LED is an extremely important energy-saving lighting products, which has greatly facilitated human life. Meanwhile, it also makes a positive contribution to global carbon neutrality and carbon peaking. Defect detection is a vital part of the production process to control the quality of LED chips. The traditional methods use a microscope for manual visual inspection, which is time-consuming and has inconsistent testing standards, low efficiency, and other deficiencies. To solve these problems, a hybrid algorithm based on geometric computation and a convolutional neural network is proposed for LED chip defect detection. The method takes advantage of the dimensionality reduction of geometric computation to perform coarse detection of defects on preprocessed chip lithography graphs in the form of grid segmentation, which realizes fast coarse screening of large-scale chip samples and reduces postcomputational costs. The convolutional neural network model is used for the secondary fine detection of “suspected defective” chips after geometric coarse screening, and the SPP (spatial pyramid pooling) network model is improved by directly introducing the original feature map into the SPP pooling layer for summation to enhance the global and local feature information of the output feature map. Furthermore, we construct an LED chip image acquisition platform using a high-frequency multimagnification zoom lens, collect training samples of defective chips, and increase the number of samples through image processing techniques. The research introduces the R-CNN, SDD, and YOLO methods to evaluate the superiority of our method in a number of experiments. The experimental results show that our algorithm proposed in this paper has an average precision (AP) of 96.7% for large-scale chip detection with a low defect rate. Compared with other methods, the testing mean average precision (mAP) is 10.39% higher than traditional YOLO v2. The testing mIoU is also 3.63% higher than traditional YOLO v5, the detection speed is also significantly improved, and it has good robustness.
Based on the analysis of the advantages and disadvantages of existing surface flattening algorithms, this paper proposes a free‐form surface flattening algorithm that minimizes geometric deformation by combining the advantages of the geometric flattening method and the mechanical energy flattening method to address the problems of many iterations, large changes in convergence, and weak visualization of deformation. The point cloud surface is meshed using the triangular slice search method, and the 3D surface mesh is wrapped around the surface using geometric mapping relationships. The initial correction and deformation analysis of the ring flatten graphic are carried out according to the average flattening error, and a global geometric deformation energy model is established to obtain the energy‐minimizing unfolding conditions and optimize the initial flattening graph by iterative optimization. The verification of the algorithm shows that the algorithm is general, has good robustness, high flattening accuracy, and visualizes the flattening deformation. The method is suitable for some design occasions of machining, such as the flattening calculation of automobile outer cladding parts and sheet metal parts. It is especially suitable for the flattening calculation of ring‐like parts, and is a good guide for the design calculation and stamping process of sheet‐metal‐like parts.
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