NSF Org: |
CMMI Div Of Civil, Mechanical, & Manufact Inn |
Recipient: |
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Initial Amendment Date: | June 3, 2022 |
Latest Amendment Date: | June 3, 2022 |
Award Number: | 2152908 |
Award Instrument: | Standard Grant |
Program Manager: |
Reha Uzsoy
ruzsoy@nsf.gov (703)292-2681 CMMI Div Of Civil, Mechanical, & Manufact Inn ENG Directorate For Engineering |
Start Date: | July 1, 2022 |
End Date: | June 30, 2025 (Estimated) |
Total Intended Award Amount: | $509,721.00 |
Total Awarded Amount to Date: | $509,721.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
3 RUTGERS PLZ NEW BRUNSWICK NJ US 08901-8559 (848)932-0150 |
Sponsor Congressional District: |
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Primary Place of Performance: |
33 Knightsbridge Road Piscataway NJ US 08854-3925 |
Primary Place of Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | CDS&E |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
Metal additive manufacturing (AM) offers a great opportunity for making complex parts. However, the collective impact of complex part geometry, nonuniform heat dissipation, and diverse laser scanning often cause overheating of the melt pool during the printing process. The overheating problem leads to various quality issues. Therefore, the understanding and fast prediction of melt pool behaviors are necessary for printing high-quality parts. Data science models (e.g., deep learning, or DL) may use diverse types of melt pool data for efficient prediction of overheating. But the data science models lack transparency, are computationally expensive, and need massive training data. On the other hand, computational models may understand the complex melt pool behaviors, but require continuous updates of model parameters and are not suitable for fast prediction. This award provides an integrated approach by using the strength of both models for fast prediction of melt pool overheating. The outcome of this project will not only contribute to the fundamental knowledge of deep learning but also enable the broad acceptance of the project's testbed as a public tool for the AM community. The results will help many industry sectors including aerospace, healthcare, tools, and mold, automotive, and others. The project?s interdisciplinary nature also helps train the future digital manufacturing workforce by broadening the participation of women and underrepresented minority groups in data science-driven research and education.
This research bridges the knowledge gap in fundamental understanding and real-time prognosis of melt pool dynamics by developing a new computation-informed deep learning (Co-DL) approach. The research team will: (1) develop a computational fluid dynamics (CFD) model of selective laser melting (SLM) to generate complementary data which cannot be measured otherwise; (2) create cyberinfrastructure to enable multimodal data curation, contextualization, integration, and interoperability, extracting knowledge from data analytics, and interfacing Co-DL testbed; (3) develop a Co-DL modeling method to integrate physical laws of melt pool dynamics and augmented data from the CFD model into DL training and learning algorithm; (4) create a set of DL acceleration and semi-supervised learning approaches with small data; and (5) create a real-time online Co-DL testbed for the metal AM community. The resulting method will solve a major limitation of pure data-driven DL models for lacking explainability, significantly reduce the time-latency of the Co-DL model training and inference, and create cyberinfrastructure to enable data curation, contextualization, integration, interoperability, and interfacing with the Co-DL testbed.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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