Efficient description of shape perturbations

Airbus wish to have efficient ways of describing perturbations of a manu- factured aerofoil from its design shape. The typical kind of perturbations expected are waves, steps, and bumps, and automatic classification into the classes is desired. Various possible methods of analysis were pro- posed and studied in some detail, including projection onto suitable basis functions, wavelets, and radial basis functions. Other methods were studied in less detail, but with the aim of giving a digital signature of defects that could be used to classify them

Introduction to Critical Strain and a New Method for the Assessment of Mechanical Damage in Steel Line Pipe

The pipeline industry has conducted a vast amount of research on the subject of mechanical damage. Mechanical damage makes up a large portion of the total amount of pipeline failures that occur each year. The current methods rely on engineering judgment and experience rather than scientific theory. The method for the assessment of mechanical damage introduced in this study uses a material property called critical strain to predict the onset of cracking within the pipe wall. The critical strain is compared to the strain within a dent using a ductile failure damage indicator (DFDI). To investigate the use of the DFDI to indicate the onset of cracking within a dent, the study attempted to accomplish three tasks. The first was to investigate the use of various techniques to locate the critical strain from the stress-strain curve. Five samples taken from the pipe material was used to generate both engineering and true stress-strain curves. A sensitivity analysis was conducted to show the effects of different variables on the critical strain value. The DFDI compares the critical strain value to the calculated strain at the deepest depth location within a dent. The strain calculations use the curvature of the dent and thus require a dent profile. A high resolution laser scanner was used to extract dent profiles from a pipe. The second task of the study was to investigate the reliability of the laser scanner equipment used for this study. The results from the investigation showed that the laser scanner could be used to scan the inside of the pipe despite its design for external scanning. The results also showed that the scans should be 1 mm in length along the axis of the pipe at a resolution of 0.5 mm and 360 degrees around the pipe. The final task was to conduct the denting test. The test used a spherical indenter to dent the pipe at increments of 3% of the outside diameter. The results from the test showed that a visible crack did not form on the inside pipe surface as expected from the DFDI method. This does not mean a crack did not form. During the denting test distinct popping sounds were observed possibly indicating cracks forming within the pipe wall

Calibration Methods of Characterization Lens for Head Mounted Displays

This thesis concerns the calibration, characterization and utilization of the HMD Eye, OptoFidelity’s eye-mimicking optical camera system designed for the HMD IQ, a complete test station for near eye displays which are implemented in virtual and augmented reality systems. Its optical architecture provides a 120 degree field of view with high imaging performance and linear radial distortion, ideal for analysis of all possible object fields. HMD Eye has an external, mechanical entrance pupil that is of the same size as the human entrance pupil. Spatial frequency response (the modulation transfer function) has been used to develop sensor focus calibration methods and automation system plans. Geometrical distortion and its relation to the angular mapping function and imaging quality of the system are also considered. The nature of the user interface for human eyes, called the eyebox, and the optical properties of head mounted displays are reviewed. Head mounted displays consist usually of two near eye displays amongst other components, such as position tracking units. The HMD Eye enables looking inside the device from the eyebox and collecting optical signals (i.e. the virtual image) from the complete field of view of the device under test with a single image. The HMD Eye under inspection in this thesis is one of the ’zero’ batch, i.e. a test unit. The outcome of the calibration was that the HMD Eye unit in this thesis is focused to 1.6 m with an approximate error margin of ±10 cm. The drop of contrast reaches 50% approximately at angular frequency of 11 cycles/degree which is about 40% of the simulated values, prompting improvements in the mechanical design. Geometrical distortion results show that radial distortion is very linear (maximum error of 1%) and that tangential distortion has a diminishable effect (0.04 degrees of azimuth deviation at most) within the measurement region

Analysis and Manipulation of Repetitive Structures of Varying Shape

Self-similarity and repetitions are ubiquitous in man-made and natural objects. Such structural regularities often relate to form, function, aesthetics, and design considerations. Discovering structural redundancies along with their dominant variations from 3D geometry not only allows us to better understand the underlying objects, but is also beneficial for several geometry processing tasks including compact representation, shape completion, and intuitive shape manipulation. To identify these repetitions, we present a novel detection algorithm based on analyzing a graph of surface features. We combine general feature detection schemes with a RANSAC-based randomized subgraph searching algorithm in order to reliably detect recurring patterns of locally unique structures. A subsequent segmentation step based on a simultaneous region growing is applied to verify that the actual data supports the patterns detected in the feature graphs. We introduce our graph based detection algorithm on the example of rigid repetitive structure detection. Then we extend the approach to allow more general deformations between the detected parts. We introduce subspace symmetries whereby we characterize similarity by requiring the set of repeating structures to form a low dimensional shape space. We discover these structures based on detecting linearly correlated correspondences among graphs of invariant features. The found symmetries along with the modeled variations are useful for a variety of applications including non-local and non-rigid denoising. Employing subspace symmetries for shape editing, we introduce a morphable part model for smart shape manipulation. The input geometry is converted to an assembly of deformable parts with appropriate boundary conditions. Our method uses self-similarities from a single model or corresponding parts of shape collections as training input and allows the user also to reassemble the identified parts in new configurations, thus exploiting both the discrete and continuous learned variations while ensuring appropriate boundary conditions across part boundaries. We obtain an interactive yet intuitive shape deformation framework producing realistic deformations on classes of objects that are difficult to edit using repetition-unaware deformation techniques

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Automated shape analysis and visualization of the human back.

Spinal and back deformities can lead to pain and discomfort, disrupting productivity, and may require prolonged treatment. The conventional method of assessing and monitoring tile de-formity using radiographs has known radiation hazards. An alternative approach for monitoring the deformity is to base the assessment on the shape of back surface. Though three-dimensional data acquisition methods exist, techniques to extract relevant information for clinical use have not been widely developed. Thi's thesis presentsthe content and progression of research into automated analysis and visu-alization of three-dimensional laser scans of the human back. Using mathematical shape analysis, methods have been developed to compute stable curvature of the back surface and to detect the anatomic landmarks from the curvature maps. Compared with manual palpation, the landmarks have been detected to within accuracy of 1.15mm and precision of 0.8111m.Based on the detected spinous process landmarks, the back midline which is the closest surface approximation of the spine, has been derived using constrained polynomial fitting and statistical techniques. Three-dimensional geometric measurementsbasedon the midline were then corn-puted to quantify the deformity. Visualization plays a crucial role in back shape analysis since it enables the exploration of back deformities without the need for physical manipulation of the subject. In the third phase,various visualization techniques have been developed, namely, continuous and discrete colour maps, contour maps and three-dimensional views. In the last phase of the research,a software system has been developed for automating the tasks involved in analysing, visualizing and quantifying of the back shape. The novel aspectsof this research lie in the development of effective noise smoothing methods for stable curvature computation; improved shape analysis and landmark detection algorithm; effective techniques for visualizing the shape of the back; derivation of the back midline using constrained polynomials and computation of three dimensional surface measurements.

Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics