Predictive Iterative Learning Control with Data-Driven Model for Optimal Laser Power in Selective Laser Sintering
Building high quality parts is still a key challenge for Selective Laser Sintering machines today due to a lack of sufficient process control. In order to improve process control, we propose a Predictive Iterative Learning Control (PILC) controller that minimizes the deviation of the postsintering temperature profile of a newly scanned part from a desired temperature. The controller does this by finding an optimal laser power profile and applying it to the plant in a feedforward manner. The PILC controller leverages machine learning models that accurately capture the process’ temperature dynamics based on in-situ measurement data while still guaranteeing low computational cost. We demonstrate the controller’s performance in regards to the control objective with heat transfer simulations by comparing the PILC-controlled laser power profiles to constant laser power profiles.