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February 11, 2026The International Journal of Advanced Manufacturing Technology1 citations

In situ monitoring and control of laser-directed energy deposition with wire – part 2: geometry and hardness modeling and closed-loop control

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MDMostafa Rahmani DehaghaniKOKevin OldknowMNMorgan Nilsen

Key Points

  • The aim is to enable in situ monitoring and control of geometry and hardness in laser-directed energy deposition processes.
  • Developed a closed-loop control strategy for L-DED/W using MIMO framework.
  • Utilized an LSTM network to model relationships between process parameters and melt pool signatures.
  • Integrated a fuzzy logic controller with the control strategy for effective regulation of final qualities.
  • Achieved regulation of melt pool geometry and improved hardness outcomes under process uncertainties.
  • Demonstrated the feasibility of real-time control of unmeasurable qualities during deposition.

Abstract

Laser-directed energy deposition with wire (L-DED/W) offers high deposition and material utilization rates but often suffers from geometric inaccuracies and anisotropic mechanical properties due to unstable deposition dynamics and complex thermomechanical phenomena. To enable in situ monitoring and closed-loop control of final qualities such as geometry and hardness—which cannot be directly measured during deposition—this study employs the parameter–signature–quality (PSQ) framework. This work presents the first in situ multi-input multi-output (MIMO) closed-loop control of both deposition geometry and hardness in a directed energy deposition process. Building on the experimental foundation established in Part 1 of this study, a real-time MIMO control strategy is developed for the L-DED/W process. A long-short-term-memory (LSTM) network is used to model the nonlinear and dynamic relationships between process parameters (power and speed) and melt pool signatures, while hardness is classified using a combined set of process parameters and melt pool signatures within the PSQ framework. These models are integrated with a fuzzy logic controller to achieve closed-loop MIMO control, demonstrating effective regulation of melt pool geometry while maximizing the likelihood of achieving high hardness under process uncertainties. The results demonstrate the feasibility of in situ regulation of otherwise unmeasurable final qualities and highlight fuzzy logic control as a flexible and computationally efficient approach for multi-objective control in metal additive manufacturing.

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Cite This Study

Dehaghani et al. (2026) studied this question.

synapsesocial.com/papers/698be001058ab1890a13baa7https://doi.org/10.1007/s00170-026-17618-4
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