For Printed Circuit Board(PCB)surface defect detection,traditional detection methods mostly focus on template matching-based reference method and manual detections,which have the disadvantages of low defect detection ...For Printed Circuit Board(PCB)surface defect detection,traditional detection methods mostly focus on template matching-based reference method and manual detections,which have the disadvantages of low defect detection efficiency,large errors in defect identification and localization,and low versatility of detectionmethods.In order to furthermeet the requirements of high detection accuracy,real-time and interactivity required by the PCB industry in actual production life.In the current work,we improve the Youonly-look-once(YOLOv4)defect detection method to train and detect six types of PCB small target defects.Firstly,the original Cross Stage Partial Darknet53(CSPDarknet53)backbone network is preserved for PCB defect feature information extraction,and secondly,the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process.Then,the K-means++clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection,which further improves the recognition and localization of small PCB defects.Finally,the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms.The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%,which has better detection capability,lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.展开更多
Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including lo...Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.展开更多
This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transfo...This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transform. The experimental results denote that this algorithm can locate the circular mark of Printed Circuit Board (PCB).展开更多
From the background of poor responses of metallic particles in printed circuit board comminution fines to chemical conditioning froth flotation schemes, contrary to expectations based on native metal flotation, surfac...From the background of poor responses of metallic particles in printed circuit board comminution fines to chemical conditioning froth flotation schemes, contrary to expectations based on native metal flotation, surface studies were carried out on samples of these metallic particles in quest for the probable causatives. Auger electron spectroscopy combined with argon beam depth profiling was employed in studying the surface make-up of the metal particles. The composition profiles down to 340 nm surface depth obtained showed that the supposed metallic particles consist of organics, oxides, and various trace alloys different from the bulk material of the particles. The profiles reveal the peculiar surfaces of the particles and the matrix from which the particles were liberated. The study provides insight for better appraisal of the flotation system the sample presents. Implementing chemical conditioning flotation scheme on this sample must carefully consider the peculiar surface make up in contrast to native metal occurrences.展开更多
Epoxy resin laminate onto which a pair of copper foil was printed was employed as test samples.The samples were placed in an artificial atmospheric chamber, which was vacuumed by a rotary pump from 100 kPa to 5 kPa.Th...Epoxy resin laminate onto which a pair of copper foil was printed was employed as test samples.The samples were placed in an artificial atmospheric chamber, which was vacuumed by a rotary pump from 100 kPa to 5 kPa.The magnetic field was produced by permanent magnets that were assembled to make E×B drift away from, into and parallel to the sample surface, respectively.Magnetic flux density was adjusted at 120 mT, 180 mT and 240 mT respectively.By applying a negative bias voltage between the electrodes, the time to surface breakdown was recorded.Obtained results show that when E×B is into the surface, the time to the breakdown is shortened;when E×B is away from the surface, the time to the breakdown is delayed;when E×B is parallel to the surface, the time to the breakdown remains approximately the same as the case without magnetic field.With the decrease of pressure, the time to the breakdown increases and the effect of magnetic field on breakdown appears to be strengthened.展开更多
The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and struct...The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and structure of PCB are getting complicated.However,there are few effective and accurate PCB defect detection methods.There are high requirements for the accuracy of PCB defect detection in the actual pro-duction environment,so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method(DDMV)and the defect detection by multi-model learning method(DDML).With the purpose of reducing wrong and missing detection,the DDMV and DDML integrate multiple defect detection networks with different fusion strategies.The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets.The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score,and the area under curve value of DDML is also higher than that of any other individual detection model.Furthermore,compared with DDMV,the DDML with an automatic machine learning method achieves the best performance in PCB defect detection,and the Fl-score on the two datasets can reach 99.7%and 95.6%respectively.展开更多
基金This work was funded by the Natural Science Research Project of Higher Education Institutions in Jiangsu Province(No.20KJA520007)Min Zhang receives the grant and the URLs to sponsors’websites are http://jyt.jiangsu.gov.cn/.
文摘For Printed Circuit Board(PCB)surface defect detection,traditional detection methods mostly focus on template matching-based reference method and manual detections,which have the disadvantages of low defect detection efficiency,large errors in defect identification and localization,and low versatility of detectionmethods.In order to furthermeet the requirements of high detection accuracy,real-time and interactivity required by the PCB industry in actual production life.In the current work,we improve the Youonly-look-once(YOLOv4)defect detection method to train and detect six types of PCB small target defects.Firstly,the original Cross Stage Partial Darknet53(CSPDarknet53)backbone network is preserved for PCB defect feature information extraction,and secondly,the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process.Then,the K-means++clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection,which further improves the recognition and localization of small PCB defects.Finally,the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms.The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%,which has better detection capability,lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.
基金supported in part by the IoT Intelligent Microsystem Center of Tsinghua University-China Mobile Joint Research Institute.
文摘Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.
基金supported by Science and Technology Project of Fujian Provincial Department of Education under contract JAT170917Youth Science and Research Foundation of Chengyi College Jimei University under contract C16005.
文摘This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transform. The experimental results denote that this algorithm can locate the circular mark of Printed Circuit Board (PCB).
文摘From the background of poor responses of metallic particles in printed circuit board comminution fines to chemical conditioning froth flotation schemes, contrary to expectations based on native metal flotation, surface studies were carried out on samples of these metallic particles in quest for the probable causatives. Auger electron spectroscopy combined with argon beam depth profiling was employed in studying the surface make-up of the metal particles. The composition profiles down to 340 nm surface depth obtained showed that the supposed metallic particles consist of organics, oxides, and various trace alloys different from the bulk material of the particles. The profiles reveal the peculiar surfaces of the particles and the matrix from which the particles were liberated. The study provides insight for better appraisal of the flotation system the sample presents. Implementing chemical conditioning flotation scheme on this sample must carefully consider the peculiar surface make up in contrast to native metal occurrences.
基金Supported by National Natural Science Foundation of China (No.50777048)
文摘Epoxy resin laminate onto which a pair of copper foil was printed was employed as test samples.The samples were placed in an artificial atmospheric chamber, which was vacuumed by a rotary pump from 100 kPa to 5 kPa.The magnetic field was produced by permanent magnets that were assembled to make E×B drift away from, into and parallel to the sample surface, respectively.Magnetic flux density was adjusted at 120 mT, 180 mT and 240 mT respectively.By applying a negative bias voltage between the electrodes, the time to surface breakdown was recorded.Obtained results show that when E×B is into the surface, the time to the breakdown is shortened;when E×B is away from the surface, the time to the breakdown is delayed;when E×B is parallel to the surface, the time to the breakdown remains approximately the same as the case without magnetic field.With the decrease of pressure, the time to the breakdown increases and the effect of magnetic field on breakdown appears to be strengthened.
基金the Natural Science Foundation of Shanghai(No.20ZR1420400)the State Key Program of National Natural Science Foundation of China(No.61936001)。
文摘The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and structure of PCB are getting complicated.However,there are few effective and accurate PCB defect detection methods.There are high requirements for the accuracy of PCB defect detection in the actual pro-duction environment,so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method(DDMV)and the defect detection by multi-model learning method(DDML).With the purpose of reducing wrong and missing detection,the DDMV and DDML integrate multiple defect detection networks with different fusion strategies.The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets.The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score,and the area under curve value of DDML is also higher than that of any other individual detection model.Furthermore,compared with DDMV,the DDML with an automatic machine learning method achieves the best performance in PCB defect detection,and the Fl-score on the two datasets can reach 99.7%and 95.6%respectively.
