TY - GEN
T1 - Nozzle-to-Work Distance Measurement and Control in Wire Arc Additive Manufacturing
AU - Reisch, Raven Thomas
AU - Hauser, Tobias
AU - Franke, Jan
AU - Heinrich, Florian
AU - Theodorou, Konstantinos
AU - Kamps, Tobias
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/11/19
Y1 - 2021/11/19
N2 - In multi-axes Wire Arc Additive Manufacturing, keeping the correct nozzle-to-work distance is crucial to avoid collisions and process defects. Measuring this distance is challenging as the welding arc complicates the usage of conventional distance measurements without positional offset in-process. For that reason, this study investigated and evaluated the usage of several sensors (wire feed sensor, current and voltage sensor, microphone, welding camera, spectrometer, structural acoustic sensor) for a direction independent in-process measurement. Features were extracted based on domain knowledge and selected by means of a correlation analysis. The spectrometer (Pearson's r = -0.90) showed the most robust measurements for stable process parameters when computing the relative intensity at a wavelength of 960 nm, followed by the welding camera (Pearson's r = 0.84) when analyzing the images with a convolutional neural network. Based on the findings, a closed-loop-control was created. As a system identification revealed a high impact of the welding speed on the track height in comparison to the wire feed rate (Pearson's r - 0.90 < > - 0.16), the closed-loop-control was realized by means of a simple P-control for the welding speed. The proposed approach enabled the manufacturing of multi-layer multi-bead parts with multi-axes deposition paths.
AB - In multi-axes Wire Arc Additive Manufacturing, keeping the correct nozzle-to-work distance is crucial to avoid collisions and process defects. Measuring this distance is challenging as the welding arc complicates the usage of conventional distance measurements without positional offset in-process. For that reason, this study investigated and evaluated the usage of several sensors (wire feed sensor, current and voltage sensor, microphone, welding camera, spectrometer, structural acoustic sensor) for a direction independent in-process measurement. Features were extracted based on domain knowledge and selected by means of a correlation analysis. The spectrometer (Pearson's r = -0.90) showed the most robust measurements for stable process parameters when computing the relative intensity at a wavelength of 960 nm, followed by the welding camera (Pearson's r = 0.84) when analyzing the images with a convolutional neural network. Based on the findings, a closed-loop-control was created. As a system identification revealed a high impact of the welding speed on the track height in comparison to the wire feed rate (Pearson's r - 0.90 < > - 0.16), the closed-loop-control was realized by means of a simple P-control for the welding speed. The proposed approach enabled the manufacturing of multi-layer multi-bead parts with multi-axes deposition paths.
KW - closed-loop-control
KW - neural networks
KW - process monitoring
KW - sensors
KW - signal processing
KW - wire arc additive manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85127484634&partnerID=8YFLogxK
U2 - 10.1145/3501774.3501798
DO - 10.1145/3501774.3501798
M3 - Conference contribution
AN - SCOPUS:85127484634
T3 - ACM International Conference Proceeding Series
SP - 163
EP - 172
BT - ESSE 2021 - 2nd European Symposium on Software Engineering
PB - Association for Computing Machinery
T2 - 2nd European Symposium on Software Engineering, ESSE 2021
Y2 - 6 November 2021 through 8 November 2021
ER -