Conference Logo ProgramMaster Logo
Conference Tools for 2026 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 2026 TMS Annual Meeting & Exhibition
Symposium Algorithms Development in Materials Science and Engineering
Presentation Title Using Machine Learning to Overcome Image Clarity Derived Dendritic Characterisation Uncertainty within In-Situ, X-Ray Solidification Videos
Author(s) Jonathan Stewart Mullen, Laetitia Frost, Christos Koromilas, Wim Sillekens
On-Site Speaker (Planned) Jonathan Stewart Mullen
Abstract Scope One of the principal limiting factors in obtaining useful and timely measurements of observable dendrites within non-synchrotron, x-ray images is the issue of high image noise and low dynamic range. Given the nature of x-ray images, and the linkage between pixel intensity and dendrite position, conventional, pixel-value-altering solutions to this issue cannot be applied. While automated dendrite analysis modules have been developed, these can still struggle in instances of particularly challenging image characteristics. Thus, a new, complimentary approach is needed, one which is not solely dependent on input image clarity. By linking machine learning with dendritic arm modelling, a new solution has been developed in which higher clarity dendrites within the experimental images are used to inform the modelling of lower clarity instances. These models, oriented to match the observable experimental image as closely as possible, allow for dendritic characterisation to be successfully undertaken without a dependence on image clarity.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Modeling and Simulation, Characterization

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Comparative Study on Finite Volume and Analytical Methods for Melt Pool Predictions in Laser Powder Bed Fusion Process
A Coupled Thermal-Mechanical Deep Material Network
A Data-Driven Probabilistic Framework for Quantifying Uncertainty in Predicting the Yield Strength of Precipitate-Strengthened FCC Alloys
A Mechanistic and Data Hybrid Model for Predicting Si and Mn Contents at LF Refining Endpoint
A Multiphysics SPH Framework for Modeling Battery Electrode Drying
A Two-Step Physics-Informed Machine Learning Approach for NiTi-X Shape Memory Alloy Design
An Effort Towards the Standardization of Disorientations in Texture
An Optimization Method for Continuous Casting Start-Time Decision Considering Equipment Capacity and Metal Resource Characteristics
Analysis and Reduction of DFT Database for Rapid Artificial Neural Network Interatomic Potentials
Application of Alteration Analysis Coupled with Two-Dimensional Correlation Analysis to Multidimensional Gas Chromatography High-Resolution Mass Spectral Data
Atomistic Roughening of Micrometer-Long Dislocation Lines Under Multi-Physical Stimuli
BESFEM: A Multiphysics Simulation Toolkit for Complex Battery Electrode Microstructures
Computational Approaches for Estimating Chemical Potential Differences in Non-Dilute Random Alloys
Data-Driven Analysis of Atomistic Simulations in a Symmetry-Adapted Basis
Development of Semi-Empirical Interatomic Potential to Simulate Properties of Yttria-Strengthened Ni-Based Alloys
Discovering Continuum Scale Models from Atomistic Simulations
Fast Fatigue Lifetime Prediction for as Manufactured Single Crystal CMSX4-PLUS Specimens Versus Experimental Results
Graph-Theoretical Approach for Defect Detection in Polycrystalline Atomic Specimen
Graph Neural Network-Driven Multi-Objective Bayesian Optimization for Discovering Metal-Organic Frameworks with Optimal Separation Performance
H-10: A Framework for Foundation Models in Materials Sciences: Application to 3D Polycrystalline Materials
H-11: Data-Driven Visualization of Molybdenum Deposition in Hybrid Semiconductors Using Machine Learning
H-12: High-Throughput, High-Fidelity Characterization of Complex Alloys via Machine Learning Driven 4D-STEM
Impact of Secondary Phase Particles on Creep Behavior in Diffusion Bonded Microstructures
Integration of Simulation, Crowdsourcing, and Complexity Scoring for Ambiguous Feature Evaluations in Materials Imaging
Interpretable Data-Driven Modeling of Composition-Dependent Tensile Response of Multicomponent Alloys
Label-Aware Microstructure Generation for Manufacturing Processes Using Generative Networks
LithiumX: Inverse Property Selection and Direct Design of Energy Materials via Pseudodifferential Operators in a Cross-Disciplinary Digital Twin Framework
Machine Learning–Supported High Throughput Crystal Plasticity Simulations of Stress Concentrations in Void-Containing Molybdenum Microstructures
Making FAIR Data User-Friendly with Yabadaba
Merging Experimental and Computational Tests of the Split-Hopkinson Pressure Bar: Implementing an Ontology-Based GUI with FAIR Principles.
Modeling and Simulation of the Thermo-Mechanical Behavior of Polycrystalline Energetic Systems Under 3-D Loadings
Multiscale Simulation of Dislocation-Phonon Interaction and Peierls Stress in BCC Crystals
Optimization of Microstructures for Target Mechanical Performance under Manufacturing-Informed Property Closure Constraints
Performance and Accuracy Analysis of Peridynamics and Phase‑Field Fracture Models
Resolving Plasticity Near Cracks Through 3D Discrete Dislocation Dynamics Simulations
SAGA: A Simulated Annealing / Genetic Algorithm Approach to Atom Probe Tomography Rectification
Search for New Magnetic Material Candidates by First-Principles Calculation and Machine Learning
Surrogate Models of 3D Crystal Plasticity with Variable Microstructure and Loading Condition Using Recurrent Neural Networks
The Variational Deep Materials Network: Efficient Extrapolation with Uncertainty of Homogenized Material Responses
Thermodynamically-Guided Pseudo-Fluid Deposition Algorithm for Metal Additive Manufacturing: A Voxel-Based Framework Integrating Surface Energy Minimization and Mass Redistribution
Topological Quantification of 3D Polycrystalline Microstructures
Using Machine Learning to Overcome Image Clarity Derived Dendritic Characterisation Uncertainty within In-Situ, X-Ray Solidification Videos
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement