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About this Abstract
Meeting	MS&T23: Materials Science & Technology
Symposium	Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title	Machine Learning-assisted Exploration of the Chemistry-processing Design Space Under Additive Manufacturing: Application to an FCC HEA Space
Author(s)	Raymundo Arroyave
On-Site Speaker (Planned)	Raymundo Arroyave
Abstract Scope	In this talk, we present recent work by our group in which we employ a synergistic combination of efficient physics-based models, exhaustive hand-curated databases, and state-of-the-art machine learning (ML) approaches to explore vast alloy spaces. Our focus is on High Entropy Alloys (HEAs), and we present a novel intrinsic metric for printability that we believe can be used as an alloy indicator when designing for performance and processability. We have verified our approach by comparing our predictions with available experimental data extracted from an in-house database containing hundreds of features for thousands of alloy-processing combinations. The framework has then been applied to estimate the printability of thousands of alloys within the 'Cantor' HEA system. Our work demonstrates that our methodology can significantly reduce the time and cost associated with the development of new HEAs for AM, while providing a unique metric for the intrinsic printability of an alloy.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accurate Prediction of Oxygen Vacancy Concentration with Disordered A-site Cations in High-entropy Perovskite Oxides

AI/ML Aided Drug Biomolecule and Materials Design

Autonomous Learning of Phase Trajectories via Physics-inspired Graph Neural Networks

B-1: Multi-objective Optimization for Improving Mechanical Properties of Aluminum Alloys: A Data Analytics Approach with Machine Learning and Genetic Algorithms

B-2: Simple Data Analytics Approach Coupled with Physics-based Model for Improved Prediction of Creep Rupture Life

Computing Grain Boundary "Phase" Diagrams: From Thermodynamic Models and Atomistic Simulations to Machine Learning

Development of Machine Learning Interatomic Potentials to Model Materials Processing & Performance in Multicomponent Systems

High-throughput, Ultra-fast Laser Fabrication of Alumina sample arrays and Deep-learning Based Prediction of Realistic Alumina Microstructure from Hardness

Machine Learning-assisted Exploration of the Chemistry-processing Design Space Under Additive Manufacturing: Application to an FCC HEA Space

Machine Learning-based Prediction of the Mechanical Properties of Microalloyed Steel Subjected to Thermomechanical Controlled Processing

Machine Learning for Phase Prediction of High-entropy Alloys Assisted by Imbalance Learning

Online Mechanical Properties Control for Steel Coils Using Machine Learning Model

Optical and Photothermal Property Prediction of Gold Nanoparticle/polymer Hybrid Films Through Machine Learning and Finite Element Modeling

Optimizing Heat Treatment Routes for Ni-based Alloys Using Monte Carlo Tree Search

Physics-informed Machine Learning for Crystal Plasticity Model Calibration of Ti-7Al Alloy

Prediction of the Mechanical Response of Zirconia-reinforced Metal-matrix Composite Using Deep Learning Approaches

Process Cycle Modeling with AI

Representation, Regeneration and Prediction of Microstructure in Additive Friction Stirring via Deep Regeneration Neural Network

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