China Mechanical Engineering ›› 2022, Vol. 33 ›› Issue (08): 952-959.DOI: 10.3969/j.issn.1004-132X.2022.08.009

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Laser Ultrasonic Quantitative Detection of Buried Depth for Internal Defects  in Additive Manufacturing Parts#br#

TIAN Xuexue;ZHAO Jiyuan;LU Bingheng;WANG Lei   

  1. Institute of Advanced Manufacturing Technology,Xi'an Jiaotong University,Xi'an,710049
  • Online:2022-04-25 Published:2022-05-19

增材制件内部缺陷埋藏深度的激光超声定量检测

田雪雪;赵纪元;卢秉恒;王磊   

  1. 西安交通大学先进制造技术研究所,西安,710049
  • 通讯作者: 赵纪元(通信作者),男,1969年生,研究员、博士研究生导师。研究方向为设备状态检测、故障诊断、激光超声无损检测。发表论文数34篇。E-mail:jiyuan.zhao@mail.xjtu.edu.cn。
  • 作者简介:田雪雪,女,1996年生,硕士研究生。研究方向为激光超声无损检测技术。
  • 基金资助:
    国家自然科学基金(51975452)

Abstract: A quantitative detection method for the buried depth of internal defects was proposed for the detection of internal defects in additive manufacturing parts. The wavelet packet decomposition technology was used to separate and extract the ultrasonic longitudinal waves in the signals received by the laser ultrasonic nondestructive testing technology, which solvesd the problems that the coupling of ultrasonic surface wave and longitudinal wave affected the time-domain feature extraction. According to the changes of the longitudinal wave sound path of the defects during the detection processes, the quantitative detection of the buried depth of the internal defects in the precision forging samples was realized, and the relative error of the tests is 1.81%. An abnormal point filtering algorithm was added on the basis of the original detection method, and it was applied to the detection of internal defects of manufacturing additive parts with the relative error of detection of 1.76%.

Key words: laser ultrasonic detection, additive manufacturing, internal defect, buried depth, quantitative detection

摘要: 针对增材制件内部缺陷检测,提出一种内部缺陷埋藏深度的定量检测方法。对用激光超声无损检测技术接收到的信号采用小波包分解技术进行分离并提取信号中的超声纵波,解决了超声表面波和纵波耦合影响时域特征提取的问题。根据检测过程中经过缺陷的纵波声程的变化,实现了精锻试块内部缺陷埋藏深度的定量检测,检测结果的相对误差为1.81%。在原检测方法的基础上增加异常点滤除算法,并应用于增材制件内部缺陷的检测,检测结果的相对误差为1.76%。

关键词: 激光超声检测, 增材制造, 内部缺陷, 埋藏深度, 定量检测

CLC Number: