Abstract
One approach for controlling the twin wire arc spray process is to use optical properties of the particle beam as input parameters for a process control. The idea is that changes in the process like eroded contact nozzles or variations of current, voltage, and/or atomizing gas pressure may be detected through observation of the particle beam. It can be assumed that if these properties deviate significantly from those obtained from a beam recorded for an optimal coating process, the spray particle and thus the coating properties change significantly. The goal is to detect these deviations and compensate the occurring errors by adjusting appropriate process parameters for the wire arc spray unit. One method for monitoring optical properties is to apply the diagnostic system particle flux imaging (PFI): PFI fits an ellipse to an image of a particle beam thereby defining easy to analyze characteristical parameters by relating optical beam properties to ellipse parameters. Using artificial neural networks (ANN), mathematical relations between ellipse and process parameters can be defined. It will be shown that in the case of a process disturbance through the use of an ANN-based control new process parameters can be computed to compensate particle beam deviations.
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Acknowledgments
Parts of this research and development project were funded by the German Federal Ministry of Education and Research (BMBF) within the Framework Concept “Research for Tomorrow’s Production” (funding Number 02PO2394) and managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the contents of this publication. For our investigations, we were also supported with a spray gun from the company T-Spray and with a power source from the company EWM. We thank the BMBF, the PTKA, T-Spray, and EWM.
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Hartz-Behrend, K., Schaup, J., Zierhut, J. et al. Controlling the Twin Wire Arc Spray Process Using Artificial Neural Networks (ANN). J Therm Spray Tech 25, 21–27 (2016). https://doi.org/10.1007/s11666-015-0341-0
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DOI: https://doi.org/10.1007/s11666-015-0341-0