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Chemical vapor infiltration (CVI) is a widely adopted manufacturing techniqueused in producing carbon-carbon and carbon-silicon carbide composites. Thesematerials are especially valued in the aerospace and automotive industries fortheir robust strength and lightweight characteristics. The densificationprocess during CVI critically influences the final performance, quality, andconsistency of these composite materials. Experimentally optimizing the CVIprocesses is challenging due to long experimental time and large optimizationspace. To address these challenges, this work takes a modeling-centricapproach. Due to the complexities and limited experimental data of theisothermal CVI densification process, we have developed a data-drivenpredictive model using the physics-integrated neural differentiable (PiNDiff)modeling framework. An uncertainty quantification feature has been embeddedwithin the PiNDiff method, bolstering the model's reliability and robustness.Through comprehensive numerical experiments involving both synthetic andreal-world manufacturing data, the proposed method showcases its capability inmodeling densification during the CVI process. This research highlights thepotential of the PiNDiff framework as an instrumental tool for advancing ourunderstanding, simulation, and optimization of the CVI manufacturing process,particularly when faced with sparse data and an incomplete description of theunderlying physics.

Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process