Sensor fusion for stand-off estimation in direct energy deposition

Vittorio, Sala and Ambra, Vandone and Stefano, Baraldo and Michele, Banfi and Angelo, Baj and Francesco, Impaziente and Federico, Mazzucato and Anna, Valente (2023) Sensor fusion for stand-off estimation in direct energy deposition. In: Multimodal sensing: technologies and applications 11059 Multimodal sensing: technologies and applications, 26-29 Giu 2023, Monaco. (In Press)

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Direct Energy Deposition (DED) is an additive manufacturing technology that allows for both the realization of complex shape objects and for repairing of damaged or worn metallic components. A high-power laser beam melts the substrate and the metallic powder particles ejected by nozzles converging to the same spot. The laser beam spans the substrate surface, creating overlapping solid tracks and thus building up parts in a layer-wise fashion. Despite fine parameters optimization, the process instability may lead to layer height variations that, accumulating over time, could affect the stand-off distance, i.e., the optimal printing distance between the nozzles and the substrate. Getting far from optimal stand-off, where the laser beam and the powder flow are focused, impairs the printed part quality, especially from a geometrical point of view. In the present work, an innovative process monitoring approach has been investigated. Sensors such as a pyrometer and RGB camera have been integrated into a DED machine, to measure the radiation emitted by the melted material at different spectral bandwidths. Since variations in stand-off distance affect the signals acquired, a neural network approach has been derived to estimate the stand-off of the deposition at each layer and highlight possible anomalous situations. The training dataset has been acquired by printing tracks at different stand-offs. The proposed solution has been validated by comparing the stand-off estimated on defective multilayer parts with the measurements provided by a high-resolution 3D scanner. Preliminary results show that the proposed method is a promising approach to prevent critical stand-off deviations.

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