TY - GEN
T1 - TDIP
T2 - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
AU - Akhavan, Javid
AU - Mahmoud, Youmna
AU - Xu, Ke
AU - Lyu, Jiaqi
AU - Manoochehri, Souran
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Additive Manufacturing
KW - Computer Vision
KW - Deep Learning
KW - Image Processing
KW - Melt Pool Analysis
KW - Real Time Process Monitoring
UR - http://www.scopus.com/inward/record.url?scp=85191160418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191160418&partnerID=8YFLogxK
U2 - 10.1109/CSCE60160.2023.00314
DO - 10.1109/CSCE60160.2023.00314
M3 - Conference contribution
AN - SCOPUS:85191160418
T3 - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
SP - 1899
EP - 1908
BT - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Y2 - 24 July 2023 through 27 July 2023
ER -