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<p>Directed Energy Deposition (DED) is a promising technology for producing metal matrix composites (MMXCs), but precise control over deposited layer dimensions, particularly in multi-material systems, remains difficult to achieve due to the complex interdependence of operating parameters such as laser parameters, feedstock flow rate, and beam energy attenuation. To address these problems, this work proposes an analytical model that predicts the width, height, and depth of the deposited layers using mixed feedstock compositions in-situ, with a focus on Inconel-718 and TiC MMXCs. The model takes into account variables such as laser power, feedstock density, and mixing ratios to improve layer accuracy and minimize the number of experimental attempts. The main findings reveal that TiC additions increase thermal stability and potential wear resistance properties. The model predictions closely match the experimental data, with layer dimension deviations of less than 5.0% between the simulated and observed values, indicating efficient process control for DED multi-material production.</p>

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DED printing process modeling using metal matrix composites: in-situ feedstock mixing with variable compositions and empirical validation

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Muhammad Arif Mahmood,
  • Sabin Mihai,
  • Diana Chioibasu,
  • Andrei C. Popescu,
  • Frank Liou

摘要

Directed Energy Deposition (DED) is a promising technology for producing metal matrix composites (MMXCs), but precise control over deposited layer dimensions, particularly in multi-material systems, remains difficult to achieve due to the complex interdependence of operating parameters such as laser parameters, feedstock flow rate, and beam energy attenuation. To address these problems, this work proposes an analytical model that predicts the width, height, and depth of the deposited layers using mixed feedstock compositions in-situ, with a focus on Inconel-718 and TiC MMXCs. The model takes into account variables such as laser power, feedstock density, and mixing ratios to improve layer accuracy and minimize the number of experimental attempts. The main findings reveal that TiC additions increase thermal stability and potential wear resistance properties. The model predictions closely match the experimental data, with layer dimension deviations of less than 5.0% between the simulated and observed values, indicating efficient process control for DED multi-material production.