SYNERGY BETWEEN 3D MODELING AND ARTIFICIAL NEURAL NETWORKS IN THE OPTIMIZATION OF ADDITIVE MANUFACTURING IN THE CONTEXT OF INDUSTRY 4.0

Abstract

Additive manufacturing (AM), widely known as 3D printing, emerges as one of the fundamental technologies of Industry 4.0, enabling the manufacture of parts with high geometric complexity and customization. This study investigates how the integration between 3D modeling and Artificial Neural Networks (ANNs) enhances the efficiency and quality of AM processes. 3D modeling provides support for detailed simulations of the behavior of materials and manufacturing processes, while ANNs offer predictive analysis and learning from large volumes of data, allowing automatic and dynamic adjustments to parameters such as speed, temperature, and fill patterns. The results demonstrate significant improvements in reliability, waste reduction and energy consumption, aligning production with sustainability demands. In addition, the degree of maturity of Industry 4.0 contributes to this integration, with the use of tools such as IoT, cloud computing, and big data, creating an intelligent and connected production environment. Despite the challenges related to technological infrastructure, the qualification of the workforce and the development of algorithms for ANNs, the benefits overcome the obstacles, resulting in greater flexibility and customization of production processes. This work concludes that the integration of 3D modeling and ANNs in additive manufacturing represents a milestone in the digital transformation and competitiveness of the industrial sector, standing out as a promising approach for process optimization and data-driven decision making.