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

Armstrong, Ashley Allison

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https://hdl.handle.net/2142/109347 Copy

Description

Title

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

Author(s)

Armstrong, Ashley Allison

Issue Date

2020-12-01

Director of Research (if dissertation) or Advisor (if thesis)

• Alleyne, Andrew
• Wagoner Johnson, Amy

Doctoral Committee Chair(s)

Alleyne, Andrew

Committee Member(s)

• Tsao, Tsu-Chin
• Ferreira, Placid
• Salapaka, Srinivasa

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

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

Abstract

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.

Graduation Semester

2020-12

Type of Resource

Thesis

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

Copyright and License Information

Copyright 2020 Ashley Armstrong

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