Purpose

Laser powder bed fusion (LPBF) provides the means to produce unique components with almost no restriction on geometry in an extremely short time. However, the high-temperature gradient and high cooling rate produced during the fabrication process result in residual stress, which may prompt part warpage, cracks or even baseplate separation. Accordingly, an appropriate selection of the LPBF processing parameters is essential to ensure the quality of the built part. This study, thus, aims to develop an integrated simulation framework consisting of a single-track heat transfer model and a modified inherent shrinkage method model for predicting the curvature of an Inconel 718 cantilever beam produced using the LPBF process.

Design/methodology/approach

The simulation results for the curvature of the cantilever beam are calibrated via a comparison with the experimental observations. It is shown that the calibration factor required to drive the simulation results toward the experimental measurements has the same value for all settings of the laser power and scanning speed. Representative combinations of the laser power and scanning speed are, thus, chosen using the circle packing design method and supplied as inputs to the validated simulation framework to predict the corresponding cantilever beam curvature and density. The simulation results are then used to train artificial neural network models to predict the curvature and solid cooling rate of the cantilever beam for any combination of the laser power and scanning speed within the input design space. The resulting processing maps are screened in accordance with three quality criteria, namely, the part density, the radius of curvature and the solid cooling rate, to determine the optimal processing parameters for the LPBF process.

Findings

It is shown that the parameters lying within the optimal region of the processing map reduce the curvature of the cantilever beam by 17.9% and improve the density by as much as 99.97%.

Originality/value

The present study proposes a computational framework, which could find the parameters that not only yield the lowest distortion but also produce fully dense components in the LPBF process.

中文翻译：

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Inconel 718激光粉末床熔合加工内零件变形优化

目的

激光粉末床融合 (LPBF) 提供了在极短的时间内生产几乎不受几何形状限制的独特组件的方法。然而，制造过程中产生的高温梯度和高冷却速率导致残余应力，这可能导致零件翘曲、裂纹甚至底板分离。Accordingly, an appropriate selection of the LPBF processing parameters is essential to ensure the quality of the built part. 因此，本研究旨在开发由单轨传热模型和改进的固有收缩方法模型组成的集成模拟框架，用于预测使用 LPBF 工艺生产的 Inconel 718 悬臂梁的曲率。

设计/方法/方法

悬臂梁曲率的模拟结果通过与实验观察的比较进行校准。结果表明，将模拟结果推向实验测量所需的校准因子对于激光功率和扫描速度的所有设置具有相同的值。因此，使用圆形包装设计方法选择激光功率和扫描速度的代表性组合，并将其作为输入提供给经过验证的模拟框架，以预测相应的悬臂梁曲率和密度。然后将仿真结果用于训练人工神经网络模型，以预测在输入设计空间内激光功率和扫描速度的任意组合下悬臂梁的曲率和固体冷却速率。

发现

结果表明，位于加工图最佳区域内的参数使悬臂梁的曲率降低了17.9%，密度提高了99.97%。

原创性/价值

本研究提出了一个计算框架，它可以找到不仅产生最低失真而且在 LPBF 过程中产生完全密集分量的参数。