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Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

Center Overview

The Center for Materials Data Science for Reliability and Degradation (MDS-Rely) seeks to better understand and improve the reliability and lifetime of essential materials using rigorous data science methods and analytics. Data science-informed approaches can revolutionize the development, manufacturing, and lifecycle applications of materials, parts, and products for U.S. infrastructures in energy, defense, and transportation. MDS-Rely has the potential to transform product reliability by creating technologies that enable unprecedented lifetimes and durability in extreme environments. MDS-Rely research will also deepen the understanding of active degradation and failure mechanisms and improve product design and manufacturing for long service lifetimes.

Universities

University of Pittsburgh
Case Western Reserve University

View Center Website

Center Personnel

Roger French
Center Director
+1 216 368 3655
roger.french@case.edu

Paul Leu
Site Director

pleu@pitt.edu

Liza Allison
Industry Liaison Officer

liza.allison@pitt.edu

Jonathan Steirer
Managing Director
+1 216 368 0374
ssi@pitt.edu

Research Focus

MDS-Rely’s research is grounded in three core competencies:

Materials data science
Reliability, performance, and degradation solutions
Standards and reliability study protocols

These competencies support the research areas:

Components, devices, and systems
Subtractive and additive manufacturing
Weathering and performance

These research areas allow MDS-Rely to focus on these three current value chains:

Metals and alloys
Polymers, elastomers, and coatings
Semiconductors and optoelectronics

MDS-Rely research involves statistical and machine learning, time-series modeling, spectral modeling, supervised and unsupervised machine learning of images, network graph modeling, and process modeling — and ultimately allows for the creation of transferrable codes and research pipelines.

Awards

2052662

IUCRC Phase I: University of Pittsburgh: Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

The Center for Materials Data Science for Reliability and Degradation (MDS-Rely) is a joint industry/university cooperative research center (IUCRC) between Case Western Reserve University (CWRU) and University of Pittsburgh (Pitt). Data science-informed approaches can revolutionize the development, manufacturing, and lifecycle applications of materials, parts, and products for U.S. infrastructures in energy, defense, and transportation. MDS-Rely will improve the capabilities of students, employees, and the broader workforce by bringing together research and innovation in materials-based value chains to take on technological and societal challenges. The goals of MDS-Rely include developing solutions to materials reliability, degradation, and lifetime performance problems, based on robust open source codes and datasets, coupled with improved study protocols and standards that can be effectively implemented. The team also seeks to create a cross-cutting community to address the societal and materials challenges arising from degradation and failure of materials, components, and systems and to unite industry, national labs, and academic researchers to address these challenges by developing materials data science solutions. Finally, the team seeks to develop educational programs for a diverse STEM workforce prepared for dynamic careers in the materials and data sciences and to take on research problems with industrial and commercial impact.

Through unique team research capabilities and infrastructure resources, MDS-Rely will develop reliability, performance, and degradation solutions, establish standards and reliability study protocols, and develop materials data science codes, packages and datasets to inform the materials value chains of polymers, elastomers, coatings, metals, alloys, semiconductors, and optoelectronics. This research will be achieved in three thrust areas: Weathering and Performance, Subtractive and Additive Manufacturing, and Components, Devices, and Systems. Projects include, but are not limited to: a) network models of mechanistic degradation pathways in materials and systems; b) machine learning on images to identify defects and the progress of degradation with exposure time; and c) identification of defects from tomographic images impacting the reliability of additively or traditionally manufactured parts. These new materials data science study protocols and analytics will enable MDS-Rely members to implement state of the art methods in their own research and manufacturing processes.

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. Read More >

2052776

IUCRC Phase I: Case Western Reserve University (CWRU): Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

2310663

IUCRC Planning Grant Carnegie Mellon University: Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

This project proposes to plan a new site in the Center on Data Science for Materials Reliability and Degradation (MDS-Rely) by garnering industry support and feedback on applying data science-informed research to understand reliability and lifetime of materials, and use this feedback to ensure the Center is responsive to industry needs. Reliability of materials, parts and products are essential for US infrastructure in energy, defense, and national health and welfare. MDS-Rely will recruit potential industry partners interested in conducting research with the Center to apply data science to materials development. The planning grant will then garner feedback from these industry partners on the following objectives of the Center: (1) Enhance reliability testing, (2) Develop degradation models, (3) Apply degradation models, and (4) Develop educational programs. Together these efforts will enhance national interests.

MDS-Rely will harness relationships between industry and academia to make headways in applying data science to applied materials development, design, and reliability. There will be three intellectual thrusts: (1) Soft and biomaterial formulation; (2) Natural language processing for data science of materials; and (3) Data-driven design of catalytic materials and processes. The soft and biomaterial formulation thrust will encompass machine-learning augmented design of experiments and automated science. The natural language processing thrust will focus on leveraging the existing literature more effectively in the area of materials reliability and degradation. Finally, the data-driven design of catalyst materials and processes will combine automated simulation with process systems engineering to discover materials and processes that are robust to degradation. The new site will leverage strengths at the intersections of computing, data science and machine learning with domain expertise in soft and biomaterials as well as catalysis at Carnegie Mellon University.

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. Read More >

Member Organizations

IUCRC affiliated member organizations are displayed as submitted by the Center. Non-federal organizations are not selected, approved, or otherwise endorsed by the U.S. National Science Foundation.

The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.