Article Article
NDE Data Fusion between Inconsistent Geometries

Data fusion in the NDE digital thread and digital twin requires spatial registration, but in many cases, as-designed and as-built geometries are different. Because of the additional prior knowledge of geometry and topology, a 3D CAD modeling context shows great potential to seamlessly integrate different kinds of NDE data in the digital thread. Non-Uniform Rational B-splines (NURBS) are the standard representation of surfaces in a CAD system. Recently, a differentiable NURBS framework was developed with mathematical formulations of the NURBS derivatives with respect to the input parameters. Using this framework, we perform gradient-based optimization. However, it is still challenging to transform the NDE data to a usable reference CAD geometry with a consistent as-built CAD model and a measured point cloud to support data fusion applications. We have tested this approach on a bar geometry as proof of concept. Our preliminary results show that the approach can fit the distorted surfaces to the measured NDE data well without introducing gaps between the surfaces of the CAD model.

DOI: 10.32548/RS.2022.016

References

[1] Monostori, L. (2014). Cyber-physical Production Systems: Roots, Expectations and R&D Challenges. Procedia CIRP, 17, 9–13.

[2] Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869.

[3] Rentala, V. K., Kanzler, D., & Fuchs, P. (2022). POD Evaluation: The Key Performance Indicator for NDE 4.0. Journal of Nondestructive Evaluation, 41(1), 20.

[4] Holland, S. D., McInnis, C., Radkowski, R., & Krishnamurthy, A. (2020). NDE Data Analysis and Modeling in 3D CAD Context. Materials Evaluation, 78(1), 95–103.

[5] Mai, H. N., & Lee, D. H. (2020). The Effect of Perioral Scan and Artificial Skin Markers on the Accuracy of Virtual Dentofacial Integration: Stereophotogrammetry Versus Smartphone Three-Dimensional Face-Scanning. International journal of environmental research and public health, 18(1), 229.

[6] Deva Prasad, A., Balu, A., Shah, H., Sarkar, S., Hegde, C., & Krishnamurthy, A. (2022). NURBS-Diff: A Differentiable Programming Module for NURBS. Computer-Aided Design, 146, 103199.

[7] Piegl, L., & Tiller, W. (1996). The NURBS Book. Springer Science & Business Media.

[8] Shen, W., Zhang, X., Jiang, X., Yeh, L., Zhang, Z., Li, Q., Li, B., & Qin H. (2021). Surface extraction from micro-computed tomography data for additive manufacturing. Procedia Manufacturing, 53, 568-575.

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