微尺度化学
微观结构
材料科学
选择性激光熔化
格子Boltzmann方法
合金
流体力学
制作
细胞自动机
机械工程
机械
冶金
计算机科学
医学
数学教育
数学
物理
替代医学
病理
算法
工程类
作者
Matt Rolchigo,Michael Y. Mendoza,P. Samimi,David A. Brice,Brian W. Martin,Peter C. Collins,R. LeSar
标识
DOI:10.1007/s11661-017-4120-z
摘要
Abstract Additive manufacturing (AM) processes have many benefits for the fabrication of alloy parts, including the potential for greater microstructural control and targeted properties than traditional metallurgy processes. To accelerate utilization of this process to produce such parts, an effective computational modeling approach to identify the relationships between material and process parameters, microstructure, and part properties is essential. Development of such a model requires accounting for the many factors in play during this process, including laser absorption, material addition and melting, fluid flow, various modes of heat transport, and solidification. In this paper, we start with a more modest goal, to create a multiscale model for a specific AM process, Laser Engineered Net Shaping (LENS™), which couples a continuum-level description of a simplified beam melting problem (coupling heat absorption, heat transport, and fluid flow) with a Lattice Boltzmann-cellular automata (LB-CA) microscale model of combined fluid flow, solute transport, and solidification. We apply this model to a binary Ti-5.5 wt pct W alloy and compare calculated quantities, such as dendrite arm spacing, with experimental results reported in a companion paper.
科研通智能强力驱动
Strongly Powered by AbleSci AI