Significance The multi-layer and multi-interface composite structures widely adopted in aircraft thermal protection and load-bearing components are prone to various hidden defects including structural folds, tiny pores, material inhomogeneity, interlayer delamination and interface disbond during manufacturing and service operation. These subtle defects seriously affect the structural stability and service safety of aircraft equipment. Single non-destructive testing technologies exhibit prominent limitations in defect identification accuracy, applicable material scope and visualization capability, which fail to achieve full-scale and high-precision detection of multi-type defects in complex aircraft structures. Existing multi-technology fusion detection methods mostly rely on dual-technology combination modes, lacking systematic three-dimensional (3D) reconstruction and global integration strategies for multi-source defect data. Therefore, it is urgent to develop an efficient and accurate multi-physical field fusion imaging detection technology to realize precise identification and intuitive visualization of multi-layer structural defects, which is of great engineering significance for ensuring aircraft structural integrity.

Progress  A novel 3D fusion imaging technology based on the multi-physical field response mechanisms of laser ultrasonics, X-ray detection and infrared thermography is developed for aircraft multi-layer structure defect detection (Fig.1). According to the differentiated material characteristics of aluminum alloy, composite material and thermal protection structure, as well as the morphological characteristics of typical structural defects, the optimal application scope of each single detection technology is quantitatively divided (Tab.1). Laser ultrasonic technology is applied to identify internal folds and tiny pore defects of aluminum alloy structural layers, X-ray detection is adapted to capture inhomogeneous defects of thermal protection structures, and infrared thermography is utilized to detect interlayer delamination and interface disbond defects of composite materials. On this basis, a complete technical system covering defect feature extraction, high-precision positioning and cross-source coordinate transformation is constructed. Combined with marker positioning, neural network intelligent recognition and point cloud generation algorithms, the unified global coordinate calibration of heterogeneous defect data from three detection approaches is realized, and a multi-source defect point cloud 3D integrated reconstruction model is established to complete the overall visual reconstruction of multi-layer and multi-interface structural defects.

Conclusions and Prospects  Experimental verification shows that the proposed technology can stably identify micro-defects with diameters ranging from 0.1 mm to 0.3 mm and depths of 0.1 mm to 0.15 mm, with the maximum spatial positioning error of all detected defects controlled within 0.2 mm. This technology effectively integrates the complementary advantages of three mainstream detection methods, thoroughly compensates for the single detection technology’s defects of limited detection range and fuzzy feature identification, and solves the technical bottlenecks of cross-coordinate system matching and global visual reconstruction of multi-layer complex structural defects. The established 3D reconstruction model can intuitively and accurately present the spatial distribution rules of various hidden defects in aircraft multi-layer structures. Future research will focus on optimizing the synchronous acquisition efficiency of multi-source detection data and improving the environmental adaptability of the technology under complex working conditions and extreme service environments, so as to further expand its engineering application scope in aircraft full-life-cycle structural health monitoring and integrity assessment.