摘要
为降低钢铁材料裂纹检测的无损检测比照差值,提高实际工作中的检测效率和质量,扩大对应的检测范围,提出基于机器视觉的钢铁材料裂纹无损检测方法。首先,提取裂纹视觉特征,布设二值化视觉检测节点;其次,构建钢材裂纹机器视觉无损检测模型,采用多频涡流无损视觉处理实现检测;最后,进行对比测试。测试结果表明,与基于卤素灯激励的红外热成像裂纹无损检测方法、银粉涂层损伤监测系统方法相比,利用基于机器视觉的钢铁材料裂纹无损检测方法得出的无损检测比照差值均控制在5以下,具有较高的应用价值。
In order to reduce the contrast difference of nondestructive testing for steel material crack detection, improve the detection efficiency and quality in practical work, and expand the corresponding detection range, a nondestructive testing method for steel material cracks based on machine vision is proposed. Firstly, visual features of cracks are extracted, and binary visual detection nodes are deployed.Secondly, the machine vision nondestructive testing model of steel cracks is constructed, and the multi frequency eddy current nondestructive visual processing is used to realize the detection. Finally, a comparative test is conducted. The test results show that, compared with the infrared thermal imaging crack nondestructive testing method based on halogen lamp excitation and the silver coating damage monitoring system method, the comparison difference of nondestructive testing obtained by using the machine vision based nondestructive testing method for steel material cracks is controlled below 5, which has high application value.
作者
解婧陶
XIE Jingtao(College of Machinery and Intelligent Manufacturing,Fujian Chuanzheng Communications College,Fuzhou Fujian 350000,China)
出处
《信息与电脑》
2022年第20期84-86,共3页
Information & Computer
关键词
机器视觉
钢铁材料
裂纹检测
machine vision
steel materials
crack detection
