Localized Keyhole Pore Prediction during Laser Powder Bed Fusion via Multimodal Process Monitoring and X-ray Radiography

• Published in: Additive manufacturing 2023-09, Vol.78 (N/A), p.103810, Article 103810
• Main Authors: Gorgannejad, Sanam, Martin, Aiden A., Nicolino, Jenny W., Strantza, Maria, Guss, Gabriel M., Khairallah, Saad, Forien, Jean-Baptiste, Thampy, Vivek, Liu, Sen, Quan, Peiyu, Tassone, Christopher J., Calta, Nicholas P.
• Format: Article
• Language: English
• Subjects: In situ monitoring; Keyhole pore identification; Laser powder bed fusion; MATERIALS SCIENCE; Sensor fusion; X-ray radiography
• ISSN: 2214-8604; 2214-7810
• DOI: 10.1016/j.addma.2023.103810

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naïve Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors. [Display omitted]