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<p>Directed energy deposition using laser beam and metal wire (DED─LB⁄MW) offers notable advantages for additive manufacturing (AM) of large metal components but remains limited by process variability and challenges in ensuring internal quality. This study presents an integrated multi-sensor monitoring approach combined with a spatiotemporal data fusion framework to enhance process understanding. Key thermal, geometric, and visual signals were acquired during deposition using a coaxial pyrometer, an optical coherence tomography (OCT) sensor, and a welding camera, and subsequently synchronized with robot trajectory data. Post-process computed tomography (CT) scans were used to incorporate internal porosity information. The fused dataset was mapped onto the manufactured geometry through a layer-wise 3D representation, enabling the spatial correlation of process features and defect formation. Results demonstrate that thermal accumulation, surface irregularities, and deficient bead overlap significantly affect deposition stability and internal quality. The proposed methodology supports advanced process analysis and lays the groundwork for data-driven control and quality assurance strategies in DED─LB⁄MW.</p>

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Multiple data management for quality analysis in DED─LB⁄MW

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Jon Flores,
  • David Alberdi,
  • Itziar Cabanes,
  • Oscar Gonzalo,
  • Carlos Soriano

摘要

Directed energy deposition using laser beam and metal wire (DED─LB⁄MW) offers notable advantages for additive manufacturing (AM) of large metal components but remains limited by process variability and challenges in ensuring internal quality. This study presents an integrated multi-sensor monitoring approach combined with a spatiotemporal data fusion framework to enhance process understanding. Key thermal, geometric, and visual signals were acquired during deposition using a coaxial pyrometer, an optical coherence tomography (OCT) sensor, and a welding camera, and subsequently synchronized with robot trajectory data. Post-process computed tomography (CT) scans were used to incorporate internal porosity information. The fused dataset was mapped onto the manufactured geometry through a layer-wise 3D representation, enabling the spatial correlation of process features and defect formation. Results demonstrate that thermal accumulation, surface irregularities, and deficient bead overlap significantly affect deposition stability and internal quality. The proposed methodology supports advanced process analysis and lays the groundwork for data-driven control and quality assurance strategies in DED─LB⁄MW.