 more
TL;DR: In this paper, the authors presented an effective method to analyze the transmission performance of linkages that have joint clearance and treated the virtual link as virtual link to simplify the study, where equivalent kinematical pairs were used to model the motion freedoms caused by the joint clearances.
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Accuracy analysis of a multi-loop linkage with joint clearances
Ming-June Tsai,Tien-Hsing Lai +1 more
TL;DR: In this paper, a generalized method for error analysis of multi-loop mechanisms with joint clearance is introduced, which uses the properties of reciprocal screws to determine the instantaneous configurations of six-bar linkages with different specified input links.
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Efficient automatic polishing process with a new compliant abrasive tool
Ming-June Tsai,J. F. Huang +1 more
TL;DR: In this paper, an efficient polishing process is proposed for precision polishing tasks using a new compliant abrasive tool, which is conducted by a force-controllable five-axes robot.
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Development of a high-precision surface metrology system using structured light projection
Ming-June Tsai,Chuan-Cheng Hung +1 more
TL;DR: In order to acquire higher measurement resolution, this paper proposes a correspondence matching method which combines Gray codes encoding and sub-pixel edge detection and with a line-shifting procedure, the measurement resolution is elevated four times higher.