刘永超. 基于振动声学的螺栓疲劳开裂检测方法[J]. 制造技术与机床, 2022, (8): 142-148. DOI: 10.19287/j.mtmt.1005-2402.2022.08.022
引用本文: 刘永超. 基于振动声学的螺栓疲劳开裂检测方法[J]. 制造技术与机床, 2022, (8): 142-148. DOI: 10.19287/j.mtmt.1005-2402.2022.08.022
LIU Yongchao. Detection method of bolt fatigue cracking based on vibration acoustics[J]. Manufacturing Technology & Machine Tool, 2022, (8): 142-148. DOI: 10.19287/j.mtmt.1005-2402.2022.08.022
Citation: LIU Yongchao. Detection method of bolt fatigue cracking based on vibration acoustics[J]. Manufacturing Technology & Machine Tool, 2022, (8): 142-148. DOI: 10.19287/j.mtmt.1005-2402.2022.08.022

基于振动声学的螺栓疲劳开裂检测方法

Detection method of bolt fatigue cracking based on vibration acoustics

  • 摘要: 提出了一种利用振动声学技术检测螺栓裂纹缺陷的方法,并通过敲击试验、模态仿真模拟以及理论推导论证此方法对螺栓裂纹损伤识别的可行性。试验证明采用振动声学的方法可以有效识别螺栓裂纹缺陷的位置和深度,且对于M30×200 mm的螺栓最小可识别缺陷深度为2 mm,位于螺栓1/2及1/4位置的缺陷最易被识别,结合工程现场螺栓常见疲劳断裂形式,此方法可有效识别螺栓的疲劳开裂问题,具有一定的工程应用意义。另外,提出了振动声学技术与机器学习技术融合研究方向,为螺栓损伤检测提供智能化研究思路。

     

    Abstract: In this paper, a method to detect bolt crack defects using vibroacoustics technology is proposed. The feasibility of this method to identify bolt crack damage is demonstrated through percussion test, modal simulation and theoretical derivation. The test proves that the vibroacoustics method can effectively identify the location and depth of bolt crack defects. In addition, the minimum discernible defect depth of M30×200 mm bolt is 2 mm, and the defects located in 1/2 and 1/4 positions of bolts are most easily identified. Combined with the common fatigue fracture forms of bolts in engineering field, this method can effectively identify the fatigue cracking of bolts, which has certain engineering application significance. In addition, this paper proposes the research direction of the fusion of vibroacoustics technology and machine learning technology, providing intelligent research ideas for bolt damage detection.

     

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