摘要
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 physicsintegrated 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.
基金
The authors would like to acknowledge the funds from the Air Force Office of Scientific Research(AFOSR),United States of America,under award number FA9550-22-1-0065
J.X.W.would also like to acknowledge the funding support from the Office of Naval Research under award number N00014-23-1-2071
the National Science Foundation under award number OAC-2047127 in supporting this study.
