Integrated Computational and Experimental Design of High Performance Metal Matrix Composites
Abstract
The development of high performance Metal Matrix Composites (MMCs) requires careful microstructure design which can improve fracture toughness while maintaining high strength. Although a great deal of research has focused on the effect of microstructural properties on fracture toughness and strength of MMCs, the relations being established are mostly qualitative. The interplay between plastic deformation and the fracture mechanism being activated during the failure process has not been systematically studied and much less quantified. The objective of the proposed research is to elucidate the competition between the deformation and failure mechanisms in MMCs. To this end, an integrated computational and experimental framework is proposed to quantify the energy allocation within plastic deformation, heat generation, crack surface formation and inertia energy dissipation by considering the microstructure attributes and loading conditions. The conclusions will provide insight on how to tailor the microstructure so that the energy can be allocated in a way which can best enhance material strength and fracture toughness. Based on the conclusions, a probabilistic model which allows the scatter of fracture toughness to be predicted. Weibull distribution parameters will be correlated with the statistical measures of microstructure characteristics and the statistical characterization of the competitions between deformation and failure mechanisms. The parameters employed in the computational modeling will be calibrated through Digital Image Correlation (DIC) analysis. If successful, the proposed study will push the limit of the fracture toughness of MMCs through microstructure tailoring and material processing. The quantitative relations to be established will serve as the roadmap for fabricating high performance MMCs in aerospace, automotive and defense applications. The implementation of microstructure-based simulation and DIC analysis will significantly increase the education and research opportunities for students at California State University, Long Beach and surrounding high schools. As a Hispanic-Serving Institution, the research activity will allow more underrepresented minorities and women to have exposure to interdisciplinary research, education symposiums, and outreach activities. The developed tool which implements complex algorithms into design decisions will be useful for educating people with a broad, industrially relevant perspective on engineering research and practice.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 14, 2019
- Source ID
- W911NF1610541
Entities
People
- Yan Li
Organizations
- Army Contracting Command
- California State University
- United States Army