Meta-modeling of high-fidelity FEA simulation for efficient product and process design in additive manufacturing

Finite element analysis (FEA) has been widely adopted to identify potential defects in additive manufacturing (AM) processes. For personalized product realization, it is necessary to validate a number of heterogeneous product and process designs before or during manufacturing by using FEA. Multi-fidelity FEA simulations can be readily implemented with different capabilities in terms of simulation accuracy. However, due to its complexity, high-fidelity FEA simulation is time-consuming and decreases the efficiency of product realization in AM, while low-fidelity FEA simulation has fast computation speed yet limited capability. Hence, our objective is to improve the capability of FEA by providing an efficient data-driven model. In this research, a Gaussian process-constrained general path model is proposed to approximate the high-fidelity FEA simulation results based on low-fidelity results voxel-by-voxel. The proposed model quantifies the heterogeneous discrepancies between low- and high-fidelity FEA simulation results by incorporating the product design information (e.g., Cartesian coordinates of deposition sequence) and process design information from inputs of FEA simulation (e.g., input heat). Therefore, it enables the validation of new product and process designs based on the simulation results with the desired capability in a timely manner. The advantages of the proposed method are illustrated by FEA simulations of the fused deposition modeling (FDM) process with two levels of fidelity (i.e., low- and high-fidelity).