Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling: Data-Driven Investigations of Fatigue
Sponsored by: TMS Structural Materials Division, TMS Materials Processing and Manufacturing Division, TMS: Additive Manufacturing Committee, TMS: Advanced Characterization, Testing, and Simulation Committee, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee, TMS: Mechanical Behavior of Materials Committee
Program Organizers: Garrett Pataky, Clemson University; Ashley Spear, University of Utah; Antonios Kontsos, Drexel University; Brian Wisner, Ohio University; J.C. Stinville, University of Illinois Urbana-Champaign
Thursday 2:00 PM
March 18, 2021
Room: RM 34
Location: TMS2021 Virtual
Session Chair: Ashley Spear, University of Utah
2:00 PM
Discovering the Structural Signature of Fatigue Crack Growth Rate Using Computer Vision and Machine Learning: Katelyn Jones1; William Musinski2; Adam Pilchak2; Reji John2; Paul Shade2; Anthony Rollett1; Elizabeth Holm1; 1Carnegie Mellon University; 2Air Force Research Laboratory
Machine Learning in materials science permits a deeper understanding of the relationship between microstructure and mechanical properties through efficient analysis of large amounts of data. Convolutional neural networks (CNNs) have been used to connect images of microstructure with processing history and properties such as fatigue life. This project uses CNNs on experimental images of fracture surfaces that have been augmented and/or segmented to predict features in crack growth such as direction, length, and rate. This study focuses on Ti-6Al-4V because of its wide usage in aerospace and medicine, abundance of data, favorable mechanical properties, and corrosion resistance. The resulting model aims to predict crack growth behavior. The application of CNNs in this instance, images used, and identified causes of crack rate transition will be presented.
2:20 PM
A Microstructural Model for Fatigue in NiTi Shape Memory Alloy Based on Information Fusion from Advanced Experiments and Simulation: Harshad Paranjape1; Darren Pagan2; Sivom Manchiraju3; Peter Anderson4; Craig Bonsignore1; Justin Gilbert1; Ich Ong1; Lot Vien1; 1Confluent Medical; 2Pennsylvania State University; 3Ansys, Inc.; 4The Ohio State University
Prediction of the fatigue lifetime of phase transforming structural materials such as NiTi shape memory alloys requires inputs that characterize the microstructure and the deformation due to cyclic loading. We fused information from multiple experiments and simulations — cycles-to-failure from fatigue experiments spanning the typical loading condition space, spatially-resolved phase transformation extent during fatigue loading from in-situ high-energy X-ray diffraction, local strains from microstructural finite element simulations and stereo digital image correlation measurements, non-metallic impurity inclusion size distribution from micro CT scans — to develop a microstructural model for fatigue in NiTi. The proposed microstructural model for fatigue life is based on the size distribution of impurity inclusions and the amplitude of phase transformation volume during a cycle. These factors control the amount of local damage that leads to fatigue crack initiation. We also present results that elucidate the mechanistic difference between high-cycle and low-cycle fatigue in shape memory alloys.
2:40 PM
In-situ Diffraction and Cohesive-zone Studies of the Fatigue-crack-growth Behavior in the ZK60 Mg Alloy: Di Xie1; Peter Liaw1; Yang Ren2; Yanfei Gao1; 1University of Tennessee; 2Argonne National Laboratory
Our limited knowledge of materials failure mechanisms places the ultimate restrictions on the technological viability of Mg alloys. The measurements of strain at the microstructural level are crucial to understanding fatigue crack behavior. In this work, we exploit the in-situ synchrotron X-ray diffraction to provide the high-resolution strain mapping near a fatigue tip in the ZK60 Mg alloy. Microscopic deformation mechanisms near the crack are identified. And an irreversible, hysteretic cohesive interface model is adopted to simulate a steady fatigue crack, which allows us to generate the stress/strain distribution and history near the fatigue crack tip. Coupled the Hill’s plasticity model with the in-situ diffraction technique, the plastic anisotropy on the retardation of fatigue crack growth and the elastic strain fields is investigated. This work, combining the micromechanical modeling with in situ diffraction studies, enhances the fundamental knowledge of Mg alloys by connecting the macroscopic behavior with microscopic mechanisms.