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<p>Additive Friction Stir Deposition (AFSD) is an emerging solid-state additive manufacturing process capable of creating metallic parts with forged-like mechanical properties. Understanding the relationship between process parameters and deposition rate is crucial for realizing the full potential of AFSD. This paper presents the first analytical model designed to predict key thermal characteristics of AFSD. The model establishes a direct relationship between process parameters and deposition rate, enabling the determination of optimal machine process windows for maximizing both deposition speed and energy efficiency. Validation against experimental deposition data demonstrates the model’s ability to accurately predict thermal behaviour and identify potential pathways for improved process control. The resulting process maps provide a fundamental framework for optimizing AFSD, offering insights into achievable deposition rates and energy efficiencies. This work paves the way for further exploration of AFSD’s capabilities and optimization for industrial applications.</p>

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Analytical modelling of AFSD points the way to faster and more efficient deposition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

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  • Alban de Vaucorbeil,
  • Seyed Mehdi Alavizadeh,
  • Daniel Fabijanic,
  • Matthew Barnett

摘要

Additive Friction Stir Deposition (AFSD) is an emerging solid-state additive manufacturing process capable of creating metallic parts with forged-like mechanical properties. Understanding the relationship between process parameters and deposition rate is crucial for realizing the full potential of AFSD. This paper presents the first analytical model designed to predict key thermal characteristics of AFSD. The model establishes a direct relationship between process parameters and deposition rate, enabling the determination of optimal machine process windows for maximizing both deposition speed and energy efficiency. Validation against experimental deposition data demonstrates the model’s ability to accurately predict thermal behaviour and identify potential pathways for improved process control. The resulting process maps provide a fundamental framework for optimizing AFSD, offering insights into achievable deposition rates and energy efficiencies. This work paves the way for further exploration of AFSD’s capabilities and optimization for industrial applications.