节点文献
焊缝超声检测缺陷类型的模糊C均值聚类识别
Fuzzy C-Means Clustering Algorithm Used in Ultrasonic Test to Recognize Weld Defects
【摘要】 提出了一种采用模糊C均值聚类的方法来识别焊缝超声检测缺陷类型的方法.以夹渣、气孔、未焊透、未熔合4种焊缝常见缺陷为对象,对25个实际焊缝样本进行识别计算,并与射线检测结果进行比对.结果表明:该方法正确识别率达88%,具有较好的识别效果.
【Abstract】 This paper presents a fuzzy C-Mean clustering algorithm to recognize the type of a weld defect pattern in ultrasonic test.By the method,twenty-five weld samples with four types of welding flaws were recognized and the results were compared with those of the X-Ray test.Experimental results show that the method is feasible and effective.Its recognized accuracy can reach 88%.
【关键词】 超声波检测;
焊缝缺陷;
模糊C均值聚类分析;
模式识别;
【Key words】 ultrasonic test; weld defects; fuzzy C-mean clustering analysis; pattern recognition;
【Key words】 ultrasonic test; weld defects; fuzzy C-mean clustering analysis; pattern recognition;
- 【文献出处】 测试技术学报 ,Journal of Test and Measurement Technology , 编辑部邮箱 ,2007年05期
- 【分类号】TG441.7
- 【被引频次】7
- 【下载频次】220