A Physics-Informed Neural Network Modeling Approach to Direct Ink Writing 3D Printing Process

Vaibhav Sharma; Rong Pan; Giulia Pedrielli

doi:10.1109/CASE56687.2023.10260639

A Physics-Informed Neural Network Modeling Approach to Direct Ink Writing 3D Printing Process

Vaibhav Sharma, Rong Pan, Giulia Pedrielli

Engineering, Ira A. Fulton Schools of (IAFSE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Direct ink writing is a 3D printing process and the quality of this process depends on the steady-state flow of materials at the tip of nozzle. In this paper, we investigate a data-driven approach, the physics informed neural network, for predicting flows, and compare different neural network architectures and their performance.

Original language	English (US)
Title of host publication	2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350320695
DOIs	https://doi.org/10.1109/CASE56687.2023.10260639
State	Published - 2023
Event	19th IEEE International Conference on Automation Science and Engineering, CASE 2023 - Auckland, New Zealand Duration: Aug 26 2023 → Aug 30 2023

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2023-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Country/Territory	New Zealand
City	Auckland
Period	8/26/23 → 8/30/23

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/CASE56687.2023.10260639

Cite this

Sharma, V., Pan, R., & Pedrielli, G. (2023). A Physics-Informed Neural Network Modeling Approach to Direct Ink Writing 3D Printing Process. In 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023 (IEEE International Conference on Automation Science and Engineering; Vol. 2023-August). IEEE Computer Society. https://doi.org/10.1109/CASE56687.2023.10260639

A Physics-Informed Neural Network Modeling Approach to Direct Ink Writing 3D Printing Process. / Sharma, Vaibhav; Pan, Rong ; Pedrielli, Giulia.
2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. IEEE Computer Society, 2023. (IEEE International Conference on Automation Science and Engineering; Vol. 2023-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sharma, V, Pan, R & Pedrielli, G 2023, A Physics-Informed Neural Network Modeling Approach to Direct Ink Writing 3D Printing Process. in 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. IEEE International Conference on Automation Science and Engineering, vol. 2023-August, IEEE Computer Society, 19th IEEE International Conference on Automation Science and Engineering, CASE 2023, Auckland, New Zealand, 8/26/23. https://doi.org/10.1109/CASE56687.2023.10260639

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author = "Vaibhav Sharma and Rong Pan and Giulia Pedrielli",

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A Physics-Informed Neural Network Modeling Approach to Direct Ink Writing 3D Printing Process

Abstract

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Conference

ASJC Scopus subject areas

Access to Document

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