  In the era of Industry 4.0, Additive Manufacturing (AM), particularly metal
AM, has emerged as a significant contributor due to its innovative and
cost-effective approach to fabricate highly intricate geometries. Despite its
potential, this industry still lacks real-time capable process monitoring
algorithms. Recent advancements in this field suggest that Melt Pool (MP)
signatures during the fabrication process contain crucial information about
process dynamics and quality. To obtain this information, various sensory
approaches, such as high-speed cameras-based vision modules are employed for
online fabrication monitoring. However, many conventional in-depth analyses
still cannot process all the recorded data simultaneously. Although
conventional Image Processing (ImP) solutions provide a targeted tunable
approach, they pose a trade-off between convergence certainty and convergence
speed. As a result, conventional methods are not suitable for a dynamically
changing application like MP monitoring. Therefore, this article proposes the
implementation of a Tunable Deep Image Processing (TDIP) method to address the
data-rich monitoring needs in real-time. The proposed model is first trained to
replicate an ImP algorithm with tunable features and methodology. The TDIP
model is then further improved to account for MP geometries and fabrication
quality based on the vision input and process parameters. The TDIP model
achieved over 94% estimation accuracy with more than 96% R2 score for quality,
geometry, and MP signature estimation and isolation. The TDIP model can process
500 images per second, while conventional methods taking a few minutes per
image. This significant processing time reduction enables the integration of
vision-based monitoring in real-time for processes and quality estimation.
