TDIP: Tunable Deep Image Processing, a Real Time Melt Pool Monitoring Solution

Javid Akhavan, Youmna Mahmoud, Ke Xu, Jiaqi Lyu, Souran Manoochehri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 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.

Original languageEnglish
Title of host publicationProceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Pages1899-1908
Number of pages10
ISBN (Electronic)9798350327595
DOIs
StatePublished - 2023
Event2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 - Las Vegas, United States
Duration: 24 Jul 202327 Jul 2023

Publication series

NameProceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

Conference

Conference2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Country/TerritoryUnited States
CityLas Vegas
Period24/07/2327/07/23

Keywords

  • Additive Manufacturing
  • Computer Vision
  • Deep Learning
  • Image Processing
  • Melt Pool Analysis
  • Real Time Process Monitoring

Fingerprint

Dive into the research topics of 'TDIP: Tunable Deep Image Processing, a Real Time Melt Pool Monitoring Solution'. Together they form a unique fingerprint.

Cite this