基于声发射技术的金属烧伤监测  

Metal burn monitoring based on acoustic emission

作　　者：陈珍 吴聘 杨壮 高哲瑜 CHEN Zhen;WU Pin;YANG Zhuang;GAO Zheyu(China Power Engineering Consulting Group Co.,Ltd.,Beijing 100029,China;CPECC Smart Energy Storage Technology(Shanghai)Co.,Ltd.,Shanghai 200333,China;School of Mechanical and Electrical Engineering,Xi’an Polytechnic University,Xi'an 710048,Shaanxi,China)

机构地区：[1]中国电力工程顾问集团有限公司,北京100029 [2]中电智慧储能科技(上海)有限公司,上海200333 [3]西安工程大学机电工程学院,陕西西安710048

出　　处：《金属加工（热加工）》2024年第9期15-20,24,共7页MW Metal Forming

基　　金：中电工程公司重大科技专项(DG3-F01-2023);陕西省自然科学基础研究计划(2024JC-YBQN-0550)。

摘　　要：机床加工过程中因冷却液无法及时带走磨削热量而导致零部件表面产生金属烧伤,会造成强度降低、残余应力增加等性能改变,产生巨大安全隐患。声发射(AE)技术具有对微弱信号敏感、可监测高频信号等优势而广泛应用于机床磨削烧伤监测。由于磨削过程中存在金属颗粒剥落、摩擦等强干扰信号,同时金属烧伤信号特征不明确,因此磨削烧伤监测精度不理想。为提高烧伤信号识别准确率,有必要单独对金属烧伤声发射信号的特性进行研究。为获取纯净金属烧伤信号,设计并开展了激光试验模拟磨削烧伤过程。通过调整激光能量产生了4种程度的金属表面烧伤并获得相应的声发射信号。试验发现:随着烧伤程度增加,声发射信号频谱产生向高频跃迁现象。根据这一现象,利用小波包变换(WPT)提取烧伤信号主导频带并进行特征提取。将提取出的特征向量输入主成分分析(PCA)算法,对不同程度金属烧伤信号进行聚类与识别。In the process of grinding processing,grinding heat results in metal surface burn on parts,leading to reduced strength,increased residual stress,and other performance changes with significant security risks.Acoustic Emission(AE)technology is widely employed for monitoring grinding burn in machine tools due to its sensitivity to weak signals and ability to detect high-frequency signals.However,the accuracy of grinding burn monitoring is not ideal due to strong interference signals such as metal particle spalling and friction during the grinding process.Meanwhile,the characteristics of metal burn signal is unknown.To improve the accuracy of burn signal recognition,it is necessary to investigate the characteristics of metal burn AE signals independently.Therefore,a laser experiment was designed and conducted to simulate the grinding burn process in order to obtain pure metal burn signals.By adjusting laser energy levels,four degrees of surface metal burns were generated.It was observed that as the metal burn degree increased,the spectrum of AE signals moved to higher frequency bandwidth.Based on this phenomenon,Wavelet packet transform(WPT)was utilized to extract dominant frequency bandwidths of metal burn signals.The extracted feature vectors were then input into Principal Component Analysis(PCA)algorithm for clustering and identification metal burn signals.

关 键 词：磨削烧伤 声发射信号 小波包变换 激光烧伤 

分 类 号：TG115.28[金属学及工艺—物理冶金]