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首页> 外文期刊>Journal of manufacturing science and engineering: Transactions of the ASME >Melt Pool Flow and Surface Evolution During Pulsed Laser Micro Polishing of Ti6Al4V
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Melt Pool Flow and Surface Evolution During Pulsed Laser Micro Polishing of Ti6Al4V

机译:Ti6Al4V脉冲激光微抛光过程中的熔池流动和表面演变

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摘要

Extensive experimental work has shown that pulsed laser micro polishing (PLμP) is effective for polishing micro metallic parts. However, the process physics have not been fully understood yet, especially with respect to the melt pool flow. A reliable physical model can be of significant assistance in understanding the fluid flow in the melt pool and its effect on PLμP. In this paper, a two-dimensional axisymmetric transient model that couples heat transfer and fluid flow is described that was constructed using the finite element method. The model not only provided the solutions to the temperature and velocity fields but also predicted the surface profile evolution on a free deformable surface. The simulated melt depth and resolidified surface profiles matched those obtained from optical images of PLμPed Ti6Al4V sample cross-sections. The model was also used to study the effect of laser pulse duration on the melt pool flow. The study suggests that longer pulses produce more significant fluid flows. The cut-off pulse duration between capillary and thermocapillary regimes, below which minimal Maragoni flow should be expected, was estimated to be 0.66 [μs for Ti6Al4V, which also matched well with the experimental results. It is evident that the coupled model offers reliable predictions and thus can be extended for a more complex parametric study to provide further insights for PLμP.
机译:大量的实验工作表明,脉冲激光微抛光(PLμP)可有效抛光微金属零件。然而,还没有完全理解过程物理,特别是关于熔池流动。可靠的物理模型可以帮助理解熔池中的流体流动及其对PLμP的影响。在本文中,描述了使用有限元方法构造的耦合传热和流体流动的二维轴对称瞬态模型。该模型不仅提供了温度和速度场的解决方案,而且还预测了自由变形表面上的表面轮廓演变。模拟的熔体深度和再凝固的表面轮廓与从PLμPedTi6Al4V样品横截面的光学图像获得的相匹配。该模型还用于研究激光脉冲持续时间对熔池流量的影响。研究表明,更长的脉冲会产生更大的流体流动。毛细管和热毛细管状态之间的截止脉冲持续时间估计为0.66 [μs(对于Ti6Al4V为μs),这与实验结果非常吻合,在该截止脉冲之前应期望最小的Maragoni流量。显然,耦合模型提供了可靠的预测,因此可以扩展为更复杂的参数研究,从而为PLμP提供更多的见解。

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