An optimized clustering-based spraying trajectory planning method for complex parts with separated and hollow features in cold spray additive manufacturing-Copy

PDF-Download zu https://doi.org/10.53192/ITSC2026582

An optimized clustering-based spraying trajectory planning method for complex parts with separated and hollow features in cold spray additive manufacturing-Copy

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Produktinformationen

Jun Wang, jyunwang@stu.xjtu.edu.cn, Xi‘an Jiaotong University; Xiao-Tao Luo, luoxiaotao@mail.xjtu.edu.cn, Xi‘an Jiaotong University; Dr.-Prof. Chang-Jiu Li, licj@mail.xjtu.edu.cn, Xi‘an Jiaotong University; 

https://doi.org/10.53192/ITSC2026582

Cold spray (CS), as a solid-state material deposition process, exhibits significant advantages in additive manufacturing (AM) of large-scale parts made from thermally sensitive materials, high-reflectivity materials or limited solid-solution dissimilar metals due to its low processing temperature and high material building rate. However, challenges such as edge loss caused by uneven deposition, and unintended deposition due to delayed start-stop response persist in geometry control of CSAM deposits. For parts containing separated structures, accurate contour point clustering constitutes the essential basis for trajectory planning of edge compensation and inter-structure transition. Existing clustering algorithms struggle to effectively separate contour points of complex discrete structures and often rely on complicated parameter tuning. To address this, in the present study, a contour point clustering method is proposed based on connected component algorithm, which enables accurate separation of discrete structure contour points without requiring parameter optimization. This method is further extended to distinguish between inner and outer contour points of hollow structures, ultimately achieving trajectory planning for complex parts with separated and hollow features. The present work provides an effective trajectory planning solution for CSAM of complex structures, offering significant implications for expanding the application scope of CSAM.

 

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PDF-Download zu https://doi.org/10.53192/ITSC2026582
Erscheinungsdatum
März 2026
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