Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets
Abstract
New manufacturing technologies such as additive manufacturing require research and development to minimize the uncertainties in the produced parts. The research involves experimental measurements and large simulations, which result in huge quantities of data to store and analyze. We address this challenge by alleviating the data storage requirements using lossy data compression. We select wavelet bases as the mathematical tool for compression. Unlike images, additive manufacturing data is often represented on irregular geometries and unstructured meshes. Thus, we use Alpert tree-wavelets as bases for our data compression method. We first analyze different basis functions for the wavelets and find the one that results in maximal compression and miminal error in the reconstructed data. We then devise a new adaptive thresholding method that is data-agnostic and allows a priori estimation of the reconstruction error. Finally, we propose metrics to quantify the global and local errors in the reconstructed data. One of the error metrics addresses the preservation of physical constraints in reconstructed data fields, such as divergence-free stress field in structural simulations. While our compression and decompression method is general, we apply it to both experimental and computational data obtained from measurements and thermal/structural modeling of the sintering of a hollowmore »
- Authors:
-
[1];
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1465181
- Report Number(s):
- SAND-2018-7410J
Journal ID: ISSN 0178-7675; 665640
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Computational Mechanics
- Additional Journal Information:
- Journal Volume: 63; Journal Issue: 3; Journal ID: ISSN 0178-7675
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; Wavelets; Compression; Unstructured mesh; Additive manufacturing; Temperature; Stress; Compression ratio; Reconstruction; Threshold; Error metric; Divergence
Citation Formats
Salloum, Maher, Johnson, Kyle L., Bishop, Joseph E., Aytac, Jon M., Dagel, Daryl, and van Bloemen Waanders, Bart G. Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets. United States: N. p., 2018.
Web. doi:10.1007/s00466-018-1605-6.
Salloum, Maher, Johnson, Kyle L., Bishop, Joseph E., Aytac, Jon M., Dagel, Daryl, & van Bloemen Waanders, Bart G. Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets. United States. https://doi.org/10.1007/s00466-018-1605-6
Salloum, Maher, Johnson, Kyle L., Bishop, Joseph E., Aytac, Jon M., Dagel, Daryl, and van Bloemen Waanders, Bart G. Tue .
"Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets". United States. https://doi.org/10.1007/s00466-018-1605-6. https://www.osti.gov/servlets/purl/1465181.
@article{osti_1465181,
title = {Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets},
author = {Salloum, Maher and Johnson, Kyle L. and Bishop, Joseph E. and Aytac, Jon M. and Dagel, Daryl and van Bloemen Waanders, Bart G.},
abstractNote = {New manufacturing technologies such as additive manufacturing require research and development to minimize the uncertainties in the produced parts. The research involves experimental measurements and large simulations, which result in huge quantities of data to store and analyze. We address this challenge by alleviating the data storage requirements using lossy data compression. We select wavelet bases as the mathematical tool for compression. Unlike images, additive manufacturing data is often represented on irregular geometries and unstructured meshes. Thus, we use Alpert tree-wavelets as bases for our data compression method. We first analyze different basis functions for the wavelets and find the one that results in maximal compression and miminal error in the reconstructed data. We then devise a new adaptive thresholding method that is data-agnostic and allows a priori estimation of the reconstruction error. Finally, we propose metrics to quantify the global and local errors in the reconstructed data. One of the error metrics addresses the preservation of physical constraints in reconstructed data fields, such as divergence-free stress field in structural simulations. While our compression and decompression method is general, we apply it to both experimental and computational data obtained from measurements and thermal/structural modeling of the sintering of a hollow cylinder from metal powders using a Laser Engineered Net Shape process. The results show that monomials achieve optimal compression performance when used as wavelet bases. The new thresholding method results in compression ratios that are two to seven times larger than the ones obtained with commonly used thresholds. Overall, adaptive Alpert tree-wavelets can achieve compression ratios between one and three orders of magnitude depending on the features in the data that are required to preserve. Furthermore, these results show that Alpert tree-wavelet compression is a viable and promising technique to reduce the size of large data structures found in both experiments and simulations.},
doi = {10.1007/s00466-018-1605-6},
url = {https://www.osti.gov/biblio/1465181},
journal = {Computational Mechanics},
issn = {0178-7675},
number = 3,
volume = 63,
place = {United States},
year = {2018},
month = {7}
}
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