Guler, O.Dincer, E.2024-06-132024-06-132023979835033752510.1109/HORA58378.2023.101567042-s2.0-85165643978https://doi.org/10.1109/HORA58378.2023.10156704https://hdl.handle.net/11501/10185th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 8 June 2023 through 10 June 2023 -- -- 190025Wire Arc Additive Manufacturing (WAAM) is one of the emerging technologies which have been demanded from industry. Monitoring the manufacturing process is very important in order to avoid any deposition defects, errors like dimensional errors, detecting problems and also to get better texture recognition and higher performance in WAAM. The thermal cameras are being used to monitor the weld pools in general and the information captured can be processed by using computer programs for different outcomes. In this study, we processed three WAAM videos and calculated the size of the thermal light regions simultaneously to get momentary welding amount in order to measure the welding quality and stability. K-means clustering method and Teh-Chin chain approximation algorithm have been applied to process videos, separately. We found that Teh-Chin chain approximation was 27.5% more successful to detect the thermal light regions. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessK-meansTeh-Chin Chain Algorithm)Thermal cameravideo processingwelding monitoring3D printingAdditivesApproximation algorithmsCamerasInfrared devicesK-means clusteringTexturesVideo signal processingEmerging technologiesK-meansManufacturing ISManufacturing processTeh-chin chain algorithm)Thermal cameraThermal lightVideo processingWelding monitoringWire arcWeldingConference ObjectN/A