In this contribution, we validate a physical model based on a transient temperature equation (including latent heat), w.r.t. the experimental set AMB2018-02 provided within the additive manufacturing benchmark series, established at the National Institute of Standards and Technology, USA. We aim at predicting the following quantities of interest, width, depth, and length of the melt pool by numerical simulation, and report also on the obtainable numerical results of the cooling rate. We first assume the laser to possess a double-ellipsoidal shape and demonstrate that a well-calibrated, purely thermal model based on isotropic thermal conductivity is able to predict all the quantities of interest, up to a deviation of maximum 7.3% from the experimentally measured values. However, it is interesting to observe that if we directly introduce, whenever available, the measured laser profile in the model (instead of the double-ellipsoidal shape), the investigated model returns a deviation of 19.3% from the experimental values. This motivates a model update by introducing anisotropic conductivity, which is intended to be a simplistic model for heat material convection inside the melt pool. Such an anisotropic model enables the prediction of all quantities of interest mentioned above with a maximum deviation from the experimental values of 6.5%. We note that, although more predictive, the anisotropic model induces only a marginal increase in computational complexity.

中文翻译：

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含相变的非线性温度方程精确预测激光粉末床熔合中熔池的形状

在此贡献中，我们验证了基于瞬态温度方程（包括潜热）的物理模型，并使用了美国国家标准技术研究院在增材制造基准系列中提供的实验套件AMB2018-02。我们旨在通过数值模拟来预测熔池的以下感兴趣的数量，宽度，深度和长度，并且还报告可获得的冷却速率数值结果。我们首先假定激光器具有双椭圆形形状，并证明基于各向同性热导率的经过良好校准的纯热模型能够预测所有感兴趣的量，与实验测量值的最大偏差为7.3％价值观。但是，有趣的是，如果我们直接介绍，只要有可用的模型中测得的激光轮廓（而不是双椭圆形），所研究的模型将返回与实验值的19.3％的偏差。这通过引入各向异性电导率来激发模型更新，该模型旨在成为熔池内部热对流的简化模型。这种各向异性模型可以预测上述所有感兴趣的量，并且与实验值的最大偏差为6.5％。我们注意到，尽管更具预测性，但各向异性模型仅引起计算复杂性的少量增加。这通过引入各向异性电导率来激励模型更新，该模型旨在简化熔池内部热对流的模型。这种各向异性模型可以预测上述所有感兴趣的量，并且与实验值的最大偏差为6.5％。我们注意到，尽管更具预测性，但各向异性模型仅引起计算复杂性的少量增加。这通过引入各向异性电导率来激励模型更新，该模型旨在简化熔池内部热对流的模型。这种各向异性模型可以预测上述所有感兴趣的量，并且与实验值的最大偏差为6.5％。我们注意到，尽管更具预测性，但各向异性模型仅引起计算复杂性的少量增加。