Mechanical Behavior of Advanced Structural AlloysRichard W. Neu, Professor of Mechanical Engineering and Materials Science and Engineering

Research Group: Morris Satin (with Dr. Steven Johnson), Sanam Gorgannejad , Anirudh Srinivas Bhat, Jonathan Leung, Chuchu Zhang, Zach Towner (with Dr. Ashok Saxena and Dr. Chris Muhlstein), Alex Caputo

Temperature-Dependent Fretting Wear of AISI 310 Austenitic Stainless Steel• Develop wear law to describe coefficient of friction and wear

volumes loss change with temperature in 4 identified domains.• Identify coupling effect of frequency and temperature on friction and

wear and their influence on glaze layer formation.

Surface Crack Modeling in IN-718• Better prediction of life to mitigate catastrophic failure.• Develop computer based model to describe transition of surface to

through cracks.• Comparison of model predictions to experimental data.

Microstructure-sensitive Modeling of Rolling Contact Fatigue• Improve computational and experimental tools to support accelerated

design of bearing steels beyond traditional empirical techniques• Develop computational model that predicts the role of retained

austenite and strain-induced phase transformations during RCF• Develop Fatigue Indicating Parameters (FIPs) to predict crack

nucleation and propagation

Hertzian Contact Stresses Nanoindentation Experiments

High-Throughput (HT) Mechanical Property Characterization of Additive Manufactured (AM) metal alloys• To further accelerate investigating PSP relationships of AM materials

there is a need to use HT property characterization techniques.• Model developed based on nondimensional analysis, using FEA

simulations to extract uniaxial from indentation stress-strain curves.• HT high cycle fatigue fixture capable of testing 4 samples in bending

fatigue simultaneously has been developed.• Future work: fixture design for automated miniature tensile testing.

Creep and Creep-Fatigue Crack Growth Behavior in Ni-base Superalloys• Develop a unifying approach to extend time-dependent fracture

mechanics concepts to creep-brittle materials (i.e. Ni-base superalloys)

• Understand the nature of crack tip stress fields for growing cracks in the presence of small-scale creep and transient conditions

• Data-driven modeling provides verification and validation between experiment and FEA

Cree

p-Br

ittle

Crac

k Gr

owth

Creep strain development with time vs. crack growth

1mm

EDM Notch

Crack Growth

Machine Learning and Statistical Approaches for Development of Process-Structure-Property Linkages• Establishing the functional relationships of the PSP linkages is a

prominent effort in providing core knowledge for the design and manufacturing of advanced material systems that serve as a tool for performance prediction of the engineering components as well.

• Rigorous statistical microstructure quantification framework (n-point correlation functions) and machine learning algorithms (regression analysis, principal component analysis, tensor regression), are employed to predict the evolution of the γ/γ′ phaseattributes of Ni-base superalloys.

Glaze layer

Third body debris

Microstructure-sensitive Crystal Viscoplasticity for Ni-base Superalloys• Consider influence of microstructure evolution over long-term

exposure in gas turbine systems on the mechanical behavior.• Explore influence of microstructure on creep-fatigue interactions.

Digital Twin Material Model for Real Time Fatigue Assessment