基于激光超声机械零件内部缺陷的表征方法研究
Research on Characterization Method of Internal Defects Based on Laser Ultrasound
张昊楠 1程砺锋 1曹建树 1王伟 1高群勇1
作者信息
- 1. 北京石油化工学院机械工程学院,北京 102617
- 折叠
摘要
机械零件稳定运行是保证安全生产的关键,内部缺陷是造成机械零件失效的主要因素,但目前针对机械零件内部缺陷的定位定量表征较为困难.为实现对内部缺陷参数的定位定量表征,提出一种基于激光超声无损检测表征内部缺陷参数方法.采用有限元仿真结合实验验证的方法,研究内部缺陷与激光超声波作用机理,得到内部缺陷的位置、深度、长度和角度等参数定位定量表征方法.实验结果表明,采用脉冲反射法可以准确表征内部缺陷参数,通过A扫实验对内部缺陷深度定量表征,相对误差小于5％;通过B扫实验对内部缺陷长度定量表征,相对误差小于4％;通过A扫和B扫相结合的方式,实现对内部缺陷角度的定量表征.研究结果表明,基于激光超声无损检测表征内部缺陷参数方法可以实现内部缺陷参数定位定量表征,具有较高的检测精度,同时提出一种内部缺陷角度表征的新方法,可用于机械零件内部缺陷的检测.
Abstract
The stable operation of mechanical parts is the top priority to ensure the safety of production.Internal defects are an important factor causing the failure of mechanical parts.However,it is difficult to locate and quantify the internal defects of mechanical parts at present.Therefore,this paper realized the quantitative characterization of internal defect parameters,and proposed a method to characterize the Angle of internal defect.In this paper,based on the principle of laser ultrasound,the in-fluence of internal defects on ultrasonic body wave time domain signals is analyzed through numerical simulation combined with experimental methods,and the location,depth,length and Angle of the quantitative characterization of the main internal de-fects are studied.Experimental results show that the pulse reflection method can accurate characterization of the internal defect parameters,swept through A experiment on the depth of internal defect characterization,the relative error within 5％,the length of the internal defect characterization by B scan experiment,the relative error is within 4％,through the way of combi-ning A flicking A and B,realizes the quantitative characterization of Angle of internal defects.The results provide a reference for the quantitative characterization of internal defects of mechanical parts.
关键词
激光超声/内部缺陷/定位表征/定量表征/无损检测Key words
laser ultrasound/Internal defects/localization characterization/quantitative characterization/non-destruc-tive testing引用本文复制引用
基金项目
北京市自然科学基金资助项目(3182008)
出版年
2024
NETL
NSTL
万方数据