scholarly journals An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

2019 ◽  
Vol 9 (11) ◽  
pp. 2185 ◽  
Author(s):  
Jiange Liu ◽  
Tao Feng ◽  
Xia Fang ◽  
Sisi Huang ◽  
Jie Wang
Keyword(s):  
Neural Networks ◽  
Defect Detection ◽  
Vision System ◽  
Control Panel ◽  
Quality Inspection ◽  
Defect Inspection ◽  
Vision Inspection ◽  
Front End ◽  
Inspection Process ◽  
Front End Module

Automatic vision inspection technology shows a high potential for quality inspection, and has drawn great interest in micro-armature manufacturing. Given that the inspection process is highly influenced by the lack of real standardization and efficiency performed with the human eye, thus, it is necessary to develop an automatic defect detection process. In this work, an elaborated vision system for the defect inspection of micro-armatures used in smartphones was developed. It consists of two parts, the front-end module and the deep convolution neural networks (DCNNs) module, which are responsible for different areas. The front-end module runs first and the DCNNs module will not run if the output of the front-end module is negative. To verify the application of this system, an apparatus consisting of an objective table, control panel, and a camera connected to a Personal Computer (PC) was used to simulate an industrial position of production. The results indicate that the developed vision system is capable of defect detection of micro-armatures.

Application of Optical Inspection Method for Testing the Correctness of Ball Bearings Assembly

2014 ◽  
Vol 223 ◽  
pp. 264-271
Author(s):  
Piotr Garbacz ◽  
Piotr Czajka
Keyword(s):  
Vision System ◽  
Ccd Camera ◽  
Inspection Method ◽  
Vision Inspection ◽  
Optical Inspection ◽  
Inspection Process ◽  
Rolling Elements ◽  
Computer Based ◽  
The Individual ◽  
Low Sensitivity

The article presents the optical inspection method for the correct assembly of bearings with rolling elements in the form of balls. During the assembly process, faults can occur of incomplete fill of the rolling elements in a bearing cage or a lack of rivets for fixing both parts of the cage. These are critical defects, disqualifying the bearing from the operation. In order to detect these faults, the method of backlighting was used in the form of a modular LED panel illuminator located under the inspected bearing. The chosen method of lighting provides a high contrast and good sharpness with a simultaneous low sensitivity to contamination that may arise in the field of view of the camera. In order to verify the developed method, an experimental automated research stand was made. For vision inspection, a modular vision controller with a monochromatic CCD camera was used. Due to the range of bearings subjected to vision inspection, the algorithm of the program allows automatic detection of the bearing type based on its characteristics. The operation of the vision system was presented for each of the individual stages of the inspection process. The functions used in the field of computer-based image processing and analysis were described. Examples of bearing inspections, with use of the developed method, were presented.

scholarly journals Automated Quality Inspection on Tile Border Detection using Vision System

2019 ◽  
Vol 8 (3) ◽  
pp. 3737-3745
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Defect Detection ◽  
Ceramic Tile ◽  
Vision System ◽  
Extreme Temperature ◽  
Detection Technique ◽  
Quality Inspection ◽  
Border Detection ◽  
Image Capturing

Most of the ceramic tile industry still doing the quality control by manually. The accuracy of the manual inspection by human is lower due to the harsh industrial environment with noise, extreme temperature and humidity. A camera should replace the human eyes to recognise the defect tile effectively. Thus, a suitable method have to investigate for implementing this function. This project aim to design and develop an automated quality inspection in ceramic tile industry using vision system. The performance of the system is analysed. An Imaging Source CMOS industrial camera is use to capture the tile border. Image processing with edge detection technique is use to analyse the captured image of tile border and identify the defective tiles. The image filtering and intensity of the light are adjust to evaluate the performance of the system. The overall automation process involves image capturing, image processing, and decision making. The defect detection algorithms are develop to differentiate the defective tile based on the edge detection technique. The system using background subtraction method has achieved 50% accuracy in identify the status of tile since it consist of many limitation. By evaluate the gradient variation on the tile border edge, the accuracy of the defect detection has achieved 80% in identify the tile condition.

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

Electronics ◽  
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.

Rapid Failure Analysis of Low-Yielding Electrical Test Structures Using E-beam Physical and Voltage Contrast Inspection

2013 ◽  
Author(s):  
Oliver D. Patterson ◽  
Deborah A. Ryan ◽  
Xiaohu Tang ◽  
Shuen Cheng Lei
Keyword(s):  
Failure Analysis ◽  
Defect Detection ◽  
High Volume ◽  
The Other ◽  
Defect Inspection ◽  
Electrical Test ◽  
Detection Mode ◽  
Rapid Generation ◽  
Voltage Contrast ◽  
Additional Support

Abstract In-line E-beam inspection may be used for rapid generation of failure analysis (FA) results for low yielding test structures. This approach provides a number of advantages: 1) It is much earlier than traditional FA, 2) de-processing isn’t required, and 3) a high volume of sites can be processed with the additional support of an in-line FIB. Both physical defect detection and voltage contrast inspection modes are useful for this application. Voltage contrast mode is necessary for isolation of buried defects and is the preferred approach for opens, because it is faster. Physical defect detection mode is generally necessary to locate shorts. The considerations in applying these inspection modes for rapid failure analysis are discussed in the context of two examples: one that lends itself to physical defect inspection and the other, more appropriately addressed with voltage contrast inspection.

scholarly journals Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1511
Author(s):  
Taylor Simons ◽  
Dah-Jye Lee
Keyword(s):  
Neural Networks ◽  
Visual Inspection ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Quality Inspection ◽  
Agricultural Produce ◽  
Resource Limited ◽  
Inspection Tasks ◽  
Computational Resources ◽  
Small Models

There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target embedded visual inspection tasks, like quality inspection sorting on manufactured parts and agricultural produce sorting. We propose a new binarized convolutional layer, called the neural jet features layer, that learns well-known classic computer vision kernels that are efficient to calculate as a group. We show that on visual inspection tasks, neural jet features perform comparably to standard BNN convolutional layers while using less computational resources. We also show that neural jet features tend to be more stable than BNN convolution layers when training small models.

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