文章摘要
陈兴彬,曹伟,闵新和,李妮妮,肖舜仁,张鹏.基于数字成像机理的齿轮疲劳试验方法研究[J].机床与液压,2022,50(12):45-50.
CHEN Xingbin,CAO Wei,MIN Xinhe,LI Nini,XIAO Shunren,ZHANG Peng.Study on Gear Fatigue Test Method Based on Digital Imaging Theory[J].Machine Tool & Hydraulics,2022,50(12):45-50
基于数字成像机理的齿轮疲劳试验方法研究
Study on Gear Fatigue Test Method Based on Digital Imaging Theory
  
DOI:10.3969/j.issn.1001-3881.2022.12.009
中文关键词: 齿轮疲劳试验  数字图像相关法  非接触检测  数字散斑
英文关键词: Gear fatigue test  Digital image correlation method  Non-contact detection  Digital speckle
基金项目:国家自然科学基金青年基金项目(52105148);广东省科技基础条件建设项目(2018B030323027);国家重点研发计划项目(2017YFB1301400);广州机械科学研究院有限公司博士后专项(1014300036)
作者单位E-mail
陈兴彬 广州机械科学研究院有限公司,华南理工大学机械与汽车工程学院,中汽检测技术有限公司 cxb19862003@163.com 
曹伟 广州机械科学研究院有限公司,中汽检测技术有限公司  
闵新和 广州机械科学研究院有限公司,中汽检测技术有限公司  
李妮妮 广州机械科学研究院有限公司,中汽检测技术有限公司  
肖舜仁 华南理工大学机械与汽车工程学院  
张鹏 广州机械科学研究院有限公司,中汽检测技术有限公司  
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中文摘要:
      由于齿轮副啮合接触和内封装等特点,其疲劳强度的检测存在较大限制,尤其是对疲劳裂纹、轮齿折断等疲劳失效状态的监测大多采取间隔停机并拆解后检视的方式。整个过程耗时耗力,容易因人工主观判断和经验差异造成观测误差,而且无法感知齿轮早期疲劳阶段的微动疲劳特征。为此,基于双目视觉技术和数字图像相关理论提出一种面向齿轮疲劳强度试验的非接触式检测方法。通过双电机对拖驱动的方式搭建包含双目摄像头的减速机试验台,在齿轮上预制作高质量散斑进行疲劳试验,应用双目视觉方法采集全寿命周期的疲劳特征图像。通过图像匹配算法搜索跟踪轮齿上的目标区域,并分析计算得到区域内数据点的位移及应变等信息。结果表明:所提方法可以在无须拆装的情况下,实时在线监控齿轮疲劳试验过程并准确识别早期微小裂纹等疲劳失效特征,具有检测精度高、抗干扰能力强的特点,有利于提高试验效率,降低成本。
英文摘要:
      Due to the characteristics of meshing contact and inner package of gear pair, the detection of fatigue strength is greatly limited, especially, the fatigue failure state monitoring such as fatigue crack and gear breakage is usually conducted by stopping at intervals and inspecting after disassembly. The whole process is time-consuming and laborious, the observation errors are caused from manual subjective judgment and experience differences, moreover, the micro-fatigue characteristics of gear in early fatigue stage cannot be perceived. Therefore, a non-contact detection method for gear fatigue strength test was proposed based on binocular vision technology and digital image correlation theory. A reducer test platform with binocular cameras was set up through the transmission means of dual motors to drag, high-quality speckles were prefabricated on gears for fatigue test, the fatigue characteristic images of whole life cycle were collected by using binocular vision method. The target areas on the gear teeth were searched and tracked by using the methods of image matching algorithm, so that the displacement or strain of data points in the area were analyzed and calculated. The results show that by using the proposed method, the gear fatigue test process can be monitored online and the fatigue failure characteristics can be accurately identified such as early micro-cracks without disassembly, which has features of high detection accuracy and strong anti-interference ability that benefit to improving the test efficiency and reducing costs.
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