晶粒度是高强度铝合金微观组织分析的关键参数.通常是由人工手动获得,整个过程耗时且容易出错.目前随着数字图像处理技术和模式识别技术的快速发展,为定量金相分析提供了一种新的方法.利用人工智能实现自动金相分析可以克服手工工艺的缺点.在本文中提出了确定的金相图像的晶粒尺寸的数字图像处理的一种新方法.基于模糊逻辑的边缘检测算法的提取晶界.并对不同方法的金相图像提取方法进行了对比,验证了该方法的有效性.并基于美国材料试验学会(ASTM)标准获得了晶粒度等微观组织参数.
Grain size is one of the crucial parameters in the microstructure analysis of high strength aluminum alloy.This information is commonly derived based on manual processes.However,these manual processes may take long time and error are prone to occur.Nowadays,the rapid development of the digital image processing and the pattern recognition technologies provides a new methodology for the quantitative metallographic analysis.Artificial intelligence utilized in realizing automatic metallographic analysis can overcome the drawbacks of the manual processes.In the present paper we presented a new method of digital image processing for determining the grain sizes of the metallographic images.To derive the grain sizes of the digital metallographic images,the digital image processing was applied to extract grain boundary by proposing a new edge detection algorithm based on fuzzy logic.Extensive metallographic images with different qualities were tested to validate this method.Practical application cases were presented here.The grain size was calculated in accordance with American Society for Testing Material (ASTM) standards.
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