## 1 Introduction  
Humanity has undergone several technological advances, and is currently experiencing the fourth industrial revolution, with effects in several areas. New technologies are constantly being developed, always requiring new knowledge. One of them is 3D printing, which still undergoes constant evolution [1]. Three-dimensional printing is a manufacturing area that builds parts, starting from a virtual model, by the automatic layer-by-layer deposition method [2]. In the late 1980s, 3D printing had a high cost and detailed coverage. In the 2000s, mainly due to the fall in patents, this process became less expensive, allowing for applications in other areas, such as education and medicine. In addition to having become a technology with several applications, 3D printing has gained strength by using low-cost products and generating little waste [1]. There are a few 3D printing methods, the most common being fused filament fabrication (FFF). This method consists of the hot deposition of a filament through the extrusion of a specific material [3]. Despite being a widely used process, 3D printing is a very elaborate technology. This high complexity is due to the many parameters influencing the process and the final effect. Geometry, speed, temperature, raw material and other factors influence the final properties of the product, such as mechanical strength and dimensional accuracy. Several works have already been developed to define how each 3D printing parameter influences the outcome. However, even with this large amount of studies, there are some difficulties in developing mathematical models that describe the final properties of the products [2]. Some works applied a computational strategy known as an artificial neural network (ANN) to control and optimize the parameters of the 3D printing process in the best possible way. Neural networks can present a supervised, unsupervised and reinforcement learning application methodology, being part of an even broader branch that is known as artificial intelligence (AI) [4]. The ANN represents an information processing system that simulates the functions of the human brain computationally. Neural networks, as well as other supervised learning methods, start from a data set, with input values associated with their respective output, so, after executing an algorithm, known as a learning algorithm, the network will have the ability to classify or predict the outcome of cases where the inputs do not have a known output [5]. The aforementioned concept can be beneficial for several applications, but these types of methods have several parameters that can be considered. Several tests can be carried out to define the best configuration for the ANN; however, performing this task can take a long time. Several methods can be used to mitigate the situation above. This paper applies a genetic algorithm (GA), commonly used to tackle optimization problems and grounded on [PERSON]'s theory of evolution, addressing steps such as natural selection, reproduction and mutation [6]. Combining ANN with the GA is called the neuroevolutionary strategy [7]. In this context, our paper aims to develop a model that combines artificial neural networks and genetic algorithms in a neuroevolutionary strategy, capable of predicting the rupture stress of materials manufactured by the 3D printing method of the EFF type. The main idea is to realize the training procedure of an ANN using the genetic algorithm strategy and to validate the weights representation throughout as a feasible solution. We highlight that models applying neural networks are commonly different in the configurations and representation of weights, activation functions and learning methods. Therefore, in this paper, we focus on presenting the choice composition of all of the above characteristics to obtain our proposal in the 3D printing process. The remaining part of this paper is organized as follows.