Scholarlink

<p>Cold spray (CS) is a solid-state additive manufacturing technique where high-velocity particles impact a substrate to form coatings or 3D structures. Key factors such as particle velocity, temperature, residual stress, and porosity influence the final material properties. Digital deposition prior to manufacturing offers a cost-effective approach to optimising these parameters, reducing defects like cracking and delamination, and enhancing mechanical properties. Numerical modelling plays a crucial role in predicting particle behaviour and optimising deposition efficiency. However, most models focus on single- or multi-particle impact with over-simplified assumptions, limiting their applicability to thick coatings. Advancing digital construction capabilities requires simulating multiple-particle interactions while optimising computational efficiency for industrial applications. Such advancements reduce inaccuracies by minimising assumptions and enabling direct validation of model outcomes<Emphasis Type="BoldItalic">.</Emphasis> This review evaluates current computational approaches for CS digital deposition, highlighting their strengths and limitations. Results demonstrate that advanced numerical models can accurately predict residual stress and porosity, improving adhesion strength and structural integrity while minimising defects. Optimised simulations enhance scalability and sustainability, reducing material waste and experimental costs. While significant progress has been made, further research is required to refine multi-particle impact simulations, particularly in predicting interfacial bonding and long-term performance. Integrating high-fidelity digital twins with real-time validation will be essential for achieving reliable and scalable CS manufacturing solutions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessment of current capabilities for cost effective digital deposition of cold spray additive structures: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月29日

查看全文
  • Abdoolah Badaloo,
  • Muhammad Faizan-Ur-Rab,
  • Saden H. Zahiri,
  • Syed. H. Masood,
  • Suresh Palanisamy

摘要

Cold spray (CS) is a solid-state additive manufacturing technique where high-velocity particles impact a substrate to form coatings or 3D structures. Key factors such as particle velocity, temperature, residual stress, and porosity influence the final material properties. Digital deposition prior to manufacturing offers a cost-effective approach to optimising these parameters, reducing defects like cracking and delamination, and enhancing mechanical properties. Numerical modelling plays a crucial role in predicting particle behaviour and optimising deposition efficiency. However, most models focus on single- or multi-particle impact with over-simplified assumptions, limiting their applicability to thick coatings. Advancing digital construction capabilities requires simulating multiple-particle interactions while optimising computational efficiency for industrial applications. Such advancements reduce inaccuracies by minimising assumptions and enabling direct validation of model outcomes. This review evaluates current computational approaches for CS digital deposition, highlighting their strengths and limitations. Results demonstrate that advanced numerical models can accurately predict residual stress and porosity, improving adhesion strength and structural integrity while minimising defects. Optimised simulations enhance scalability and sustainability, reducing material waste and experimental costs. While significant progress has been made, further research is required to refine multi-particle impact simulations, particularly in predicting interfacial bonding and long-term performance. Integrating high-fidelity digital twins with real-time validation will be essential for achieving reliable and scalable CS manufacturing solutions.