Prediction of material toughness using ensemble learning and data augmentation

The present work investigates the impact resistance of metallic parts produced using Laser Powder Bed Fusion and the possibility of its prediction using machine learning algorithms. The challenge lies in finding optimal process parameters before printing based on the existing data. Economic constraints often result in the availability of only a limited amount of data for predictive purposes. In this work, around one hundred data points from Charpy impact tests on AlSi10Mg0.5 were used to analyse the correlation between the impact resistance and process parameters, including information about sample porosity. The present research implements a data augmentation technique that artificially increases the volume of training data by applying domain-specific transformations to the original limited dataset. Using this technique, the dataset had been extended to over one thousand data points. To identify the most suitable approach for the specific issue at hand, several algorithms were explored: Regression Neural Network, K-Nearest Neighbours, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, as well as ensemble combinations of Random Forest with AdaBoost, Gradient Boosting, and XGBoost algorithms. The results suggest that the Random Forest and the boosting algorithms generalise best given the sparse testing data. The best-performing models yield a prediction fitness reaching 86 percent. Therefore, an effective model for predicting the impact resistance had been developed and can be used to optimise the quality of additively manufactured parts.