A Multi-Input Single-Output iterative learning control for improved material placement in extrusion-based additive manufacturing

A major limitation in extrusion-based additive manufacturing (AM) is the lack of process monitoring and control tools material deposition frame. Iterative learning (ILC) has been demonstrated to be an effective strategy (Hoelzle et al., 2009; Bristow & Alleyne, 2003) due repetitive nature processes, but for much prior work ILC AM, focus was on precise machine components. To improve fabricated part quality, we apply directly account uncertainty behavior imperfect coordination between flow axes. This paper presents a novel Multi-Input Single-Output (MISO) Learning Control method that couples extrusion input axis speed width along trajectory. MISO partitions error different stages through frequency separation avoid saturation. uses feedback frame placement. The stability convergence properties system are presented lifted domain. Simulation experimental results printing demonstrate achieves improved placement path. While specific system, present general can implemented other applications.