利用高保真多物理场仿真了解激光粉末床熔融中光束整形的热流体-微结构影响

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引用次数: 0

摘要

在金属增材制造(MAM)工艺中,激光光束整形是一个关注度相对较低的课题。这种技术可以对激光的强度轮廓进行调制或空间交替。由于 MAM 的大部分工作主要围绕高斯光束轮廓展开,其他光束形状的确切影响和潜力仍是一个未解之谜。在这项工作中,开发了无粉 Ti6Al4V 激光粉末床熔化 (LPBF) 过程的多物理场数值模型,该模型可以预测热流体微结构条件。模型预测结果与单轨试样的实验数据进行了对比,对比结果显示两者非常吻合。结果表明,与使用高斯光束轮廓(GBP)形成的熔池相比,环形光斑光束轮廓(RSBP)形成的熔池要宽得多。微观结构预测显示,在 GBP 条件下,晶粒向熔池中心线靠拢,而在环形光束剖面 (RBP) 条件下,晶粒倾向于向单点靠拢。最后,研究了不同的环形半径对 RBP 的影响,结果表明,在较大的环形半径下,液态金属会在激光束正下方形成明显的隆起。
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Understanding the thermo-fluid-microstructural impact of beam shaping in Laser Powder Bed Fusion using high-fidelity multiphysics simulation

Beam shaping of lasers is a topic that has received relatively less attention in the context of metal additive manufacturing (MAM) processes. This technique allows for modulation or spatial alternation of the intensity profile of the laser. As the bulk of the work within MAM primarily revolves around Gaussian beam profiles, the precise impact and potential of other beam shapes is still an unanswered question. In this work a multiphysics numerical model of the laser powder bed fusion (LPBF) process of Ti6Al4V without powder is developed and the model can predict thermo-fluid-microstructural conditions. The model predictions are compared with experimental data from single-track specimens, and the comparison shows a very good agreement. It is shown that the ring spot beam profile (RSBP) results in substantially wider melt pools as compared to the ones forming using the Gaussian beam profile (GBP). The microstructural predictions show that for GBP the grains converge to the center line of the melt pool, while for ring beam profile (RBP), the grains tend to converge to a single point. Finally, the impact of different ring radii for RBP is studied and the results show that at larger ring radii, a noticeable bulge of liquid metal forms right beneath the laser beam.

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Editorial Preface Off-axis monitoring of the melt pool spatial information in Laser Metal Deposition process Machine learning-assisted collection of reduced sensor data for improved analytics pipeline Demand-Oriented Optimization of Machine Tools: a Closed Loop Approach for Safe Exploration in Series Production
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