Sensor fusion for stand-off estimation in direct energy deposition

Vittorio Sala; Ambra Vandone; Stefano Baraldo; Michele Banfi; Angelo Baj; Francesco Impaziente; Federico Mazzucato; Anna A. Valente

doi:10.1117/12.2673816

9 August 2023 Sensor fusion for stand-off estimation in direct energy deposition

Vittorio Sala, Ambra Vandone, Stefano Baraldo, Michele Banfi, Angelo Baj, Francesco Impaziente, Federico Mazzucato, Anna A. Valente

Author Affiliations +

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210B (2023) https://doi.org/10.1117/12.2673816
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

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.

Conference Presentation

Citation Download Citation

Vittorio Sala, Ambra Vandone, Stefano Baraldo, Michele Banfi, Angelo Baj, Francesco Impaziente, Federico Mazzucato, and Anna A. Valente "Sensor fusion for stand-off estimation in direct energy deposition", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210B (9 August 2023); https://doi.org/10.1117/12.2673816

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