Planning IUCRC at Case Western Reserve University: Center on Data Science for Materials Reliability and Degradation (MDS-Rely)
Planning IUCRC at Case Western Reserve University: Center on Data Science for Materials Reliability and Degradation (MDS-Rely)
批准号:
1841426
负责人:
Roger French
金额:
$1.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-12-01 至 2019-11-30
中文摘要
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英文摘要
Case Western Reserve University (CWRU) and the University of Pittsburgh (Pitt) are planning to form a new IUCRC named the Center on Materials Data Science for Reliability and Degradation (MDS-Rely). The primary goal of MDS-Rely is to apply data science-informed research to better understand the reliability and lifetime of essential materials. This IUCRC builds on established centers at both locations focusing on Energy Materials, Advanced and Additive Manufacturing, and in Data Science and Analytics. Recent advances in materials data science have the potential to transform this field in creating new technologies that enable unprecedented lifetimes, durability in extreme environments, and understanding of various degradation and failure mechanisms. Our established relationships with industry and our multidisciplinary capabilities in protocols, modeling, and applications are key to taking on this grand challenge of long-lived technologies. MDS-Rely will engage a diverse cohort of students, and develop broad data science capabilities to respond to employers' needs. MDS-Rely will harness relationships between industry and academia to make headways in applying data science to materials development, design, and reliability. MDS-Rely will include three Thrusts: (1) Enhanced Reliability Study Protocols, (2) Modeling & Service Life Prediction, and (3) Reliability and Degradation Applications. Thrust 1 will focus on designing data informed experimental studies of materials and developing a data science-based experimental design with both non-destructive and destructive evaluations to monitor material degradation. Thrust 2 will focus on degradation and reliability modeling of materials and will encompass graph-based network modeling, machine learning, image analysis, and time-series analysis. Data analytic pipelines will be created to inform future analysis. These models will give insight back to the design of data informed experimental studies. Finally, Thrust 3 will inform materials design choices for different stress conditions by understanding the main contributors of degradation in a material system. This will also allow for much more rapid material selection in the material choice portion of a system design. The workshop meetings further define specific projects to direct respond to industry needs.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.
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