SIM-AM 2025

Qualification of High-value Industrial Metallic Components through DED Remanufacturing Processes

  • Chiumenti, Michele (CIMNE)
  • Moreira, Carlos Augusto (CIMNE)
  • Caicedo, Manuel (CIMNE)
  • Ramma, Runeal (CIMNE)
  • Molotnikov, Andrey (RMIT)
  • Baiges, Joan (CIMNE)

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In the circular economy, the goal is to maximize the utility and value of products, components, and materials throughout their entire life cycle. Remanufacturing and repair operations play a crucial role in this approach by modifying and restoring used products at the end of their life, enabling them to achieve equal or superior performance compared to new ones. This not only preserves product value but also contributes to a more resource-efficient manufacturing industry. Among metal Additive Manufacturing (AM) technologies, Direct Energy Deposition (DED) stands out as the most suitable option for remanufacturing metallic parts due to its flexibility, precision, and adaptability. The primary objective of this research is to develop a systematic qualification strategy for the remanufacturing of end-of-life parts, with a focus on molds and dies as case studies. The proposed strategy addresses two key aspects. On the one hand, the qualification of the DED process by establishing an optimal process window to ensure stable and repeatable metal deposition. This is achieved by implementing closed-loop control for the DED machine, allowing real-time adjustments based on melt-pool stability, independent of substrate temperature and preheating conditions. On the other hand, the qualification of the remanufactured component is discussed. This includes: (i) assessing dimensional accuracy to ensure compliance with design specifications, (ii) evaluating residual stresses induced by the DED process and their impact on part integrity, and (iii) analysing microstructural changes in the heat-affected zone (HAZ) and their influence on mechanical performance. The qualification framework is established by leveraging the high-fidelity in-house software (Add2Man) for the numerical simulation of the DED process, as well as for generating the synthetic data necessary for optimizing the actual process window.