ProgramMaster Logo
Conference Tools for MS&T21: 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&T21: Materials Science & Technology
Symposium Accelerating Materials Science with Big Data and Machine Learning
Presentation Title Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos
Author(s) Akanksh Shetty, Chunshan Hu, Mohammad Sadeq Saleh, Jack Beuth, Rahul Panat, Amir Farimani
On-Site Speaker (Planned) Akanksh Shetty
Abstract Scope Mechanical properties of cellular materials are important in fields such as lightweight-materials, bone-implants, and energy storage devices. Obtaining force-displacement curves from experiments, however, is costly, and time consuming. FEA is computationally intensive and theoretical models cannot fully capture various geometries of cellular materials. In this paper, metallic microlattices were fabricated by Aerosol Jet printing and subjected to uniaxial compression. High-resolution videos of the compression tests, along with measured force-displacement curves were used to train dataset for a Convolutional-Neural-Network-Long Short-Term-Memory-Network(CNN-LSTM) model. Force-displacement curve was predicted based on compression videos of untrained samples and was compared with experimental data. To further improve the performance, physics-based features were extracted from the videos and used for the training of LSTMs. Excellent prediction capability is demonstrated with average-Intersection-Over-Union score of >0.95 for train test split of 0.1. This study demonstrates that deep learning can be used to accurately predict the mechanical behavior of cellular materials.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Data-driven Simulator for High-throughput Prediction of Electromigration-mediated Damage in Polycrystalline Interconnects
Accelerating Discovery in Computational Materials Science Using CAMD
Bridging the Gap between Literature Data Extraction and Domain Specific Materials Informatics
Characterization of Microscopic Deformation of Materials Using Deep Learning Methods
Considerations for Interpretability, Reliability, And Data-efficiency in Machine Learning Properties of Solid-state Materials
Data Science as Bridge – Materials Characterization and Modeling
Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos
Designing Alloys with Process-mapping AI Pre-trained on Empirical Knowledge
Developing Physics-based Descriptors for Property Prediction in Oxide Glasses
Learning Synthesis: Engineering Metal Nanoclusters for Specific Material Properties
Machine Learning in 2D Materials: Benchmarking Crystal Graph Based Convolutional Neural Network (CGCNN) for Open Databases
Machine Learning to Predict Mechanical Properties of Steel Alloys Based on Chemical Composition and Heat Treatment Process
Materials Graph Ontology for Improving the Standardization and Utilization of Materials Data
Molecular Dynamics Simulation Using Lagrangian Neural Networks
Multi-target Prediction of Concrete Engineering Properties Based on a Single Deep Learning Model
P3-18: Rashba Spin Splitting and Photocatalytic Properties of GeC−MSSe (M=Mo, W) Van Der Waals Heterostructures
P3-19: Thermo-mechanical Property Prediction of High-temperature Materials Using a Python Based Interface With Quantum Espresso
Predicting Glass Behaviour from Optical Microscopy Images Using Interpretable Machine Learning
Scalable Gaussian Processes for Predicting the Optical, Physical, Thermal, and Mechanical Properties of Inorganic Glasses Using Compositions for Large Datasets
Searching for New Ferroelectric Materials Browsing a High-throughput Phonon Database
Semantic Segmentation of Plasma Transferred Arc Additively Manufactured NiBSi-WC Optical Microscopy Images Using a Convolutional Neural Network
Slip Band Characterization with Microtensile Testing Using Digital Image Processing
There is No Time for Science as Usual
Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression

Questions about ProgramMaster? Contact programming@programmaster.org