TDIP: Tunable Deep Image Processing, a Real Time Melt Pool Monitoring Solution
Javid Akhavan, Youmna Mahmoud, Ke Xu, Jiaqi Lyu, Souran Manoochehri

TL;DR
This paper introduces TDIP, a tunable deep image processing method that enables real-time melt pool monitoring in metal additive manufacturing, significantly improving processing speed and accuracy over traditional techniques.
Contribution
The paper presents a novel tunable deep image processing model that replicates and enhances conventional image analysis for real-time melt pool monitoring in additive manufacturing.
Findings
Achieved over 94% estimation accuracy.
Processed 500 images per second, vastly faster than traditional methods.
Enabled real-time process and quality estimation in metal AM.
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Image and Signal Denoising Methods · Advanced Data Compression Techniques
MethodsAttention Model
