The Use of Iterative Learning Control to Improve the Accuracy of Additive Manufacturing

Fused deposition modeling (FDM) additive manufacturing, while versatile, has performance limitations in certain geometries, such as arcs, and holes. Previous research led to iterative learning control (ILC) being selected as the control algorithm to procedurally generate parts that are more accurate than their prior counterparts. Since a baseline for the printer and scanner has been established, experiential testing with the ILC is in progress. Currently a “bounding box” approach is used to determine an error metric which is fed into the ILC to create the next iteration of printed parts. If this ILC approach is successful, the final part resulting from this iterative process of improvement will be more accurate, as it will more closely resemble the desired part, compared to the result achieved if only the default control approach was used. The ultimate goal of the research and development of the ILC is to allow for inexpensive desktop machines to produce parts with a level of accuracy that is comparable to high end industrial FDM machines.