Data-driven Multiscale Damage and Failure Prediction

点击翻译按钮获取中文摘要

Damage and failure of materials is commonplace; the ability to predict damage and subsequent failure in engineered systems is foundational to design, and critically important when failures are expensive and even life-threatening. As manufacturing technologies become more advanced, particularly with the advent of additive manufacturing where nearly any shape or form can be made by local application of material and heat, so too must the methods used to predict the mechanical response of these components. The computational modeling framework in this research will enable a wider application of these advanced manufacturing technologies thorough a rigorous understanding of the material performance of parts made with these methods. An extensive experimental characterization and validation effort will form the basis of this computational framework. As such, this research will promote manufacturing sciences and knowledge for the fields where shape and form considerations outweigh production rate concerns, e.g., in biomedical and aerospace industries. The manufacturing advances enabled by this research will directly benefit the U.S. economy, advance national health, prosperity, and welfare, and secure national defense through technological innovations, e.g., through reduced aircraft fuel consumption from lighter additively manufactured parts. The intersection of domains required for this research, including: manufacturing, mechanical engineering, materials science, and computational sciences, will support interdisciplinary collaboration that can lead to crosscutting improvements in engineering education for the modern age. As part of this project, outreach to high school students will be performed to foster interest in engineering, undergraduate summer interns will be recruited to conduct state-of-the-art research, and specialized graduate student projects will be created related to advanced modeling and simulation.The anticipated outcome of the research is a predictive computational theory for damage and failure of complex, hierarchical materials such as metal alloys. The effort builds on data-driven, reduced order, and multiscale principles under the traditional framework of mechanics with the potential for a transformative new theory. Initially, fundamental characterization experiments (including x-ray tomography and diffraction) will be conducted to understand the relationship between material microstructures and mechanical properties in additively manufactured metals. This information will be used to calibrate micromechanical models, and simulations will be used to populate a database of synthetic microstructures and their mechanical response. From this, a new concurrent multiscale theory based on reduced-order methods will be developed, capable of capturing nonlinearity both in geometric and material response. This method will query the database constructed in the first phase for mechanical information and use that data to predict damage and failure, particularly for metals parts made with additive manufacturing.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.