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  • 主管:中国科学技术协会
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陈思晨,吴伟,郑雪鹏,张全红.超声特征信号评价金属蜂窝构件钎焊质量[J].数据采集与处理,2021,36(2):270-279
超声特征信号评价金属蜂窝构件钎焊质量
Evaluation of Brazing Quality of Metal Honeycomb Components by Ultrasonic Characteristic Signals
投稿时间:2020-08-27  修订日期:2021-03-01
DOI:10.16337/j.1004-9037.2021.02.008
中文关键词:  蜂窝构件  超声信号  聚类算法  机器学习  钎焊质量
英文关键词:honeycomb component  ultrasonic signal  clustering algorithm  machine learning  brazing quality
基金项目:
作者单位邮编
陈思晨 南昌航空大学无损检测技术教育部重点实验室南昌 330063 330063
吴伟 南昌航空大学无损检测技术教育部重点实验室南昌 330063 330063
郑雪鹏 上海航天精密机械研究所上海 201600 201600
张全红 南昌航空大学无损检测技术教育部重点实验室南昌 330063 330063
摘要点击次数: 60
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中文摘要:
      金属蜂窝构件钎焊质量通常以钎着率(单位面积内检测到的焊合面积占比)为指标进行评价。实际生产中采用超声C扫幅值成像无损检测,以GH4099高温合金薄壁窄筋蜂窝板件为研究对象,提出基于超声A扫信号特征值参数的无监督机器学习分类方法。首先在数字超声信号提取时域、功率谱上各8个特征值;其次对数据进行标准化处理、主成分分析 (Principal components analysis,PCA)降维,得到各自贡献率为95%以上的前3组共6个主成分值;然后以这些值为特征值作为输入进行K均值、高斯混合模型聚类、模糊C均值聚;最后采用多分类器融合算法提高模型准确率,将分类结果可视化与超声C扫图像比对,验证分类评价效果。12组数据实验结果表明:3种聚类算法成像结果与超声C扫结果一致,其中融合投票计算比于单分类器更为准确,为非监督机器学习方法在超声信号评价蜂窝构件钎焊质量中的应用提供了新思路。
英文摘要:
      The brazing quality of metal honeycomb components is usually evaluated by the brazing rate (the proportion of the welded area detected in the unit area) as an indicator. In actual production, the GH4099 superalloy thin-walled narrow-ribbed honeycomb panel is used as the research object, ultrasonic C-scan amplitude imaging is used for non-destructive testing, and an unsupervised machine learning classification method based on the eigenvalue parameters of ultrasonic A-scan signal is proposed. Firstly, eight eigenvalues are extracted in the time domain and power spectrum of the digital ultrasound signal, respectively. Secondly, the data is standardized and reduced the dimensionality by using principal components analysis (PCA) to obtain the top three groups with six principal component values, which have respective contribution rates of more than 95%. Then these values are used as eigenvalues to perform K-means clustering, Gaussian mixture model clustering, and fuzzy C-means clustering as the input. Finally, the multi-classifier fusion algorithm is used to improve the accuracy of the model, and the classification results are visualized and compared with the ultrasound C-scan amplitude imaging to verify the classification evaluation effect. Experimental results of twelve groups of data show that the imaging results of the three clustering algorithms are consistent with those of the ultrasound C-scan amplitude imaging, in which the fusion voting calculation is more accurate than the single classifier. The study provides new ideas for an unsupervised machine learning method in ultrasound signal for evaluating the quality of honeycomb component brazing.
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