基于生成扩散模型的深度学习光学图像超分辨率用于逐层原位LPBF监测
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring
AI总结：
本研究提出基于条件潜在扩散模型的深度学习超分辨率方法，将LPBF过程监测中的低成本低分辨率图像提升为高分辨率图像，并利用Segment Anything框架重建3D形态，实现经济高效的原位监测。
摘要：
激光粉末床熔融（L-PBF）过程中缺陷的随机形成对其在高精度应用中的采用产生了负面影响。光学监测技术可用于基于逐层成像识别缺陷，但由于成本和内存限制，这些方法难以扩展到高分辨率。因此，我们采用生成式深度学习模型，将构建板的低成本、低分辨率图像与构建板的详细高分辨率光学图像关联起来，从而实现经济高效的过程监测。为此，我们训练了一个条件潜在概率扩散模型，从低分辨率网络摄像头图像生成构建板的逼真高分辨率图像，恢复小尺度特征和表面粗糙度的分布。我们首先通过分析生成图像的峰值信噪比（PSNR）、结构相似性指数（SSIM）和描述高频信息保留情况的小波协方差指标来评估模型的性能。此外，我们设计了一个基于Segment Anything基础模型的框架，以重建打印部件的3D形态并分析重建样品的表面粗糙度。最后，我们通过创建合成低分辨率数据，探索了所实现框架对其他零件几何形状的零样本泛化能力。
英文摘要：
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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