Modeling and Topography Control for Microscale Additive Manufacturing

Abstract

Additive manufacturing (AM) processes are so radically different in their approach to fabricate parts, assemblies, or even entire devices that the traditional machining processes they replace are now labeled ‘subtractive.’ Additionally, the recent proliferation and maturation of AM processes is amplifying the digital and networked transformation in manufacturing capabilities often considered a new industrial revolution. While many engineering challenges in AM are sufficiently addressed at the macroscale (resolutions of approximately 100 μm and above), the microscale remains a formidable frontier for AM. Microscale Additive Manufacturing (μAM) offers new material and geometric capabilities and mask-free fabrication not available in traditional lithographic micromachining or soft lithography, so μAM promises expanded design palettes in bio, optical, and electrical microdevice fabrication and customization. However, the bottleneck in μAM microdevice development is in repeatably achieving microscale geometric tolerances. This problem is especially severe for candidate material jetting μAM processes such as 3D inkjet and Electrohydrodynamic Jet (E-jet) printing, which rely upon liquid interface motion. Modeling, sensing, and closed loop topography control are needed to replace the current practice of heuristically tuning manufacturing process parameters to achieve geometric tolerances. Only by automated control will application development time and manufacturing process yield improve to economically feasible levels. The objective of this dissertation is twofold: first, to develop more accurate process models for topography control; and second, to improve topography controllers for material jetting μAM. A testbed for E-jet research is presented with an integrated atomic force microscope for topography feedback. A demonstration of inline metrology and quality monitoring is performed on the testbed. The spreading of printed build material is investigated as it relates to the final shape of a printed drop. A process model to capture the drop spreading transient is proposed from the molecular kinetic theory of contact line motion and experimentally validated for E-jet printing using high speed optical microscopy on the printing testbed. Subsequently, a process model for topography control is developed to capture the perturbation to printed drop shape (and thus topography evolution) caused by nonflat substrate topography. A relatively low complexity numerical simulation of drop spreading on nonflat surfaces is developed to identify and evaluate the models for topography control. The key to low complexity in the numerical simulation is to neglect inertial and viscous effects by treating steady state drop shape prediction as a process driven to equilibrium by interfacial energy minimization of a fixed volume drop. The identified models for topography control are linear, parameter-varying for use in model-based control. Lastly, directions for controller improvement are identified, emphasizing constrained optimization and input signal sparsity in Spatial Iterative Learning Control (SILC). Additionally, this work has implications for broader AM process control.