Relationship between subsurface damage and surface roughness of glass BK7 in rotary ultrasonic machining and conventional grinding processes
Relationship between subsurface damage and surface roughness of glass BK7 in rotary ultrasonic machining and conventional grinding processes
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DOI：
10.1007/s00170-012-4509-1
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Dongxi Lv;Yanhua Huang;Yongjian Tang;Hong-xiang Wang
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文献类型：
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作者：
Dongxi Lv;Yanhua Huang;Yongjian Tang;Hong-xiang Wang
光学零件在研磨加工过程中产生的亚表面损伤（SSD）严重影响了光学零件的工艺应用。然而，到目前为止，还没有快速有效的方法来检测SSD的深度。为了准确、无损地评价旋转超声加工（RUM）和常规磨削（CG）过程中产生的SSD深度，基于脆性材料压痕断裂力学，通过分析锋利压头引起的中间裂纹系统和侧向裂纹系统之间的相关性，提出了一种理论模型.结果表明，表面粗糙度（SSD）深度与表面粗糙度（SR）的平方成非线性单调递增关系，即SSD =χ SR ~ 2 + 1。利用该模型，可以通过SR（p-vvalue）快速准确地预测SSD深度。加工表面的几何特征、磨料的几何特征和材料的机械性能。为了验证该方法的可行性，使用Sauer Ultrasonic 20对BK 7玻璃试样进行了RUM和CG测试。随后，这些标本的SSD曝光与抛光蚀刻技术。SSD深度的测量结果与该模型的预测值一致，反映了利用该模型快速准确预测SSD深度的可行性。
Subsurface damage (SSD) induced during the abrasive machining process considerably influences the technological application of the optical components. However, to date, there is no rapid and effective method to inspect the depth of SSD. For the purpose of precise and nondestructive evaluation of the SSD depth generated in rotary ultrasonic machining (RUM) and conventional grinding (CG) processes, a theoretical model, ground on indentation fracture mechanics of brittle material, was proposed by analyzing the correlation between the median and lateral crack systems aroused by a sharp indenter. It was found that the SSD depth was nonlinear monotone increasing with square of surface roughness (SR), namely, SSD =χSR2+l. Utilizing this model, the SSD depth could be quickly and precisely predicted through the SR (p–vvalue.) of the machined surface, geometrical features of the abrasive, and the material mechanical properties. To validate the feasibility of this method, both RUM and CG tests were conducted on the BK7 glass specimens with a Sauer Ultrasonic 20. Subsequently, the SSD of these specimens was exposed with the polishing–etching technique. The measurement results of SSD depth were consistent with the prediction values of this model, which reflected the feasibility of using this model to rapidly and accurately predict the SSD depth.