多层结构中脱粘缺陷的超声检测方法
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摘要
固体火箭发动机多层装药结构中的脱粘类型、位置和尺寸决定了其对整体安全性能构成的威胁程度。该文研究了多层结构中脱粘缺陷的超声检测方法,通过对不同脱粘缺陷超声脉冲回波的特征分析与统计,实现缺陷的定性、定位和定量。首先,采集含有多类脱粘缺陷的粘接结构的超声脉冲回波信号,分析信号中主能量波包所代表的声程,提取五种声程的波峰时刻和幅值作为特征值,组建已知脱粘类型训练样本并输入至BP神经网络,实现特征值域到类别域的非线性映射,即脱粘类型分类;其次,采用阈值法确定缺陷的界面位置;最后,提出分段线性插值-相关性定量法将待检测缺陷的定量结果缩小到±2 mm以内。该文利用COMSOL有限元仿真和实验操作验证了多层粘接结构中脱粘缺陷的定性、定位和定量方法的可行性和可靠性。
The type,location and size of the debonding defects in a multi-layer charge structure of a solid rocket motor determines its threat degree to overall safety performance.In this paper,the ultrasonic detection method of debonding defects in multilayer structure is studied.According to the analysis and statistics of ultrasonic pulse echoes of different debonding defects,the judge of type,position and area of defects is realized.Firstly,the ultrasonic pulse echo signals of the bonding structure containing multiple types of debonding defects are acquired,the sound paths represented by the main energy wave packet in the signal are analyzed,and the peak moments and amplitudes of the five sound paths are extracted as the eigenvalues to construct the known debonding defects' training samples and input the samples into the BP neural network to realize the nonlinear mapping from the eigenvalue domain to the category domain,equivalent to the classification of debonding defects.Secondly,the threshold method is used to determine the interface position of the defect.Finally,the segmented linear interpolation-correlation is proposed to reduce the quantitative result of defects to be detected to within ±2 mm.In this paper,the feasibility and reliability of the judge method of type,position and area for debonding defects in multilayer bonded structures are verified by COMSOL finite element simulation and experimental operations.
The type,location and size of the debonding defects in a multi-layer charge structure of a solid rocket motor determines its threat degree to overall safety performance.In this paper,the ultrasonic detection method of debonding defects in multilayer structure is studied.According to the analysis and statistics of ultrasonic pulse echoes of different debonding defects,the judge of type,position and area of defects is realized.Firstly,the ultrasonic pulse echo signals of the bonding structure containing multiple types of debonding defects are acquired,the sound paths represented by the main energy wave packet in the signal are analyzed,and the peak moments and amplitudes of the five sound paths are extracted as the eigenvalues to construct the known debonding defects' training samples and input the samples into the BP neural network to realize the nonlinear mapping from the eigenvalue domain to the category domain,equivalent to the classification of debonding defects.Secondly,the threshold method is used to determine the interface position of the defect.Finally,the segmented linear interpolation-correlation is proposed to reduce the quantitative result of defects to be detected to within ±2 mm.In this paper,the feasibility and reliability of the judge method of type,position and area for debonding defects in multilayer bonded structures are verified by COMSOL finite element simulation and experimental operations.
引文
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[10]韦鹏宇,潘福成,李帅.改进人工蜂群优化BP神经网络的分类研究[J].计算机工程与应用,2018,54(10):158-163.Wei Pengyu,Pan Fucheng,Li Shuai.Study on classification of improved artificial bee colony algorithm to optimization of BP neural network[J].Computer Engineering&Applications,2018,54(10):158-163.
[11]Raju P,Rao B P,Rao V M.Gray wolf optimization-based artificial neural network for classification of kidney images[J].Journal of Circuits Systems&Computers,2018,27(14):1850231.
[12]艾春安,曾一平,李剑,等.基于功率谱形心SWF的SRM复合材料壳体脱粘缺陷识别方法[J].固体火箭技术,2012,35(6):833-837.Ai Chun’an,Zeng Yiping,Li Jian,et al.Debonding defect identification method of SRM composite shell based on the power spectral centroid of SWF[J].Journal of Solid Rocket Technology,2012,35(6):833-837.
[13]戴永.基于激光超声检测金属材料表面缺陷的数值模拟[D].镇江:江苏大学,2011.
[14]王嵘冰,徐红艳,李波,等.BP神经网络隐含层节点数确定方法研究[J].计算机技术与发展,2018,28(4):31-35.Wang Rongbing,Xu Hongyan,Li Bo,et al.Research on method of determining hidden layer nodes in BP neural network[J].Computer Technology and Development,2018,28(4):31-35.
[15]许鑫.复合材料脱粘的红外无损检测及数值模拟[J].检测技术,2014(3):54-58.Xu Xin.Infrared nondestructive testing of composites debonding defects and numerical simulation[J].Aerospace Manufacturing Technology,2014(3):54-58.
[2]朱敏,于光辉,卢洪义,等.固体火箭发动机装药缺陷特征及危害性影响分析[J].弹箭与制导学报,2013,33(3):95-97.Zhu Min,Yu Guanghui,Lu Hongyi,et al.Characters and hazard analysis of charge defects in solid rocket motor[J].Journal of Projectiles,Rockets,Missiles and Guidance,2013,33(3):95-97.
[3]李正义,陈刚.玻璃纤维缠绕壳体在固体火箭发动机一二级上的应用研究[J].航天制造技术,2011(1):49-52.Li Zhengyi,Chen Gang.Application of glass fiber winding shell in first and second stage solid rocket motor[J].Aerospace Manufacturing Technology,2011(1):49-52.
[4]唐盛明,齐子诚,刘子瑜,等.电子束焊缝超声波C扫描与工业CT检测方法测试结果比较[J].无损检测,2014,36(10):49-52.Tang Shengming,Qi Zicheng,Liu Ziyu,et al.Comparison of test method between ultrasonic C-scan and industrial CT in electron beam welding testing[J].Nondestructive Testing,2014,36(10):49-52.
[5]李建文,王增勇,汤光平.金属壳体粘接结构件的超声检测[J].无损检测,2010,32(4):283-285.Li Jianwen,Wang Zengyong,Tang Guangping.The ultrasonic testing of the metal shell bonds[J].Nondestructive Testing,2010,32(4):283-285.
[6]艾春安,蔡堃,吴安法.声-超声技术在固体火箭发动机无损检测中的应用[C]//中国宇航学会固体火箭推进24届年会,2007.
[7]贾卫东,周建,刘亚冰,等.长期贮存固体火箭发动机界面脱粘安全性研究[J].战术导弹技术,2013(5):101-106.Jia Weidong,Zhou Jian,Liu Yabing,et al.Research on the safety of long term storage solid rocket motor with debonded layer[J].Tactical Missile Technology,2013(5):101-106.
[8]詹映,罗华平,彭云发,等.BP人工神经网络南疆红枣颜色分级方法的研究[J].食品工业,2015,36(1):165-167.Zhan Ying,Lou Huaping,Peng Yunfa,et al.Research of the BP artificial neural network in southern jujube color classification method[J].Food Industry,2015,36(1):165-167.
[9]郭少文,赵其华,张群,等.BP人工神经网络在岩体质量分级中的应用[J].人民黄河,2015,37(1):111-114,118.Guo Shaowen,Zhao Qihua,Zhang Qun,et al.Application of BP artificial neural network in rock mass quality classification[J].Yellow River,2015,37(1):111-114,118.
[10]韦鹏宇,潘福成,李帅.改进人工蜂群优化BP神经网络的分类研究[J].计算机工程与应用,2018,54(10):158-163.Wei Pengyu,Pan Fucheng,Li Shuai.Study on classification of improved artificial bee colony algorithm to optimization of BP neural network[J].Computer Engineering&Applications,2018,54(10):158-163.
[11]Raju P,Rao B P,Rao V M.Gray wolf optimization-based artificial neural network for classification of kidney images[J].Journal of Circuits Systems&Computers,2018,27(14):1850231.
[12]艾春安,曾一平,李剑,等.基于功率谱形心SWF的SRM复合材料壳体脱粘缺陷识别方法[J].固体火箭技术,2012,35(6):833-837.Ai Chun’an,Zeng Yiping,Li Jian,et al.Debonding defect identification method of SRM composite shell based on the power spectral centroid of SWF[J].Journal of Solid Rocket Technology,2012,35(6):833-837.
[13]戴永.基于激光超声检测金属材料表面缺陷的数值模拟[D].镇江:江苏大学,2011.
[14]王嵘冰,徐红艳,李波,等.BP神经网络隐含层节点数确定方法研究[J].计算机技术与发展,2018,28(4):31-35.Wang Rongbing,Xu Hongyan,Li Bo,et al.Research on method of determining hidden layer nodes in BP neural network[J].Computer Technology and Development,2018,28(4):31-35.
[15]许鑫.复合材料脱粘的红外无损检测及数值模拟[J].检测技术,2014(3):54-58.Xu Xin.Infrared nondestructive testing of composites debonding defects and numerical simulation[J].Aerospace Manufacturing Technology,2014(3):54-58.
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