Developing dense, well-adhered coatings with minimal heterogeneity via cold spray deposition has been a challenge because in-flight and layer-by-layer diagnostics has not yet been adequately adapted to complement existing post-deposition analyses and parameter process mapping. This study presents an empirical and analytical approach for development and optimization of requisite spray parameters via the cold spray solid-state consolidation process using the in situ beam curvature technique to assess particle dynamics during deposition, in conjunction with post-deposition analyses. Here, CuNi (62/38) powder was deposited onto CuNi (70/30) substrates and monitored with an in situ beam curvature sensor to provide real-time feedback of the coating’s evolving stress states during the deposition process and subsequent residual stress determination immediately after deposition. The resulting beam curvature compressive and tensile stress amplitudes detected layer-by-layer coating variations during deposition resulting from adjusted carrier gas flow rates, substrate surface preparations, nozzle deposition angle, robot raster speeds, powder feed rate, substrate preheat, as well as identifying nozzle clogging. The acquired stress-state data enhanced post-deposition analyses that directed and accelerated the downselection process to define and optimize cold spray parameters, as it pertained to coating adhesion, cohesion, and the corresponding microstructure.

中文翻译：

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通过冷喷涂 CuNi 涂层的原位残余应力测量和沉积后分析进行参数开发

通过冷喷涂沉积开发具有最小异质性的致密、粘附良好的涂层一直是一项挑战，因为飞行中和逐层诊断尚未充分适应以补充现有的沉积后分析和参数过程映射。本研究提出了一种经验和分析方法，通过冷喷涂固态固结过程开发和优化必要的喷涂参数，使用原位束曲率技术评估沉积过程中的粒子动力学，并结合沉积后分析。这里，CuNi (62/38) 粉末沉积在 CuNi (70/30) 基材上，并使用原位梁曲率传感器进行监测，以提供沉积过程中涂层应力状态的实时反馈以及沉积后立即确定的残余应力. 由此产生的光束曲率压缩和拉伸应力幅度检测到沉积过程中由于调整的载气流速、基材表面准备、喷嘴沉积角度、机器人光栅速度、粉末进料速度、基材预热以及识别喷嘴堵塞。获得的应力状态数据增强了沉积后分析，指导和加速了向下选择过程，以定义和优化冷喷涂参数，因为它与涂层附着力、内聚力、