AM-DefectNet: Additive Manufacturing Defect Classification Using Machine Learning -- A comparative Study

Abstract

Additive Manufacturing (AM) processes present challenges in monitoring and controlling material properties and process parameters, affecting production quality and defect detection. Machine Learning (ML) techniques offer a promising solution for addressing these challenges. In this study, we introduce a comprehensive framework, AM-DefectNet, for benchmarking ML models in melt pool characterization, a critical aspect of AM. We evaluate 15 ML models across 10 metrics using 1514 training and 505 test datasets. Our benchmarking reveals that non-linear tree-based algorithms, particularly CatBoost, LGBM, and XGBoost, outperform other models, achieving accuracies of 92.47%, 91.08%, and 90.89%, respectively. Notably, the Deep Neural Network (DNN) also demonstrates competitive performance with an accuracy of 88.55%. CatBoost emerges as the top-performing algorithm, exhibiting superior performance in precision, recall, F1-score, and overall accuracy for defect classification tasks. Learning curves provide insights into model performance and data requirements, indicating potential areas for improvement. Our study highlights the effectiveness of ML models in melt pool characterization and defect detection, laying the groundwork for process optimization in AM.