About this Abstract |
Meeting |
MS&T24: Materials Science & Technology
|
Symposium
|
Manufacturing and Processing of Advanced Ceramic Materials
|
Presentation Title |
Probabilistic Physics-Integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process |
Author(s) |
Tengfei Luo, Deepak Akhare, Jianxun Wang, Zeping Chen, Richard Gulotty |
On-Site Speaker (Planned) |
Tengfei Luo |
Abstract Scope |
Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing composites. The densification in CVI critically influences the final performance, quality, and consistency. Experimentally optimizing the CVI processes is challenging. To address these challenges, this work takes a modeling-centric approach. Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework. An uncertainty quantification feature has been embedded within the PiNDiff method, bolstering the model's reliability and robustness. Through numerical experiments involving both synthetic and real-world manufacturing data, the proposed method showcases its capability in modeling densification during the CVI process. This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding, simulation, and optimization of the CVI manufacturing process, particularly when faced with sparse data and incomplete description of the underlying physics. |