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作 者:徐立青[1] XU Liqing(Shaanxi Railway Institute,Weinan Shaanxi 714000,China)
机构地区:[1]陕西铁路工程职业技术学院,陕西渭南714000
出 处:《自动化与仪器仪表》2022年第11期36-39,45,共5页Automation & Instrumentation
基 金:陕西铁路工程职业技术学院科研项目“基于机器视觉的精密零件表面缺陷检测技术的研究”(KT2022-33)。
摘 要:汽车精密零件表面缺陷会导致零件故障,为了提高缺陷检测能力,提出基于机器视觉的汽车精密零件表面缺陷自动检测方法。构建汽车精密零件表面缺陷的机器视觉图像采集模型,采用单背景约束下的表面光泽点提取的方法,分析汽车精密零件表面的结构纹理特征值,在视觉传感下通过对汽车精密零件的形变、相变参数分析,分析汽车精密零件的机械性能和服役性能,在切削载荷作用下,通过疲劳裂纹的视觉特征重构,在机器视觉下采用表面残余应力及变质层的动态特征分析,通过缺陷特征检测和有限元仿真分析,实现对汽车精密零件表面缺陷自动检测。测试结果表明,采用该方法进行汽车精密零件表面缺陷检测的纹理匹配度较高,检测性能较好,对零件表面的断屑、化学磨损等各类缺陷检测的可靠性较高。The surface defects of automotive precision parts will lead to parts failure. In order to improve the defect detection ability, an automatic detection method of automotive precision parts surface defects based on machine vision is proposed. The machine vision image acquisition model of the surface defects of automobile precision parts is constructed. The surface gloss point extraction method under the single background constraint is adopted to analyze the structural texture characteristic values of the surface of automobile precision parts. The mechanical properties and service performance of automobile precision parts are analyzed by analyzing the deformation and phase change parameters of automobile precision parts under visual sensing. Under the action of cutting load, the visual characteristics of fatigue cracks are reconstructed, the dynamic characteristics of surface residual stress and deterioration layer are analyzed under machine vision, and the automobile precision is realized through defect characteristics detection and finite element simulation analysis. The test results show that this method has high texture matching degree, good detection performance and high reliability in detecting various defects such as chip breaking and chemical wear on the surface of automobile precision parts.
关 键 词:机器视觉 汽车精密零件 缺陷 自动检测 纹理 有限元
分 类 号:TG501[金属学及工艺—金属切削加工及机床]
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