基于Lamb波能量和飞行时间的碳纤维复合材料疲劳损伤成像

Imaging of fatigue damage for carbon fiber reinforced polymer based on Lamb wave energy and time-of-flight

碳纤维复合材料被广泛应用于航空航天等高新技术领域,其在服役过程中会产生疲劳损伤,埋下安全隐患,因此需要对其健康状况进行监测,利用损伤概率成像算法能够得到直观反映结构健康状况的图像,但传统的损伤概率成像算法在无损伤区域的损伤概率高,难以准确定位损伤,针对以上问题,提出基于Lamb波能量和飞行时间的损伤概率成像算法。将待测区域均匀划分成N个像素点,计算每条通道的Lamb波能量与飞行时间损伤因子,确定各通道损伤因子影响区域的概率值并叠加,得到每个像素点的损伤概率并成像。实验结果表明,与目前常用的基于能量损伤因子和互相关损伤因子的损伤概率成像算法进行对比,提出的方法能够直观地反映碳纤维复合材料缺陷情况,并且识别效果更优,成像误差显著减小,误差error分别降低了4.420、2.117、2.055和4.732、2.380、2.647,能够更准确地识别缺陷,有效地保障碳纤维复合材料结构的安全应用。

Carbon fiber reinforced polymer is widely used in high-tech fields such as aerospace,fatigue damage will occur during service,it will bury potential safety hazards.Therefore,its health needs to be monitored,and damage probability imaging algorithms can be used to intuitively reflect structural health.However,the traditional damage probability imaging algorithms have a high damage probability in the non-damaged area and it is difficult to accurately locate the damage.In view of the above problems,a damage probability imaging algorithm based on Lamb wave energy and time of flight is proposed.The area measured is evenly divided into N pixels,calculate the Lamb wave energy and time of flight damage factor of each channel,determine and superimpose the probability value in the affected area of each channel damage factor,and obtain the damage probability of each pixel and image it.The experimental results show that compared with the frequently-used damage probability imaging algorithms based on energy damage factor and cross-correlation damage factor,the proposed method can intuitively reflect the defects of carbon fiber reinforced polymer,and the recognition effect is better,the imaging error is significantly reduced,the error is reduced by 4.420,2.117,2.055 and 4.732,2.380,2.647 respectively,which can identify defects more accurately and guarantee the safe application of carbon fiber reinforced polymer structure effectively.