Effects of DoE Sampling and Hyperparameter Selection on ANN-Based Surrogate Models for Large-Scale 3D Concrete Printer Frames
In mechanical design optimization, ANN-based surrogate models are increasingly used to replace computationally expensive simulations such as the Finite Element Method (FEM) and Computational Fluid Dynamics (CFD). However, their performance strongly depends on the quality of training data (Design of Experiments - DoE) and ANN configuration. Many studies still rely on random sampling and trial-and-error approaches, leading to suboptimal accuracy. This paper develops an ANN-based surrogate model for the structural design of a large-scale 3D concrete printer frame. A dataset is generated using an automated computational module integrating MATLAB with ANSYS APDL. The ANN models are used to evaluate the influence of design parameters on structural displacements and natural frequencies. Comparative results show that the ANN model based on random sampling (RS) and trial-and-error yields a maximum relative error of approximately 8%. The use of Latin Hypercube Sampling (LHS) reduces this error to below 3.5%, while the combination of LHS and Reduced Grid Search (RGS) achieves the best performance, with the maximum relative error predominantly below 2%. These results demonstrate that combining LHS with hyperparameter optimization significantly improves the accuracy and robustness of surrogate models, providing a reliable alternative to FEM in structural design optimization.