scholarly journals Defect Inspection in Display Panel Using Concentrated Auto Encoder

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
DongHun Ku
Keyword(s):  
Critical Dimension ◽  
Image Similarity ◽  
Inspection Time ◽  
Performance Parameters ◽  
Defect Inspection ◽  
Alignment Position ◽  
Similarity Comparison ◽  
Comparison Methods ◽  
Inspection Performance

In this paper, concentrated auto encoder (CAE) is proposed for aligning photo spacer (PS) and for local inspection of PS. The CAE method has two characteristics. First, unaligned images can be moved to the same alignment position, which makes it possible to move the measured PS images to the same position in order to directly compare the images. Second, the characteristics of the abnormal PS are maintained even if the PS is aligned by the CAE method. The abnormal PS obtained through CAE has the same alignment as the reference PS and has its abnormal characteristics. The presence or absence of defects and the location of defects were identified without precisely measuring the height of the PS and critical dimension (CD). Also, alignment and defect inspection were performed simultaneously, which shortened the inspection time. Finally, inspection performance parameters and inspection time were analyzed to confirm the validity of the CAE method and were compared with the image similarity comparison methods used for defect inspection.

scholarly journals Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 313
Author(s):  
Jonas Aust ◽  
Antonija Mitrovic ◽  
Dirk Pons
Keyword(s):  
Classification Accuracy ◽  
Visual Inspection ◽  
False Positive Rate ◽  
Aircraft Engine ◽  
Tactile Perception ◽  
Inspection Time ◽  
Positive Rate ◽  
Training Programmes ◽  
Engine Maintenance ◽  
Inspection Performance

Background—In aircraft engine maintenance, the majority of parts, including engine blades, are inspected visually for any damage to ensure a safe operation. While this process is called visual inspection, there are other human senses encompassed in this process such as tactile perception. Thus, there is a need to better understand the effect of the tactile component on visual inspection performance and whether this effect is consistent for different defect types and expertise groups. Method—This study comprised three experiments, each designed to test different levels of visual and tactile abilities. In each experiment, six industry practitioners of three expertise groups inspected the same sample of N = 26 blades. A two-week interval was allowed between the experiments. Inspection performance was measured in terms of inspection accuracy, inspection time, and defect classification accuracy. Results—The results showed that unrestrained vision and the addition of tactile perception led to higher inspection accuracies of 76.9% and 84.0%, respectively, compared to screen-based inspection with 70.5% accuracy. An improvement was also noted in classification accuracy, as 39.1%, 67.5%, and 79.4% of defects were correctly classified in screen-based, full vision and visual–tactile inspection, respectively. The shortest inspection time was measured for screen-based inspection (18.134 s) followed by visual–tactile (22.140 s) and full vision (25.064 s). Dents benefited the most from the tactile sense, while the false positive rate remained unchanged across all experiments. Nicks and dents were the most difficult to detect and classify and were often confused by operators. Conclusions—Visual inspection in combination with tactile perception led to better performance in inspecting engine blades than visual inspection alone. This has implications for industrial training programmes for fault detection.

Visual Inspection of Circuit Boards: Effect of Gender, Age, Defect type, and Defect Proportion

1998 ◽  
Vol 42 (16) ◽  
pp. 1161-1164
Author(s):  
J. Klamklay ◽  
R.R. Bishu
Keyword(s):  
Visual Inspection ◽  
Printed Circuit Boards ◽  
Basic Program ◽  
Inspection Time ◽  
Circuit Boards ◽  
Defect Type ◽  
Printed Circuit ◽  
Independent Variables ◽  
And Gender ◽  
Inspection Performance

Visual inspection of printed circuit boards was evaluated through a simulated experiment. Photographs of circuit boards were scanned into a PC and the images were manipulated using a Visual Basic program. The visual inspection performance measured reaction time in seconds and accuracy by measuring the number of incorrect responses given. The independent variables studied in this research were age, gender, defect type, board size, inspection pace, and proportion of defects. Forty subjects participated in this experiment. In summary, the study showed that defect proportion, defect type, age and gender do influence both inspection time and inspection accuracy. Perhaps the most interesting result is that rate of false alarm (decision 2) and misses (decision 1) depend on defect type and proportion. This has interesting ramification for quality professionals.

scholarly journals GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3165
Author(s):  
Pekka Peltola ◽  
Jialin Xiao ◽  
Terry Moore ◽  
Antonio Jiménez ◽  
Fernando Seco
Keyword(s):  
Dead Reckoning ◽  
Urban Setting ◽  
Measurement Unit ◽  
Similarity Comparison ◽  
Pedestrian Navigation ◽  
Comparison Methods ◽  
Challenging Environment ◽  
Gnss Receivers ◽  
Distance Difference ◽  
Pedestrian Dead Reckoning

The urban setting is a challenging environment for GNSS receivers. Multipath and other anomalies typically increase the positioning error of the receiver. Moreover, the error estimate of the position is often unreliable. In this study, we detect GNSS trajectory anomalies by using similarity comparison methods between a pedestrian dead reckoning trajectory, recorded using a foot-mounted inertial measurement unit, and the corresponding GNSS trajectory. During a normal walk, the foot-mounted inertial dead reckoning setup is trustworthy up to a few tens of meters. Thus, the differing GNSS trajectory can be detected using form similarity comparison methods. Of the eight tested methods, the Hausdorff distance (HD) and the accumulated distance difference (ADD) give slightly more consistent detection results compared to the rest.

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenjie Chen ◽  
Nian Cai ◽  
Huiheng Wang ◽  
Jianfa Lin ◽  
Han Wang
Keyword(s):  
Solder Joint ◽  
Ensemble Learning ◽  
Solder Joints ◽  
Inspection Time ◽  
Subsequent Stage ◽  
Learning Stage ◽  
Content Type ◽  
Automatic Optical Inspection ◽  
Optical Inspection ◽  
Inspection Performance

Purpose Automatic optical inspection (AOI) systems have been widely used in many fields to evaluate the qualities of products at the end of the production line. The purpose of this paper is to propose a local-to-global ensemble learning method for the AOI system based on to inspect integrated circuit (IC) solder joints defects. Design/methodology/approach In the proposed method, the locally statistically modeling stage and the globally ensemble learning stage are involved to tackle the inspection problem. At the former stage, the improved visual background extraction–based algorithm is used for locally statistically modeling to grasp tiny appearance differences between the IC solder joints to achieve potential defect images for the subsequent stage. At the latter stage, mean unqualified probability is introduced based on a novel ensemble learning, in which an adaptive weighted strategy is proposed for revealing different contributions of the base classifier to the inspection performance. Findings Experimental results demonstrate that the proposed method achieves better inspection performance with an acceptable inspection time compared with some state-of-the-art methods. Originality/value The approach is a promising method for IC solder joint inspection, which can simultaneously grasp the local characteristics of IC solder joints and reveal inherently global relationships between IC solder joints.

scholarly journals Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2095
Author(s):  
In Yong Moon ◽  
Ho Won Lee ◽  
Se-Jong Kim ◽  
Young-Seok Oh ◽  
Jaimyun Jung ◽  
...  
Keyword(s):  
Surface Defects ◽  
Raw Materials ◽  
Region Of Interest ◽  
Defect Inspection ◽  
Number Of Layers ◽  
Important Region ◽  
Optimized Model ◽  
Human Inspection ◽  
Inspection Performance ◽  
Hierarchical Pattern

A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.

scholarly journals Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6135
Author(s):  
Jonas Aust ◽  
Antonija Mitrovic ◽  
Dirk Pons
Keyword(s):  
Visual Acuity ◽  
Statistical Analysis ◽  
Eye Tracking ◽  
Visual Inspection ◽  
Search Strategy ◽  
Aircraft Engine ◽  
Inspection Time ◽  
Defect Type ◽  
Inspection Performance ◽  
Different Levels

Background—The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor ‘cleanliness’ was analysed among other factors. Method—Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results—The results show an inspection accuracy of 86.8% and 66.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions—Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.

Systematic defect inspection and verification for distributions of critical dimension in OPC models utilizing design based metrology tool

2008 ◽  
Author(s):  
Jeong-Geun Park ◽  
Sang-ho Lee ◽  
Young-Seog Kang ◽  
Young-Kyou Park ◽  
Tadashi Kitamura ◽  
...  
Keyword(s):  
Critical Dimension ◽  
Defect Inspection
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