Optimal Feed-Forward and Iterative Learning Control Framework for Enhanced Precision in Extrusion-Based Additive Manufacturing

Abstract Extrusion-based printing is a widely used additive manufacturing (AM) process, typically controlled by motion inputs (stage speed) with a constant extrusion rate. However, relying solely on stage speed can introduce printing resolution errors, particularly in complex pattern regions like sharp angles. This paper develops a modeling framework for line width based on extrusion rate and stage speed, using the control volume concept. An optimal feedforward control framework is then designed to compute control inputs that accurately track line width while respecting system constraints. To refine these inputs, they are adjusted to match the system’s slowest time constant, yielding the final feed-forward signal, which significantly reduces errors in complex regions. To further address modeling limitations, an iterative learning control (ILC) framework is integrated with the feed-forward approach, mitigating errors across both printing and iteration directions. Experimental results strongly align with the model, demonstrating the framework’s reliability. The combined control system effectively enhances line width precision and pattern consistency across individual prints and repeated iterations, even in intricate designs.