Diseño y calibración de un sistema modular de inspección inteligente para verificación y calidad en línea 4.0
In the context of Industry 4.0, this project addresses the growing demand for in-line quality control and automated repair in high-stakes manufacturing. While complex inspection tasks often require manual intervention due to detection difficulties, traditional industrial robots are frequently limited by rigid safety requirements. To overcome this, the research focuses on Collaborative Robots (Cobots), which offer a safe, cost-effective, and flexible symbiosis between humans and machines, enabling high levels of automation in intricate assembly and visual inspection processes.
The primary objective is the development of intelligent sensors based on mobile and low-cost devices integrated into both collaborative and conventional robotic platforms. These systems leverage Deep Learning models and neural networks for the advanced recognition, segmentation, and precise measurement of surface features and defects. Beyond simple detection, the project develops a comprehensive framework for sensor-to-robot calibration, allowing the robot to be precisely directed to a detected flaw for autonomous or semi-autonomous repair.
Primarily focused on the automotive and aerospace sectors, the system operates through a consistent functional cycle: robot pre-positioning, defect detection, coordinate generation, and real-time action commands. Furthermore, the project integrates industrial connectivity solutions, linking sensor data with plant-wide systems to enable Big Data analytics. This approach not only improves operational efficiency but also facilitates the transition toward smarter, self-correcting production environments.