Adaptive acquisition and recognition system of blade surface defects during machining process

This study proposes an adaptive acquisition and recognition system of blade surface defects during machining process. The study has developed a hardware system consisting of motion platforms with four degrees of freedom (DOF) and industrial camera systems, along with an optimized workflow for the acquisition of blade surface defects. Subsequently, the hill-climbing algorithm, the energy gradient function and an adaptive evaluate image definition method are utilized to acquire clear images of the blade surface which contains tiny machined surface defects. An improved you only look once v5 (YOLOv5) algorithm is finally proposed to recognize the type and location of blade surface defects. The improved YOLOv5 algorithm uses K-means++ algorithm to cluster marking boxes, introduces a convolutional block attention module (CBAM) attention mechanism in the cross stage partial network with 3 convolutions (C3 module), and adopts the efficient intersection over union (EIoU) loss function instead of the complete intersection over union (CIoU) loss function to improve the recognition accuracy. The result shows that the proposed adaptive acquisition and recognition system can clearly collect the blade surface defects. The improved YOLOv5 algorithm can identify the type and location of blade surface defects, and the mean average precision (mAP) improved by 1.4 % compared to the original YOLOv5.