Conference Logo ProgramMaster Logo
Conference Tools for MS&T25: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract

Meeting MS&T25: Materials Science & Technology
Symposium Enhancing the Accessibility of Machine Learning-Enabled Experiments
Presentation Title High-throughput, Ultra-fast Laser Sintering of Ceramics and Machine-learning Based Prediction on Processing-Microstructure-Property Relationships
Author(s) Ningxuan Wen, Jianhua Tong, Rajendra K Bordia, Dongsheng Li, Hai Xiao, Fei Peng
On-Site Speaker (Planned) Fei Peng
Abstract Scope We report high-throughput, ultra-fast laser sintering of alumina micro-arrays and characterization of microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. These experimental data were used to train generative adversarial networks (GAN)-based machine-learning (ML) models. Accurate ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power, and (2) prediction of the expected microstructure from the desired hardness. A pre-trained CNN was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict the ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Scientific Discovery with Machine Learning: Data Analysis for Computational Beamlines
Adaptive Workflows for Lab of the Future
ATOMIC: Autonomous Characterization of 2D Materials Through Foundation Models
Autonomous Atomic Force Microscopy using Large Language Model Agents
DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns Using a Generative Pretrained Transformer
Foundational Workflows for Processing Legacy Data and Realizing Domain-Specific Multi-Modal AI Models
From automated to autonomous – creating a general active learning service for self-driving laboratories
High-throughput, Ultra-fast Laser Sintering of Ceramics and Machine-learning Based Prediction on Processing-Microstructure-Property Relationships
Hypothesis Formation and Predictive Modeling of 2D Perovskite Spacer Cations Using Retrieval Augmented LLMs and Deep Kernel Learning
Ptychography Data Pipelines at the Advanced Photon Source
Pycroscopy, AEcroscopy, and data workflows: integrating customized control, data analysis and workflows in an autonomous microscopy facility

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement