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 the 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.

化学气相渗透（CVI）是一种广泛采用的制造技术，用于生产碳-碳和碳-碳化硅复合材料。这些材料因其坚固的强度和轻质的特性而在航空航天和汽车工业中特别受到重视。 CVI 期间的致密化过程严重影响这些复合材料的最终性能、质量和一致性。由于实验时间长和优化空间大，实验优化 CVI 过程具有挑战性。为了应对这些挑战，这项工作采用了以建模为中心的方法。由于等温 CVI 致密化过程的复杂性和有限的实验数据，我们使用物理集成神经可微分 (PiNDiff) 建模框架开发了数据驱动的预测模型。 PiNDiff 方法中嵌入了不确定性量化功能，增强了模型的可靠性和鲁棒性。通过涉及合成和真实制造数据的综合数值实验，所提出的方法展示了其在 CVI 过程中建模致密化的能力。这项研究强调了 PiNDiff 框架作为促进我们对 CVI 制造过程的理解、模拟和优化的工具的潜力，特别是在面对稀疏数据和对底层物理的不完整描述时。