斑点图案
表面粗糙度
人工神经网络
表面光洁度
人工智能
计算机科学
激光器
研磨
特征(语言学)
计算机视觉
特征提取
材料科学
光学
模式识别(心理学)
物理
哲学
复合材料
语言学
出处
期刊:International Conference on Mechatronics and Automation
日期:2009-08-01
卷期号:: 4474-4478
被引量:5
标识
DOI:10.1109/icma.2009.5244847
摘要
A non-contact surface roughness measurement method based on laser speckle, image processing and neural network technique is introduced. As the laser speckle patterns contain a lot of information about illuminated surface, four feature vectors correlative to surface roughness, which include contrast, dark region ratio, gray distribution and binary feature, are extracted and taken as inputs of the neural network to realize the surface roughness measurement. Neural network has characteristics, such as automatically organizing, automatically studying and memory capability etc; therefore, after training the network by a number of examples, the measurement can be implemented. 4 flat-grinding specimens with different roughness values are measured in the experiments. The results indicate that the measurement method has the advantages of not-contact, fast, precise, simple and easy to implement.
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