Abstract
Additive Manufacturing (AM) simulations are often employed to replace the expensive experiments to study the effects of processing conditions. In process modeling, one of the key limitations is the lack of reliable validation techniques. The stochastic nature and the spatial heterogeneity of microstructures make it difficult to validate the simulated microstructures against experimentally obtained images through statistical measures (e.g. average and standard deviation of grain sizes). In this work, a validation metric is proposed that can effectively quantify the dissimilarity between two AM microstructures. The methodology involves first calculating the Angularly Resolved Chord Length Distribution (ARCLD) at representative angles and then computing the Earth Mover's Distance (EMD) to obtain the final unitless score that is named Dissimilarity Score (DS). The efficacy of the proposed methodology was first tested on synthetic microstructures, and then on AM simulations that employ the solidification model-Cellular Automaton (CA) with IN625. Results show that DS effectively measures the dissimilarity between different microstructures. The use of DS is also extended to calibrate the CA processing simulation code to match with experimental AM images from NIST AM-Bench Challenge.
Original language | English (US) |
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Title of host publication | 2020 International Symposium on Flexible Automation, ISFA 2020 |
Publisher | American Society of Mechanical Engineers (ASME) |
ISBN (Electronic) | 9780791883617 |
DOIs | |
State | Published - 2020 |
Event | 2020 International Symposium on Flexible Automation, ISFA 2020 - Virtual, Online Duration: Jul 8 2020 → Jul 9 2020 |
Publication series
Name | 2020 International Symposium on Flexible Automation, ISFA 2020 |
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Conference
Conference | 2020 International Symposium on Flexible Automation, ISFA 2020 |
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City | Virtual, Online |
Period | 7/8/20 → 7/9/20 |
Funding
The authors are thankful to National Institute of Standards and Technology (NIST) for providing the EBSD data in section 4.3. Funding support from the doctoral predictive science & engineering design (PS&ED) cluster fellowship from Northwestern and the Center for Hierarchical Materials Design (ChiMaD NIST 70NANB19H005) are also acknowledged.
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering