材料科学
航空航天
制作
机械工程
刚度
陶瓷
组分(热力学)
有限元法
过程(计算)
拓扑优化
机械加工
工艺工程
复合材料
结构工程
计算机科学
工程类
冶金
医学
病理
航空航天工程
物理
替代医学
操作系统
热力学
作者
F.N. Lomo,Milan Patel,Alejandro Vargas-Uscategui,Peter C. King,Ivan Cole
标识
DOI:10.1016/j.addma.2023.103891
摘要
Cold spray additive manufacturing (CSAM) is a solid-state deposition process with the potential to produce near-net shape components with complex geometry at a high fabrication rate, making it an attractive alternative to more widely established additive manufacturing (AM) processes. However, CSAM is still in its early stages and requires numerous advancements. The current literature highlights the lack of a design framework for fabricating structural components that encompasses the advantages and constraints of CSAM. This work proposes such a framework to guide product and process engineers, with its novel aspects including (i) accounting for different spray trajectories and their effect on anisotropic mechanical properties, (ii) accounting for the primary constraint for toolpath planning (maximum overhang angle ‘MOA’), and (iii) virtual development and optimisation of a real-world structural component with complex geometry. To exemplify this framework, tensile properties under two spray trajectories were determined experimentally for a common lightweight metal (titanium) supplemented with a ceramic to form a metal matrix composite with improved strength and hardness. Optimisation of the design was conducted via finite element analysis and topology optimisation (TO). Two different TO processes were conducted, namely (i) minimising the strain energy of the structure and reducing the weight by 60% (best stiffness-to-weight ratio) and (ii) minimising the weight by targeting a maximum factor of safety (FoS) value of 1.2. The final design was fabricated via CSAM with relatively little raw material wastage and reasonably close geometric accuracy. Fabrication defects were, however, noticed after making a demonstration component and mitigation measures are discussed within the context of the design framework proposed here.
科研通智能强力驱动
Strongly Powered by AbleSci AI