利用声发射信号时频特征在线监测慢走丝线切割加工表面粗糙度

王艳; 王健波; 王强; 熊巍; 贺独醒

doi:10.3788/OPE.20172508.2090

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利用声发射信号时频特征在线监测慢走丝线切割加工表面粗糙度

微纳技术与精密机械 | 更新时间：2020-07-07

- 利用声发射信号时频特征在线监测慢走丝线切割加工表面粗糙度
- On-line monitoring of WEDM-LS surface roughness by utilizing time-frequency characteristics of AE signal
- 光学精密工程 2017年25卷第8期页码：2090-2097
- 作者机构：
  
  1.上海理工大学机械工程学院, 上海 200093
  2.上海理工大学材料科学与工程学院, 上海 200093
- 作者简介：
  
  王艳(1969-), 女, 上海人, 博士, 教授, 1998年于上海交通大学获得硕士学位, 2006年于浙江大学获得博士学位, 2012年南京航空航天大学博士后出站, 主要从事精密加工、特种加工技术的研究。E-mail:yanwang909909@163.com WANG Yan, E-mail:yanwang909909@163.com
  [ "王健波(1990-), 男, 江苏南通人, 2013年于徐州工程学院获得学士学位, 2016年于上海理工大学获得硕士学位, 主要从事特种加工技术的研究。E-mail:15721092081@163.com" ]
- 基金信息：
  
  江苏省创新创业项目(2012055)
- DOI：10.3788/OPE.20172508.2090
  中图分类号： TP206+.1
- 收稿日期：2017-02-09，
  
  录用日期：2017-4-17，
  
  纸质出版日期：2017-08-25
- 稿件说明：
移动端阅览
王艳, 王健波, 王强, 等. 利用声发射信号时频特征在线监测慢走丝线切割加工表面粗糙度[J]. 光学精密工程, 2017,25(8):2090-2097.

Yan WANG, Jian-bo WANG, Qiang WANG, et al. On-line monitoring of WEDM-LS surface roughness by utilizing time-frequency characteristics of AE signal[J]. Optics and precision engineering, 2017, 25(8): 2090-2097.
王艳, 王健波, 王强, 等. 利用声发射信号时频特征在线监测慢走丝线切割加工表面粗糙度[J]. 光学精密工程, 2017,25(8):2090-2097. DOI： 10.3788/OPE.20172508.2090.

Yan WANG, Jian-bo WANG, Qiang WANG, et al. On-line monitoring of WEDM-LS surface roughness by utilizing time-frequency characteristics of AE signal[J]. Optics and precision engineering, 2017, 25(8): 2090-2097. DOI： 10.3788/OPE.20172508.2090.

摘要

针对慢走丝线切割加工（WEDM-LS）时因温度高、切缝窄等因素造成的加工过程难以监测的问题，利用声发射检测技术对占空比可调脉冲的慢走丝线切割加工过程进行在线监测。首先利用小波包能谱算法将AE信号分解到8个独立的频段上：分别为W1~W8，且频率依次降低；然后提取各频段上的能量特征，研究其与加工工件表面粗糙度值之间的相关性。试验结果表明：W8频段的能量与表面粗糙度值之间具有较高的相关性，该频段的能量与脉冲放电能量均随着脉冲信号占空比的增大而增大，且加工表面粗糙度值也随之逐渐增大。最后通过回归分析得到了反应材料表面粗糙度值与W8频段能量占比关系的数学预测模型，该模型的预测结果与实际测得的表面粗糙度值误差仅为3.51%。说明该模型具有较高的预测精度，可用于加工表面粗糙度的在线监测。

Abstract

Aiming at the problem that monitoring of Wire Electrical Discharging Machining-Low Speed (WEDM-LS) process is difficult because of high temperature

narrow kerf and other factors

Acoustic Emission (AE) was adopted to implement on-line monitoring of WEDM-LS process with adjustable duty ratio pulse. Firstly

AE signal was decomposed into eight independent frequency bands by energy spectrum algorithm of wavelet packet

respectively being W1~W8 and frequency reduces in sequence; then energy characteristic of all frequency bands was extracted to research the relativity between it and surface roughness value of machining work-piece. Experimental results show that energy of W8 frequency band is highly related to surface roughness value and energy of this frequency band and pulse discharge energy increase with increase of pulse duty ratio

and roughness value of machining surface increases with it gradually. Finally

a mathematical prediction model between the surface roughness value and the energy in W8 frequency band was established via regression analysis

and error was only 3.51% between prediction result and surface roughness actually measured. It can be illustrated that this model has high prediction precision for online monitoring of machining surface roughness.

关键词

Keywords

references

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