Linking process parameters with lack-of-fusion porosity for laser powder bed fusion metal additive manufacturing
Linking process parameters with lack-of-fusion porosity for laser powder bed fusion metal additive manufacturing
复制标题
将工艺参数与激光粉末床熔融金属增材制造的未熔合孔隙率联系起来
DOI：
10.1016/j.addma.2023.103500
复制
发表时间：
2023
影响因子：
11
通讯作者：
Liu, Wing Kam
中科院分区：
文献类型：
--
作者：
Mojumder, Satyajit;Gan, Zhengtao;Li, Yangfan;Amin, Abdullah Al;Liu, Wing Kam
孔隙率等结构缺陷对增材制造部件具有不利影响，可以通过选择最佳工艺条件来减少。在这项工作中，研究了Ti-6Al-4V合金（Ti64）的激光粉末床熔合（L-PBF）工艺参数与未熔合（LOF）孔隙率之间的关系。基于物理的热流体模型被用来预测多层多轨道PBF过程中的LOF孔隙率。为了有效地映射高维工艺参数与孔隙率，主动学习框架已被采用的实验的优化设计。此外，一个定制的基于神经网络的符号回归工具已被用来识别加工条件和LOF孔隙率之间的机械关系。结果表明，结合物理为基础的热流体模型PBF孔隙度预测与主动学习和符号回归可以找到一个适当的机械关系LOF孔隙度是预测范围广泛的处理条件。通过无量纲数对其他金属AM材料系统（IN 718，SS 316 L）的这种机理关系进行了进一步检验。所提出的工作流程有效地探索了不同增材制造材料系统的高维工艺设计空间。
Structural defects such as porosity have detrimental effects on additively manufactured parts which can be reduced by choosing optimal process conditions. In this work, the relationship between process parameters and lack-of-fusion (LOF) porosity has been studied for the laser powder bed fusion (L-PBF) process of the Ti‐6Al‐4V alloy (Ti64). A physics-based thermo-fluid model is used to predict LOF porosity in the multilayer multitrack PBF process. To effectively map the high-dimensional processing parameters with porosity, an active learning framework has been adopted for the optimal design of experiments. Furthermore, a customized neural network-based symbolic regression tool has been utilized to identify a mechanistic relationship between processing conditions and LOF porosity. Results indicate that combining the physics-based thermo-fluid model for PBF porosity prediction with active learning and symbolic regression can find an appropriate mechanistic relationship of LOF porosity that is predictive for a wide range of processing conditions. This mechanistic relationship was further tested for other metal AM materials systems (IN718, SS316L) through non-dimensional numbers. The presented workflow effectively explores the high-dimensional process design space for different additive manufacturing materials systems.