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SLM成形316L不锈钢微观组织演化模拟与试验验证

Simulation and experimental validation of microstructure evolution in SLM-formed 316L stainless steel

  • 摘要: 为研究激光选区熔化(selective laser melting,SLM)中工艺参数对成形过程及成形质量的影响,文中以316L不锈钢粉末为材料,设计了SLM成形 316L不锈钢对照试验,并以试验为基础进行了数值模拟分析,首先采用离散元法(discrete element method,DEM)建立单层厚度为40 μm的粉末模型,其次采用计算流体力学(computational fluid dynamics,CFD) 方法模拟了激光对粉床的熔化过程,最后基于仿真温度场通过元胞自动机(cellular automaton, CA)对熔化区域微观组织进行模拟. 结果表明,当激光热输入范围为136.4 ~ 250.0 J/m时,单道质量较高. 当选定扫描间距为70 μm时,激光热输入不应低于181.8 J/m,微观组织模拟结果表明,熔池内部的晶粒尺寸与激光功率和扫描速度密切相关. 在合理的激光热输入范围内,当工艺参数组合为铺粉厚度为40 μm,扫描间距为70 μm,激光功率为200 W,扫描速度为1.1 m/s时,熔池微观组织中晶粒尺寸最小. 文中采用的热流体流动模型和微观组织演化模型能够在不同尺度上对SLM成形结果进行准确的预测,并能在试验的基础上对工艺参数优化提供指导.

     

    Abstract: To investigate the influence of process parameters on the forming process and quality during selective laser melting (SLM), 316L stainless steel powder was selected as the material, and controlled experiments of SLM-formed 316L stainless steel were conducted. On this basis, numerical simulations were carried out. A powder model with a single-layer thickness of 40 μm was established using the discrete element method (DEM), and computational fluid dynamics (CFD) was performed to simulate laser-induced powder bed melting processes. Finally, cellular automaton (CA) was used to simulate the microstructure of the melting region based on the simulated temperature field. The results showed that when the laser heat input was in the range of 136.4 ~ 250.0 J/m, the single-track quality was higher. When the scanning interval was 70 μm, the laser heat input should not be lower than 181.8 J/m. The microstructure simulation results showed that the grain size in the molten pool was closely related to the laser power and scanning speed. In the range of reasonable laser heat input , when the combination of process parameters was powder thickness of 40 μm, scanning interval of 70 μm, laser power of 200 W, and scanning speed of 1.1 m/s, the grain size in the molten pool microstructure was the smallest. The thermal fluid flow model and microstructure evolution model used in this paper could accurately predict the SLM forming results at different scales and provide guidance for the optimization of process parameters on the basis of experiments.

     

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