ProgramMaster Logo
Conference Tools for 2023 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Accelerated Discovery and Insertion of Next Generation Structural Materials
Presentation Title High-throughput Prediction of Fracture and Brittle to Ductile Transition in Tungsten using Variable Temperature Nanoindentation
Author(s) Kevin Schmalbach, Radhika Laxminarayana, Douglas Stauffer, William Gerberich, Nathan Mara
On-Site Speaker (Planned) Kevin Schmalbach
Abstract Scope Methods to predict material fracture frequently rely on large experimental datasets tuned to the properties of one material or are based on computationally expensive modeling. Development of analytical models with easily measured physically meaningful parameters are key to alleviating bottlenecks in new materials development. Here, I describe the use of nanoindentation strain rate jump tests, applied at low temperature (-100 C) and high temperature (50-300 C), to measure the effective stress and activation volume as a function of temperature. These activation parameters, in combination with an analytical model for the strain energy release rate, accurately predict the brittle-ductile transition temperature along particular fracture systems in single crystal tungsten. Activation parameters measured from both nanoindentation and bulk tension of single crystal tungsten accurately predict the fracture toughness and brittle-ductile transition in macroscale tungsten single crystals.
Proceedings Inclusion? Planned:
Keywords Mechanical Properties, High-Temperature Materials,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Design Space for Tunable Ceramic-polymer Composites
A Diffusion Couple Approach to β-Ti Alloy Development: Evaluating the Oxidation Performance of Ti-Fe-X+ Alloys
A High-throughput Setup for Materials Exposure to Simultaneous Irradiation-corrosion Conditions
Accelerated Discovery of Novel Titanium Alloys using High-throughput Manufacturing, Characterization and Testing
Accelerating Multimodal Data Collection: A Workflow for Metallic Films
AI and Machine Learning Tools for Development and Analysis of Image Driven 2D Materials
Combinatorial Mechanical Microscopy via Correlated Nanoindentation and EDX Mapping
Computational Design of an Ultra-strong High-entropy Alloy
Computational Design of High Entropy Alloy Hardmetals
Design of a Compact Morphology Cobalt-based Superalloy for Additive Manufacturing
Efficient Conductivity and Hardness Optimization in Cu-Ag-Ni Alloys using Bayesian Active Learning
High-throughput Electric-Field-assisted Sintering and Characterization Techniques for Materials Discovery
High-throughput Prediction of Fracture and Brittle to Ductile Transition in Tungsten using Variable Temperature Nanoindentation
High-throughput Synthesis and Mechanical Characterization of Sputtered Metallic Alloys
How Should You Select an Algorithm for a Materials Discovery Campaign with Multiple Objectives, Complex and High-dimensional Structure-processing-property Relationships, and a Small Adaptive Design Budget?
Machine Learning-assisted Discovery of Novel High Temperature Ni-rich NiTiHfZr Multi-component Shape Memory Alloys
Rapid Characterisation of Active Slip Systems in Titanium Ordered-bcc Compounds using an Algorithm for Automated Indentation Slip Trace Analysis.
Using Machine Intuitive Learning to Predict Advanced Steel Properties

Questions about ProgramMaster? Contact programming@programmaster.org