Energy Absorption Analysis in an Auxetic Lattice Structure Using Artificial Neural Network Machine Learning and Genetic Algorithm

Today, with the advancement of technology, improvement and quality of life, artificial intelligence has found a special place in the world. In this research, the optimization of the curved mesh structure made of polylactic acid has been discussed. Mesh structures are widely used in various industries due to their advantages such as high energy absorption. The geometric parameters of this structure can have a noticeable effect on the amount of energy absorption of these structures. In this regard, optimization of geometrical parameters has been done using genetic algorithm. In this research, the parameters of radius of curvature R1, angle of curvature Ɵ1, radius of curvature R2, angle of curvature Ɵ2, length L relative to the energy absorbed by the structure, maximum force and modulus of elasticity have been optimized. A large number of optimization parameters confirm the use of genetic algorithm for the optimization process of this structure. Optimization using genetic algorithm requires the existence of an objective function to which the geometric parameters are optimized. This objective function should be a continuous function that can produce the necessary outputs for each geometric parameter. In this research, artificial neural network has been used to construct the objective function. The artificial neural network makes it possible to create a continuous function with a limited number of inputs and outputs that can produce suitable outputs for receiving different inputs. After extracting the optimal geometric parameters, the optimal structure was made using a 3D printer and subjected to quasi-static pressure testing.