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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
Presentation Title A Validation Framework for Microstructure-sensitive Fatigue Simulation Models
Author(s) Ali Riza Durmaz, Nikolai Arnaudov, Erik Natkowski, Petra Sonnweber-Ribic, Sebastian Münstermann, Chris Eberl, Peter Gumbsch
On-Site Speaker (Planned) Ali Riza Durmaz
Abstract Scope Fatigue crack initiation under very-high-cycle-fatigue (VHCF) conditions is highly susceptible to microstructural extrema. Therefore, VHCF life simulation depends on micromechanical crack initiation models. While corresponding computational models exist, their systematic validation is difficult. This is attributed to the lack of costly experimental data on the microstructure scale and the absence of validation methodologies. To this end, EN1.4003 ferritic steel mesoscale specimens were tested in a bending-resonant fatigue setup that allows sensitive damage detection. The experiment was mimicked in a sub-modeling simulation embedding the measured microstructure into the specimen geometry, on which experimental boundary conditions are applied. An elastic continuum simulation of the specimen geometry imposes load on the embedded microstructure, for which deformation is evaluated by phenomenological crystal plasticity FE. Simulated mechanical fields are compared with experimental semantically segmented damage locations from micrographs. This open-access framework enables user subroutine statistical validation and serves as a benchmark for future modeling approaches.
Proceedings Inclusion? Planned:
Keywords ICME, Modeling and Simulation, Iron and Steel

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Quest for Re-using 3D Materials Data
A Validation Framework for Microstructure-sensitive Fatigue Simulation Models
Added Value and Increased Organization: Capturing Experimental Data Provenance in Materials Commons 2.0
Challenges in Producing, Curating, and Sharing Large Multimodal, Multi-institutional Data Sets for Additive Manufacturing
Data-driven Model Based Comparison of Public Datasets for Online State of Charge Estimation in Lithium-ion Batteries
Filling Data Gaps in 3D Microstructure with Deep Learning
Generating, Sharing, and Using Halide Perovskite Exploratory Synthesis Data to Discover New Materials
Graph Convolutional Neural Networks for Fast, Accurate Prediction of Material Properties for Solid Solution High Entropy Alloys Using Open-source Datasets
Holistic Merging of Experimental and Computational Datasets – A Case Study for Diffusion Coefficients
Materials Innovation and Design Enabled by the Materials Project
Mg Database Project: Mapping Trends and Data Sets of Magnesium and Its Alloys for Improved Mechanical Performance
NOW ON-DEMAND ONLY - Hard Fought Lessons on Open Data and Code Sharing and the Terra Infirma of Ground Truth
The Status of ML Algorithms for Structure-property Relationships Using Matbench as a Test Protocol

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