Laser Powder Bed Fusion Parameter Selection via Machine-Learning-Augmented Process Modeling
Laser Powder Bed Fusion Parameter Selection via Machine-Learning-Augmented Process Modeling
复制标题
通过机器学习增强过程建模进行激光粉床熔融参数选择
DOI：
10.1007/s11837-020-04383-2
复制
发表时间：
2020
期刊：
影响因子：
2.6
通讯作者：
M. Groeber
中科院分区：
文献类型：
--
作者：
S. Srinivasan;Brennan Swick;M. Groeber
激光粉末床熔融增材制造（AM）是材料和制造界的一个高度活跃的研究领域，其推动力是缩短交货时间，增加设计灵活性和潜在的特定位置过程控制。然而，复杂的处理空间抵消了这些益处，并且在尝试开发跨不同部件几何形状和子几何形状的工艺参数集时导致困难。我们开发了一种将基于物理的过程建模与机器学习和优化方法相结合的方法，以加速搜索AM处理空间以获得合适的打印参数集。我们首先在尺寸不同的简单几何形状上展示了该方法，然后在更复杂的几何形状上展示了局部定制工艺参数对组件加工历史的好处。
Laser powder bed fusion additive manufacturing (AM) is a highly active research area in the materials and manufacturing community, driven by promises of reduced lead time, increased design flexibility, and potentially location-specific process control. However, a complex processing space counters these benefits and results in difficulties when attempting to develop process parameter sets across different component geometries and subgeometries. We develop a procedure for coupling physics-based process modeling with machine learning and optimization methods to accelerate searching the AM processing space for suitable printing parameter sets. We demonstrate the approach first on simple geometries that vary in size to show the methodology and then on a more complicated geometry to show the benefit of locally tailored process parameters on component processing history.