At Eleven Dynamics, we work with automation systems that need to understand the real geometry of a part, not just the nominal CAD model. This principle is central to modern metrology automation, but it also applies directly to production processes such as gluing. When robots, sensors, software, and digital models work together, adhesive application can become more precise, flexible, and much less dependent on perfect part positioning.
Automated gluing uses robots or automated motion systems to apply adhesives, sealants, thermal materials, or similar compounds in a controlled way. The robot manages the movement while the dispensing system controls the material flow. When both are synchronized correctly, manufacturers can achieve more consistent quality, especially in high-volume production or on complex geometries.
A conventional robot usually follows a predefined trajectory based on CAD data, taught points, or fixture positions. But this assumes that every component arrives exactly where the digital model says it should. In real production, parts move, deform, or vary within tolerance. This is a challenge we already know well from automated metrology: repeating a robot path accurately does not mean the path is correct for the real component.
This is where adaptive automation becomes relevant. A sensor or metrology system can identify the real position and geometry of a part and compare it with the nominal model. Based on that information, the robot path can be adjusted before execution. The same concept is highly relevant to what we do at Eleven Dynamics: use real measurement data to make robot motion more intelligent and less dependent on fixed assumptions.
The workflow starts with the digital part and the desired application path. The real component is then measured or localized, and deviations are identified. From there, the robot trajectory can be adjusted, simulated, checked for collisions, and executed. This is very similar to the type of workflow we build around automated metrology: digital planning, sensor input, robot programming, execution, and validation all connected in one process.
A modern gluing cell combines a robot, dispensing equipment, sensors, simulation, path-planning software, and cell control. This is where the overlap with Eleven Dynamics becomes clear. Our work with EDAS focuses on connecting robots, sensors, digital models, and automation workflows in one environment. The dispensing process itself is specialist technology, but the orchestration, robot programming, simulation, and adaptation around it follow many of the same principles we already use in metrology automation.
The value of adaptive gluing is not only higher precision. It can reduce the dependence on complex fixtures, improve flexibility for different part variants, and make the process more robust against real production variation. This is also why adaptive automation matters to us: instead of trying to mechanically eliminate every deviation, we can use sensor data and software to let the automation respond intelligently.
For us, automated gluing is a good example of where industrial automation is heading. The future is not just robots executing predefined programs. It is robots working together with sensors, software, and real production data to make better decisions about how a process should be executed. That is closely aligned with our vision at Eleven Dynamics: building the software foundation for a Smart Factory where measurement, automation, and production become part of one connected system.
