Height dependent laser metal deposition process modeling

Laser Metal Deposition (LMD) is used to construct functional parts in a layer-by-layer fashion. The heat transfer from the melt region to the solid region plays a critical role in the resulting material properties and part geometry. The heat transfer dynamics can change significantly as the layers increase, depending on the geometry of the sub layers. However, this effect is unaccounted for in previous analytical models, which are only valid for a single layer. This thesis develops a layer dependent model of the LMD process for the purpose of designing advanced layer-to-layer controllers. A lumped-parameter model of the melt pool is introduced and then extended to include elements that capture height dependent effects on the melt pool dimensions and temperature. The model dynamically relates the process inputs (e.g., laser power, material mass flow rate, and scan speed) to the melt pool dimensions and temperature. A finite element analysis is then conducted to determine the effect of scan speed and track height on the solid region temperature gradient at the melt pool solidification boundary. Experimental results demonstrate that the model successfully predicts multilayer phenomenon for two deposits on two different substrates. Finally, an investigation into the sensitivity of track width to changes in process parameters is conducted --Abstract, page iv

Repetitive process control of additive manufacturing with application to laser metal deposition

Additive Manufacturing (AM) is a set of manufacturing processes which has promise in the production of complex, functional structures that cannot be fabricated with conventional manufacturing and the repair of high-value parts. However, a significant challenge to the adoption of additive manufacturing processes to these applications is proper process control. In order to enable closed-loop process control compact models suitable for control design and for describing the layer-by-layer material addition process are needed. This dissertation proposes a two-dimensional modeling and control framework, with an application to a specific metal-based AM process, whereby the deposition of the current layer is affected by both in-layer and layer-to-layer dynamics, both of which are driven by the state of the previous layer. The proposed modeling framework can be used to create two-dimensional dynamic models for the analysis of layer-to-layer stability and as a foundation for the design of layer-to-layer controllers for AM processes. In order to analyze the stability of this class of systems, linear repetitive process results are extended enabling the treatment of the process model as a two-dimensional analog of a discrete time system. For process control, the closed-loop repetitive process is again treated as a two-dimensional analog of a discrete time system for which controllers are designed. The proposed methodologies are applied to a metal-based AM process, Laser Metal Deposition (LMD), which is known to exhibit layer-to-layer unstable behavior and is also of significant interest to high-value manufacturing industries --Abstract, page iii

A connectome and analysis of the adult Drosophila central brain.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain

A connectome of the adult drosophila central brain

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain

High-Resolution Electrohydrodynamic Jet Printing of Molten Polycaprolactone

Polycaprolactone (PCL) is a biocompatible and biodegradable polymer that is commonly used in drug delivery systems, medical structures, and tissue engineering applications. Typical additive manufacturing methods of PCL structures for tissue engineering applications either require harsh organic solvents or are only capable of producing relatively large feature sizes, which are not compatible with some of the desired applications. Electrohydrodynamic jet (e-jet) printing, an additive manufacturing process which uses an electric field to induce jetting from a microcapillary nozzle, is an attractive method for producing PCL tissue engineering structures due to the achievable resolution and the ability to print highly viscous inks. In this work, experimental investigation into the ability to print pure, molten PCL using the e-jet process is carried out. A characterization of the process inputs that yield suitable printing regimes is presented. Demonstration of the achievable resolution with e-jet printing is presented in the form of printed, high-resolution structures.Mechanical Engineerin

Numerical Modeling of High Resolution Electrohydrodynamic Jet Printing Using OpenFOAM

Electrohydrodynamic jet (e-jet) printing is a micro-/nano-scale additive manufacturing (AM) method that has emerged in recent years as a process to rival similar technologies such as inkjet printing. By utilizing electrostatic forces to induce fluid flow, e-jet has shown the ability to fabricate high resolution features with a wide variety of materials. Because it is a relatively new AM process, the printing behavior of many different types of inks are not completely understood, as existing knowledge of the process relies almost entirely on empirical methods that can be time intensive and difficult to implement for materials that are hazardous or not readily available. This paper discusses procedures for modeling both the electric and fluid dynamics of the e-jet printing process within OpenFOAM, an open-source computational fluid dynamics (CFD) solver. Further discussion has been provided to show simulations of the process at typical high resolution e-jet printing scales and to present existing limitations of the model.Mechanical Engineerin

DC-Gain Layer-To-Layer Stability Criterion in Laser Metal Deposition Processes

In Laser Metal Deposition (LMD), a blown powder metal additive manufacturing process, functional metal parts are fabricated in a layer-by-layer fashion. In addition to the in-layer dynamics, which describe how the process evolves within a given layer, the additive-fabrication property of LMD creates a second set of dynamics which describe how the process evolves from layer-to-layer. While these dynamics, termed layer-to-layer dynamics, are coupled with both the in-layer dynamics and the process operating conditions, they are not widely considered in the modeling, process planning, or process control of LMD operations. Because of this, seemingly valid choices for process parameters can lead to part failure - a phenomenon commonly encountered when undergoing the laborious procedure of tuning a new LMD process. Here, a layer-to-layer stability condition for LMD fabrications is given, based on the shape of the powder catchment efficiency function, which provides insight into the layer-to-layer evolution of LMD processes and can be useful in process planning and control. The stability criterion is evaluated for various operating points, allowing stable and unstable operating regions to be identified. Simulation results are presented showing both stable and unstable layer-to-layer LMD fabrications. The simulated behavior successfully predicts the results seen in both stable and unstable experimental depositions