Abstract

Global competition is pushing the composites industry to advance and become more cost effective. Liquid Composite Molding or LCM is a family of processes that has shown significant promise in its potential to reduce process times and cost while maintaining high levels of part quality. However, the majority of research and information on composite processes have been related to prepreg-autoclave processing which is significantly different than LCM. In order for LCM processes to gain large scale implementation, significant research is required in order to model and simulate the unique nature of the resin infusion process. The purpose of this research is to aid in the development of in situ void measurement and characterization during LCM processing, particularly for carbon fiber composites. This will allow for the gathering of important empirical data for the validation of models and simulations that aid in the understanding of void formation and movement during LCM. For such data to be useful, it needs to include details on the formation, mobility and evolution of the void over time during infusion. This was accomplished by creating a methodology that allowed for in situ images of voids to be captured during the infusion process. A clear mold was used to visually monitor infusions during RTM with UV dye and lighting to enhance contrast. Consecutive images were acquired through the use of macro lens photography. This method proved capable of yielding high quality images of a variety of in situ voids during infusions with carbon fiber composites. This is believed to be the first instance where this was accomplished. A second methodology was then developed for the analysis of the collected images. This was done by using ImageJ software to analyze and process the acquired images in order to identify and characterize the voids. Success was found in quantifying the size and circularity of a wide range of micro and macrovoids in both a satin weave and double bias NCF woven fabrics. To facilitate the burden of collecting large amounts of data, this process was made to be automated. A user generated macro script could be applied to large sets of images for rapid processing and analysis. This automated method was then evaluated against manually processed images to determine its overall effectiveness and accuracy as tool for validating void theory.

Degree

College and Department

Ira A. Fulton College of Engineering and Technology; Technology

Rights

http://lib.byu.edu/about/copyright/