Capability determination of in-process monitoring for laser powder bed fusion

Abstract

This thesis investigates the capability of optical tomography (OT) as an in-situ process monitoring tool for detecting spatter-induced defects in components produced via Laser Powder Bed Fusion (LPBF). The study evaluates whether OT imaging, combined with custom image processing techniques, can reliably identify internal porosities typically verified through high-resolution X-ray Computed Tomography (XCT). Two Inconel 718 demonstrator parts were manufactured using an EOS M290 LPBF machine, with process parameters deliberately adjusted to promote defect formation. OT images captured during the build were processed using a custom MATLAB script that applied Laplacian of Gaussian (LoG) and erosion filtering to identify thermal anomalies. These anomalies were compiled into point cloud datasets and digitally compared with XCT data of the segmented demonstrator. The results revealed limited correlation between OT-based indications and actual porosities detected through XCT, with alignment errors, image noise, and the resolution of OT data identified as key limitations. Despite this, the study highlights spatial patterns in OT data that may still be indicative of defect-prone regions, suggesting potential for further development. The findings underscore the need for enhanced filtering techniques, improved data alignment, and the integration of additional OT data types to improve detection accuracy. This work contributes to the ongoing development of in-situ monitoring systems for LPBF, particularly in critical applications like aerospace, where early defect detection is vital for part certification and safety assurance.