Enhancing Uniformity Detection in Woven Aeroengine Fan Blades Through Multiscale Feature Interaction

Woven composite fan blades are renowned for their superior material properties and play a crucial role in the performance of aeroengines. During the manufacturing, the uniformity of fan blade preforms is a critical factor that directly affects engine reliability. However, it’s challenging to accurately detect interlaced points due to their dense distribution and varied angular orientations caused by the weaving process and mechanical tension, making the uniformity detection of preform surfaces even more difficult. In this article, we develop a novel method for detecting the uniformity of woven aeroengine fan blade preforms. First, we design high-resolution surface image acquisition equipment tailored to the forming and weaving characteristics of preforms. Subsequently, we propose a two-stage uniformity detection framework, termed hierarchical dense interlaced-point detection framework (HDID Framework). The first stage of this framework is the angular rectification network (ARNet), which standardizes the orientation of interlaced points to a horizontal alignment using a rotational object detection method. The second stage involves the dense feature refinement network (DFRNet), which utilizes multiscale feature extraction modules and cross-attention mechanisms to effectively transform the dense detection problem into a feature point prediction task. This enables precise identification of densely interlaced points, ensuring robust performance in uniformity analysis preform detection. Ablation studies verify the effectiveness of each module, and comparative experiments demonstrate superior performance over state-of-the-art (SOTA) networks. Field measurements further validate the network’s practical applicability.