基于数字相关法初始值优化的微悬臂梁弯曲变形与应变测量方法

摘要/Abstract

摘要：

提出了一种基于数字相关法初始值优化的微悬臂梁弯曲变形与应变测量方法.基于放大的微悬臂梁表面的纹理特征，在尺度空间内进行特征点的测量与匹配，在特征点处进行高阶的数字相关法计算以得到变形参数；将特征点处的变形参数进行二维插值运算以得到任意像素点处变形参数的初始值；采用牛顿拉普森迭代方法计算在特定网格点处的变形参数，进而得到网格点处的位移和应变.通过仿真变形图像的计算结果来确定数字相关法的设定参数，对实际的微悬臂梁变形图像进行应变场计算，并与有限元法的计算结果进行比较，以验证所提出方法的有效性.

关键词: 微悬臂梁, 特征点匹配, 数字相关法, 初始值优化, 应变测量

Abstract:

A method for bending and strain analysis of micro-cantilever with initialization optimization by digital image correlation was proposed. Feature points were detected and matched in the scale space based on the image context obtained under microscope. First, the transformation parameters were calculated by digital image correlation with high order in the feature points. Then, the initial transformation parameters of every pixel of the cantilever were obtained by the interpolation in twodimension which could be used as the initial value for the iterative spatial domain cross correlation algorithm (NewtonRaphson method). Finally, the refined displacement and strain information at some special grid were calculated. While the algorithm parameters were optimized in the experiment of the simulated bended images, the performance of the proposed method was verified through the strain comparison with the finite element method.

Key words: micro-cantilever, feature points matching, digital image correlation method, initialization optimization, strain measurement

中图分类号:

TB 124

刘洪涛，梁振宁，胡文，莫锦秋，王石刚. 基于数字相关法初始值优化的微悬臂梁弯曲变形与应变测量方法[J]. 上海交通大学学报（自然版）, 2014, 48(05): 693-701.

LIU Hongtao,LIANG Zhenning,HU Wen,MO Jinqiu,WANG Shigang. A Method for Bending and Strain Measurement of Micro-Cantilever Based on Digital Image Correlation with Initialization Optimization[J]. Journal of Shanghai Jiaotong University, 2014, 48(05): 693-701.

参考文献

［1］Gates R S, Reitsma M G, Kramar J A, et al. Atomic force microscope cantilever flexural stiffness calibration: Toward a standard traceable method ［J］. Journal of Research of the National Institute of Standards and Technology, 2011, 116(4): 703727.

［2］Gianola D S, Eberl C. Micro and nanoscale tensile testing of materials ［J］. The Journal of the Minerals, Metals and Materials Society, 2009, 61(3): 2435.

［3］Grédiac M. The use of fullfield measurement methods in composite material characterization: Interest and limitations ［J］. Composites Part A: Applied Science and Manufacturing, 2004, 35(78): 751761.

［4］Avril S, Bonnet M, Bretelle A S, et al. Overview of identification methods of mechanical parameters based on fullfield measurements ［J］. Experimental Mechanics, 2008, 48(4): 381402.

［5］Pan B, Qian K M, Xie H M, et al. Twodimensional digital image correlation for inplane displacement and strain measurement: A review ［J］. Measurement Science and Technology, 2009, 20(6): 062071.

［6］Eberl C, Gianola D S, Hemker K J. Mechanical characterization of coatings using microbeam bending and digital image correlation techniques ［J］. Experimental Mechanics, 2010, 50(1): 8597.

［7］Chen J L, Zhang X C, Zhan N, et al. Deformation measurement across crack using twostep extended digital image correlation method ［J］. Optics and Lasers in Engineering, 2010, 48(11): 11261131.

［8］Zhou Y H, Pan B, Chen Y Q. Large deformation measurement using digital image correlation: A fully automated approach ［J］. Applied Optics, 2012, 51(31): 76747683.

［9］Zhou Y H, Chen Y Q. Propagation function for accurate initialization and efficiency enhancement of digital image correlation ［J］. Optics and Lasers in Engineering, 2012, 50(12): 17891797.

［10］Lu H, Cary P D. Deformation measurements by digital image correlation: Implementation of a secondorder displacement gradient ［J］. Experimental Mechanics, 2000, 40(4): 393400.

［11］Cheng P, Sutton M, Schreier H, et al. Fullfield speckle pattern image correlation with Bspline deformation function ［J］. Experimental Mechanics, 2002, 42(3): 344352.

［12］Zhao J Q, Zeng P, Lei L P, et al. Initial guess by improved populationbased intelligent algorithms for large interframe deformation measurement using digital image correlation ［J］. Optics and Lasers in Engineering, 2012, 50(3): 473490.

［13］Bornert M, Brémand F, Doumalin P, et al. Assessment of digital image correlation measurement errors: Methodology and results ［J］. Experimental Mechanics, 2009, 49(3): 353370.

［14］Crammond G, Boyd S W, DulieuBarton J M. Speckle pattern quality assessment for digital image correlation ［J］. Optics and Lasers in Engineering, 2013, 51(12): 13681378.

［15］Tuytelaars T, Mikolajczyk K. Local invariant feature detectors: A survey ［J］. Foundations and Trends in Computer Graphics and Vision, 2007, 3(3): 177280

［16］Mikolajczyk K, Tuytelaars T, Schmid C, et al. A comparison of affine region detectors ［J］. International Journal of Computer Vision, 2005, 65(12): 4372.

［17］Lowe D G. Distinctive image features from scaleinvariant key points ［J］. International Journal of Computer Vision, 2004, 60(2): 91110.

［18］Suk T, Flusser J. Affine moment invariants generated by graph method ［J］. Pattern Recognition, 2011, 44(9): 20472056.

［19］Lai W M, Rubin D, Krempl E. Introduction to continuum mechanics［M］. Fourth edition. USA: Elsevier, 2009: 257261.

［20］Lava P, Cooreman S, Coppieters S, et al. Assessment of measuring errors in DIC using deformation fields generated by plastic FEA ［J］. Optics and Lasers in Engineering, 2009, 47(78): 747753.