Menon Nandana, Mondal Sudeepta, Basak Amrita
Department of Mechanical Engineering, The Pennsylvania State University, University Park, State College, PA 16802, USA.
Materials (Basel). 2022 Apr 15;15(8):2902. doi: 10.3390/ma15082902.
A multi-fidelity (MF) surrogate involving Gaussian processes (GPs) is used for designing temporal process maps in laser directed energy deposition (L-DED) additive manufacturing (AM). Process maps are used to establish relationships between the melt pool properties (e.g., melt pool depth) and process parameters (e.g., laser power and scan velocity). The MFGP surrogate involves a high-fidelity (HF) and a low-fidelity (LF) model. The Autodesk Netfabb® finite element model (FEM) is selected as the HF model, while an analytical model developed by Eagar-Tsai is chosen as the LF one. The results show that the MFGP surrogate is capable of successfully blending the information present in different fidelity models for designing the temporal forward process maps (e.g., given a set of process parameters for which the true depth is not known, what would be the melt pool depth?). To expand the newly-developed formulation for establishing the temporal inverse process maps (e.g., to achieve the desired melt pool depth for which the true process parameters are not known, what would be the optimal prediction of the process parameters as a function of time?), a case study is performed by coupling the MFGP surrogate with Bayesian Optimization (BO) under computational budget constraints. The results demonstrate that MFGP-BO can significantly improve the optimization solution quality compared to the single-fidelity (SF) GP-BO, along with incurring a lower computational budget. As opposed to the existing methods that are limited to developing steady-state forward process maps, the current work successfully demonstrates the realization of temporal forward and inverse process maps in L-DED incorporating uncertainty quantification (UQ).
一种涉及高斯过程（GP）的多保真度（MF）代理模型被用于在激光定向能量沉积（L-DED）增材制造（AM）中设计时间过程图。过程图用于建立熔池特性（如熔池深度）与工艺参数（如激光功率和扫描速度）之间的关系。MF GP代理模型包含一个高保真（HF）模型和一个低保真（LF）模型。选择Autodesk Netfabb®有限元模型（FEM）作为HF模型，而将Eagar-Tsai开发的解析模型选作LF模型。结果表明，MF GP代理模型能够成功融合不同保真度模型中的信息，用于设计时间正向过程图（例如，给定一组未知真实深度的工艺参数，熔池深度会是多少？）。为了扩展用于建立时间反向过程图的新公式（例如，要实现未知真实工艺参数的所需熔池深度，作为时间函数的工艺参数的最佳预测会是多少？），在计算预算约束下，通过将MF GP代理模型与贝叶斯优化（BO）相结合进行了一个案例研究。结果表明，与单保真（SF）GP-BO相比，MF GP-BO可以显著提高优化解的质量，同时计算预算更低。与现有仅限于开发稳态正向过程图的方法不同，当前工作成功展示了在包含不确定性量化（UQ）的L-DED中实现时间正向和反向过程图。
