TY - GEN
T1 - Research of Image Segmentation-Based Layer Height Estimation Method for WAAM Process
AU - Wu, Di
AU - David, Joe
AU - Kuosmanen, Jari
AU - Coatanea, Eric
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2025
Y1 - 2025
N2 - The contact tip to workpiece distance (CTWD) has a considerable effect on the current of the wire arc additive manufacturing (WAAM) process, which in turn affects the height of the deposited layer. An unstable CTWD may result in significant deviation in the height direction. Accordingly, it is necessary to monitor and regulate the CTWD for each individual layer. In practice, CTWD can be calculated from the gaps between the torch and the current layer edge. An image-based layer height estimation method is developed to ascertain the height of the newly deposited layer. A welding camera, affixed to the torch, is utilized to document the deposition process. A segmentation network is utilized to identify the perimeter of the newly deposited layer. Given that the camera is continuously focused on the molten pool, it is essential to ascertain the camera's position to calculate the layer height. This is achieved by synchronizing the robot position data and the images in ROS2 (Robot operation system 2) to locate the position of camera in the real world. To find out a better choice, three distinct deep learning-based segmentation algorithms, namely Unet3 +, YOLOv11, and PIDNet, are evaluated in terms of accuracy and efficiency. Then, the layer height estimation method is tested with a 10-layer thin wall. As a result, the proposed method can provide accurate height estimation. Among the segmentation algorithms, PIDNet using 256x256 resolution is considered as a better choice to balance the accuracy and efficiency.
AB - The contact tip to workpiece distance (CTWD) has a considerable effect on the current of the wire arc additive manufacturing (WAAM) process, which in turn affects the height of the deposited layer. An unstable CTWD may result in significant deviation in the height direction. Accordingly, it is necessary to monitor and regulate the CTWD for each individual layer. In practice, CTWD can be calculated from the gaps between the torch and the current layer edge. An image-based layer height estimation method is developed to ascertain the height of the newly deposited layer. A welding camera, affixed to the torch, is utilized to document the deposition process. A segmentation network is utilized to identify the perimeter of the newly deposited layer. Given that the camera is continuously focused on the molten pool, it is essential to ascertain the camera's position to calculate the layer height. This is achieved by synchronizing the robot position data and the images in ROS2 (Robot operation system 2) to locate the position of camera in the real world. To find out a better choice, three distinct deep learning-based segmentation algorithms, namely Unet3 +, YOLOv11, and PIDNet, are evaluated in terms of accuracy and efficiency. Then, the layer height estimation method is tested with a 10-layer thin wall. As a result, the proposed method can provide accurate height estimation. Among the segmentation algorithms, PIDNet using 256x256 resolution is considered as a better choice to balance the accuracy and efficiency.
KW - CTWD
KW - ROS2
KW - Segmentation
KW - WAAM
UR - https://www.scopus.com/pages/publications/105020569620
U2 - 10.1007/978-3-032-05610-8_23
DO - 10.1007/978-3-032-05610-8_23
M3 - Conference contribution
AN - SCOPUS:105020569620
SN - 9783032056092
T3 - Lecture Notes in Mechanical Engineering
SP - 231
EP - 238
BT - Flexible Automation and Intelligent Manufacturing
A2 - Srihari, Krishnaswami
A2 - Khasawneh, Mohammad T.
A2 - Yoon, Sangwon
A2 - Won, Daehan
PB - Springer
T2 - 34th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2025
Y2 - 21 June 2025 through 24 June 2025
ER -