MAAL

This project establishes a dynamic model of industrial robots considering the link and joint flexibility. The geometrical nonlinearity of the robot is captured with low dimensions via the strain-based beam elements and modal amplitudes. The dynamic model captures the industrial robot’s position- and posture-dependent vibration. A nonlinear filtered B-spline approach is developed to alleviate the motion-induced vibration.

This project aims to establish a unified model of additive manufacturing process combining the process model and the feed drive dynamic model. Thermal cameras and accelerometers are exploited to acquire vibration, temperature, position, and process data. Local and cloud controllers are deployed to enable model-based and data-driven control for enhanced manufacturing performance.

This project addresses the modeling and control in continuous fiber additive manufacturing. It aims to establish a computationally efficient model of free-form continuous-fiber-reinforced parts. The model is further exploited in fiber path optimization in a unified consideration of part modulus, strength, cost, and weights.

This project proposes the boom lift-mounted robot platform, combining the scale model of boom lift vehicle with the six-axis industrial robot. The aim is to replace human to do the dangerous tasks at high altitude. This platform is deployed with the real-time controller, which can stabilize against the external disturbance, such as strong wind.