Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring

• URL: http://arxiv.org/abs/2409.13171v1
• Date: Fri, 20 Sep 2024 02:59:25 GMT
• Title: Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring
• Authors: Francis Ogoke, Sumesh Kalambettu Suresh, Jesse Adamczyk, Dan Bolintineanu, Anthony Garland, Michael Heiden, Amir Barati Farimani,
• Abstract summary: We implement generative deep learning models to link low-cost, low-resolution images of the build plate to detailed high-resolution optical images of the build plate. A conditional latent probabilistic diffusion model is trained to produce realistic high-resolution images of the build plate from low-resolution webcam images. We also design a framework to recreate the 3D morphology of the printed part and analyze the surface roughness of the reconstructed samples.
• Score: 4.667646675144656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The stochastic formation of defects during Laser Powder Bed Fusion (L-PBF) negatively impacts its adoption for high-precision use cases. Optical monitoring techniques can be used to identify defects based on layer-wise imaging, but these methods are difficult to scale to high resolutions due to cost and memory constraints. Therefore, we implement generative deep learning models to link low-cost, low-resolution images of the build plate to detailed high-resolution optical images of the build plate, enabling cost-efficient process monitoring. To do so, a conditional latent probabilistic diffusion model is trained to produce realistic high-resolution images of the build plate from low-resolution webcam images, recovering the distribution of small-scale features and surface roughness. We first evaluate the performance of the model by analyzing the reconstruction quality of the generated images using peak-signal-to-noise-ratio (PSNR), structural similarity index measure (SSIM) and wavelet covariance metrics that describe the preservation of high-frequency information. Additionally, we design a framework based upon the Segment Anything foundation model to recreate the 3D morphology of the printed part and analyze the surface roughness of the reconstructed samples. Finally, we explore the zero-shot generalization capabilities of the implemented framework to other part geometries by creating synthetic low-resolution data.

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