Investigations into nondestructive evaluation and surface metrology of 3D printed parts using optical techniques - Rukundo Simeon (Nanyang Technological University, 2024)
Surface metrology of 3D printed parts is a powerful exploration tool, improving process knowledge to develop improved additive manufacturing processes for producing specification-compliant parts. It is the science that focuses on micro-scale surface features and their measurement. The quality of 3D printed parts mainly depends on the 3D printing technology employed, the printer quality, and the build material. In this research, the surface roughness is characterized using non-contact optical methods and compared with a conventional stylus. Compared to the conventional stylus method, optical techniques are more advantageous owing to their non-destructive nature and accurate characterization. One of the optical methods used is a commercial optical profiler that uses a confocal technique to acquire a three-dimensional (3D) map of the surface. The research will also investigate surface material characteristics of 3D printed parts. This research also deals with the optimization of speckle-based Structured Illumination Microscope, a technique traditionally challenged by its speed in high-throughput environments. By employing various pattern projections, such as concentric rings, grids, and checkerboards, this research significantly provided improved contrast images that can further be used for roughness quantification. This optimization not only reduces the imaging and processing times but also improves the contrast and detail resolution of captured images, thereby accelerating the evaluation process without compromising the depth of surface analysis. Furthermore, the research explores the application of spectral imaging to assess surface contamination and post-processing effects, providing a comprehensive understanding of their impact on surface quality. Current methods in 3D printing inspection rely on RGB cameras, lacking sensitivity to detect defects such as corrosion or oxidation at early stages. In this context, this research proposes and demonstrates the use of spectral imaging integrated with SAM algorithm which can serve as a solution for automated detection of corrosion with high sensitivity. This research investigates surface measurement approaches in 3D printing and sets an example for future studies aiming to improve additive manufacturing. It highlights the importance of advanced non-destructive surface analysis techniques for ensuring product quality in 3D printed samples.