AI-Based Image Analysis for Particle Size and Shape Detection in Casting Sand
The shape and particle size distribution of casting sand are important factorsin the production and utilization of the casting sand.To address the issues of measurement efficiency and measurement errors associated with sieving methods,this study proposed a casting sand particle size and shape testing method based on artificial intelligence(AI)image analysis,which involves capturing images of the casting sand using an industrial camera and employing AI image processing techniques for instance segmentation.Subsequently,the images were subjected to feature extraction to statistically determine the particle size and shape distribution of the casting sand.The research findings indicated that the AI-based BlendMask instance segmentation model,could effectively separate agglomerated sand particles.By utilizing three feature parameters-circularity,shape factor,and rectangularity-for K-means clustering of casting sand particles,the method analyzed the particle shape characteristics accurately.Furthermore,employing the equivalent ellipse method and the area proportion equivalent mass ratio method enabled precise measurement of the particle size distribution for three types of the casting sand:baked sand,dried sand,and zircon sand,respectively,which meets the industry accuracy requirements.
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