A rapid virtual autoclave for carbon fiber reinforced plastics

Book excerpt: Structural carbon fiber reinforced plastic parts are usually manufactured through autoclave processing for high-performance aerospace applications. Today’s 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. 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.