Co-training of multiple neural networks for simultaneous optimization and training of physics-informed neural networks for composite curing
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Epoxy Resin Curing Processes · Advanced machining processes and optimization · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Autoclave curing—comparisons of model and test results10.1177/002199839202600106 ↗
2. Autoclave curing of thermosetting composites: process modeling for the cure assembly10.1016/0735-1933(94)90032-9 ↗
3. Curing pressure influence of out‐of‐autoclave processing on structural composites for commercial aviation
4. Process modeling of composite materials: residual stress development during cure. Part I. Model formulation10.1177/002199839202601604 ↗
5. Curing of epoxy matrix composites10.1177/002199838301700204 ↗
6. Experimental and numerical study of the effect of cure cycle, tool surface, geometry, and lay-up on the dimensional fidelity of autoclave-processed composite parts10.1016/S1359-835X(01)00123-3 ↗
7. Multiscale characterization and representation of composite materials during processing
8. The origins of residual stress and its evaluation in composite materials
9. Cure kinetics and viscosity models for Hexcel 8552 epoxy resin
10. Heat of reaction, degree of cure, and viscosity of Hercules 3501-6 resin10.1177/002199838201600605 ↗
11. Rapidly cured out-of-autoclave laminates: Understanding and controlling the effect of voids on laminate fracture toughness10.1016/j.compositesa.2015.02.023 ↗
12. Finite element modeling of polymer curing in natural fiber reinforced composites10.1016/j.compscitech.2006.06.021 ↗
13. A partial differential equation model predictive control strategy: application to autoclave composite processing10.1016/j.compchemeng.2003.08.007 ↗
14. Three-dimensional cure simulation of composite structures by the finite element method10.1016/S0263-8223(03)00083-7 ↗
15. Modelling of residual stress during curing of a polymer under autoclave conditions and experimental validation10.1016/j.commatsci.2024.113038 ↗
16. Cure simulation of thick composite structures using the finite element method10.1177/002199801772662217 ↗
17. A numerical approach to the modeling of polymer curing in fibre-reinforced composites10.1016/S0266-3538(98)00138-9 ↗
18. Two-dimensional cure simulation of thick thermosetting composites10.1177/002199839102500302 ↗
19. A finite element/control volume approach to mold filling in anisotropic porous media10.1002/pc.750110613 ↗
20. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations10.1016/j.jcp.2018.10.045 ↗
21. Physics-informed neural networks (PINNs) for fluid mechanics: A review10.1007/s10409-021-01148-1 ↗
22. Paris Perdikaris, and George Em Karniadakis. Physics-informed neural networks for heat transfer problems10.1115/1.4050542 ↗
23. Physics-informed neural networks for power systems
24. Applications of physics-informed neural networks for property characterization of complex materials10.21809/rilemtechlett.2022.174 ↗
25. Applications of artificial intelligence/machine learning to high-performance composites
26. Cure process evaluation of CFRP composites via neural network: From cure kinetics to thermochemical coupling10.1016/j.compstruct.2022.115341 ↗
27. Physics-guided neural operator for data-driven composites manufacturing process modelling10.1016/j.jmsy.2023.07.010 ↗
28. Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
29. An advanced physics-informed neural operator for dynamic, zero-shot, and near real-time simulation of aerospace composite material curing process
30. A data-driven multi-fidelity physics-informed learning framework for smart manufacturing: a composites processing case study
31. Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry10.1016/j.cma.2023.116042 ↗
32. Barry-Straume, J., Sarshar, A., Popov, A.A. and Sandu, A., “Physics-informed neural networks for PDE-constrained optimization and control.” Computational Science Laboratory Report (2022).
33. Solving real-world optimization tasks using physics-informed neural computing10.1038/s41598-023-49977-3 ↗
34. Physics-informed neural networks with hard constraints for inverse design10.1137/21M1397908 ↗
35. Review of thermal conductivity in composites: Mechanisms, parameters and theory10.1016/j.progpolymsci.2016.05.001 ↗
36. Cure modeling and monitoring of epoxy/amine resin systems. I. Cure kinetics modeling10.1002/1097-4628(20000815)77:7<1419::AID-APP3>3.0.CO;2-N ↗
37. Dykeman, D., 2008. “Minimizing Uncertainty in Cure Modeling for Composites Manufacturing.” Electronic Theses and Dissertations (ETDs), University of British Columbia.
38. Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks10.1016/j.cma.2021.114333 ↗
39. A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method10.1016/j.cma.2022.115616 ↗
40. Adam: A method for stochastic optimization
41. “Aluminum 6061-T6; 6061-T651.” MatWeb Material Property Data. Accessed January 17th, 2025. https://www.matweb.com/search/datasheet.aspx? MatGUID=b8d536e0b9b54bd7b69e4124d8f1d20a&ckck=1.
42. “3900 Prepreg System, Data Sheet.” Toray Composite Materials America, Inc. Accessed January 17, 2025. https://www.toraycma.com/wp-content/uploads/3900-Prepreg-System.pdf.