TDD‐net: a tiny defect detection network for printed circuit boards

An integration degree of modern printed circuit boards (PCB) is continually increasing. One of the important stages in the manufacturing of electronic devices is component soldering onto PCB pads. The soldering process is affected by many factors, which may lead to the formation of undesirable soldering defects. Some kinds of defects including bridge defects are detected visually. In the current article the possibility of bridge defect detection in a multi-pin surface mount component PCB images using wavelet transform is analyzed.

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The Impact of a Number of Samples on Unsupervised Feature Extraction, Based on Deep Learning for Detection Defects in Printed Circuit Boards

Future Internet ◽

10.3390/fi14010008 ◽

2021 ◽

Vol 14 (1) ◽

pp. 8

Author(s):

Ihar Volkau ◽

Abdul Mujeeb ◽

Wenting Dai ◽

Marius Erdt ◽

Alexei Sourin

Keyword(s):

Deep Learning ◽

Defect Detection ◽

Printed Circuit Boards ◽

Learning Model ◽

Circuit Boards ◽

Printed Circuit ◽

Unsupervised Deep Learning ◽

The Impact ◽

Deep Learning Model ◽

Unsupervised Feature Extraction

Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these approaches in manufacturing, due to lack of images of defects before the actual manufacturing starts. In contrast, we propose to train a defect detection unsupervised deep learning model, using a much smaller number of images without defects. We propose an unsupervised deep learning model, based on transfer learning, that extracts typical semantic patterns from defect-free samples (one-class training). The model is built upon a pre-trained VGG16 model. It is further trained on custom datasets with different sizes of possible defects (printed circuit boards and soldered joints) using only small number of normal samples. We have found that the defect detection can be performed very well on a smooth background; however, in cases where the defect manifests as a change of texture, the detection can be less accurate. The proposed study uses deep learning self-supervised approach to identify if the sample under analysis contains any deviations (with types not defined in advance) from normal design. The method would improve the robustness of the AOI process to detect defects.

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Defect Detection in Printed Circuit Boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

Computers Materials & Continua ◽

10.32604/cmc.2022.019527 ◽

2022 ◽

Vol 70 (1) ◽

pp. 637-652

Author(s):

Mohammed A. Alghassab

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Defect Detection ◽

Printed Circuit Boards ◽

Circuit Boards ◽

Printed Circuit ◽

Convolution Neural Networks

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Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks

Electronics ◽

10.3390/electronics9091547 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1547

Author(s):

Venkat Anil Adibhatla ◽

Huan-Chuang Chih ◽

Chi-Chang Hsu ◽

Joseph Cheng ◽

Maysam F. Abbod ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Defect Detection ◽

Printed Circuit Boards ◽

Learning Algorithms ◽

Detection Accuracy ◽

Quality Inspection ◽

Circuit Boards ◽

Printed Circuit

In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32.

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