背散射技术对缠绕复合材料均匀性的表征
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摘要
通过对纤维缠绕成型复合材料声学特性的研究,针对常规超声底波法检测该材料的困难,提出了一种利用超声背向散射信号表征材料均匀性的无损检测方法。通过大功率超声脉冲反射法提取玻璃纤维缠绕复合材料试块多个区域的回波信号,引入超声背向散射积分作为参数对背向散射信号进行时域和频域的分析,最后利用超声非线性系数对其表征效果进行验证。结果表明:时域背向散射积分对缺陷的敏感性高于频域积分、低于超声非线性系数,背向散射积分是有效、准确评价材料内部质量均匀性的参数。
By studying the acoustic characteristics of filament winding composites and the difficulty of detecting the material by conventional ultrasonic bottom wave method,this paper presents a non-destructive testing method using ultrasonic backscattering signals to characterize the homogeneity of materials. High-power ultrasonic pulse reflection method was used to extract the echo signals from multiple regions of the glass fiber-wound composite material. The ultrasonic backscatter integral was used as a parameter to analyze the time-domain and frequency-domain of the backscatter signal. Finally,the ultrasonic nonlinearity was used. The coefficient validates its characterization effect. The results show that the time-domain backscatter integral is more sensitive to defects than the frequency domain integral and lower than the ultrasonic nonlinearity coefficient. The backscatter integral is an effective and accurate parameter for evaluating the internal quality uniformity of the material.
By studying the acoustic characteristics of filament winding composites and the difficulty of detecting the material by conventional ultrasonic bottom wave method,this paper presents a non-destructive testing method using ultrasonic backscattering signals to characterize the homogeneity of materials. High-power ultrasonic pulse reflection method was used to extract the echo signals from multiple regions of the glass fiber-wound composite material. The ultrasonic backscatter integral was used as a parameter to analyze the time-domain and frequency-domain of the backscatter signal. Finally,the ultrasonic nonlinearity was used. The coefficient validates its characterization effect. The results show that the time-domain backscatter integral is more sensitive to defects than the frequency domain integral and lower than the ultrasonic nonlinearity coefficient. The backscatter integral is an effective and accurate parameter for evaluating the internal quality uniformity of the material.
引文
[1]孔庆保.高强2号玻璃纤维无捻粗纱物理力学性能和工艺性能的分析[J].纤维复合材料,1981(1):17-25.
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[6]丁珊珊,金士杰,何晓晨,等.基于仿真方法的CFRP复合材料孔隙率与超声衰减系数的关系[J].复合材料学报,2018,35(1):89-94.
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[9]李伟,高维佳,陈平,等.连续纤维增强PEK-C复合材料的缠绕成型工艺及性能研究[D].沈阳:沈阳航空航天大学,2011.
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[11]赵振凯,孔春花,鲍伟宏,等.超声背散射硬化层深度检测仪的原理及应用[C]//全国机电企业工艺年会暨机械工业节能减排工艺技术研讨会. 2014.
[12]陈越超.基于超声背散射信号处理的碳纤维复合材料孔隙检测研究[D].杭州:浙江大学,2016.
[13]杨锦茹.超声背向散射积分对肾实质弥漫性疾病的诊断[J].陕西医学杂志,2003,32(1):47-49.
[14]童林军,张书增,黄惠媛.考虑声源非线性影响下材料非线性系数测量研究[J].机械工程学报,2016,52(14):8-14.
[15] Potter J,Croxford A. Characterization of nonlinear ultrasonic diffuse energy imaging[J]. IEEE Transactions on Ultrasonics Ferroelectrics&Frequency Control,2018,PP(99):1-1.
[16] Maharbiz M M,Carmena J M,Anwar M,et al. Implants using ultrasonic backscatter for sensing physiological conditions:US20180085605[P]. 2018.
[2]孙雪坤,郭艳阳,杜善义,等.纤维缠绕固体火箭发动机壳体的应力及强度分析[J].复合材料学报,1997,14(1):116-121.
[3]闫景玉.大型风机叶片的疲劳寿命估算[D].南京:南京航空航天大学,2013.
[4]江珍珍.不同超声背向散射信号提取技术的临床应用[J].中国医学影像技术,2010,26(9):1793-1795.
[5]魏宏远.基于背散射信号时频分析的碳纤维复合材料微缺陷研究[D].杭州:浙江大学,2015.
[6]丁珊珊,金士杰,何晓晨,等.基于仿真方法的CFRP复合材料孔隙率与超声衰减系数的关系[J].复合材料学报,2018,35(1):89-94.
[7]宋永锋,李雄兵,吴海平,等. In718晶粒尺寸对超声背散射信号的影响及其无损评价方法[J].金属学报,2016,52(3):378-384.
[8]栗永峰,申志彬,张赋,等.纤维缠绕复合材料壳体热承载能力分析方法及应用[J].固体火箭技术,2018(1).
[9]李伟,高维佳,陈平,等.连续纤维增强PEK-C复合材料的缠绕成型工艺及性能研究[D].沈阳:沈阳航空航天大学,2011.
[10]承德兆,阮尚弘.缠绕玻璃钢的超声波无损检测[J].玻璃钢/复合材料,1986(3):17-20.
[11]赵振凯,孔春花,鲍伟宏,等.超声背散射硬化层深度检测仪的原理及应用[C]//全国机电企业工艺年会暨机械工业节能减排工艺技术研讨会. 2014.
[12]陈越超.基于超声背散射信号处理的碳纤维复合材料孔隙检测研究[D].杭州:浙江大学,2016.
[13]杨锦茹.超声背向散射积分对肾实质弥漫性疾病的诊断[J].陕西医学杂志,2003,32(1):47-49.
[14]童林军,张书增,黄惠媛.考虑声源非线性影响下材料非线性系数测量研究[J].机械工程学报,2016,52(14):8-14.
[15] Potter J,Croxford A. Characterization of nonlinear ultrasonic diffuse energy imaging[J]. IEEE Transactions on Ultrasonics Ferroelectrics&Frequency Control,2018,PP(99):1-1.
[16] Maharbiz M M,Carmena J M,Anwar M,et al. Implants using ultrasonic backscatter for sensing physiological conditions:US20180085605[P]. 2018.