论文标题
随机添加剂制造模拟：从实验到表面粗糙度和孔隙率预测
Stochastic additive manufacturing simulation: from experiment to surface roughness and porosity prediction
论文作者
论文摘要
除非使用昂贵的粉末规模分析，否则激光粉末床融合（LPBF）过程的确定性计算建模无法捕获扫描轨道的不规则性和粗糙度。在这项工作中，我们开发了一个基于马尔可夫链蒙特卡洛（MCMC）的随机计算建模框架，能够捕获LPBF扫描的不规则性。使用特殊设计的张量分解方法，即高阶适当的通用分解（HOPGD）对AFRL单轨扫描数据进行校准，该方法依赖于非侵入性数据学习和减少订单替代模型的构建。一旦校准，与详细的粉末尺度确定性模拟相比，随机模型可用于以零件尺度的零件尺度预测粗糙度和孔隙率。与常规的确定性仿真结果相比，对AFRL多层和多站实验的随机模拟预测进行了验证，并报告更准确。
Deterministic computational modeling of laser powder bed fusion (LPBF) process fails to capture irregularities and roughness of the scan track, unless expensive powder-scale analysis is used. In this work we developed a stochastic computational modeling framework based on Markov Chain Monte Carlo (MCMC) capable of capturing the irregularities of LPBF scan. The model is calibrated against AFRL single track scan data using a specially designed tensor decomposition method, i.e., Higher-Order Proper Generalized Decomposition (HOPGD) that relies on non-intrusive data learning and construction of reduced order surrogate models. Once calibrated, the stochastic model can be used to predict the roughness and porosity at part scale at a significantly reduced computational cost compared to detailed powder-scale deterministic simulations. The stochastic simulation predictions are validated against AFRL multi-layer and multitrack experiments and reported as more accurate when compared with regular deterministic simulation results.
