Towards deep-learning-based image enhancement for optical camera-based monitoring system of laser powder bed fusion process

Abstract

ABSTRACT With the increasing demand of quality assurance and reliability of additive manufacturing (AM), the demand of development of advanced in-situ monitoring systems is increased to monitor the process behavior. Optical-based camera monitoring systems are proved as the effective ways to observe part surface layer wise. For certain camera-based monitoring system, the coverage of the build platform and the resolution of the images are always a trade-off. In the low-resolution images, detailed features (e.g. scan vector) are often lost. Super resolution (SR) algorithms are often discussed in the literature, but there are no specific applications in AM area. In this paper, the authors present a U-Net-based super-resolution (SR) algorithm to enhance details of monitoring image of the optical camera for the LPBF process. A test setup was built in the laboratory to generate high-resolution images for training. To have precise original images for the validation, low-resolution images were downscaled and blurred from high-resolution images. SR results were evaluated by peak signal to noise ratio (PSNR) and plausibility of details. The SR algorithm shows the ability to reconstruct detailed features from low-resolution images for LPBF process.