[1]舒小英*,李小梅,史洪松.doi: 10.3969/j.issn.1001-3849.2025.11.003机械冲击荷载下电镀合金断裂损伤精准识别研究[J].电镀与精饰,2025,(11):22-30.
　Shu Xiaoying*,Li Xiaomei,Shi Hongsong.Research on accurate identification of fracture damage of electroplating alloy under mechanical impact load[J].Plating & Finishing,2025,(11):22-30.
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doi: 10.3969/j.issn.1001-3849.2025.11.003机械冲击荷载下电镀合金断裂损伤精准识别研究()

《电镀与精饰》[ISSN:1001-3849/CN:12-1096/TG]

卷:
期数:
2025年11
页码:
22-30
栏目:
出版日期:
2025-11-30

文章信息/Info

Title:
Research on accurate identification of fracture damage of electroplating alloy under mechanical impact load
作者:
舒小英*; 李小梅; 史洪松
(江西工程学院 智能制造产业学院,江西 新余 338000)
Author(s):
Shu Xiaoying*;  Li Xiaomei;  Shi Hongsong
(Industry Manufacturing Intelligent of School, Jiangxi College of Engineering, Xinyu 338000, China)
关键词:
机械冲击荷载; 电镀合金材料; 断裂损伤; 特征提取; 支持向量机
Keywords:
mechanical impact load;  electroplating alloy material;  fracture damage;  feature extraction;  support Vector Machine
分类号:
V241.07
文献标志码:
A
摘要:
电镀合金镀层表面钝化膜的局部击穿会引发蠕变、疲劳、氧化等损伤，与机械冲击荷载协同作用产生应力断裂，这种断裂具有瞬态特性，仅依靠视觉特征识别会降低识别精度。为此，将声发射信号与图像检测技术结合，提出机械冲击荷载下电镀合金断裂损伤精准识别方法。通过声发射监测传感器采集电镀合金在机械冲击下的声发射信号，并将信号映射至二维图像空间，获取电镀合金断裂损伤图像。利用分形特征快速定位电镀合金图像裂纹大致位置，而细分割阶段则在粗分割基础上进一步细化裂纹边界，精确提取电镀合金的断裂位置瞬态特征。将电镀合金断裂位置裂缝瞬态特征作为支持向量机(Support Vector Machine，SVM)的输入，实现对电镀合金断裂损伤的精准识别。实验结果表明，所提方法通过声发射信号映射和图像处理优化，在80 ms后捕捉到裂纹扩展特征，且支持向量机分类准确区分了不同断裂类型，识别精度显著提高。
Abstract:
The local breakdown of passivated film on the surface of electroplating alloy coating will lead to creep, fatigue, oxidation and other damage, and the synergistic effect with mechanical impact load will produce stress fracture, which has transient characteristics, and only relying on visual feature recognition will reduce the recognition accuracy. Therefore, an accurate method for identifying fracture damage of electroplating alloy under mechanical impact load is proposed by combining acoustic emission signal with image detection technology. The acoustic emission signal of electroplating alloy under mechanical impact is collected by the acoustic emission monitoring sensor, and the signal is mapped to the two-dimensional image space to obtain the fracture damage image of electroplating alloy. The fractal feature is used to quickly locate the crack location of the electroplating alloy image, and the fine segmentation stage further refines the crack boundary on the basis of the rough segmentation, and accurately extracts the transient characteristics of the electroplating alloy fracture location. The crack transient characteristics of electroplating alloy fracture position were used as the input of support vector machine to realize the accurate identification of electroplating alloy fracture damage. The experimental results show that the proposed method captures the crack propagation characteristics after 80 ms by means of acoustic emission signal mapping and image processing optimization, and the SVM classification accurately distinguishes different fracture types, and the recognition accuracy is significantly improved

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更新日期/Last Update: 2025-11-20