论文标题
通过遗传编程和维度分析在3D打印金属中发现通用缩放定律
Discovering universal scaling laws in 3D printing of metals with genetic programming and dimensional analysis
论文作者
论文摘要
我们利用维数分析和遗传编程（一种机器学习）来发现两种非常简单但通用的缩放定律，这些定律对于金属三维（3D）印刷中的不同材料，加工条件和机器仍然准确。第一个是从高保真性高速同步加速器X射线成像中提取的，并定义了一个新的无尺寸数字，钥匙孔数量，以预测熔体池蒸气抑郁深度。第二个可以使用钥匙孔数量和另一个无量纲数，即归一化的能量密度来预测孔隙率。通过降低这些长期问题的维度，低维度缩放定律将有助于过程优化和缺陷消除，并有可能导致金属3D打印中关键问题的定量预测框架。此外，该方法本身广泛适用于一系列科学领域。
We leverage dimensional analysis and genetic programming (a type of machine learning) to discover two strikingly simple but universal scaling laws, which remain accurate for different materials, processing conditions, and machines in metal three-dimensional (3D) printing. The first one is extracted from high-fidelity high-speed synchrotron X-ray imaging, and defines a new dimensionless number, Keyhole number, to predict melt-pool vapor depression depth. The second predicts porosity using the Keyhole number and another dimensionless number, normalized energy density. By reducing the dimensions of these longstanding problems, the low-dimensional scaling laws will aid process optimization and defect elimination, and potentially lead to a quantitative predictive framework for the critical issues in metal 3D printing. Moreover, the method itself is broadly applicable to a range of scientific areas.
