Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique
used in producing carbon-carbon and carbon-silicon carbide composites. These
materials are especially valued in the aerospace and automotive industries for
their robust strength and lightweight characteristics. The densification
process during CVI critically influences the final performance, quality, and
consistency of these composite materials. Experimentally optimizing the CVI
processes is challenging due to long experimental time and large optimization
space. 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 comprehensive 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 an incomplete description of the
underlying physics.

透过数据驱动的预测模型以及物理整合的神经微分模型，该研究提出了一种在稀少数据和不完整物理描述条件下，优化化学气相渗透制造过程的工具，并展示了该工具在模拟材料致密化过程方面的潜力。