Hard work, plastic flow: a data-centric approach to dislocation-based plasticity
This project aims to bridge the gap between individual and collective dislocation behavior in metals by utilizing data-driven analysis of dislocation trajectories to develop novel plasticity models.
Projectdetails
Introduction
Metals as structural materials are at the core of our society. Almost everything we physically interact with includes some form of metal manufactured to specific properties and formed into a desired shape. Consequently, the understanding and design of the balance between ductility and strength of metals are one of the primary disciplines of materials science.
Dislocation Fundamentals
On a fundamental level, this is the description of crystalline line defects called dislocations. At the atomic scale, the current understanding of dislocations is often on the level of individual dislocation properties. At the component scale, collective behavior is commonly formulated in continuum variables with the drawback of limited applicability over a wide range of possible scenarios.
Research Gap
Our current understanding still shows a gap in how individual dislocation properties translate into their collective behavior. To address this long-standing question, I propose a data-centric approach.
Methodology
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Dataset Creation: A comprehensive dataset of dislocation ensemble trajectories for various loading and initial conditions is created using discrete dislocation dynamics as well as molecular dynamics simulations and iteratively extended.
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Data Analysis: The trajectories are subsequently analyzed with tools borrowed from graph theory and time-series analysis to capture the network character of dislocation structures.
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Model Development: A novel class of plasticity models is developed: instead of human-derived state variables, I will `let the data speak for itself’ to bridge the gap between individual and collective dislocation behavior.
Challenges Addressed
The project solves two timely challenges in materials science:
- The described gap in understanding dislocation behavior.
- A demonstration of effective research data management of complex materials data, providing solutions to:
- Data generation
- Storage
- Accessibility
- Data fusion
- Reuse
- Analysis using the example of dislocation trajectories.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.498.839 |
Totale projectbegroting | € 1.498.839 |
Tijdlijn
Startdatum | 1-11-2024 |
Einddatum | 31-10-2029 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- RUHR-UNIVERSITAET BOCHUMpenvoerder
Land(en)
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TAILORPLAST aims to enhance understanding and prediction of plastic deformation in intermetallic phases to enable tailored properties and sustainable material design for advanced applications.
Solving the multi-scale problem in materials mechanics: a pathway to chemical design
Develop a groundbreaking computational framework to predict the viscoelastic and plastic behavior of complex materials across various deformation rates, overcoming current simulation limitations.
Deformation and Recrystallization Mechanisms in Metals
D-REX aims to enhance understanding of metal deformation and annealing by developing a high-resolution 3D X-ray diffraction microscope for real-time structural mapping in bulk metals.
Scaling limits of particle systems and microstructural disorder
This project aims to rigorously derive effective theories for many-particle systems by analyzing the impact of microstructural disorder on their dynamics, leading to new insights into complex behaviors.
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MECERDIS aims to enhance the functionality and toughness of advanced ceramics by using mechanics-guided design and external fields to manipulate dislocations for next-generation applications.