Sinergia entre modelagem 3D e redes neurais artificiais na otimização da manufatura aditiva no contexto da Indústria 4.0

Abstract

Additive manufacturing (AM), widely known as 3D printing, emerges as one of the fundamental technologies of Industry 4.0, enabling the fabrication of highly complex and customized parts. 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 material behavior and manufacturing processes, while ANNs offer predictive analysis and learning from large volumes of data, allowing automatic and dynamic adjustments in parameters such as speed, temperature, and filling patterns. The results demonstrate significant improvements in reliability, waste reduction, and energy consumption, aligning production with sustainability demands. Additionally, the maturity level 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 challenges related to technological infrastructure, workforce qualification, and ANN algorithm development, the benefits outweigh the obstacles, resulting in greater flexibility and customization of production processes. This study concludes that the integration of 3D modeling and ANNs in additive manufacturing represents a milestone in digital transformation and industrial sector competitiveness, standing out as a promising approach for process optimization and data-driven decision-making.