Comparison of deviation in aircraft casing deformation measurement based on data mapping optimization

LIU Hui; LIANG Jin; YE Meitu; GUO Jianying; LI Leigang

doi:10.37188/OPE.20233120.2930

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Comparison of deviation in aircraft casing deformation measurement based on data mapping optimization

Modern Applied Optics | 更新时间：2023-11-27

- Comparison of deviation in aircraft casing deformation measurement based on data mapping optimization
- Optics and Precision Engineering Vol. 31, Issue 20, Pages: 2930-2942(2023)
- 作者机构：
  
  1.西安交通大学机械工程学院机械制造系统工程国家重点实验室，陕西西安 710049
  2.新拓三维技术（深圳）有限公司创新实验室，广东深圳 518060
  3.中国航发四川燃气涡轮研究院强度传动试验研究室，四川绵阳 621000
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233120.2930
  CLC：
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刘辉,梁晋,叶美图等.基于数据映射优化的航空机匣变形测量偏差比对[J].光学精密工程,2023,31(20):2930-2942.

LIU Hui,LIANG Jin,YE Meitu,et al.Comparison of deviation in aircraft casing deformation measurement based on data mapping optimization[J].Optics and Precision Engineering,2023,31(20):2930-2942.
刘辉,梁晋,叶美图等.基于数据映射优化的航空机匣变形测量偏差比对[J].光学精密工程,2023,31(20):2930-2942. DOI： 10.37188/OPE.20233120.2930.

LIU Hui,LIANG Jin,YE Meitu,et al.Comparison of deviation in aircraft casing deformation measurement based on data mapping optimization[J].Optics and Precision Engineering,2023,31(20):2930-2942. DOI： 10.37188/OPE.20233120.2930.

摘要

为了解决航空发动机机匣性能试验中双目DIC变形测量偏差难以全场、精确量化的问题，提出了一种系统、全面的DIC测量数据与有限元仿真数据之间的映射方法。首先，采用FPFH特征和ICP算法精确配准两类点云数据，完成了数据坐标系的精确对齐；然后使用遗传算法优化的拟合神经网络调整有限元节点位置，从而消除两类数据间节点位置不一致的问题，完成仿真网格向DIC网格的高精度映射；最后，使用逐点最小二乘应变估计算法统一了有限元仿真和DIC测量的应变计算模式，得到与DIC属性一致的有限元比对数据，从而实现被测面全场变形的偏差估计。机匣刚度实验中肋板处的变形比对结果显示，网格节点的映射精度优于, $1 \times 10^{- 6} m m$ ,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=50498439&type=,https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=50498437&type=,17.18733406,2.70933342,，仿真变形和DIC变形的偏差云图与偏差曲线具有良好的一致性，且能够显示DIC测量偏差存在的具体位置，在未来航空发动机机匣及类匣体的研制和测试领域具有良好的应用前景。

Abstract

A method to systematically map DIC measurement data to finite element simulation data is proposed. This addresses challenges in quantifying binocular DIC deformation measurement discrepancies in full-field during the performance testing of aircraft engine casing. Initially， two sets of point cloud data were accurately registered using the FPFH feature and ICP algorithm， achieving precise alignment of their coordinate systems. Subsequently， a fitting neural network optimized by a genetic algorithm was employed to adjust the positions of finite element nodes. This ensured consistency in node positions between both data types， facilitating high-precision mapping from the simulation grid to the DIC grid. By implementing a point-by-point least squares strain estimation algorithm， the strain calculation techniques of both the finite element and DIC methods were aligned. Hence， finite element data that matches DIC attributes was produced， enabling estimation of full-field deformation deviations on the measured surface. The deformation comparison， particularly on the rib plate during the casing stiffness experiment， revealed a mapping accuracy of the mesh nodes better than 1×10,-,6, mm. Deviation images comparing simulated and DIC deformations aligned well with the deviation curve， clearly indicating the locations of DIC measurement discrepancies. This method holds significant promise for applications in the development and testing of aircraft engine casings and box-like structures.

关键词

数字图像相关变形偏差有限元仿真点云配准网格映射

Keywords

digital image correlationdeviation of deformationfinite element simulationpoint cloud registrationgrid mapping

references

郭建英，梁晋，叶美图，等. 反射镜面辅助的航空机匣三维变形多视角测量［J］. 光学精密工程， 2022， 30（24）： 3105-3115. doi: 10.37188/ope.20223024.3105http://dx.doi.org/10.37188/ope.20223024.3105

GUO J Y， LIANG J， YE M T， et al. Mirror-assisted multi-view measurement of 3D deformation for aero-engine casing［J］. Opt. Precision Eng.， 2022， 30（24）： 3105-3115. （in Chinese）. doi: 10.37188/ope.20223024.3105http://dx.doi.org/10.37188/ope.20223024.3105

叶美图，梁晋，李磊刚，等. 斜视场弱相关散斑图像的离散化匹配［J］. 光学精密工程， 2021， 29（9）： 2235-2246. doi: 10.37188/OPE.20212909.2235http://dx.doi.org/10.37188/OPE.20212909.2235

YE M T， LIANG J， LI L G， et al. Discretization matching of weakly-correlated speckle images in oblique field of view［J］. Opt. Precision Eng.， 2021， 29（9）： 2235-2246.（in Chinese）. doi: 10.37188/OPE.20212909.2235http://dx.doi.org/10.37188/OPE.20212909.2235

LUO P F， CHAO Y J， SUTTON M A， et al. Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision［J］. Experimental Mechanics， 1993， 33（2）： 123-132. doi: 10.1007/bf02322488http://dx.doi.org/10.1007/bf02322488

SCHREIER H， ORTEU J J， SUTTON M A. Image Correlation for Shape， Motion and Deformation Measurements： Basic Concepts， Theory and Applications［M］. Boston， MA： Springer US， 2009. doi: 10.1007/978-0-387-78747-3http://dx.doi.org/10.1007/978-0-387-78747-3

GARDNER N W， HILBURGER M W， HAYNIE W T， et al. Digital image correlation data processing and analysis techniques to enhance test data assessment and improve structural simulations［C］. 2018 AIAA/ASCE/AHS/ASC Structures， Structural Dynamics， and Materials Conference. Kissimmee， Florida. Reston， Virginia： AIAA， 2018： 1698. doi: 10.2514/6.2018-1698http://dx.doi.org/10.2514/6.2018-1698

MOHAMMAD M G， MARIANO CRYSTAL A， SAMANEH S， et al. Developing a lung model in the age of COVID-19： a digital image correlation and inverse finite element analysis framework［J］. Frontiers in Bioengineering and Biotechnology， 2021， 9： 684778. doi: 10.3389/fbioe.2021.684778http://dx.doi.org/10.3389/fbioe.2021.684778

TAYLOR O A， TIPPUR HAREESH V. Measurement of mixed-mode fracture characteristics of an epoxy-based adhesive using a hybrid digital image correlation （DIC） and finite elements （FE） approach［J］. Optics and Lasers in Engineering， 2021， 140： 106544. doi: 10.1016/j.optlaseng.2021.106544http://dx.doi.org/10.1016/j.optlaseng.2021.106544

LI X H， GHASRI-KHOUZANI M， BOGNO A， et al. Investigation of compressive and tensile behavior of stainless steel/dissolvable aluminum bimetallic composites by finite element modeling and digital image correlation［J］. Materials， 2021， 14（13）： 3654. doi: 10.3390/ma14133654http://dx.doi.org/10.3390/ma14133654

SEBASTIAN C， HACK E， PATTERSON E. An approach to the validation of computational solid mechanics models for strain analysis［J］. The Journal of Strain Analysis for Engineering Design， 2013， 48（1）： 36-47. doi: 10.1177/0309324712453409http://dx.doi.org/10.1177/0309324712453409

WANG W， MOTTERSHEAD JE， SEBASTIAN CM， et al. Shape features and finite element model updating from full-field strain data［J］. International Journal of Solids and Structures， 2011， 48（11/12）： 1644-1657. doi: 10.1016/j.ijsolstr.2011.02.010http://dx.doi.org/10.1016/j.ijsolstr.2011.02.010

WANG W， MOTTERSHEAD JE， MARES C. Mode-shape recognition and finite element model updating using the Zernike moment descriptor［J］. Mechanical Systems and Signal Processing， 2009， 23（7）： 2088-2112. doi: 10.1016/j.ymssp.2009.03.015http://dx.doi.org/10.1016/j.ymssp.2009.03.015

SALLOUM M， JOHNSON K L， BISHOP J E， et al. Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets［J］. Computational Mechanics， 2019， 63（3）： 491-510. doi: 10.1007/s00466-018-1605-6http://dx.doi.org/10.1007/s00466-018-1605-6

LAVA P， JONES E M C， WITTEVRONGEL L， et al. Validation of finite-element models using full-field experimental data： levelling finite-element analysis data through a digital image correlation engine［J］. Strain， 2020， 56（4）： e12350. doi: 10.1111/str.12350http://dx.doi.org/10.1111/str.12350

BESL P J， MCKAY N D. A method for registration of 3-D shapes［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 1992， 14（2）： 239-256. doi: 10.1109/34.121791http://dx.doi.org/10.1109/34.121791

ARUN K S， HUANG T S， BLOSTEIN S D. Least-squares fitting of two 3-D point sets［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 1987， PAMI-9（5）： 698-700. doi: 10.1109/tpami.1987.4767965http://dx.doi.org/10.1109/tpami.1987.4767965

WHITLEY D， STARKWEATHER T， BOGART C. Genetic algorithms and neural networks： optimizing connections and connectivity［J］. Parallel Computing， 1990， 14（3）： 347-361. doi: 10.1016/0167-8191(90)90086-ohttp://dx.doi.org/10.1016/0167-8191(90)90086-o

DING S F， SU C Y， YU J Z. An optimizing BP neural network algorithm based on genetic algorithm［J］. Artificial Intelligence Review， 2011， 36（2）： 153-162. doi: 10.1007/s10462-011-9208-zhttp://dx.doi.org/10.1007/s10462-011-9208-z

YIN F， MAO H， HUA L. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters［J］. Materials & Design， 2011， 32（6）： 3457-3464. doi: 10.1016/j.matdes.2011.01.058http://dx.doi.org/10.1016/j.matdes.2011.01.058

PAN B， ASUNDI A， XIE H， et al. Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements［J］. Optics and Lasers in Engineering， 2009， 47（7/8）： 865-874. doi: 10.1016/j.optlaseng.2008.10.014http://dx.doi.org/10.1016/j.optlaseng.2008.10.014

AMIDROR I. Scattered data interpolation methods for electronic imaging systems： a survey［J］. Journal of Electronic Imaging， 2002， 11（2）： 157-176. doi: 10.1117/1.1455013http://dx.doi.org/10.1117/1.1455013

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