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The densification and sintering of ceramics using microwaves is first reported in the mid‐1960s. Today, the reduced carbon footprint of this process has renewed interest as it uses less energy overall compared to conventional process heating/furnaces. However, scaling up and commercializing the microwave sintering process of ceramics remains a formidable challenge. As a contactless method, microwave sintering offers geometric flexibility over other field‐assisted sintering processes. Yet, the inability to address multiscale, multiphysics‐driven heterogeneities arising during microwave coupling limits discussions about a future scale‐up process. Herein, the case is made that unlike 60 years ago, new advances in multiscale computational modeling, materials characterization, control systems, and software open up new avenues for addressing these challenges. More importantly, the rise of additive manufacturing techniques demands the innovation of sintering processes in the ceramics community for realizing near‐net‐shaped and complex parts for applications ranging from medical implants to automotive and aerospace parts.
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Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process
Abstract 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.
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- Award ID(s):
- 2047127
- PAR ID:
- 10519145
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41524-024-01307-5
