Characterization of Residual Stress and Plastic Strain in Austenitic Stainless Steel 316L(N) Weldments

Fusion welding processes commonly involve the localized input of intense heat, melting of dissimilar materials and the deposition of molten filler metal. The surrounding material undergoes complex thermo-mechanical cycles involving elastic and plastic deformation. This processing history creates large residual stress in and around the weld bead, which can be particularly detrimental in reducing the lifetime of fabricated structures, increasing their susceptibility to stress corrosion, fatigue and creep crack growth as well as reducing the fracture load. It is very important to have a proper knowledge of the residual stress distribution in and around the weld region of structured components because knowing this allows their fitness to be assessed and the service life of critical components to be predicted. Characterizing weld residual stress fields either by measurement or finite element simulation is not straightforward because of the strain field complexity, inhomogeneity of the microstructure and the complex geometry of structural weldments. The residual stress distribution in a slot weld benchmark sample made from AISI 316L(N) austenitic stainless steel was analysed using the neutron diffraction at pulsed source. The presence of crevices and hydrogen containing super glue in the stress-free cuboids are some of the main issues effecting the neutron residual stress measurements. A residual stress of 400-450MPa was observed in first pass weld metal and in the HAZ of a three pass welded plate. The strain hardening behaviour of AISI 316L(N) steel around the slot weld was studied taking account of the asymmetric cyclic deformation and the typical strain rates experienced; inferences are drawn regarding how such effects Should be modelled in finite element weld residual stress computations. The solution annealed material was tested under symmetric and asymmetric cyclic loading at both room and 550°C. During asymmetric cyclic loading, the 316L(N) material at room and high temperature was less strain hardened than in the same number of cycles of symmetric cyclic loading. At room temperature; the 316L(N) material deformed at fast strain rate showed higher strain hardening than at the slow strain rate. However, at high temperature (550°C); the 316L(N) material deformed at slow strain rate showed higher strain hardening than at the fast strain rate due to dynamic strain ageing. A mixed hardening model was to predict the strain hardening of the 316L(N) material at room and high temperature (550°C). However, the published mixed hardening parameters were unsuccessful in predicting the strain hardening of the symmetric cyclic deformation at high temperature. Finally, the accumulated cyclic plastic strain resulting from the addition of each weld bead was studied using Electron Backscatter Diffraction (EBSD) and hardness measurements. The EBSD metrics showed a gradual increase of plastic strain and equivalent yield stress from the parent zone (approximately 0.02) to the fusion boundary (approximately 0.05-0.09). Although, in strain controlled cyclic loading, none of the EBSD metrics used were capable of assessing the plastic strain, below 58% cumulative plastic strain path. The quantified plastic strain (from the EBSD) and hardness analysis of the parent material indicates that the material deformed plastically. The EBSD derived plastic strain and equivalent yield stress correlate well with hardness, finite element prediction and von Mises equivalent residual stress

A study on image calibration technique for Autonomous Robot

Abstract— Camera calibration and image processing is the most important factor in computer vision. Some of the techniques that are applied in the process of calibration are linier, technical and non technical linier with two stages. Calibration techniques can be implemented, for example in the Autonomous Robotic Soccer. The process of calibration is one of the key factors of success in robotic soccer game. Currently, the team succeeded in doing with the camera calibration is a good team who will be able to win the match

A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

This work introduces an innovative parallel, fully-distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well-known that virtual part design and qualification in additive manufacturing requires highly-accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time-to-market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high-end distributed-memory machines. It is grounded on three building blocks: (1) Hierarchical adaptive mesh refinement with octree-based meshes; (2) a parallel strategy to model the growth of the geometry; (3) state-of-the-art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder-bed technologies. After verification against a 3D benchmark, a strong-scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part-scale simulations of metal or polymer additive manufacturing, but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time

Automatic annotation for weakly supervised learning of detectors

PhDObject detection in images and action detection in videos are among the most widely studied computer vision problems, with applications in consumer photography, surveillance, and automatic media tagging. Typically, these standard detectors are fully supervised, that is they require a large body of training data where the locations of the objects/actions in images/videos have been manually annotated. With the emergence of digital media, and the rise of high-speed internet, raw images and video are available for little to no cost. However, the manual annotation of object and action locations remains tedious, slow, and expensive. As a result there has been a great interest in training detectors with weak supervision where only the presence or absence of object/action in image/video is needed, not the location. This thesis presents approaches for weakly supervised learning of object/action detectors with a focus on automatically annotating object and action locations in images/videos using only binary weak labels indicating the presence or absence of object/action in images/videos. First, a framework for weakly supervised learning of object detectors in images is presented. In the proposed approach, a variation of multiple instance learning (MIL) technique for automatically annotating object locations in weakly labelled data is presented which, unlike existing approaches, uses inter-class and intra-class cue fusion to obtain the initial annotation. The initial annotation is then used to start an iterative process in which standard object detectors are used to refine the location annotation. Finally, to ensure that the iterative training of detectors do not drift from the object of interest, a scheme for detecting model drift is also presented. Furthermore, unlike most other methods, our weakly supervised approach is evaluated on data without manual pose (object orientation) annotation. Second, an analysis of the initial annotation of objects, using inter-class and intra-class cues, is carried out. From the analysis, a new method based on negative mining (NegMine) is presented for the initial annotation of both object and action data. The NegMine based approach is a much simpler formulation using only inter-class measure and requires no complex combinatorial optimisation but can still meet or outperform existing approaches including the previously pre3 sented inter-intra class cue fusion approach. Furthermore, NegMine can be fused with existing approaches to boost their performance. Finally, the thesis will take a step back and look at the use of generic object detectors as prior knowledge in weakly supervised learning of object detectors. These generic object detectors are typically based on sampling saliency maps that indicate if a pixel belongs to the background or foreground. A new approach to generating saliency maps is presented that, unlike existing approaches, looks beyond the current image of interest and into images similar to the current image. We show that our generic object proposal method can be used by itself to annotate the weakly labelled object data with surprisingly high accuracy

Measurements and modeling of optical-equivalent snow grain sizes under arctic low-sun conditions

The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (r$_{opt}$) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Nonhydrostatic—Aerosols and Reactive Trace gases) simulations. The retrieval methods are based on optical measurements and rely on the r$_{opt}$-dependent absorption of solar radiation in snow. The measurement data were taken during a three-week campaign that was conducted in the North of Greenland in March/April 2018, such that the retrieval methods and radiation measurements are affected by enhanced uncertainties under these low-Sun conditions. An adjusted airborne retrieval method is applied which uses the albedo at 1700 nm wavelength and combines an atmospheric and snow radiative transfer model to account for the direct-to-global fraction of the solar radiation incident on the snow. From this approach, we achieved a significantly improved uncertainty (<25%) and a reduced effect of atmospheric masking compared to the previous method. Ground-based in situ measurements indicated an increase of r$_{opt}$ of 15 µm within a five-day period after a snowfall event which is small compared to previous observations under similar temperature regimes. ICON-ART captured the observed change of r$_{opt}$ during snowfall events, but systematically overestimated the subsequent snow grain growth by about 100%. Adjusting the growth rate factor to 0.012 µm$^{2}$ s$^{-1}$ minimized the difference between model and observations. Satellite-based and airborne retrieval methods showed higher r$_{opt}$ over sea ice (<300 µm) than over land surfaces (<100 µm) which was reduced by data filtering of surface roughness features. Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals revealed a large spread within a series of subsequent individual overpasses, indicating their limitations in observing the snow grain size evolution in early spring conditions with low Sun

Pressure and saturation estimation from PRM time-lapse seismic data for a compacting reservoir

Observed 4D effects are influenced by a combination of changes in both pressure and saturation in the reservoir. Decomposition of pressure and saturation changes is crucial to explain the different physical variables that have contributed to the 4D seismic responses. This thesis addresses the challenges of pressure and saturation decomposition from such time-lapse seismic data in a compacting chalk reservoir. The technique employed integrates reservoir engineering concepts and geophysical knowledge. The innovation in this methodology is the ability to capture the complicated water weakening behaviour of the chalk as a non-linear proxy model controlled by only three constants. Thus, changes in pressure and saturation are estimated via a Bayesian inversion by employing compaction curves derived from the laboratory, constraints from the simulation model predictions, time strain information and the observed fractional change in and . The approach is tested on both synthetic and field data from the Ekofisk field in the North Sea. The results are in good agreement with well production data, and help explain strong localized anomalies in both the Ekofisk and Tor formations. These results also suggest updates to the reservoir simulation model. The second part of the thesis focuses on the geomechanics of the overburden, and the opportunity to use time-lapse time-shifts to estimate pore pressure changes in the reservoir. To achieve this, a semi-analytical approach by Geertsma is used, which numerically integrates the displacements from a nucleus of strain. This model relates the overburden time-lapse time-shifts to reservoir pressure. The existing method by Hodgson (2009) is modified to estimate reservoir pressure change and also the average dilation factor or R-factor for both the reservoir and overburden. The R-factors can be quantified when prior constraints are available from a well history matched simulation model, and their uncertainty defined. The results indicate that the magnitude of R is a function of strain change polarity, and that this asymmetry is required to match the observed timeshifts. The recovered average R-factor is 16, using the permanent reservoir monitoring (PRM) data. The streamer data has recovered average R-factors in the range of 7.2 to 18.4. Despite the limiting assumptions of a homogeneous medium, the method is beneficial, as it treats arbitrary subsurface geometries, and, in contrast to the complex numerical approaches, it is simple to parameterise and computationally fast. Finally, the aim and objective of this research have been met predominantly by the use of PRM data. These applications could not have been achieved without such highly repeatable and short repeat period acquisitions. This points to the value in using these data in reservoir characterisation, inversion and history matching

Robust and affordable localization and mapping for 3D reconstruction. Application to architecture and construction

La localización y mapeado simultáneo a partir de una sola cámara en movimiento se conoce como Monocular SLAM. En esta tesis se aborda este problema con cámaras de bajo coste cuyo principal reto consiste en ser robustos al ruido, blurring y otros artefactos que afectan a la imagen. La aproximación al problema es discreta, utilizando solo puntos de la imagen significativos para localizar la cámara y mapear el entorno. La principal contribución es una simplificación del grafo de poses que permite mejorar la precisión en las escenas más habituales, evaluada de forma exhaustiva en 4 datasets. Los resultados del mapeado permiten obtener una reconstrucción 3D de la escena que puede ser utilizada en arquitectura y construcción para Modelar la Información del Edificio (BIM). En la segunda parte de la tesis proponemos incorporar dicha información en un sistema de visualización avanzada usando WebGL que ayude a simplificar la implantación de la metodología BIM.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

Long non-axisymmetric fibres in turbulent channel flow

In this work, we investigate the dynamics of long non-axisymmetric fibres in turbulent channel flow. The experimental facility is the TU Wien Turbulent Water Channel, consisting of a closed water channel (aspect ratio of 10), and the experiments are performed at a shear Reynolds number of 360. Fibres are neutrally buoyant rods that are curved and characterised by a length-to-diameter ratio of 120. Illumination is provided by a laser sheet and the motion of fibres is recorded by four high-speed cameras in a fully developed flow section. We apply multiplicative algebraic reconstruction techniques to the recorded images from four high-speed cameras to identify the three-dimensional location, shape and orientation of the fibres. The fibres are also tracked in time to obtain their three-dimensional vectors of velocity and rotation rate. We investigate the behaviour of the fibres, from the near-wall region to the channel centre, and we produce original statistics on the effect of curvature of the fibres on their orientation and rotation rate. Specifically, we measured the orientation and rotation rate of the fibres, and we can confirm that in the centre, the most homogeneous part of the channel, statistics, although influenced by the curvature, bear similarities to those obtained in previous investigations in homogeneous isotropic turbulence. In addition, we have been able to compare the tumbling rate of our long non-axisymmetric fibres with previous solutions for curved ellipsoids in simple shear flow