|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos
||Akanksh Shetty, Chunshan Hu, Mohammad Sadeq Saleh, Jack Beuth, Rahul Panat, Amir Farimani
|On-Site Speaker (Planned)
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.