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MCNN-DIC: a mechanical constraints-based digital image correlation by a neural network approach.
Applied Optics
|December 18, 2023
Summary
A new MCNN-DIC method uses neural networks and mechanical constraints to improve digital image correlation (DIC) for material deformation measurement. This approach enhances accuracy, especially for complex, non-uniform fields, without extensive pre-training.
Area of Science:
- Materials Science
- Mechanical Engineering
- Computational Mechanics
Background:
- Digital Image Correlation (DIC) is a key photomechanical technique for surface deformation analysis.
- Practical DIC applications face challenges including discontinuous fields, noise, and boundary measurement difficulties.
Purpose of the Study:
- To introduce MCNN-DIC, a novel DIC method integrating neural networks with mechanical constraints.
- To enhance the accuracy and applicability of DIC for complex deformation scenarios.
Main Methods:
- Developed MCNN-DIC by incorporating mechanical compatibility equation constraints into DIC via semi-supervised learning.
- Validated the method using simulated data and real-world nuclear graphite deformation fields.
Main Results:
- MCNN-DIC demonstrated superior accuracy in measuring non-uniform deformation fields compared to traditional methods.
- The proposed method enables rapid deformation field measurement with minimal neural network pre-training.
Conclusions:
- MCNN-DIC offers a more physically grounded and accurate approach to DIC.
- The method effectively addresses limitations of traditional DIC in challenging engineering applications.
