复合材料科学与工程Issue(10)：83-90,8.DOI:10.19936/j.cnki.2096-8000.20251028.013
飞机平尾夹芯蜂窝复合材料结构的超声智能检测技术
Ultrasonic intelligent testing technology for sandwich honeycomb composite structure of aircraft horizontal tail
摘要
Abstract
The honeycomb composite structure of aircraft horizontal tail has large size,complex material struc-ture and high quality requirements.The water-jet ultrasonic focusing imaging detection technology can realize the imaging detection of honeycomb structure.The evaluation of a large number of detected images depends on the rich engineering experience and high-intensity work of technicians,which inevitably leads to poor evaluation reliability due to the influence of subjective factors.Therefore,an intelligent recognition technology of ultrasonic C-scan de-tection image of honeycomb composite material of aircraft horizontal tail based on deep learning network is proposed.Firstly,the C-scan detection image of the horizontal tail of the aircraft is collected by the water-jet ultrasonic focu-sing detection method,and the data set of the ultrasonic detection image of the horizontal tail of the aircraft is con-structed and expanded.Secondly,based on the amplitude distribution of the detection signal corresponding to the detection image,the detection image is divided into three target area categories according to the degree of bonding integrity.Thirdly,the Faster R-CNN network is constructed and optimized to form an intelligent recognition network for small feature changes in the ultrasonic C-scan area of honeycomb composite structures.Finally,the performance of the intelligent recognition model was measured by experimental methods to verify its ability to evaluate the ultra-sonic C-scan images of honeycomb structures.The research results show that the average accuracy of the intelligent model based on deep learning for the classification and recognition of honeycomb composite materials reaches 88.2%,and the average accuracy of the worst bonding area(three class of region)can reach 91.9%,which can be used for classification and statistics of ultrasonic C-scan detection images of honeycomb composite structures.关键词
蜂窝复合材料/Faster R-CNN/喷水式超声聚焦检测/深度学习/智能识别Key words
honeycomb composites/Faster R-CNN/water-jet ultrasonic focus testing/deep learning/intelli-gent recognition引用本文复制引用
涂思敏,陈振华,章俊燕,涂东坤,徐云林,卢超..飞机平尾夹芯蜂窝复合材料结构的超声智能检测技术[J].复合材料科学与工程,2025,(10):83-90,8.基金项目
国家自然科学基金(12464059) （12464059）
江西省重点研发计划项目(20212BBE51006) （20212BBE51006）
