Flexible adaptation of iterative learning control with applications to synthetic bone graft manufacturing

Additive manufacturing processes are powerful tools; they are capable of fabricating structures without expensive structure specific tooling -- therefore structure designs can efficiently change from run-to-run -- and they can integrate multiple distinct materials into a single structure. This work investigates one such additive manufacturing process, micro-Robotic Deposition ($\mu$RD), and its utility in fabricating advanced architecture synthetic bone grafts. These bone grafts, also known as synthetic bone scaffolds, are highly porous three-dimensional structures that provide a matrix to support the natural process of bone remodeling. Ideally, the synthetic scaffold will stimulate complete bone healing in a skeletal defect site and also resorb with time so that only natural tissue remains. The objective of this research is to develop methods to integrate different regions with different porous microstructures into a single scaffold; there is evidence that scaffolds with designed regions of specific microstructures can be used to elicit a strong and directed bone ingrowth response that improves bone ingrowth rate and quality. The key contribution of this work is the development of a control algorithm that precisely places different build materials in specified locations, thereby the fabrication of advanced architecture scaffolds is feasible. Under previous control methods, designs were relegated to be composed of a single material. The control algorithm developed in this work is an adaptation of Iterative Learning Control (ILC), a control method that is typically best suited for mass manufacturing applications. This adaptation reorients the ILC framework such that it is more amenable to additive manufacturing systems, such as $\mu$RD. Control efficacy is demonstrated by the fabrication of advanced architecture scaffolds. Scaffolds with contoured forms, multiple domains with distinct porous microstructures, and hollow cavities are feasible when the developed controller is used in conjunction with a novel manufacturing workflow in which scaffolds are filled within patterned molds that support overhanging features. An additional application demonstrates controller performance on the robot positioning problem; this work has implications for additive manufacturing in general

A large displacement, high frequency, underwater microelectromechanical systems actuator

Here, we demonstrate an in situ electrostatic actuator that can operate underwater across a wide range of displacements and frequencies, achieving a displacement of approximately 10-μm at 500-Hz and 1-μm at 5-kHz; this performance surpasses that of existing underwater physical actuators. To attain these large displacements at such high speeds, we optimized critical design parameters using a computationally efficient description of the physics of low quality (Q) factor underwater electrostatic actuators. Our theoretical model accurately predicts actuator motion profiles as well as limits of bandwidth and displacement

'Seeing' the Temperature Inside the Part During the Powder Bed Fusion Process

Powder Bed Fusion (PBF) is a type of Additive Manufacturing (AM) technology that builds parts in a layer-by-layer fashion out of a bed of metal powder via the selective melting action of a laser or electron beam heat source. The technology has become widespread, however the demand is growing for closed loop process monitoring and control in PBF systems to replace the open loop architectures that exist today. This paper demonstrates the simulated efficacy of applying closed-loop state estimation to the problem of monitoring temperature fields within parts during the PBF build process. A simplified LTI model of PBF thermal physics with the properties of stability, controllability and observability is presented. An Ensemble Kalman Filter is applied to the model. The accuracy of this filters’ predictions are assessed in simulation studies of the temperature evolution of various test parts when subjected to simulated laser heat input. The significant result of this study is that the filter supplied predictions that were about 2.5x more accurate than the open loop model in these simulation studies.Mechanical Engineerin

On the Diminishing Returns of Thermal Camera Resolution for PBF Temperature Estimation

Powder Bed Fusion (PBF) faces ongoing challenges in the areas of process monitoring and control. Standard methods for alleviating these issues rely on machine learning, which requires costly and time-consuming training data. Expense is compounded by the perceived necessity of using sensors with extremely high resolutions. This research avoids this cost by employing an Ensemble Kalman Filter (EnKF), which uses measured data to correct physics-based model predictions of the process, to monitor part internal temperature fields during building. This work tests EnKF performance, in simulation, for two model architectures, using simulated cameras of varying resolution as our measuring instruments. Crucially, we show that increasing camera resolution produces diminishing returns in EnKF accuracy, relative to the model predictions, with up to 81% error reduction. This result shows that current AM quality control practices with expensive sensors may be inefficient; with appropriate algorithms, cheaper setups may be used with little additional error.Mechanical Engineerin

Interrogation of Mid-Build Internal Temperature Distributions Within Parts Being Manufactured via the Powder Bed Fusion Process

This work reports on the measurement of the internal temperature distributions of parts being manufactured via the Powder Bed Fusion (PBF) process. Eight test coupons were machined from a piece of wrought 304 stainless steel (SS). Thermocouples were inserted into the test coupon interiors to sample internal thermal history. The coupons were then placed into the open architecture laser PBF machine housed at EWI and covered to their uppermost surfaces with 316 SS powder. Three tests were executed: First, the laser rastered over the coupons without inducing melting. Second, the laser rastered over the coupons while melting the exposed faces. Lastly, five layers of 316 SS were built atop the coupons. The main result is a comprehensive data set of a multitude of measured physical inputs and outputs under typical build conditions: embedded thermocouple temperatures, laser centroid, laser power, and infrared imagery of the exposed coupon faces.Mechanical Engineerin

A microfluidic technique to probe cell deformability.

Here we detail the design, fabrication, and use of a microfluidic device to evaluate the deformability of a large number of individual cells in an efficient manner. Typically, data for ~10(2) cells can be acquired within a 1 hr experiment. An automated image analysis program enables efficient post-experiment analysis of image data, enabling processing to be complete within a few hours. Our device geometry is unique in that cells must deform through a series of micron-scale constrictions, thereby enabling the initial deformation and time-dependent relaxation of individual cells to be assayed. The applicability of this method to human promyelocytic leukemia (HL-60) cells is demonstrated. Driving cells to deform through micron-scale constrictions using pressure-driven flow, we observe that human promyelocytic (HL-60) cells momentarily occlude the first constriction for a median time of 9.3 msec before passaging more quickly through the subsequent constrictions with a median transit time of 4.0 msec per constriction. By contrast, all-trans retinoic acid-treated (neutrophil-type) HL-60 cells occlude the first constriction for only 4.3 msec before passaging through the subsequent constrictions with a median transit time of 3.3 msec. This method can provide insight into the viscoelastic nature of cells, and ultimately reveal the molecular origins of this behavior

A Microfluidic Technique to Probe Cell Deformability

Here we detail the design, fabrication, and use of a microfluidic device to evaluate the deformability of a large number of individual cells in an efficient manner. Typically, data for ~10(2) cells can be acquired within a 1 hr experiment. An automated image analysis program enables efficient post-experiment analysis of image data, enabling processing to be complete within a few hours. Our device geometry is unique in that cells must deform through a series of micron-scale constrictions, thereby enabling the initial deformation and time-dependent relaxation of individual cells to be assayed. The applicability of this method to human promyelocytic leukemia (HL-60) cells is demonstrated. Driving cells to deform through micron-scale constrictions using pressure-driven flow, we observe that human promyelocytic (HL-60) cells momentarily occlude the first constriction for a median time of 9.3 msec before passaging more quickly through the subsequent constrictions with a median transit time of 4.0 msec per constriction. By contrast, all-trans retinoic acid-treated (neutrophil-type) HL-60 cells occlude the first constriction for only 4.3 msec before passaging through the subsequent constrictions with a median transit time of 3.3 msec. This method can provide insight into the viscoelastic nature of cells, and ultimately reveal the molecular origins of this behavior