The parallel kinematic machine tool has many advantages including excellent loading capacity, high structural stiffness and small accumulated error of linkage. It has become one of the most important research fields for machine tools. In the present research, a principle for the optimization of the dimensional design parameters of a parallel kinematic machine tool is proposed. A five-degree-of-freedom (5DOF) parallel kinematic machine tool with a TRR-XY hybrid mechanism is chosen for investigating the design procedures and the optimization results. The inverse kinematics of the hybrid mechanism is first investigated. Then, the inverse solution is used to analyse and create the workspaces of the machine tool. The design parameters of the mechanical components are further optimized for constructing the maximum workspace.
The TFT-LCD panel is one of the most important and promising products in the recent years. Mura defects can be created on the display panel during its production. In this research, a linear regression diagnostic model is incorporated with digital image processing theory to automatically inspect for Mura defects. A bivariate polynomial regression model is used to simulate the brightness of background images that is used in the diagnosis of outliers and influential points. The partitions of the candidate Mura defect regions are segmented using Niblack's threshold criteria. The candidate Mura defects are further evaluated. The quantified level is defined in terms of concepts already reported in the literature. Based on Weber's law and a visual perception model, the just-noticeable intensity difference index of the Mura features can be obtained and it can be subsequently used to quantify the Mura defect level. With the obtained defect level, Mura defects can be identified for exactly labelling of the perfect and imperfect LCD panels.Experiments were performed on 13 TFT-LCD panels. There are ten bad panels and three good panels in these 13 samples as determined by human visual inspection. Each bad panel has at least one Mura defect. After the automated inspection process, the results showed that the proposed method could separate the good and bad panels accurately. Compared with human visual inspection, the Mura detection rate of the distinct size and shapes can attain over 90.9 per cent correct detection and the achieved correction rate of Mura defects on each panel can be improved by 100 per cent.
This paper derived the surface profile equations and the meshing equations of the spherical meshing elements from the homogeneous coordinate transformation matrix and the conjugate surface theory proved by the envelope theory. In the machining aspect, we demonstrated the position equation of cam profile using the coordinate transformation theory and obtained the perfected controlling position point of the tool path under the limitation of the chord errors. Finally, the design and the machining module was developed via Visual Basic that generated the cam profile points and the tool path points. The curves from UNIGRAPHICS to test and verify the cam profile equation. To verify the validation of the tool path, a cutting simulation software, VERICUT, is used to simulate the cutting geometry and kinematics of the roller gear cam, and demonstrate the practical application of the developed method.
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