Papers by Author: Y.H. Wan

Abstract: In this paper, a new type of flexible sub-size polishing tool, and an advanced polishing technique for free-form surface based on the new type of tool will be researched. The configuration of the flexible polishing tool and the method of controlling the multi-DOF precessions of the flexible polishing tool will be introduced. The rectilineal movement along X,Y, Z axis and flirts on two polar coordinates rotational axis of the polishing tool are used to control the form precision and the surface texture of polished surface. This polishing technique is enable to change continuously the polishing pressure and contact area and makes the flexible polishing tool well suited both to control the texture of work-piece surface and to control the form of work-piece surface. The influence functions the flexible polishing tool is near-Gaussian, symmetrical, and lacks the high spatial frequencies and center-zero of removal. The example of multi-DOF precessions polishing for optic spherical and aspherical will be introduced. The results show that the form, the size and the movement mode of the polishing tool will have important effect to polishing quality and multi-DOF precessions polishing can obtain better surface texture quality, form precision and higher polishing efficiency than traditional pole-down polishing for free-form surface.

Abstract: The Mahalanobis distance feature proposed by P.C .Mahalanobis, an indian statistician. In this paper, we propose a new concept, Local Region Mahalanobis Distance feature –LRMD feature, we shall discuss the structure form, the obtaining methods of LRMD feature from an image and the relations between the LRMD feature and wearing and breakage states of cutting tools. The new research results indicate that the method of automatic on-line cutting tool condition monitoring based on LRMD feature can has better inspect result than the method of Mahalanobis Distance feature.

Abstract: A new intelligent tool condition monitoring technique for metal cutting process is proposed. Fiest, the frequency spectrum analysis of the audio signal during cutting process, the pixel space projection analysis and equal gray pace analysis of the images of machined workpiece surface are introduced. Then combined the results of the audio signal analysis and the workpiece surface image analysis with artificial neural network, we implemented the intelligent tool condition monitoring based on multi-information fusion. The experimental results indicate that this method can recognize the tool condition effectively and improve the dependability of tool condition monitoring.