| 利用人工神经网络实现缺陷类型识别 |
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| 引用本文： | 陈彦华,李明轩.利用人工神经网络实现缺陷类型识别[J].应用声学,1998,17(2):1-4,10. |
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| 作者姓名： | 陈彦华 李明轩 |
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| 作者单位： | 中国科学院声学研究所!北京，100080，中国科学院声学研究所!北京，100080 |
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| 基金项目： | 国家自然科学基金，中国科学院声学研究所资助 |
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| 摘 要： | 本文在对各向同性均匀固体中横穿孔，平底孔和裂和裂缝缺陷超声散射特性分析的基础上，分别用回波幅度谱和去郑积幅度谱作为特征量，利用人工神经网络对缺陷类型进行识别。
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| 关 键 词： | 神经网络 缺陷类型识别 超声检测 无损检测 |
Classification of flaws through an artificial neural network |
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| Chen Yanhua and Li Mingxuan.Classification of flaws through an artificial neural network[J].Applied Acoustics,1998,17(2):1-4,10. |
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| Authors: | Chen Yanhua and Li Mingxuan |
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| Institution: | Institute of Acoustics, Cbinese AcadeInJ, of Sciences, Bejjing 100080;Institute of Acoustics, Cbinese AcadeInJ, of Sciences, Bejjing 100080 |
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| Abstract: | In this paper, the scattering characteristics of an ultrasonic wave in a homegeneous solid by three types of flaw are considered the three types being the traversecylindrical cavily, the blat-bottom hole and the plane crack. The flaws are then classifiedby a neural network on using the respectively amplitude spectra of ultrasonic echoes andthose of deconvolved ultrasonic echoes as characteristic features. It is demonstrated thatthe latter improve greatly the classification accuracy. |
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| Keywords: | Artifical neural network Classifcation of flaws Deconvolution |
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