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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

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【作者】 于润桥刘春缘陈海鹏马娟

【Author】 YU Runqiao,LIU Chunyuan,CHEN Haipeng,MA Juan (Key Laboratory of Nondestructive Test(Ministry of Education),Nanchang Institute of Aeronautical Technology,Nanchang 330063,China)

【机构】 南昌航空工业学院无损检测技术教育部重点实验室南昌航空工业学院无损检测技术教育部重点实验室 江西南昌330063江西南昌330063

【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%.

  • 【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
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