scholarly journals Sandblasting Defect Inspection of Investment Castings Based on Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Jenn-Kun Kuo ◽  
Jun-Jia Wu ◽  
Pei-Hsing Huang ◽  
Chin-Yi Cheng
Keyword(s):  
Prediction Accuracy ◽  
Direct Detection ◽  
Health And Safety ◽  
Defect Inspection ◽  
Labor Costs ◽  
High Noise ◽  
Learning Framework ◽  
Working Environments ◽  
Optical Inspection ◽  
Automated Optical Inspection

Abstract Investment castings often have surface impurities and pieces of shell molds can remain on the surface after sandblasting. Identification of defects involves time-consuming manual inspections in working environments of high noise and poor air quality. To reduce labor costs and increase the health and safety of employees, we applied automated optical inspection (AOI) combined with a deep learning framework based on convolutional neural networks (CNNs) to the detection of sandblasting defects. We applied the following four classic CNN models for training and predictive classification: AlexNet, VGG-16, GoogLeNet, and ResNet-34. In terms of predictive classification, AlexNet, VGG-16, and GoogLeNet v1 could accurately determine whether there were defects. Among the four models, AlexNet was the most accurate, with prediction accuracy of 99.53% for qualifying products and 100% for defective products. We demonstrate a direct detection technique based on the AOI and CNN structure with a fast and flexible computational interface.

AOI Techniques for Surface Defect Inspection

2010 ◽  
Vol 36 ◽  
pp. 297-302 ◽  
Author(s):  
Rong Sheng Lu ◽  
Yan Qiong Shi ◽  
Qi Li ◽  
Qing Ping Yu
Keyword(s):  
Rapid Development ◽  
Light Illumination ◽  
Defect Inspection ◽  
Emerging Industries ◽  
Optical Inspection ◽  
Textile Printing ◽  
Automobile Steel ◽  
Automated Optical Inspection ◽  
Key Techniques

Recent years, automated optical inspection (AOI) is developed very fast along with the rapid development of the emerging industries of semiconductor, LCD, PCB, optical communication and precision assembly, and also widely used in the industries of robot, automobile, steel, textile, printing, medicine, etc. In this paper, we will take a review of the AOI techniques, which are used for defect inspection on a large surface, such as inspecting the quality of TFT-LCD glass substrate and filter. The AOI system architecture having high inspection speed is illustrated. Some key techniques of light illumination, distributed image processing and convey mechanism, are explained.

Digital Optical Imaging Of Benzocyclobutene (Bcb) Thin Films On Silicon Wafers

1995 ◽  
Vol 381 ◽  
Author(s):  
Robert A. DeVries ◽  
Reed A. Shick ◽  
Bethany K. Johnson
Keyword(s):  
Thin Films ◽  
Optical Imaging ◽  
Fluorescent Dyes ◽  
Low Cost ◽  
Cost Effective ◽  
Silicon Wafers ◽  
Labor Costs ◽  
Or Efficiency ◽  
Optical Inspection ◽  
Automated Optical Inspection

AbstractA practical, low-cost research system for the fluorescent optical imaging of BCB (DVS-bisBCB) thin films has been used for the first time to evaluate coating quality on spin coated silicon wafers. In general, DVS-bisBCB thin films are easily produced defect free with a high degree of planarization. Various features of the coating are enhanced visually by the fluorescence making detection, digital storage, and quantification easier. Examples of features found in defective coatings made intentionally by a process to generate several common thin film defects are variations in film thickness, foreign particles, pinholes, and residual polymer in vias. The fluorescent bands of the normally transparent resin are easily excited in the near UV with a mercury lamp, causing a semi-opaque visible emission which could be observed by conventional imaging hardware. The BCB fluorescent quantum yield, or efficiency, is similar to fluorescent dyes so that only a small amount of BCB need be present in a substrate to allow optical inspection.This strong fluorescent property of DVS-bisBCB polymer, not possessed by many polyimide resins, could reduce labor costs of manual inspections and improve multichip module (MCM) processing yields. This digital imaging technique has potential for further development as a cost-effective automated optical inspection (AOI) method.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

scholarly journals Ceramic Cracks Segmentation with Deep Learning

2021 ◽  
Vol 11 (13) ◽  
pp. 6017
Author(s):  
Gerivan Santos Junior ◽  
Janderson Ferreira ◽  
Cristian Millán-Arias ◽  
Ramiro Daniel ◽  
Alberto Casado Junior ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ceramic Plate ◽  
Ceramic Tiles ◽  
Entire Area ◽  
Manual Inspection ◽  
Optical Inspection ◽  
Proposed Model ◽  
Processing Step ◽  
Long Time ◽  
Automated Optical Inspection

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

scholarly journals An Implementation of Yolo-family Algorithms in Classifying the Product Quality for the ABS Metallization

2021 ◽  
Author(s):  
YUH-WEN CHEN ◽  
Jing Mau Shiu
Keyword(s):  
Visual Inspection ◽  
Acrylonitrile Butadiene Styrene ◽  
Labor Cost ◽  
Classification Performance ◽  
Video Data ◽  
Production Lines ◽  
Learning Framework ◽  
Optical Inspection ◽  
The Neural Network ◽  
Product Surface

Abstract In the traditional electroplating industry of Acrylonitrile Butadiene Styrene (ABS), quality control inspection of the product surface is usually performed with the naked eye. However, these defects on the surface of electroplated products are minor and easily ignored under reflective conditions. If the number of defectiveness and samples is too large, manual inspection will be challenging and time-consuming. We innovatively applied Additive Manufacturing (AM) to design and assemble an automatic optical inspection (AOI) system. The system can identify defects on the reflective surface of the plated product. Based on the deep learning framework from YOLO, we successfully started the neural network model on GPU using the family of YOLO algorithms: from v2 to v5. Finally, our efforts showed an accuracy rate over an average of 70 percentage for detecting real-time video data in production lines. We also compare the classification performance among various YOLO algorithms. Our efforts of visual inspection significantly reduce the labor cost of visual inspection in the electroplating industry.

scholarly journals Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

2021 ◽  
Vol 17 (2) ◽  
pp. e1008767
Author(s):  
Zutan Li ◽  
Hangjin Jiang ◽  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Kun Lang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Biological Processes ◽  
Biological Functions ◽  
Learning Framework ◽  
Wide Range ◽  
Genomic Scale ◽  
Downstream Gene Expression ◽  
Fold Cross Validation

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

scholarly journals Sleep in wildland firefighters: what do we know and why does it matter?

2018 ◽  
Vol 27 (2) ◽  
pp. 73 ◽  
Author(s):  
Grace E. Vincent ◽  
Brad Aisbett ◽  
Alexander Wolkow ◽  
Sarah M. Jay ◽  
Nicola D. Ridgers ◽  
...  
Keyword(s):  
Multiple Stressors ◽  
Research Work ◽  
Health And Safety ◽  
Physical Work ◽  
Sleep Quantity ◽  
Working Environments ◽  
Long Duration ◽  
Physical Task ◽  
Modifiable Factor ◽  
Wildland Firefighting

Wildland firefighters perform physical work while being subjected to multiple stressors and adverse, volatile working environments for extended periods. Recent research has highlighted sleep as a significant and potentially modifiable factor impacting operational performance. The aim of this review was to (1) examine the existing literature on firefighters’ sleep quantity and quality during wildland firefighting operations; (2) synthesise the operational and environmental factors that impact on sleep during wildland firefighting; and (3) assess how sleep impacts aspects of firefighters’ health and safety, including mental and physical health, physical task performance, physical activity and cognitive performance. Firefighters’ sleep is restricted during wildfire deployments, particularly when shifts have early start times, are of long duration and when sleeping in temporary accommodation. Shortened sleep impairs cognitive but not physical performance under simulated wildfire conditions. The longer-term impacts of sleep restriction on physiological and mental health require further research. Work shifts should be structured, wherever possible, to provide regular and sufficient recovery opportunities (rest during and sleep between shifts), especially in dangerous working environments where fatigue-related errors have severe consequences. Fire agencies should implement strategies to improve and manage firefighters’ sleep and reduce any adverse impacts on firefighters’ work.

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