Stochastic Prepreg Platelet Molded Composites

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The prepreg platelet molded composite (PPMC) is a discontinuous fiber composite material form produced by molding of unidirectional fiber prepreg tape platelets. The PPMC material meso-structure allows molding of complex geometries to achieve enhanced specific strength and stiffness and thereby compete with performance of conventional structural metals at significant weight reduction. Large-scale heterogeneity of the stochastic meso-structure in a PPMC produces substantial uncertainty in macroscopic mechanical properties and a range of size effects and “atypical” macro-structural responses, wherein macro-structural geometry does not exclusively define the failure location. These characteristics are poorly understood in the absence of physics-based predictive capabilities and this creates significant obstacles to widespread adoption of PPMCs for structural applications. Insights from this project will lead to improved understanding of how the probabilistic and deterministic parameters of PPMC meso-structure translate into uncertainties in macroscopic performance of the composite and which parameters have the most impact. The research will promote the discovery of new, efficient PPMC material compositions and designs tailored to specific structural applications needed by the aerospace and automotive industries, thus advancing national prosperity. This research effort will train a diverse group of students in various aspects of composites engineering and contribute to the formation of the next generation of scientists that the U.S. composites industry critically needs to compete globally. In addition, this award will support inclusion of undergraduate students from underrepresented minority groups to serve a mission of broadening educational opportunities.The research program aims to study the fundamental failure mechanics of PPMC by developing high-fidelity computational models. The objective of this activity is to derive the material structure-property relationships associated with the interactions between the macro-structural geometry, stochastic meso-scale morphology, and platelet geometry. Probability distributions of effective mechanical properties and relevant failure modes are required inputs to deduce the structure-property relationships. The property distributions will be obtained via the statistically validated numerical simulations (“digital twins”) of standard mechanical tests for composites following the design of experiments principles. The methods that will be employed for virtual material testing are probabilistic modeling (Monte-Carlo approach) and physics-based progressive failure analysis; thereby, leveraging computational damage mechanics and finite-element methods. Statistical significance methods will be utilized to investigate the effect of the control parameters on the composite property distributions. The results of probabilistic modeling will be statistically validated by the experimental data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.