Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach
Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach
复制标题
金属增材制造多重耦合仿真模型的贝叶斯校准：贝叶斯网络方法
DOI：
10.1115/1.4052270
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Elwany, Alaa
中科院分区：
文献类型：
--
作者：
Ye, Jiahui;Mahmoudi, Mohamad;Karayagiz, Kubra;Johnson, Luke;Seede, Raiyan;Karaman, Ibrahim;Arroyave, Raymundo;Elwany, Alaa
增材制造（AM）的建模和仿真是理解工艺物理、进行工艺规划和优化以及简化资格和认证的关键推动因素。这是经常的情况下，一套分层链接（或耦合）的仿真模型是需要实现上述任务，作为整体的复杂的物理现象相关的理解过程中的背景下，AM的结构性能性能的关系排除了使用一个单一的仿真框架。在这项研究中，使用贝叶斯网络的方法，我们解决的重要问题进行不确定性量化（UQ）分析的多层次模型，以建立激光粉末床融合（LPBF）AM的工艺-微观结构的关系。更重要的是，我们提出的框架来校准和分析仿真模型，实验上不可测量的变量，这是由上游模型预测的数量感兴趣，并认为有必要为下游模型链。我们验证的框架使用的情况下，预测使用LPBF作为工艺参数的函数处理的二元镍铌合金的微观结构的研究。我们的框架被证明是能够预测铌偏析高达94.3%的预测精度的测试数据。
Modeling and simulation for additive manufacturing (AM) are critical enablers for understanding process physics, conducting process planning and optimization, and streamlining qualification and certification. It is often the case that a suite of hierarchically linked (or coupled) simulation models is needed to achieve the above tasks, as the entirety of the complex physical phenomena relevant to the understanding of process-structure-property-performance relationships in the context of AM precludes the use of a single simulation framework. In this study using a Bayesian network approach, we address the important problem of conducting uncertainty quantification (UQ) analysis for multiple hierarchical models to establish process-microstructure relationships in laser powder bed fusion (LPBF) AM. More significantly, we present the framework to calibrate and analyze simulation models that have experimentally unmeasurable variables, which are quantities of interest predicted by an upstream model and deemed necessary for the downstream model in the chain. We validate the framework using a case study on predicting the microstructure of a binary nickel-niobium alloy processed using LPBF as a function of processing parameters. Our framework is shown to be able to predict segregation of niobium with up to 94.3% prediction accuracy on test data.