Research On The Key Technology Of Surface Defect Detection Of Stranded Elastic Nano Pin Based On Deep Learning

As the main connector accessories of high-end electronic components,the stranded elastic needles are twisted by 10 strands of copper alloy wires about 6 or 7 mm long and less than 0.2 mm in diameter.It is widely used in major national aerospace projects such as manned spaceflight,moon exploration,Beidou,large aircraft,and high-resolution ground observation systems.During the processing of stranded elastic needles,due to processing errors,various quality defects will occur.To ensure product quality,unqualified products should be removed in time.However,due to the small size and high-quality requirements of the stranded elastic needles,the product cannot be held manually by manual removal,and it needs to be visually inspected under the microscope with tweezers,which is inefficient and has a large workload,which seriously affects the production efficiency of stranded elastic needles.Therefore,if the relevant stranded elastic needles can be automatically sorted into the receiving boxes such as qualified,obese,lean,and scrap,the defect detection efficiency of the stranded elastic needles can be improved and the production cycles of products can be reduced.Visual inspection of surface defects of electronic components is one of the mainstream surface defect detection methods in the industry,but many problems and challenges have been encountered in practical applications.The features of traditional image feature extraction algorithms are usually at a low level,especially based on distinguishing between defective and non-defective features,which are manually designed based on experience,the samples to be detected are diversified and have small differences from the background structure,leading to complex scene changes such as unclear borders,lighting changes,perspective distortion,occlusion and so on,the features extracted by traditional methods are not enough to deal with these problems.Deep learning has strong feature extraction capabilities in image feature extraction.Therefore,this paper relies on the Ministry of Industry and Information Technology's comprehensive standardization of intelligent manufacturing and new model application project “New Model Application of Intelligent Manufacturing of Precision Electronic Components”(Ministry of Industry and Information Technology [2016] 213 No.),taking the four common stranded elastic needles surface defects as the research object,and using deep learning as the technical method.Researching on four issues: the stability of deep learning,surface defect detection,segmentation of pixellevel defects,and engineering applications of stranded elastic needles.The main research contents and results are as follows:(1)Stability analysis of surface defect detection model of stranded elastic needles based on deep learning.Aiming at the surface defects of stranded elastic needles,a variety of defect types,difficulty in rich defect image data collection,an uneven number of defect objects among the defect types.This lack of sufficient industrial training data and the quantity imbalance between the target categories makes the deep learning model have gradient convergence instability due to different input data.Based on this,First,without affecting the original data distribution,proposing a training data category balance strategy based on Batch Normalization.Then,to further improve the feature learning ability,a feature space data augmentation method is proposed.Finally,combined with Lipschitz continuous function,it is theoretically proved that the continuity of the method using multi-strategy Batch Normalization to balance the input data is better than that without multi-strategy.The experimental results on the stranded elastic needles dataset show that the average recognition accuracy of the model for defect features is 90.27%.(2)Surface defect identification method based on the augmentation feature of stranded elastic needles.To obtain the type and approximate defect location of stranded elastic needles,a surface defect identification method for stranded elastic needles based on feature extraction is proposed.First,to improve the detection ability of small adjacent targets,and improved YOLO neural network structure is proposed.In this structure,the R-FCN method is adopted to use a fully connected layer to reduce the loss of feature information.After inputting the image,the RPN method is used to add a 2x2 maximum pooling layer to reduce the size of the picture while as much as possible save the information of the original picture,and change the grid after the multi-layer convolution and pooling operation from the original 7x7 to 14x14 to increase the size of the network feature map.Then,for the data of a single object,a combination of border regression and RPN method is proposed,and a sliding window merge algorithm based on RPN is proposed to retain more image information for the data.Finally,by increasing the number of convolution kernel units in the detection window,removing the fully connected layer,and combining border regression and sliding windows to improve feature extraction capability in complex environments.The experimental results on the stranded elastic needles dataset show that the method in this paper has higher recognition accuracy and prediction probability estimates for the four types of defects: needle tip fat offset,defect size error,needle loosening,needle flattening and bulging than the comparison algorithm.(3)Surface pixel-level defect segmentation method for stranded elastic needles.Due to the contrast between the background area and the object area of the stranded elastic needles defect image is not obvious,the background and object are separable and the object area does not exist in a small range of defects.Based on the previous research,Unet network is used as the backbone network,and a segmented method for surface defects of stranded elastic needles is proposed.First,as for the greyscale contrast between the background area and the object area of the stranded elastic needles image,the variance of the image can reflect the change of the grey value of the image,the separable background,and object,and the grey of adjacent pixels can reflect their belonging area,a K-means-based defect cluster search algorithm is proposed.Secondly,given the size and shape of the defects,Unet's prediction network and segmentation network replaced to deformable convolution and deformable interest pooling modules to improve the neural network's ability to learn deformation features.Experimental results on the stranded elastic needle dataset show that our method has learned more information about real label data and has good robustness.(4)Application of stranded elastic needles defect detection.Aiming at the problem of missed detection that is easy to appear in defect detection,combined with the built-in industrial online inspection platform,an algorithm for judging the missed detection of detection objects based on intermediate variables is proposed.We developed a prototype system for detecting surface defects of stranded elastic needles and demonstrated the implementation of various functional modules.The stranded elastic needles defect detection test bench and the prototype system for defect detection verify the rationality and effectiveness of the research method and theory.