光学 精密工程, 2020, 28 (1): 102, 网络出版: 2020-03-25   

基于概率统计的BK7玻璃磨削亚表层损伤深度在线预测技术

Prediction of subsurface damage depth in grinding of BK7 glass based on probability statistics
作者单位
1 中国科学院 宁波材料技术与工程研究所 浙江省机器人与智能制造装备技术重点实验室, 浙江 宁波 315201
2 中国科学院大学 材料与光电研究中心, 北京 100049
摘要
为了在线预测硬脆材料磨削加工引起的亚表面损伤深度, 本文利用概率统计法对磨粒高度进行分析, 建立了刀具切削力与亚表层裂纹扩展深度之间的理论关系模型。首先, 基于硬脆材料的压痕断裂力学理论, 分析了中位裂纹扩展长度与磨粒压痕深度之间的内在关联。随后, 对刀具端面边缘的磨粒数目进行了数理统计, 建立了刀具切削力与单个磨粒切削深度之间的理论关系。在此基础上, 提出了工件亚表层损伤深度的在线预测模型SSDmax=1.284×SSDmaxtheo-36.23, 并结合BK7玻璃的实际磨削实验验证了其正确性。通过对比实验结果与理论模型的预测结果发现, 该方法可以在线、准确地预测磨削加工引起的工件亚表层损伤深度。
Abstract
In order to predict online the subsurface damage depth of the specimen induced in grinding of hard-brittle material, a theoretical relationship between the cutting force of the diamond tool and the subsurface crack depth of the specimen was established. This relationship was based on the statistical analysis of abrasive height using probability and statistics. First, based on the indentation fracture mechanics of the hard-brittle materials, the intrinsic association between the propagation depth of the median crack and the indentation depth of a single abrasive was investigated. Subsequently, the number of actual abrasives located on the boundary of the tool end-face was calculated using mathematical statistics, and the internal correlation between the cutting force of the tool and the cutting depth of each single abrasive was developed. Finally, a method for quick online prediction of the subsurface damage depth was proposed (SSDmax=1284×SSDmaxtheo-3623), and its accuracy was verified by an actual grinding experiment of BK7 glass. The experimental results illustrate that this technique can achieve accurate online prediction of the depth of subsurface damage involved in the grinding process.

吕东喜, 陈明达, 姚友强, 赵岳, 祝颖丹. 基于概率统计的BK7玻璃磨削亚表层损伤深度在线预测技术[J]. 光学 精密工程, 2020, 28(1): 102. L Dong-xi, CHEN Ming-da, YAO You-qiang, ZHAO Yue, ZHU Ying-dan. Prediction of subsurface damage depth in grinding of BK7 glass based on probability statistics[J]. Optics and Precision Engineering, 2020, 28(1): 102.

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