Detail Enhancing Denoising of Digitized 3D Models from a Mobile Scanning System

The acquisition process of digitizing a large-scale environment produces an enormous amount of raw geometry data. This data is corrupted by system noise, which leads to 3D surfaces that are not smooth and details that are distorted. Any scanning system has noise associate with the scanning hardware, both digital quantization errors and measurement inaccuracies, but a mobile scanning system has additional system noise introduced by the pose estimation of the hardware during data acquisition. The combined system noise generates data that is not handled well by existing noise reduction and smoothing techniques. This research is focused on enhancing the 3D models acquired by mobile scanning systems used to digitize large-scale environments. These digitization systems combine a variety of sensors – including laser range scanners, video cameras, and pose estimation hardware – on a mobile platform for the quick acquisition of 3D models of real world environments. The data acquired by such systems are extremely noisy, often with significant details being on the same order of magnitude as the system noise. By utilizing a unique 3D signal analysis tool, a denoising algorithm was developed that identifies regions of detail and enhances their geometry, while removing the effects of noise on the overall model. The developed algorithm can be useful for a variety of digitized 3D models, not just those involving mobile scanning systems. The challenges faced in this study were the automatic processing needs of the enhancement algorithm, and the need to fill a hole in the area of 3D model analysis in order to reduce the effect of system noise on the 3D models. In this context, our main contributions are the automation and integration of a data enhancement method not well known to the computer vision community, and the development of a novel 3D signal decomposition and analysis tool. The new technologies featured in this document are intuitive extensions of existing methods to new dimensionality and applications. The totality of the research has been applied towards detail enhancing denoising of scanned data from a mobile range scanning system, and results from both synthetic and real models are presented

Three dimensionality in the wake of the flow around a circular cylinder at Reynolds number 5000

The turbulent flow around a circular cylinder has been investigated at Re=5000Re=5000 using direct numerical simulations. Low frequency behavior, vortex undulation, vortex splitting, vortex dislocations and three dimensional flow within the wake were found to happen at this flow regime. In order to successfully capture the wake three dimensionality, different span-wise lengths were considered. It was found that a length LZ=2pDLZ=2pD was enough to capture this behavior, correctly predicting different aspects of the flow such as drag coefficient, Strouhal number and pressure and velocity distributions when compared to experimental values. Two instability mechanisms were found to coexist in the present case study: a global type instability originating in the shear layer, which shows a characteristic frequency, and a convective type instability that seems to be constantly present in the near wake. Characteristics of both types of instabilities are identified and discussed in detail. As suggested by Norberg, a resonance-type effect takes place in the vortex formation region, as the coexistence of both instability mechanisms result in distorted vortex tubes. However, vortex coherence is never lost within the wake.Peer ReviewedPostprint (author's final draft

Modeling Fixed Bed Membrane Reactors for Hydrogen Production through Steam Reforming Reactions: A Critical Analysis

Membrane reactors for hydrogen production have been extensively studied in the past years due to the interest in developing systems that are adequate for the decentralized production of high-purity hydrogen. Research in this field has been both experimental and theoretical. The aim of this work is two-fold. On the one hand, modeling work on membrane reactors that has been carried out in the past is presented and discussed, along with the constitutive equations used to describe the different phenomena characterizing the behavior of the system. On the other hand, an attempt is made to shed some light on the meaning and usefulness of models developed with different degrees of complexity. The motivation has been that, given the different ways and degrees in which transport models can be simplified, the process is not always straightforward and, in some cases, leads to conceptual inconsistencies that are not easily identifiable or identified

Combining 2D2D synchrosqueezed wave packet transform with optimization for crystal image analysis

We develop a variational optimization method for crystal analysis in atomic resolution images, which uses information from a 2D synchrosqueezed transform (SST) as input. The synchrosqueezed transform is applied to extract initial information from atomic crystal images: crystal defects, rotations and the gradient of elastic deformation. The deformation gradient estimate is then improved outside the identified defect region via a variational approach, to obtain more robust results agreeing better with the physical constraints. The variational model is optimized by a nonlinear projected conjugate gradient method. Both examples of images from computer simulations and imaging experiments are analyzed, with results demonstrating the effectiveness of the proposed method

Pose-invariant, model-based object recognition, using linear combination of views and Bayesian statistics

This thesis presents an in-depth study on the problem of object recognition, and in particular the detection of 3-D objects in 2-D intensity images which may be viewed from a variety of angles. A solution to this problem remains elusive to this day, since it involves dealing with variations in geometry, photometry and viewing angle, noise, occlusions and incomplete data. This work restricts its scope to a particular kind of extrinsic variation; variation of the image due to changes in the viewpoint from which the object is seen. A technique is proposed and developed to address this problem, which falls into the category of view-based approaches, that is, a method in which an object is represented as a collection of a small number of 2-D views, as opposed to a generation of a full 3-D model. This technique is based on the theoretical observation that the geometry of the set of possible images of an object undergoing 3-D rigid transformations and scaling may, under most imaging conditions, be represented by a linear combination of a small number of 2-D views of that object. It is therefore possible to synthesise a novel image of an object given at least two existing and dissimilar views of the object, and a set of linear coefficients that determine how these views are to be combined in order to synthesise the new image. The method works in conjunction with a powerful optimization algorithm, to search and recover the optimal linear combination coefficients that will synthesize a novel image, which is as similar as possible to the target, scene view. If the similarity between the synthesized and the target images is above some threshold, then an object is determined to be present in the scene and its location and pose are defined, in part, by the coefficients. The key benefits of using this technique is that because it works directly with pixel values, it avoids the need for problematic, low-level feature extraction and solution of the correspondence problem. As a result, a linear combination of views (LCV) model is easy to construct and use, since it only requires a small number of stored, 2-D views of the object in question, and the selection of a few landmark points on the object, the process which is easily carried out during the offline, model building stage. In addition, this method is general enough to be applied across a variety of recognition problems and different types of objects. The development and application of this method is initially explored looking at two-dimensional problems, and then extending the same principles to 3-D. Additionally, the method is evaluated across synthetic and real-image datasets, containing variations in the objects’ identity and pose. Future work on possible extensions to incorporate a foreground/background model and lighting variations of the pixels are examined

Coupled Sequential Process-Performance Simulation and Multi-Attribute Optimization of Structural Components Considering Manufacturing Effects

Coupling of material, process, and performance models is an important step towards a fully integrated material-process-performance design of structural components. In this research, alternative approaches for introducing the effects of manufacturing and material microstructure in plasticity constitutive models are studied, and a cyberinfrastructure framework is developed for coupled process-performance simulation and optimization of energy absorbing components made of magnesium alloys. The resulting mixed boundary/initial value problem is solved using nonlinear finite element analysis whereas the optimization problem is decomposed into a hierarchical multilevel system and solved using the analytical target cascading methodology. The developed framework is demonstrated on process-performance optimization of a sheetormed, energy-absorbing component using both classical and microstructure-based plasticity models. Sheetorming responses such as springback, thinning, and rupture are modeled and used as manufacturing process attributes whereas weight, mean crush force, and maximum crush force are used as performance attributes. The simulation and optimization results show that the manufacturing effects can have a considerable impact on design of energy absorbing components as well as the optimum values of process and product design variables

Experimentally validated 3D MD model for AFM-based tip-based nanomanufacturing

In order to control AFM-based TBN to produce precise nano-geometry efficiently, there is a need to conduct a more focused study of the effects of different parameters, such as feed, speed, and depth of cut on the process performance and outcome. This is achieved by experimentally validating a MD simulation model of nanomachining, and using it to conduct parametric studies to guide AFM-based TBN. A 3D MD model with a larger domain size was developed and used to gain a unique insight into the nanoindentation and nanoscratching processes such as the effect of tip speed (e.g. effect of tip speed on indentation force above 10 nm of indentation depth). The model also supported a more comprehensive parametric study (than other published work) in terms of number of parameters and ranges of values investigated, as well as a more cost effective design of experiments. The model was also used to predict material properties at the nanoscale (e.g. hardness of gold predicted within 6% error). On the other hand, a comprehensive experimental parametric study was conducted to produce a database that is used to select proper machining conditions for guiding the fabrication of nanochannels (e.g. scratch rate = 0.996 Hz, trigger threshold = 1 V, for achieving a nanochannel depth = 50 nm for the case of gold device). Similar trends for the variation of indentation force with depth of cut, pattern of the material pile-up around the indentation mark or scratched groove were found. The parametric studies conducted using both MD model simulations and AFM experiments showed the following: Normal forces for both nanoindentation and nanoscratching increase as the depth of cut increases. The indentation depth increases with tip speed, but the depth of scratch decrease with increasing tip speed. The width and depth of scratched groove also depend on the scratch angle. The recommended scratch angle is at 90°. The surface roughness increases with step over, especially when the step over is larger than the tip radius. The depth of cut also increases as the step over decreases. Additional study is conducted using the MD model to understand the effect of crystal structure and defects in material when subjected to a stress. Several types of defects, including vacancies and Shockley partial dislocation loops, can be observed during the MD simulation for the case of gold, copper and aluminum. Finally, AFM-based TBN is used with photolithography to fabricate a nano-fluidic device for medical application. In fact, the photolithography process is used to create microchannels on top of a silicon wafer, and AFM-based TBN is applied to fabricate nanochannels between the microchannels that connect to the reservoirs. Fluid flow test was conducted on the devices to ensure that the nanochannel was open and the bonding sealed

Coupled Sequential Process-Performance Simulation and Multi-Attribute Optimization of Structural Components Considering Manufacturing Effects

Coupling of material, process, and performance models is an important step towards a fully integrated material-process-performance design of structural components. In this research, alternative approaches for introducing the effects of manufacturing and material microstructure in plasticity constitutive models are studied, and a cyberinfrastructure framework is developed for coupled process-performance simulation and optimization of energy absorbing components made of magnesium alloys. The resulting mixed boundary/initial value problem is solved using nonlinear finite element analysis whereas the optimization problem is decomposed into a hierarchical multilevel system and solved using the analytical target cascading methodology. The developed framework is demonstrated on process-performance optimization of a sheetormed, energy-absorbing component using both classical and microstructure-based plasticity models. Sheetorming responses such as springback, thinning, and rupture are modeled and used as manufacturing process attributes whereas weight, mean crush force, and maximum crush force are used as performance attributes. The simulation and optimization results show that the manufacturing effects can have a considerable impact on design of energy absorbing components as well as the optimum values of process and product design variables