研究目的
To develop an automated and accurate crack detection technique for pressed-panel products during manufacturing, addressing the limitations of traditional human inspection methods.
研究成果
The proposed image processing technique effectively detects cracks in pressed-panel products with high accuracy and speed, offering advantages over traditional methods. It can be integrated into manufacturing lines for automated inspection, with future work focusing on improving sensitivity to smaller cracks and reducing processing time.
研究不足
The technique may not detect very small cracks (e.g., below 1x9 mm) due to image resolution limitations, and processing time increases with higher resolution. False positives or negatives can occur with certain angle thresholds and circularity values.
1:Experimental Design and Method Selection:
The study uses an image processing-based technique involving object extraction from backgrounds using color factors, edge-line extraction via a percolation process, and crack detection through edge-line analysis.
2:Sample Selection and Data Sources:
Lab-scale experiments with a thin aluminum plate and real panel images from a press line (e.g., front panels of washing machines) were used. Images were captured using a camera system.
3:List of Experimental Equipment and Materials:
A cell phone camera (Samsung Note 3), Intel 4th Generation i5 processor (
4:2 GHz) with Windows 7 and 8GB RAM, MATLAB software, and aluminum plates with simulated cracks. Experimental Procedures and Operational Workflow:
Images were captured, converted to binary using RGB thresholding, edge lines extracted via percolation, and cracks detected by analyzing angle variations and circularity.
5:Data Analysis Methods:
MATLAB was used for signal processing, with analysis of relative angle variances and circularity values to identify cracks.
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