Machine learning and feature representation approaches to predict stress-strain curves of additively manufactured metamaterials with varying structure and process parameters

Lattice structures, one type of mechanical metamaterials, exhibit excellent mechanical properties such as a high strength-to-weight ratio and energy absorption capacity. However, predicting the stress-strain relationship of lattice structures remains a challenge because the mechanical behaviors of additively manufactured lattices are affected by both the structure parameters of a lattice and process conditions. This study proposes a machine learning (ML)-based predictive modeling framework to predict the entire stress-strain curves, compressive modulus, and energy absorption of lattice structures with varying structure and process parameters. Feature engineering techniques are used to transform raw data into new data representations compatible with ML models, including artificial neural networks, long short-term memory (LSTM), and convolutional neural networks (CNN). Two novel feature representations, including polygons and colormaps, are developed to transform structure and process parameters from tabular format into image format for CNN. The LSTM model that predicts stress-strain curves achieves a minimum mean absolute percentage error (MAPE) of 0.147. The CNN (colormap) model achieves a minimum MAPE of 0.174 when predicting compressive modulus. The ANN and LSTM models achieve a minimum MAPE of 0.068 when predicting energy absorption. The training time to build these ML models is less than 6 min.