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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