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Process Model and Sensor Based Optimization of Polyimide Prepreg Compaction During Composite Cure

Magato, James
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1533144776251201

Abstract Details

2018, Doctor of Philosophy (Ph.D.), University of Dayton, Materials Engineering.
PMR-type polyimide prepregs are challenging to fabricate into high quality composites due to volatiles that are generated and must be removed in-situ during processing. Despite several decades of academic and industrial study, the core challenge of effective and repeatable volatile removal has still plagued manufacturers of polyimide composite structures. A method for the in-situ characterization and modeling of polyimide compaction during composite fabrication would greatly help in understanding and controlling the process. To this end, the current work was conducted to develop a polyimide prepreg compaction model, as well as practical characterization techniques for the resin rheology, volatile generation, and subsequent volatile removal from the prepreg stack during composite fabrication. Two PMR-type polyimide / carbon fiber prepreg systems were studied: one containing a simplified / model resin system, and one containing the commercially available RM-1100 resin system. Thermal analysis was used to characterize volatile generation, reaction rates, and rheology for each prepreg system. A novel approach was used to measure the thickness of a prepreg stack in-situ during vacuum bag / oven processing using a high-temperature LVDT. Neural networks were then used to model the volatile generation, rheology, and composite compaction. These tools were then combined in a global model that showed the key interrelationships in these coupled phenomena and how that information can be used to select the optimum temperature of pressure application to minimize void content. Two autoclave trials were conducted for RM-1100 which demonstrated the ability to reduce void content from 9.5% to 3.1% by using the information and criteria developed by this methodology. Overall the results showed that the methods developed in this study were able to accurately measure polyimide prepreg thickness in-situ during the imidization phase of the cure and were effective in cure cycle optimization. This model based approach is intended to greatly reduce experimental trial-and-error in cure cycle development for PMR type polyimide composites.
Donald Klosterman (Advisor)
127 p.
Materials Science
polyimide; prepreg; autoclave; process modeling;

Recommended Citations

Citations

  • Magato, J. (2018). Process Model and Sensor Based Optimization of Polyimide Prepreg Compaction During Composite Cure [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1533144776251201

    APA Style (7th edition)

  • Magato, James. Process Model and Sensor Based Optimization of Polyimide Prepreg Compaction During Composite Cure. 2018. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1533144776251201.

    MLA Style (8th edition)

  • Magato, James. "Process Model and Sensor Based Optimization of Polyimide Prepreg Compaction During Composite Cure." Doctoral dissertation, University of Dayton, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1533144776251201

    Chicago Manual of Style (17th edition)

dayton1533144776251201
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© 2018, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.