In Situ Process Monitoring for Laser-Powder Bed Fusion using Convolutional Neural Networks and Infrared Tomography

Additive Manufacturing (AM) is a growing field for various industries of avionics, biomedical, automotive and manufacturing. The onset of Laser Powder Bed Fusion (LPBF) technologies for metal printing has shown exceptional growth in the past 15 years. Quality of parts for LPBF is a concern for the industry, as many parts produced are high risk, such as biomedical implants. To address these needs, a LPBF machine was designed with in-situ sensors to monitor the build process. Image processing and machine learning algorithms provide an efficient means to take bulk data and assess part quality, validating specific internal geometries and build defects. This research will analyze infrared (IR) images from a Selective Laser Melting (SLM) machine using a Computer Aided Design (CAD) designed part, featuring specific geometries (squares, circles, and triangles) of varying sizes (0.75–3.5 mm) on multiple layers for feature detection. Applying image processing to denoise, then Principal Component Analysis (PCA) for further denoising and applying Convolution Neural Networks (CNN) to identify the features and identifying a class which does not belong to a dataset, where a dataset are created from CAD images. Through this automated process, 300 geometric elements detected, classified, and validated against the build file through CNN. In addition, several build anomalies were detected and saved for end-user inspection.