Multimodal sensor fusion for real-time standoff estimation in directed energy deposition

Herberger, Callan; Kimmell, Jeffrey; Feldhausen, Thomas; Post, Brian; MacDonald, Eric; Orlyanchik, Vladimir

doi:10.1016/j.addma.2024.104598

Title: Multimodal sensor fusion for real-time standoff estimation in directed energy deposition

Journal Article · 09 December 2024 · Additive Manufacturing

DOI: https://doi.org/10.1016/j.addma.2024.104598 · OSTI ID:2483417

^[1]; Kimmell, Jeffrey ^[2];

^[1];

^[2]; MacDonald, Eric ^[1];

^[2]

Univ. of Texas at El Paso, TX (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

In Laser Powder-based Direct Energy Deposition (LP-DED) systems, achieving consistency, precision and quality of produced parts requires tight control over printing parameters. One of the critical parameters is the standoff distance. Maintaining an optimal standoff height is crucial for achieving correct laser power density and powder catchment efficiency, as both laser and powder streams are focused at this distance. Here, this study introduces a novel approach using multimodal sensor fusion to predict standoff height in real-time. The proposed system integrates two low-profile, cost-effective sensors: an RGB coaxial camera and a high frequency and high dynamic range microphone. By utilizing a simple fully connected neural network, trained on a limited dataset, data fusion of these sensors allowed for the real-time prediction of the standoff height. The results demonstrate high resolution and accuracy of the predictions across multiple geometries and a wide range of standoff heights. This approach offers a simple, and cost-effective solution for real-time standoff height monitoring and lays the groundwork for future integration into commercial LP-DED systems.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2483417

Journal Information:: Additive Manufacturing, Journal Name: Additive Manufacturing Vol. 97; ISSN 2214-8604

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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