Direct process feedback in extrusion-based additive manufacturing using an improved iterative learning control approach

Additive manufacturing (AM) is one of the largest and most exciting growth areas of manufacturing research for the near future. While the impact of AM covers many market segments, our focus in this effort is restricted to extrusion-based bioprinting for applications in tissue engineering. A major limitation in extrusion-based printing is the lack of process monitoring tools in the material reference frame, which limits the spatial resolution and results in defects that can influence the biological and mechanical outcomes of the fabricated structures. Extrusion-based printing also lacks appropriate control tools for material deposition to correct for and avoid defects. Iterative learning control (ILC) is a candidate control strategy for manufacturing applications due to the repetitive nature of manufacturing processes. However, there are current knowledge gaps in ILC that must be addressed before it can be implemented to improve material fabrication. For much of the prior work of ILC in manufacturing applications, the focus was on precise control of the machine components. High precision 3D AM, however, requires precise control of material deposition. The machine axis motions cannot be reliably used to predict material placement due to imperfect coordination between the machine and material reference frames as well as nonlinear behavior of the material between extrusion nozzle and substrate. Further, for approaches to date, the speed of convergence to the appropriate input signal for ILC is limited by the level of knowledge of the plant model. As a result, the convergence rate for uncertain systems, such as material systems in AM, is slow and uncertain, which requires a lot of material and can potentially cause system damage. This dissertation uses a two-pronged approach to address two main gaps including 1) the lack of process monitoring and control tools in the material deposition frame and 2) the slow convergence rate of ILC for uncertain systems. The first key contribution of this work includes the development of a process monitoring and control strategy to monitor material placement. We use a non-contact, laser scanner that is integrated into the AM system and develop a custom image processing script to define and correct for the material placement error. The second key contribution is a novel ILC approach to speed up convergence for systems with significant model uncertainty. We experimentally validate the process monitoring method and novel ILC system on a custom-built extrusion printer. While we apply the process monitoring technique to a specific printing platform, the generalized approach can be extended to other extrusion-based platforms and other AM techniques to improve the spatial material placement in other printing applications.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Armstrong, Ashley Allison

Contributors dc:contributor

• Alleyne, Andrew
• Wagoner Johnson, Amy
• Tsao, Tsu-Chin
• Ferreira, Placid
• Salapaka, Srinivasa

• extrusion-based printing
• extrusion-based bioprinting
• iterative learning control
• additive manufacturing
• process monitoring
• process monitoring and controls
• scaffolds

Statement dc:rights

• Copyright 2020 Ashley Armstrong

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/109347

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/109347