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<p>The wire laser-based Directed Energy Deposition (DED-LB) Additive Manufacturing (AM) process supports the manufacturing and repairing of medium- and large-scale components. The process achieves high-density, cost-efficient parts with good mechanical properties and precise geometries. However, its broader adoption, particularly by small- and medium-sized enterprises (SMEs), is impeded due to the limited documented process knowledge and the lack of solutions for in-line performance evaluation, process history tracking, and control available in the market. This work presents a monitoring system that leverages data from a vision camera and an embedded load cell to guide process understanding and control methodologies. It is an all-in-one system that evaluates the heat accumulation and the distance between the deposition head and the working surface (Standoff distance) based on the melt-pool area and the loads acting on the feeding system. A nested experimental investigation evaluates the process performance as a function of key process variables (cooling time, laser power, working distance deviation, wire feed rate, and deposition head speed). The process variables and monitoring data are correlated with metrology data, indicating the effects of process mechanisms on part quality. Finally, the generated process knowledge drives the corrective actions, enabling first-time-right processes and process optimization.</p>

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Digital transformation of wire DED-LB: enabling industrial scalability through monitoring and control systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月8日

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  • Konstantinos Tzimanis,
  • Michail S. Koutsokeras,
  • Nikolas Bourlesas,
  • Nikolas Porevopoulos,
  • Georgios Pastras,
  • Panagiotis Stavropoulos

摘要

The wire laser-based Directed Energy Deposition (DED-LB) Additive Manufacturing (AM) process supports the manufacturing and repairing of medium- and large-scale components. The process achieves high-density, cost-efficient parts with good mechanical properties and precise geometries. However, its broader adoption, particularly by small- and medium-sized enterprises (SMEs), is impeded due to the limited documented process knowledge and the lack of solutions for in-line performance evaluation, process history tracking, and control available in the market. This work presents a monitoring system that leverages data from a vision camera and an embedded load cell to guide process understanding and control methodologies. It is an all-in-one system that evaluates the heat accumulation and the distance between the deposition head and the working surface (Standoff distance) based on the melt-pool area and the loads acting on the feeding system. A nested experimental investigation evaluates the process performance as a function of key process variables (cooling time, laser power, working distance deviation, wire feed rate, and deposition head speed). The process variables and monitoring data are correlated with metrology data, indicating the effects of process mechanisms on part quality. Finally, the generated process knowledge drives the corrective actions, enabling first-time-right processes and process optimization.