A rapid virtual autoclave for carbon fiber reinforced plastics
Junhong Zhu
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Junhong Zhu, A rapid virtual autoclave for carbon fiber reinforced plastics (2023), Logos Verlag, Berlin, ISBN: 9783832583781
Beschreibung / Abstract
Structural carbon fiber reinforced plastic parts are usually manufactured through autoclave processing for high-performance aerospace applications. Todayâs aerospace composite manufacturing techniques require high quality with robust manufacturing processes. Manufacturing process simulation enables the investigations of physical effects and manufacturing process mechanisms. This approach has been increasingly used to predict and optimize the manufacturing process for high part quality at low manufacturing costs. Owing to a complicated manufacturing environment involving multi-physics characteristics, there is a critical need to develop an efficient and cost-effective numerical methodology with a systematic study.
This thesis contributes to the systematic investigations of the process modeling, simulation, thermal measurement, and optimization in composite manufacturing of autoclave processing. The method provides a correct and efficient thermal analysis and optimization in autoclave processing to achieve better process control and ensure the quality of composite parts. The presented framework can be applied directly in autoclave production with larger dimensions and full-scale tools for aerospace structures. The developed methodology allows quick delivery guidelines of production plans and optimization strategies for composite manufacturing in a highly useful and cost-effective way, thereby reducing the cost in the design and manufacturing phase.
This thesis contributes to the systematic investigations of the process modeling, simulation, thermal measurement, and optimization in composite manufacturing of autoclave processing. The method provides a correct and efficient thermal analysis and optimization in autoclave processing to achieve better process control and ensure the quality of composite parts. The presented framework can be applied directly in autoclave production with larger dimensions and full-scale tools for aerospace structures. The developed methodology allows quick delivery guidelines of production plans and optimization strategies for composite manufacturing in a highly useful and cost-effective way, thereby reducing the cost in the design and manufacturing phase.
Beschreibung
Since July 2017, Mr. Junhong Zhu has been working as a research assistant in the department of modeling and simulation at the FIBRE (Faserinstitut Bremen e.V.) at the University of Bremen. He deals with the process modeling and simulation in composite manufacturing of autoclave processing. His research focuses on numerical methods, such as computational fluid dynamics and finite element methods, muti-physics coupling schemes, and process optimization. He is also interested in the use of artificial intelligence in the manufacturing process.
Inhaltsverzeichnis
- BEGINN
- Acknowledgements
- Kurzfassung der Dissertation
- Abstract
- List of Symbols
- 1 Introduction
- 1.1 Overview of autoclave processing
- 1.2 Motivation and research objectives
- 1.3 Thesis outline
- 2 Theoretical background
- 2.1 Autoclave processing models
- 2.2 Description of heat transfer in autoclave processing
- 2.3 Characterization of convective heat transfer
- 2.4 Governing equations
- 2.5 Cure kinetics
- 3 Modeling approach
- 3.1 Finite volume method
- 3.2 Finite element method
- 3.3 Turbulence modeling
- 3.4 Thermo-physical property of composite material
- 3.5 Computational chain
- 3.6 Summary and Discussion
- 4 CFD simulation and validation
- 4.1 Numerical scheme
- 4.2 Mesh independency study
- 4.3 Model optimization
- 4.4 Validation
- 4.5 Conclusion
- 5 Quasi-transient coupling approach
- 5.1 Assumptions and limitations
- 5.2 Time scale analysis
- 5.3 Coupling scheme
- 5.4 Numerical algorithm
- 5.5 Validation and case study
- 5.6 Conclusion
- 6 Experimental and numerical study in composite manufacturing
- 6.1 Experiment
- 6.2 Simulation
- 6.3 Conclusion
- 7 Thermal optimization based on simulation and machine learning
- 7.1 Computational study
- 7.2 Machine learning-based thermal optimization
- 7.3 Results and discussion
- 7.4 Conclusion
- 8 Conclusions and further work
- 9 Appendix
- 9.1 Supplementary information on data and results
- 9.2 List of Abbreviations
- 9.3 List of Figures
- 9.4 List of Tables
- References
- Publications and supervised student theses