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A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle.

Huang KY, Ye YT - Sensors (Basel) (2015)

Bottom Line: These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures.A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles.The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan. kuoyi@nchu.edu.tw.

ABSTRACT
In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

No MeSH data available.


The profile of the micro-spray nozzle.
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sensors-15-15326-f002: The profile of the micro-spray nozzle.

Mentions: Micro-spray nozzles (shown in Figure 1) are provided by Natural Fog Multi-Tech Precision Industry Corporation Ltd. (Tai-Chung, Taiwan). The structure profile of the micro-spray nozzle is shown in Figure 2. The outlet diameter of the micro-spray nozzle is 0.1 mm. There are two inclined annular-planes, A and B (as shown in Figure 2), on the inside surface of the micro-spray nozzle. Figure 3 depicts the circle textures in the inner image of micro-spray nozzles after CNC machine manufacturing. The defects of micro-spray nozzles were made by the CNC machine during the manufacturing procedures. The defects appear on the outlet and inclined planes A and B. Four possible defects, which include the outlet shape and deckle edge, pellet and stripe metal filings on inclined planes, are shown in Figure 3. The purpose of this study is to inspect the defects of micro-spray nozzles automatically using a machine vision system.


A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle.

Huang KY, Ye YT - Sensors (Basel) (2015)

The profile of the micro-spray nozzle.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541833&req=5

sensors-15-15326-f002: The profile of the micro-spray nozzle.
Mentions: Micro-spray nozzles (shown in Figure 1) are provided by Natural Fog Multi-Tech Precision Industry Corporation Ltd. (Tai-Chung, Taiwan). The structure profile of the micro-spray nozzle is shown in Figure 2. The outlet diameter of the micro-spray nozzle is 0.1 mm. There are two inclined annular-planes, A and B (as shown in Figure 2), on the inside surface of the micro-spray nozzle. Figure 3 depicts the circle textures in the inner image of micro-spray nozzles after CNC machine manufacturing. The defects of micro-spray nozzles were made by the CNC machine during the manufacturing procedures. The defects appear on the outlet and inclined planes A and B. Four possible defects, which include the outlet shape and deckle edge, pellet and stripe metal filings on inclined planes, are shown in Figure 3. The purpose of this study is to inspect the defects of micro-spray nozzles automatically using a machine vision system.

Bottom Line: These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures.A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles.The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan. kuoyi@nchu.edu.tw.

ABSTRACT
In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

No MeSH data available.