K9玻璃单磨料冲击划伤的实验研究与预测

Q3 Engineering
Chen Li, Fei-hu Zhang, Xiaoshuang Rao, B. Leng
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引用次数: 3

摘要

设计了正交试验L16(43),利用维氏金刚石压头在DMG超声70-5直线上对K9玻璃进行了冲击刮擦试验。利用扫描电镜(SEM)观察了玻璃表面的三维形貌,并与准静态状态下的形貌进行了比较。选择接触区长度作为冲击接触长度作为冲击评价指标,得到磨削过程的应变速率。首先分析了应变率与径向裂纹深度、应变率与横向裂纹深度、应变率与法向划痕力之间的关系。结果表明:随着应变速率的增大,裂纹径向深度、横向裂纹深度和法向划痕力均减小;建立了以应变率为输入变量的双层BP神经网络。对径向裂纹深度、横向裂纹深度和法向划痕力进行了预测,误差在10%以内,表明BP神经网络预测结果可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experimental study and prediction on impact scratching of single abrasive for K9 glass
The orthogonal test L16 (43) was designed, and the impact scratching experiment for K9 glass was carried out by using Vickers diamond indenter on the DMG ULTRASONIC 70-5 linear. The three-dimensional morphology of the surface for glass was observed by scanning electron microscope (SEM), which was compared with that in the quasi static state. The strain rate of the grinding process was obtained by choosing the contact zone length as the impact contact length, which was the evaluation Index of impact. The relationships between strain rate and the depth of radial crack, strain rate and the depth of transverse crack, strain rate and normal scratching force were first analysed. The results showed that the depth of radial of crack, the depth of transversal crack and the normal scratching force decreased with the increase of strain rate. The two-layer BP neural network was established, which took the strain rate as input variables. The depth of radial crack, the depth of transversal crack and normal scratching force were predicted and the errors were within 10%, which indicated that the prediction results of BP neural network were reliable.
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来源期刊
International Journal of Nanomanufacturing
International Journal of Nanomanufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
0.60
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