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Meeting MS&T23: Materials Science & Technology
Symposium Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title Process Cycle Modeling with AI
Author(s) Vyacheslav Romanov
On-Site Speaker (Planned) Vyacheslav Romanov
Abstract Scope Modeling the process‐structure‐property‐performance relationships hidden in materials data can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. Here a convoluted process model-filtering technique is presented that can build and successfully train the structural property artifacts of materials after multiple heat treatment cycles. The artifacts were pre-trained on Fe-based alloy data to filter out deep models that did not reflect the domain knowledge. The Graph based approach facilitated development of the microstructure evolution models with reduced overfitting to limited datasets.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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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
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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
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