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Robust and automatic measurement of grinding-induced subsurface damage in optical glass K9 based on digital image processing

Zhao Y. J., Yan Y. H., Song K. C., Li H. N.

Archives of Civil and Mechanical Engineering

2018

Vol. 18, no. 1

320--330

Optical glass K9 is a critical kind of optical materials, however experiments have indicated that the mechanical grinding of K9 easily led to subsurface damage (SSD). Although substantial SSD measurement methods have been suggested, the problems including the prior knowledge of SSD and slow measurement speed still impede the reported method applications. To this end, this paper has presented an image-process-based method that can identify and measure the grinding-induced SSD in K9 specimens. By performing grinding trials, the method has been found to be able to accurately (with biggest relative error of 3.13% in comparison with the manually measured results) and quickly (with the measurement speed of 1.68 s per image) measure SSD depths. Without any parameter presetting, the method enables automatic SSD measurements, allowing the users without SSD knowledge to be able to use the method. Moreover, the method has shown the good robustness to the input image size, illumination, tilted specimen placement, and material flaws. The method is therefore anticipated to be meaningful for the industrial manufacturing, design and application of optical glass.

Analytical modeling of ground surface topography in monocrystalline silicon grinding considering the ductile-regime effect

Li H. N., Yu T. B., Zhu L. D., Wang W. S.

Archives of Civil and Mechanical Engineering

2017

Vol. 17, no. 4

880--893

Grinding process of monocrystalline silicon easily leads to fractured surfaces, therefore an analytical model of the ground silicon surface is presented. In the model, the ductile-regime effect is considered by determining grain-workpiece interaction mode (ductile and brittle modes) at each grinding moment. Validation experiments proved that the model can, to a large extent, describe realistic silicon grinding and predict the machined surfaces in terms of (i) brittle and ductile area, (ii) roughness and waviness, and (iii) potential chipping zone sizes. The model therefore is anticipated to be not only meaningful to guide and optimize the industrial silicon grinding process, but also transferable to other kinds of brittle materials.