Droplet based microfluidics
Direct-ink-writing (DIW) is a method of additive manufacturing whereby fluid material, referred to as ink, is extruded through a syringe and nozzle onto a substrate to build a part later-by-layer (Ink Printing). DIW is proving to be an emergent technology with applications in:
There are two most common base modalities to produce a back force to move the piston head which pushes ink through the syringe and nozzle:
Both have their drawbacks in terms of performance, especially when printing with compressible inks. Indeed, compressible inks change in volume when a pressure is applied to them in a closed space (such as the syringe), which means some of the energy applied by the piston is lost to compressing instead of extruding the ink. Pressure-based printing requires in-depth optimization prior to printing to determine the rheological complexities and pressure-to-flow rate relationship of each ink, and velocity-driven printing harbors very slow response to ink transients which makes printing with micro-nozzles impractical on a reasonable timescale.
Iterative learning techniques have become prevalent in recent years, and here [2] we apply this principle to DIW to effectively combine pressure-based and velocity-based printing to more accurately control ink volume flow rate and therefore final printed part shape fidelity.
This can be performed with automatic settling-detection of transients and material model generation followed by model updates during extrusion via a closed-loop control method involving velocity feedback from the motor controller and pressure feedback from the Elveflow sensor.
A foundational component to the operation of the flow control setup is a nested control loop.
Flow rate is considered to be known only when both the velocity of the piston that is pushing the ink and the pressure read by the sensor at the nozzle entrance are in a steady state. Because of this, model generation and flow control are possible through an automatic settling-detection algorithm which takes a moving average of the piston velocity and pressure to determine when the system is at steady state.
Model generation is done by prescribing three set pressures to the system. The corresponding flow rates to those pressures are then automatically found by the settling detection algorithm.
This sequence yields a rough material model consisting of three data points in the form of:
P(Q) = bQn
where P is pressure, Q is volume flow rate, and b and n are Power Law Model fit parameters. The three-point model is sent to the main controller and flow rate control iterations can begin.
NB : P_target corresponds to the desired pressure, and the graph represents the read pressure as the piston in the syringe compresses and extrudes the ink.If there is some error between the calculated flow rate at that pressure and the desired flow rate, then a change in pressure based on how much the flow rate needs to change to correct that error is calculated from the derivative of the material model equation, so the new P_target would be P_target_old + dP/dQ.
To control flow rate, the pressure for a desired flow rate is calculated from the Material Model and then the piston accelerates until the desired pressure is met. This pressure is then held constant, and the settling-detection will determine when the piston velocity is at steady-state and then report the volume flow rate. A flow rate error is then calculated, and from that error a new target pressure is calculated using P(Q) + dP/dQ, where dP/dQ is the derivative of the material model. In this way, the target pressure is constantly being updated based on the error between the read and the desired flow rate. The loop then begins again, iterating for pressure values and reporting the associated flow rate until a pressure which yields the target flow rate within an acceptable range is found.
This continues indefinitely and can then overcome the time-dependent properties of certain materials. Thixotropic materials, for example, become less viscous over time at a constant shear rate, and therefore have a changing pressure-to-flow rate relationship. This means that the constant observation and correction of flow rate through pressure is essential for accurate microscale printing.
Direct-Ink-Writing demonstration. Video obtained from WSU
Controlling the flow of compressible fluids, especially on the microscale, has proven to be a difficult challenge in the world of additive manufacturing. With the assistance of microfluidic sensors and mechatronic integration, a new level of accuracy and repeatability in a manageable timeframe is now feasible. This will pave a path for enhanced micro-printing with DIW through reactive in situ monitoring of pressure and flow rate, which also has promise for defect detection.
If you want to learn more about this work we invite you to visit Washington State University, School of Mechanical and Materials Engineering.
This work was financially supported by the National Science Foundation (NSF) grant 1825872 and NSF Graduate Research Fellowship Award 1842493.
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