金刚石砂轮磨削性能退化评估

毕果; 王惠雪; 周炼; 邵升阳

doi:10.3788/OPE.20192707.1508

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金刚石砂轮磨削性能退化评估

微纳技术与精密机械 | 更新时间：2020-08-13

- 金刚石砂轮磨削性能退化评估
- Gringing performance degradation of diamond wheel
- 光学精密工程 2019年27卷第7期页码：1508-1515
- 作者机构：
  
  1.厦门大学航空航天学院，福建厦门 361005
  2.中国工程物理研究院激光聚变研究中心，四川绵阳 610041
- 作者简介：
  
  [ "毕果 (1978-)，女，河南南阳人，博士，副教授，2000年、2003年于郑州大学分别获得学士、硕士学位，2007年于上海交通大学获得博士学位，主要从事精密加工过程监测等方面的研究。E-mail:guobi@xmu.edu.cn" ]
  [ "王惠雪(19-)，女，福建漳州人，硕士研究生，主要从事精密加工、在线监测等方面的研究。E-mail:15759219774@163.com" ]
- 基金信息：
  
  科学挑战计划专题资助项目(JCKY2016212A506-0504);国家自然科学基金资助项目(51805459)
- DOI：10.3788/OPE.20192707.1508
  中图分类号： TN911.6;TN305.1
- 收稿日期：2019-01-02，
  
  录用日期：2019-2-13，
  
  纸质出版日期：2019-07-15
- 稿件说明：
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毕果, 王惠雪, 周炼, 等. 金刚石砂轮磨削性能退化评估[J]. 光学精密工程, 2019,27(7):1508-1515.

Guo BI, Hui-xue WANG, lian ZHOU, et al. Gringing performance degradation of diamond wheel[J]. Optics and precision engineering, 2019, 27(7): 1508-1515.
毕果, 王惠雪, 周炼, 等. 金刚石砂轮磨削性能退化评估[J]. 光学精密工程, 2019,27(7):1508-1515. DOI： 10.3788/OPE.20192707.1508.

Guo BI, Hui-xue WANG, lian ZHOU, et al. Gringing performance degradation of diamond wheel[J]. Optics and precision engineering, 2019, 27(7): 1508-1515. DOI： 10.3788/OPE.20192707.1508.

摘要

为了提取高精度磨削干涉中的声发射信号特征，实现砂轮磨削性能退化评估，针对熔石英开展全寿命周期金刚石砂轮磨削实验，基于小波包分析确定砂轮磨损敏感频段为低频段，然后计算声发射信号在低频段的归一化能量占比，再利用主成分分析对能量占比进行特征降维获得单值特征，利用该单值特征绘制砂轮磨削性能退化曲线。研究结果表明，监测特征能够清晰反映砂轮初期磨损、正常磨损和过度磨损三个阶段，且监测结果不受加工参数影响；砂轮磨削材料破裂尺度与声发射频率具有一定关系，伴随砂轮磨损的加剧，较大尺度破裂的比例上升，造成65 kHz以下低频段特征的能量占比增大，监测特征显著增加，磨粒崩碎产生新的切削刃，砂轮的去除能力有所改善，监测特征数值回落，但是，不同样本的声发信号频谱差异性显著增加，说明砂轮加工状态不稳定，不利于精密与超精密加工中维持稳定质量的要求。砂轮形貌图像的白像素占比变化曲线验证了声发射特征对砂轮磨损状态判断的正确性。

Abstract

Acoustic emission is the most direct and sensitive characterization of grinding interference. Therefore

to ensure high-precision grinding

acoustic emission signals were used to evaluate the degradation in grinding performance of a diamond wheel. A fused silica grinding experiment was conducted using a newly trimmed diamond grinding wheel to record the acoustic emission signal and surface morphology of the grinding wheel during its life cycle. A wavelet packet analysis was used to determine the typical low-frequency band of the abrasive wear of the grinding wheel. The frequency energy ratio was then extracted by using principal component analysis to characterize the degradation of grinding wheel grinding performance. The results showed that the acoustic emission frequency energy ratio was completely independent of the processing parameters. As the abrasive wear of the grinding wheel intensified

both the proportion of large-scale rupture and the amplitude of the corresponding low-frequency characteristic increased. When the abrasive particles were worn to the limit

the abrasive grain was broken to produce a new cutting edge

improving the removal ability of the grinding wheel

but the variance of the spectrum was significantly increased

inhibiting the wheel from maintaining a stable quality in ultra-precision machining. The variation of the white pixel ratio of the grinding wheel shape image verified the correctness of the acoustic emission signal analysis results.

关键词

Keywords

references

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