Uncertainty analysis of microsegregation during laser powder bed fusion
Uncertainty analysis of microsegregation during laser powder bed fusion
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DOI：
10.1088/1361-651x/ab01bf
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发表时间：
2019-01
影响因子：
1.8
通讯作者：
Supriyo Ghosh;M. Mahmoudi;L. Johnson;A. Elwany;R. Arróyave;D. Allaire
中科院分区：
文献类型：
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作者：
Supriyo Ghosh;M. Mahmoudi;L. Johnson;A. Elwany;R. Arróyave;D. Allaire
增材制造中的质量控制可以通过兴趣量（qi）的变化控制来实现。在这项工作中，我们选择微观结构微偏析作为我们的qi。微偏析是由于激光粉末床熔合过程中合金熔池凝固过程中溶质元素在固液界面上的空间再分布造成的。由于工艺和合金参数对微观结构特征的统计变化有影响，因此对qi的不确定性分析是必不可少的。高通量相场模拟估计了由有限元模拟估计的熔池凝固条件下生长的固液界面。在不同的工艺和合金参数下，从模拟界面上确定了微偏析。使用相关、回归和替代模型分析来量化不同不确定性来源对QoI变异性的贡献。我们发现热梯度和Gibbs-Thomson系数对qi的贡献可以忽略不计，而凝固速度、液体扩散系数和偏析系数对qi的贡献相当大。利用累积分布函数和概率密度函数分析了qi在凝固过程中的分布。我们的方法首次确定了与增材制造相关的凝固过程中qi的不确定性来源和频率密度。
Quality control in additive manufacturing can be achieved through variation control of the quantity of interest (QoI). We choose in this work the microstructural microsegregation to be our QoI. Microsegregation results from the spatial redistribution of a solute element across the solid–liquid interface that forms during solidification of an alloy melt pool during the laser powder bed fusion process. Since the process as well as the alloy parameters contribute to the statistical variation in microstructural features, uncertainty analysis of the QoI is essential. High-throughput phase-field simulations estimate the solid–liquid interfaces that grow for the melt pool solidification conditions that were estimated from finite element simulations. Microsegregation was determined from the simulated interfaces for different process and alloy parameters. Correlation, regression, and surrogate model analyses were used to quantify the contribution of different sources of uncertainty to the QoI variability. We found negligible contributions of thermal gradient and Gibbs–Thomson coefficient and considerable contributions of solidification velocity, liquid diffusivity, and segregation coefficient on the QoI. Cumulative distribution functions and probability density functions were used to analyze the distribution of the QoI during solidification. Our approach, for the first time, identifies the uncertainty sources and frequency densities of the QoI in the solidification regime relevant to additive manufacturing.