Quality Inspection of the Riveting Process by Neural Networks

2013 ◽  
Vol 284-287 ◽  
pp. 2276-2280
Author(s):  
Huang Chi Chen ◽  
Yu Ju Chen ◽  
Hsing Ling Wang ◽  
Chuo Yean Chang ◽  
Pin Hsuin Weng ◽  
...  
Keyword(s):  
Neural Network ◽  
Inspection System ◽  
Quality Inspection ◽  
Quantum Neural Network ◽  
Riveting Process ◽  
Inspection Systems ◽  
Institute Of Technology ◽  
Trained Neural Network ◽  
The Impact ◽  
Chinese Air Force

This paper presents an automatic quality inspection system for the riveting process by using neural network (NN) techniques. Two types of neural models were used in studies. One is the conventional neural network and the other one is the quantum neural network which is expected to deal with the signals with fuzziness and uncertainty. The well-trained neural network could make an immediate diagnosis of the riveting quality based on the impact signals sensed. Thus, such NN inspection system can not only monitor the real time riveting process, but also give the assistance on the riveting quality verification. In order to demonstrate the superiority of neural network inspection system developed, the experimental data provided Chinese Air Force Institute of Technology was studied and simulated. The method of riveting quality index (RQI) was also performed as a comparison. From the simulation results shown, both of the proposed neural network inspection systems have the better verification accuracy than RQI method.

scholarly journals Product Inspection Methodology via Deep Learning: An Overview

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5039
Author(s):  
Tae-Hyun Kim ◽  
Hye-Rin Kim ◽  
Yeong-Jun Cho
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Inspection System ◽  
Quality Inspection ◽  
Learning Models ◽  
Unified Framework ◽  
Learning Techniques ◽  
Inspection Systems ◽  
System Maintenance ◽  
Test Scenarios

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

Automated Multichannel Signal Classification Systems for Ultrasonic Nondestrucitve Evaluation

2006 ◽  
Vol 321-323 ◽  
pp. 1266-1269 ◽  
Author(s):  
J. Kim ◽  
P. Ramuhalli ◽  
L. Udpa ◽  
S. Udpa
Keyword(s):  
Neural Network ◽  
Spatial Correlation ◽  
Principal Component ◽  
Automated Analysis ◽  
Classification Systems ◽  
Inspection Systems ◽  
Weld Inspection ◽  
Correlation Information ◽  
Trained Neural Network ◽  
Analysis System

A key requirement in most ultrasonic weld inspection systems is the ability for rapid automated analysis to identify the type of flaw. Incorporation of spatial correlation information from adjacent A-scans can improve performance of the analysis system. This paper describes two neural network based classification techniques that use correlation of adjacent A-scans. The first method relies on differences in individual A-scans to classify signals using a trained neural network, with a post-processing mechanism to incorporate spatial correlation information. The second technique transforms a group of spatially localized signals using a 2-dimensional transform, and principal component analysis is applied to the transform coefficients to generate a reduced dimensional feature vectors for classification. Results of applying the proposed techniques to data obtained from weld inspection are presented, and the performances of the two approaches are compared.

scholarly journals Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

2020 ◽  
Vol 10 (2) ◽  
pp. 545 ◽  
Author(s):  
Wenyuan Cui ◽  
Yunlu Zhang ◽  
Xinchang Zhang ◽  
Lan Li ◽  
Frank Liou
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Quality Inspection ◽  
Weight Decay ◽  
Metal Additive Manufacturing ◽  
Low Efficiency ◽  
The Impact ◽  
Metal Additive

Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

scholarly journals Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 313
Author(s):  
Mengdi Li ◽  
Anumol Mathai ◽  
Stephen L. H. Lau ◽  
Jian Wei Yam ◽  
Xiping Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Similarity Index ◽  
Super Resolution ◽  
Inspection System ◽  
Underwater Environment ◽  
Reconstruction Performance ◽  
Underwater Object ◽  
The Impact ◽  
Single Pixel

Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.

scholarly journals Using a Neural Network to Analyze the Impact of Passenger Activity on Bus Dwell Time and Travel Time

2010 ◽  
Author(s):  
Mei Chen ◽  
Xiaobo Liu
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Dwell Time ◽  
Sensitivity Analyses ◽  
Neural Network Modeling ◽  
Time Data ◽  
Relative Significance ◽  
Improvement Measures ◽  
Trained Neural Network ◽  
The Impact

This paper applies neural network modeling approach to analyze the impact of passenger activities on bus dwell time and station-to-station travel time. Data used to develop the model was collected by onboard AVL/APC devices. Sensitivity analyses based on a trained neural network were performed to evaluate the relative significance of each passenger activity variable to variation of dwell time and/or station-to-station travel time. Transit providers can use these methods to identify the causes of schedule deviation and to develop improvement measures that are most effective to transit service.

An intelligent defect detection system for warp-knitted fabric

2021 ◽  
pp. 004051752110600
Author(s):  
Xie Guosheng ◽  
Xu Yang ◽  
Yu Zhiqi ◽  
Sun Yize
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Detection System ◽  
Color Difference ◽  
Detection Algorithm ◽  
Inspection System ◽  
Knitted Fabric ◽  
Single Function ◽  
Inspection Systems ◽  
Warp Knitted Fabric

In textile factories, the most typical warp-knitted fabric defects include point defects, holes, and color differences. Traditional manual inspection methods are inefficient for detecting these defects. Existing intelligent inspection systems often have a single function. Factories require a real-time inspection system that can detect common defects and color difference. The YOLO (you only look once) neural network is faster than the two-stage neural network and has lower hardware requirements. The system’s color difference detection algorithm compares the color difference between the standard image and the image to be measured and records where the color difference value is exceeded. Finally, the comparison of the factory application proves that the designed system has good real-time performance and accuracy and can meet the fabric inspection requirements of warp-knitted fabric factories.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

European Economies Stability Faced With Potential Outburst of Sovereign Debt Crisis. An Empirical Study Using Neural Network

2013 ◽  
Vol 12 (2) ◽  
pp. 3255-3260
Author(s):  
Stelian Stancu ◽  
Alexandra Maria Constantin
Keyword(s):  
Neural Network ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Banking System ◽  
Sovereign Debt Crisis ◽  
European Level ◽  
Continuous Growth ◽  
Economic Context ◽  
European Economies ◽  
The Impact

Instilment, on a European level, of a state incompatible with the state of stability on a macroeconomic level and in the financial-banking system lead to continuous growth of vulnerability of European economies, situated at the verge of an outburst of sovereign debt crises. In this context, the current papers main objective is to produce a study regarding the vulnerability of European economies faced with potential outburst of sovereign debt crisis, which implies quantitative analysis of the impact of sovereign debt on the sensitivity of the European Unions economies. The paper also entails the following specific objectives: completing an introduction in the current European economic context, conceptualization of the notion of “sovereign debt crisis, presenting the methodology and obtained empirical results, as well as exposition of the conclusions.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

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