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Cold spray additive manufacturing (CSAM) is a cutting-edge high-speed additive manufacturing process enabling the production of high-strength components without relying on traditional high-temperature methods. Unlike other techniques, CSAM produces oxide-free deposits and preserves the feedstock’s original characteristics without adversely affecting the substrate. This makes it ideal for industries requiring materials that maintain structural integrity. This paper explores strategies for improving material quality, focusing on nozzle design, particle size distribution, and fine-tuning of process parameters such as gas pressure, temperature, and spray distance. These factors are key to achieving efficient deposition and optimal bonding, which enhance the mechanical properties of the final products. Challenges in CSAM, including porosity control and achieving uniform coating thickness, are discussed, with solutions offered through the advancements in machine learning (ML). ML algorithms analyze extensive data to predict optimal process parameters, allowing for more precise control, reduced trial-and-error, and improved material usage. Advances in material strength, such as enhanced tensile strength and corrosion resistance, are also highlighted, making CSAM applicable to sectors like aerospace, defense, and automotive. The ability to produce high-performance, durable components positions CSAM as a promising additive-manufacturing technology. By addressing these innovations, this study offers insights into optimizing CSAM processes, guiding future research and industrial applications toward more efficient and high-performing manufacturing systems.
Cold spray additive manufacturing (CSAM) is a cutting-edge high-speed additive manufacturing process enabling the production of high-strength components without relying on traditional high-temperature methods. Unlike other techniques, CSAM produces oxide-free deposits and preserves the feedstock’s original characteristics without adversely affecting the substrate. This makes it ideal for industries requiring materials that maintain structural integrity. This paper explores strategies for improving material quality, focusing on nozzle design, particle size distribution, and fine-tuning of process parameters such as gas pressure, temperature, and spray distance. These factors are key to achieving efficient deposition and optimal bonding, which enhance the mechanical properties of the final products. Challenges in CSAM, including porosity control and achieving uniform coating thickness, are discussed, with solutions offered through the advancements in machine learning (ML). ML algorithms analyze extensive data to predict optimal process parameters, allowing for more precise control, reduced trial-and-error, and improved material usage. Advances in material strength, such as enhanced tensile strength and corrosion resistance, are also highlighted, making CSAM applicable to sectors like aerospace, defense, and automotive. The ability to produce high-performance, durable components positions CSAM as a promising additive-manufacturing technology. By addressing these innovations, this study offers insights into optimizing CSAM processes, guiding future research and industrial applications toward more efficient and high-performing manufacturing systems.
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