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Chemical shrinkage in thermosetting polymers drives residual stress development and induces residual deformation in composite materials. Accurate characterization of chemical shrinkage during curing is therefore vital to minimize residual stresses through process modeling and optimize composite performance. This work introduces a novel methodology to measure the pre- and post-gelation chemical shrinkage of an epoxy resin using three-dimensional digital image correlation (3D-DIC). Differential scanning calorimetry (DSC) is employed to calculate reaction kinetics and correlate chemical shrinkage with the degree of cure. Rheology experiments are conducted to quantify gelation and validate post-gelation. 3D-DIC post-gelation results show excellent agreement with rheology. Pre-gelation results show the effect of the in-situ curing in the proximity of constraints on the global strain behavior. This work introduced an innovative approach to characterize the chemical shrinkage of thermosets during curing, which will enable accurate residual stress prediction for enhancing thermoset composite performance and provide insight into the in-situ polymer behavior during processing.
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Design and Optimization of Manufacturing Process of Polymer Composites Through Multiscale Cure Analysis and NSGA‑II
Thermoset polymer composite structures are heavily used in the aerospace, defense, transport, and energy sectors due to their lightweight and high-performance behavior. Thermoset polymer resins require external heat for manufacturing/curing. The behavior of these polymer composite materials is highly dependent on curing process as it affects evolution of material properties as well as residual stresses and deformation. Various cure process parameters, mainly related to cure thermal cycle, need to be optimized to get the desired properties of these structures. In this paper, the polymer cure process is explicitly modeled through finite element method. Its effects at the structural level are captured by modeling thermo-chemical-mechanical analysis through multiple length scales. The multi-scale analysis is carried out by surrogate models to reduce run time. In this study, non-dominated sorting genetic algorithm II is used for multi-objective cure process optimization. The objectives are to minimize the spring-in angle and minimize the process time with achieving degree of cure above given requirement. Insights from such optimization can be utilized by product designers as well as manufacturers to take timely decisions to improve the performance of these composite structures.
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- Award ID(s):
- 2316628
- PAR ID:
- 10523537
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Integrating materials and manufacturing innovation
- ISSN:
- 2193-9772
- Subject(s) / Keyword(s):
- Thermoset polymer composites · Cure kinetics · Multi-scale method · Finite element analysis · Genetic algorithm · NSGA-II
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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