利用遗传编程和dimensional analysis发现金属3D打印中的通用标度律
Discovering universal scaling laws in 3D printing of metals with genetic programming and dimensional analysis
摘要：
我们利用尺寸分析和遗传编程（一种机器学习）来发现两个非常简单但通用的缩放定律，这些定律对于不同的材料，加工条件和金属三维（3D）打印机仍然是准确的。第一个是从高保真高速同步辐射X射线成像中提取的，并定义了一个新的无量纲数，锁眼数，预测熔池蒸汽凹陷深度。第二个预测孔隙度使用的匙孔数和另一个无量纲数，归一化能量密度。通过减少这些长期存在的问题的维度，低维标度律将有助于工艺优化和缺陷消除，并可能为金属3D打印中的关键问题提供定量预测框架。此外，该方法本身广泛适用于一系列科学领域。
英文摘要：
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.
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