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| Inverse method of model parameters based on deformation measurement data using principal component compression |
| Received:February 24, 2019 Revised:June 03, 2019 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20190224004 |
| KeyWord:digital image correlation data compression model parameters inverse least square method Gauss-Newton method |
| Author | Institution |
| 李建宇 |
天津科技大学 机械工程学院, 天津市轻工与食品工程机械装备集成设计与在线监控重点实验室, 天津 |
| 任朝 |
天津科技大学 机械工程学院, 天津市轻工与食品工程机械装备集成设计与在线监控重点实验室, 天津 |
| 张丽丽 |
天津职业技术师范大学 理学院, 天津 |
| 魏凯杰 |
天津科技大学 机械工程学院, 天津市轻工与食品工程机械装备集成设计与在线监控重点实验室, 天津 |
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| Abstract: |
| The principal component analysis method is used to compress the deformation information of the structure measured by the digital image correlation technology,and further to calculate the unknown parameters of the mechanical model.Firstly,in order to reduce the application cost of the huge data measured by digital image correlation technology,the principal component analysis method is applied to compress the surface deformation data of the structure,so as to reduce the amount of data significantly under the premise of retaining the main features of the surface deformation information.Secondly,the inverse model of mechanical model parameters based on the least-square method is established for the compressed data,and solved by Gauss-Newton method.Finally,numerical examples are given to demonstrate the effect of data compression on the inverse calculation of model parameters.The results show that the proposed method significantly reduces the amount of data,and can effectively improve the convergence speed of the inverse calculation for mechanical model parameters with higher accuracy and better stability,especially for the inverse calculation of multiple model parameters. |
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