Integrating Machine Learning with Multimodal Monitoring System Utilizing Acoustic and Vision Sensing to Evaluate Geometric Variations in Laser Directed Energy Deposition

Laser directed energy deposition (DED) additive manufacturing faces significant challenges in maintaining consistent part quality due to complex melt pool dynamics and process variations. While extensive research has focused on part defect detection, limited work has systematically validated the capability of utilizing process monitoring systems to evaluate melt pool dynamics, and process quality. This study presents a novel multimodal monitoring framework that synergistically integrates contact-based acoustic emission (AE) sensing with vision-based coaxial camera monitoring to enable layer-wise identification and evaluation of geometric variations in DED fabricated parts. The experimental study employs three distinct part configurations: a baseline part without any holes, the part with 3mm diameter through-hole, and the part with 5mm diameter through-hole. These specimens are served as test cases to evaluate the monitoring system's discerning capabilities. Raw sensor data undergoes preprocessing, where acoustic signals are subjected to filtering and feature extraction using time-domain and frequency-domain analysis, while coaxial camera data undergoes melt pool segmentation followed by morphological feature extraction. Multiple machine learning algorithms including support vector machines (SVM), neural networks, random forest, gradient boosting, XGBoost, and logistic regression are evaluated to identify optimal models for layer-wise geometric variations classification. The integrated multimodal strategy demonstrates superior classification performance of 94.4% as compared to individual sensing modalities of 87.8% (AE only) and 86.7% (Camera only). Experimental validation reveals that the integrated multimodal monitoring system effectively captures both structural vibration signatures and surface morphological changes associated with studied geometric variations. While this study focuses on specified part geometric variations, the demonstrated capability to discriminate between different geometric features establishes the technical foundation for future applications in characterizing any part variations such as geometric accuracies and manufacturing-induced defects.