Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling: Data-Driven Investigations of Fatigue
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS Structural Materials 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: Jean-Charles Stinville, University of Illinois Urbana-Champaign; Garrett Pataky, Clemson University; Ashley Spear, University of Utah; Antonios Kontsos, Drexel University; Brian Wisner, Ohio University; Orion Kafka, National Institute of Standards and Technology

Tuesday 2:30 PM
March 1, 2022
Room: 254B
Location: Anaheim Convention Center

Session Chair: Ashley Spear, University of Utah; Orion L. Kafka, National Institute of Standards and Technology

2:30 PM  
Combining Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN: Katelyn Jones1; Elizabeth Holm1; Anthony Rollett1; 1Carnegie Mellon University
    Machine learning and computer vision techniques can be used in materials science to improve and facilitate the analysis of microstructural data and images. Additionally, it can work on large amounts of data and diverse images when enough training data is provided. Convolutional neural networks (CNNs) are a key tool in making connections between fracture images, microstructure, and fatigue characteristics such as stress intensity factor, crack length, and load values. This project collects data from Ti-6Al-4V fracture surfaces in the form of BSE and SEM images, and height data from Scanning White Light Interferometry, and combines them to train a CNN and identify high stress points, crack initiation sites, and predict values such as stress intensity factor and plastic zone size. The images used to develop this model, creation of the CNNs, identified fatigue properties, and fracture characteristics will be presented.

2:50 PM  
Process-microstructure-behavior Relationships for Fatigue of Additively Manufactured Al Alloys : Emine Tekerek1; Vignesh Perumal1; Alex Riensche2; Prahalada Rao2; Lars Jacquemetton3; Darren Beckett3; Harold Halliday4; Joseph T. Mckeown5; Antonios Kontsos1; 1Drexel University; 2University of Nebraska-Lincoln; 3Sigma Labs, Inc.; 4Navajo Technical University; 5Lawrence Livermore National Laboratory
    Thermal histories associated with extreme heating/cooling rates and temperature gradients in laser powder bed fusion (LPBF) cause complex, process-dependent, material phase changes and a variety of microstructural defects. In this context, the effects of non-equilibrium metallurgy near the meltpool region of the “as-printed” microstructures must be investigated to improve quality control and connect process parameter selection with material behavior. To achieve this goal, direct meltpool observations are first made on an aluminum alloy leveraging a Dynamic Transmission Electron Microscope setup capable of observing the solidification process. Then, thermomechanical process simulations are used to define print geometries optimized for thermal effects. Subsequently, in-situ meltpool measurements are performed during printing using a commercially available co-axial monitoring system. Such measurements are then correlated post-mortem with microscopy and X-ray micro-computed tomography data, to identify defects. Based on this pre-test analysis, the fatigue behavior is then investigated using mechanical testing inside the Scanning Electron Microscope.

3:10 PM  
Revealing the Critical Role of Volumetric Defects in the Fatigue Behavior of LB-PBF Ti-6Al-4V: Muztahid Muhammad1; Arun Poudel1; Salman Yasin1; Shuai Shao1; Nima Shamsaei1; 1Auburn University
    Fatigue failure of laser beam powder bed fused (LB-PBF) Ti-6Al-4V parts in machined surface condition can almost always be traced back to one or a group of volumetric defects, including gas-entrapped pores, key holes, and lack-of-fusion defects. Their size, shape, and location can influence the initiation of fatigue cracks, which occupies a major portion of fatigue lives in the high cycle fatigue regime. Building upon a fracture mechanics-based defect sensitive fatigue model, this work aimed to correlate the defect characteristics with the fatigue performance of LB-PBF Grade 5 Ti-6Al-4V specimens. Prior to tests, extensive non-destructive evaluation via X-ray computed tomography were performed to first characterize various types of defects via several geometrical metrics. The metrics of the fatigue critical defects measured from the fracture surfaces post-test, together with their location information, were then correlated with the fatigue performance of Ti-6Al-4V specimens via a data-analytics approach.

3:30 PM  
Correlation of Microstructure and Damage Tolerant Properties of Additive Manufactured Ti-6Al-4V: Ralph Bush1; Tessa Barbosa1; Elijah Palm1; Benjamin Smith1; 1US Air Force
    Microstructure, tensile properties, fracture toughness and fatigue crack growth properties of additive manufactured (AM) Ti-6Al-4V, fabricated via powder bed fusion were measured and compared to those of ingot fabricated mill (MA) and beta annealed (BA) products. The MA product microstructure was bimodal in nature, whereas the AM and BA product microstructures exhibited a fine lamellar structure. The grain/packet size of the AM product was 20 to 30 times smaller than for the BA product. Mechanical property / microstructure correlations reported in historical literature were observed. The coarse-grained lamellar BA product exhibited damage tolerant properties superior to those of the fine-grained AM product, which were superior to those of the bi-modal MA product. The MA material exhibited tensile properties superior to those of the AM product, which were superior to those of the BA material. Post-beta annealing of MA and AM products altered microstructures and improved damage tolerant properties.