共查询到16条相似文献,搜索用时 171 毫秒
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无合作目标激光绝对测距中,远距离、真实表面的后向散射特性机理不明朗,严重影响测距结果,是制约测距技术发展的重要瓶颈。以立铣、平铣、平磨等三种典型实际机械加工方式下的粗糙表面为研究对象,测量了在1 550 nm红外激光照射下形成的后向散射场,探究了不同加工方式下特殊后向散射场形成的原因,深入分析了表面纹理、入射方位角、入射角度、粗糙度对后向散射场分布的影响。实验结果分析表明,加工制造表面的后向散射光谱形态分布受加工方式的影响很大,且相互入射几何关系和粗糙度对每种加工方式下的实际粗糙表面均有规律性影响。为了能够获取足够的后向散射能量,对表面参数的识别反演显得十分重要。进一步构建了一种加工表面多维参数反演模型,采用另外一种加工方式(刨床)的样块数据进行验证,加工方式能够被准确区分,入射方位角和粗糙度反演的相对误差分别达到1.21%和1.03%,反演精度较高。经实验验证,通过表面参数的反演极大拓宽了无合作目标激光绝对测距的范围,有效降低了表面纹理、入射方位角、粗糙度等对测距范围的影响。这一研究结果还对具有纹理特征加工表面的后向散射光谱的研究和在其他领域的应用均具有一定的参考价值。 相似文献
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微光图像对比度较低,目标显著性不明显,目标自动探测难度大.针对此问题,本文提出一种噪声鲁棒性较好的图像局部纹理粗糙度算法,并给出一种适用于微光图像显著分析的纹理显著性算法.首先,提出一种新的局部纹理粗糙度算法,该算法利用最佳尺寸计算局部纹理粗糙度,对纹理图像进行加噪实验,与基于局部分形维的粗糙度方法相比,本文局部纹理粗糙度算法表现出较好的噪声鲁棒性;其次,在提取图像粗糙度特征图的基础上,给出一种针对纹理的显著性度量算法;最后,将纹理显著性算法应用于微光图像目标检测,实验结果证明了该算法的有效性. 相似文献
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超精密车削技术适于加工KDP(磷酸二氢钾)等频率转换类型的强光光学零件,但车削表面存在明显的加工纹理,导致抗激光损伤阈值降低。以加工表面误差幅值及其频谱分布为对象,分析了KDP光学零件超精密车削的加工特征和误差形态,采用功率谱密度(PSD)评价方法研究了工艺参数与误差频谱的内在关系,结果表明:不同进给速度及主轴转速将使螺旋形刀痕的间距发生变化,进而影响KDP表面误差的频率成分;切削深度虽然对误差频谱影响很小,但会改变PSD的幅值;当主轴转速高于500 r/min、进给速度小于2 mm/min、切削深度小于2 μm时能够加工出rms值优于20 nm的KDP面形。在此基础上,以典型KDP光学零件加工为例,通过超精密补偿车削方法将低频误差的PSD控制在300 nm2·mm以内,中高频误差的PSD控制到国家点火装置(NIF)标准线以下,满足强光系统的工作要求。 相似文献
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《光子学报》2020,(7)
针对薄壁零件变形量测量困难且过程繁琐的问题,提出了一种基于双目视觉的薄壁零件变形量测量方法 .对于零件表面变形量的测量,将具有基准坐标系标记点的刚性金属块安装在被测零件上,并在零件表面粘贴多个设计的编码标志点和彩色圆形定位点.利用彩色标记点分割出基准坐标系和编码标记点的有效图像区域,排除干扰图像特征.进行基准坐标系标记点和编码标记点的识别和检测,利用设计的角点结构实现标记点圆心的精确定位,并计算得到测量点的三维坐标.在基准坐标系下,通过计算零件变形前后表面关键点的三维坐标变化得到工件表面变形量.对于零件边缘变形,采用改进Canny边缘检测算法提取零件边缘的有效轮廓信息,并利用极线几何约束和灰度相似性对边缘特征进行了立体匹配和三维重建.薄壁零件变形量测量实验和测量精度验证实验表明该测量方法合理有效且测量精度满足要求. 相似文献
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为了获取磨削工件表面特征信息,提出一种基于激光扫描的磨削工件表面检测方法。利用机械臂带动激光传感器扫描放置在激光测量平面中的磨削工件,从而获得工件在激光测量平面中的三维坐标信息,通过相邻2个扫描点之间的高度变化求出工件边界点的三维坐标信息,结合x轴和y轴坐标的极值点利用最小二乘法拟合出工件边界在激光测量平面中的解析式,进一步求出附着在工件上的坐标系相对于激光测量坐标系的位姿,最后利用工件在激光测量坐标系中的位置矢量信息得出其表面特征信息。实验结果表明,利用该方法对工件表面进行检测,得到工件表面检测误差为0.11 mm,检测平均时间在1 s内,满足工件表面特征检测要求。 相似文献
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基于多色散斑延长效应的表面粗糙度测量及影响因素分析 总被引:6,自引:1,他引:5
粗糙表面在多波长激光束照射下形成的多色散斑场显示出散斑延长效应,利用此效应可以测量表面粗糙度,并且测量结果在一定条件下不受粗糙表面横向特征的影响。通过模拟计算随机粗糙表面的多色散斑场,以空间平均的多色散斑场局部自相关函数研究了平均散斑延长率〈χ〉对表面轮廓均方根偏差σh的依赖关系,分析了测量系统因素,如入射激光波长组合、成像器件光敏单元尺寸和动态范围对测量结果的影响。结果表明,以空间平均的局部自相关函数代替集平均的散斑自相关函数描述多色散斑延长效应是有效的;为达到一定的粗糙度测量精度,应选择合适的入射激光波长组合和合适的成像器件光敏单元尺寸。 相似文献
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This paper proposes an integrated roughness measurement system that is based on adaptive optics (AO) and binary analysis of speckle pattern images. The aim of this study was to demonstrate the necessity for AO compensation in regions containing both heat and fluid flow turbulences. A speckle image was obtained by projecting a laser beam onto the specimen surface, and the laser pattern image reflected from the surface was binarized to experimentally correlate the intensity with the surface roughness. In the absence of the AO correction scheme, induced turbulences can severely increase the residual rms error from 0.14 to 1.4 μm. After a real-time closed-loop AO correction, we can reduce the wavefront root mean square (rms) error to 0.12 μm, which not only compensates for the aberration error from induced disturbances but also improves the overall performance of the optical system. In addition, an AO system having different gains was investigated, and a threshold gain value was found to be able to steadily compensate for the wavefront errors in less than 2 s. Measurement results of five steel samples having roughness ranging from 0.2 to 3.125 μm (0.3λ and 5λ, where λ is the diode laser wavelength) demonstrate an excellent correlation between the intensity distribution of binary images and average roughness with a correlation coefficient of 0.9982. Furthermore, the proposed AO-assisted system is in good agreement with the stylus method and less than 9.73% error values can be consistently obtained. 相似文献
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The measurement of surface roughness using stylus equipment has several disadvantages. A non-contact optical method is needed for measuring the surface roughness of engineering metals with improved accuracy. One candidate for an optical method is the use of a laser source, where the laser light intensity reflected from the surface represents the surface roughness of the illuminated area. A relation can be developed between the reflected laser beam intensity and the surface roughness of the metal. The present study examines the measurement of the surface roughness of the stainless steel samples using a He-Ne laser beam. In the measurement a Gaussian curve parameter of a Gaussian function approximating the peak of the reflected intensity is measured with a fast response photodetector. In order to achieve this, an experimental setup is designed and built. In the experimental apparatus, fiber-optic cables are used to collect the reflected beam from the surface. The output of the fiber-optic system is fed to a back-propagation neural network to classify the resulting surface profile and predict the surface roughness value. The results obtained from the present study are then compared with the stylus measurement results. It is found that the resolution of the surface texture improves considerably in the case of optical method and the neural network developed for this purpose can classify the surface texture according to the control charts developed mathematically. 相似文献