Implementation of a surrogate model for structural optimization based on artificial neural network and genetic algorithm

The use of computational optimization methods in structural design, employing surrogate models based on artificial neural networks (ANNs), is a promising approach for achieving efficient and cost-effective solutions compared to traditional optimization relying solely on the finite element method (FEM). These methods are particularly effective for composite material structures, which can be tailored to meet varying mechanical requirements and are fabricated via fused deposition modeling (FDM), such as 3D printing, due to their flexibility and capability to efficiently produce complex geometries. This study aims to implement a numerical optimization algorithm combined with an ANN trained and tested using a database to optimize the support arm of an unmanned aerial vehicle (UAV), manufactured using FDM. The methodology was divided into two main stages. In the first stage, an algorithm was developed in Python to generate a database from FEM simulations conducted in ABAQUS software. These simulations considered the arm, fabricated using polylactic acid (PLA) as the constituent material, with one fixed end and the other subjected to thrust and torsional loads, replicating the UAV's operational conditions. Four geometric parameters of the arm (structure height and width, thickness, and the number of diagonal reinforcement cells) were modified to obtain the masses and stiffness values for each configuration. In the second stage, the algorithm was enhanced by incorporating three additional geometric variables (thickness of the edges and bottom reinforcement, and width of the top reinforcement), resulting in a more extensive database. This expanded dataset was used to train the ANN, which predicted masses and stiffness values based on the geometric variables. The multi-objective optimization problem was then defined using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to minimize mass and maximize stiffness, constrained by the geometric variables, with the ANN employed to evaluate the multi-objective function as a surrogate for FEM. From the solutions obtained on the Pareto front, one was selected for experimental validation. The optimized and standard models were designed in computer-aided design (CAD) software, fabricated using FDM with PLA, and subjected to bending tests to replicate the conditions implemented in the FEM analyses. The results demonstrated that the ANN exhibited high accuracy, with correlation coefficients of 0.99 for stiffness and 0.99 for mass, reducing analysis time by 99.65% compared to FEM. The optimized model showed an average specific stiffness increase of 51.14% and a mass reduction of 42.13% compared to the standard model. Finally, the results indicated that the proposed methodology is efficient in determining optimized solutions. While the database generation required substantial computational time, analyses conducted using the trained neural network demonstrated a significant reduction in processing time. Moreover, the proposed method is adaptable for application in various engineering projects involving composite materials