Deep Learning and Big Data Framework for Real-Time Porosity Monitoring in Laser Powder Bed Fusion

Laser-based powder-bed fusion (LPBF) is an additive manufacturing process renowned for its ability to fabricate complex metallic components with intricate geometries. However, its broad industrial adoption is hindered by inherent defects, particularly porosity, which undermine the structural integrity of fabricated parts. Reducing porosity during fabrication through machine learning could significantly enhance the industrial viability of LPBF components, yet the scarcity of in situ data poses a major challenge for training models to detect pores in real time. This study proposes a novel Big Data framework that leverages ex situ porosity data to train machine learning and deep learning models for in situ pore detection and classification during LPBF. We investigate state-of-the-art models such as Support Vector Machines (SVR), eXtreme Gradient Boosting (XGBoost), LSTM and Transformer-based deep learning models. The framework includes a pipeline of data streaming, processing, and storage using Big Data tools. The results showed that transformers performed noticeably better than conventional models using a large dataset of LPBF parameters, including laser power, scan speed, construct direction, and laser density. Transformers outperformed XGBoost in the categorization of porosity types, with an accuracy of 82.0% and a precision of 79.0%. LSTM outperformed in EEF regression, showing a nearly flawless prediction ability with an RMSE of 0.24. These results highlight how Transformer models and LSTM can be used to optimize LPBF procedures, opening the door to workflows for additive manufacturing that are more effective and of greater quality.