Design and implementation of automatic opto-electrical detection system for spheroidal graphite cast iron metallographic phase

Qing-xin Meng; Ze-xin Xiao; Shi-chao Deng

doi:10.1117/12.869035

11 November 2010 Design and implementation of automatic opto-electrical detection system for spheroidal graphite cast iron metallographic phase

Qing-xin Meng, Ze-xin Xiao, Shi-chao Deng

Author Affiliations +

Proceedings Volume 7855, Optical Metrology and Inspection for Industrial Applications; 78552B (2010) https://doi.org/10.1117/12.869035
Event: Photonics Asia 2010, 2010, Beijing, China

Abstract

Spheroidal graphite cast iron,with excellent mechanical properties,is widely used in manufacturing many advanced castings,such as crankshaft,gears,pistons,and a variety of machine parts.Its microstructure morphology reflects the quality performance of the products,which leads to an urgent need for a simple,accurate and automatic microstructure morphology detection technique for detecting the quality of spheroidal graphite cast iron.In this paper,opto-electrical detection technique is employed for designing a spheroidal graphite cast iron microstructure automatic detection system,in which the microstructure is imaged by optical microscopy system,and the digital images are obtained by industrial cameras and sent to the computer.A series of digital image processing algorithms,including gray transformation, binarization,edge detection,image morphology and seed filling etc,are adopted to calculate and analyze the microstructure images.The morphology and microstructure analysis methods are combined to obtain the characteristic parameters such as the size of the graphite,the ball classification,the number of graphite nodules and so on.The experiment results show that this method is simple,fast,and accurate and can be employed for assessment of the spheroidal graphite cast iron metallographic phase instead of manual detection.

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Qing-xin Meng, Ze-xin Xiao, and Shi-chao Deng "Design and implementation of automatic opto-electrical detection system for spheroidal graphite cast iron metallographic phase", Proc. SPIE 7855, Optical Metrology and Inspection for Industrial Applications, 78552B (11 November 2010); https://doi.org/10.1117/12.869035

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KEYWORDS

Iron

Image processing

Error analysis

Standards development

Image classification

Image quality

Image segmentation

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