Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making.

The integration of a KUKA robot with a Meltio Engine head to create a laser metal deposition by wire (LMD-wire) cell involves a meticulous installation and must be highly controlled to function with the required precision. To this end, the creation of a digital twin became essential to operate the cell and to generate data that enable its monitoring, through an artificial intelligence system that optimizes its performance and prevents equipment wear. The goal of this article is to present the research that the Industrial Automation Innovation Group at the University of Brasília has been developing. The procedures are detailed, such as the installation of the robot, the Meltio-KUKA integration, the elaboration of a planar slicer with Rhino3D-Grasshopper and the simulation process with KUKA.Sim. The simulations’ results confirmed the robustness of the system for planar deposition methods and demonstrated its versatility to adapt to non-planar, multiplanar and hybrid deposition methods. The integration of the LMD-wire cell will remain under evaluation until its complete installation, when it will be ready for the first deposition experiments.

Este trabalho apresenta a implementação de um Gêmeo Digital para uma célula de fabricação aditiva robotizada que utiliza o processo de Deposição de Metal a Laser com Arame (LMD-Wire). O sistema é composto por um cabeçote LMD-Wire da Meltio3D e um robô Kuka KR 70 R2100. Isso é especialmente relevante em tecnologias emergentes, como a fabricação LMD, que utiliza um laser de diodo para fundir seletivamente uma camada de material metálico alimentado por arame ou pó. Para desenvolver o Gêmeo Digital, é utilizada a ISO 23247 como referência e o ambiente CAD Rhinoceros-Grasshopper em conjunto com a plataforma de simulação Kuka.Sim, com o objetivo de criar uma plataforma CAD/CAPP/CAM para a célula robotizada.