Multi-angle valve seat machining: experimental analysis and numerical modelling

Modern automotive manufacturers operate in highly competitive markets, heavily influenced by Government regulation and ever more environmentally conscious consumers. Modern high-temperature, high-pressure engines that use high hardness multi-angle valve seats are an attractive environmental option, but one that manufacturers find requires more advanced materials and tighter geometric tolerances to maintain engine performance.Tool manufacturers meet these increasingly tougher demands by using, higher hardness cutting materials such as polycrystalline cubic boron nitride (pcBN), that on paper, promise to wear at a lower rate, require less coolant and deliver tighter tolerances than their carbide counterparts.The low brittle fracture toughness of pcBN makes tools that use it vulnerable to minute chipping. A review of literature for this work pointed to no clear answer to this problem, although suggestions range from manufacturing defects, dynamic and flexibility problems with the production line machinery and fixtures, and radial imbalances in the cutting loads.This work set about experimentally investigating those potential explanations, coming to the conclusion that the high radial imbalance of the cutting loads is responsible for pcBN cutting insert failure during multi-angle valve seat machining, and that by simply relocating the cutting inserts around the multi angle cutting tool, the imbalance can be reduced, thus extending the life of the cutting inserts.It is not always easy to predict the imbalance due to the multiple flexibilities in the system, and simulating such a system in 3D with all its associated cutting phenomena such as friction, thermal expansion, chip flow and shearing, would call upon extraordinary computational power and extremely precise experimental inputs to reduce cumulative error.This thesis proves that such a 3D simulation can be made, that runs in exceptionally short durations compared to traditional methods, by making a number of simplifications.MSC Marc was used to host the simulation, with a parametric script written in Python responsible for generating the model geometry and cutter layout. A Fortran program was developed that is called upon by Marc to calculate the required cutting load outputs and generate new workpiece meshes as material is removed.</div

Programmed design of ship forms

This paper describes a new category of CAD applications devoted to the definition and parameterization of hull forms, called programmed design. Programmed design relies on two prerequisites. The first one is a product model with a variety of types large enough to face the modeling of any type of ship. The second one is a design language dedicated to create the product model. The main purpose of the language is to publish the modeling algorithms of the application in the designer knowledge domain to let the designer create parametric model scripts. The programmed design is an evolution of the parametric design but it is not just parametric design. It is a tool to create parametric design tools. It provides a methodology to extract the design knowledge by abstracting a design experience in order to store and reuse it. Programmed design is related with the organizational and architectural aspects of the CAD applications but not with the development of modeling algorithms. It is built on top and relies on existing algorithms provided by a comprehensive product model. Programmed design can be useful to develop new applications, to support the evolution of existing applications or even to integrate different types of application in a single one. A three-level software architecture is proposed to make the implementation of the programmed design easier. These levels are the conceptual level based on the design language, the mathematical level based on the geometric formulation of the product model and the visual level based on the polyhedral representation of the model as required by the graphic card. Finally, some scenarios of the use of programmed design are discussed. For instance, the development of specialized parametric hull form generators for a ship type or a family of ships or the creation of palettes of hull form components to be used as parametric design patterns. Also two new processes of reverse engineering which can considerably improve the application have been detected: the creation of the mathematical level from the visual level and the creation of the conceptual level from the mathematical level. © 2012 Elsevier Ltd. All rights reserved. 1. Introductio

Least Squares Fitting of Analytic Primitives on a GPU

Metrology systems take coordinate information directly from the surface of a manufactured part and generate millions of (X, Y, Z) data points. The inspection process often involves fitting analytic primitives such as sphere, cone, torus, cylinder and plane to these points which represent an object with the corresponding shape. Typically, a least squares fit of the parameters of the shape to the point set is performed. The least squares fit attempts to minimize the sum of the squares of the distances between the points and the primitive. The objective function however, cannot be solved in the closed form and numerical minimization techniques are required to obtain the solution. These techniques as applied to primitive fitting entail iteratively solving large systems of linear equations generally involving large floating point numbers until the solution has converged. The current problem in-process metrology faces is the large computational times for the analysis of these millions of streaming data points. This research addresses the bottleneck using the Graphical Processing Unit (GPU), primarily developed by the computer gaming industry, to optimize operations. The explosive growth in the programming capabilities and raw processing power of Graphical Processing Units has opened up new avenues for their use in non-graphic applications. The combination of large stream of data and the need for 3D vector operations make the primitive shape fit algorithms excellent candidates for processing via a GPU. The work presented in this research investigates the use of the parallel processing capabilities of the GPU in expediting specific computations involved in the fitting procedure. The least squares fit algorithms for the circle, sphere, cylinder, plane, cone and torus have been implemented on the GPU using NVIDIA\u27s Compute Unified Device Architecture (CUDA). The implementations are benchmarked against those on a CPU which are carried out using C++. The Gauss Newton minimization algorithm is used to obtain the best fit parameters for each of the aforementioned primitives. The computation times for the two implementations are compared. It is demonstrated that the GPU is about 3-4 times faster than the CPU for a relatively simple geometry such as the circle while the factor scales to about 14 for a torus which is more complex

Neural network-based design of freeform off-axis three-mirror telescopes for space applications

openThis work explores the development of an innovative Neural Network-based framework to automate the design of freeform off-axis three-mirror imaging systems. These optical systems, consisting of three freeform optical components arranged in a non-collinear manner, have enormous potential in fields such as space exploration and astronomy, due to their compactness and superior imaging capabilities. Starting with a comprehensive overview of freeform optics, this thesis provides an in-depth explanation of the mathematical representations, fabrication, and metrology of freeform surfaces. The challenges of realizing complex optical systems are highlighted, emphasizing the need for efficient designs. Furthermore, we analyze the advantages of freeform off-axis three-mirror imaging systems in space exploration when compared to conventional designs, providing valuable context for the developed framework. In this thesis, we propose a methodology based on Neural Networks to generate effective starting points in the design process. The framework comprises several significant phases. To begin with, we identify the key parameters of the representative system which include the Field of View, F-number, and Entrance Pupil Diameter. Next, we establish the System Parameter Space (SPS) by taking into account the design requirements and the parameters involved in the system. Then, we create a dataset through systematic sampling within the SPS, using a system evolution approach to derive the corresponding surface parameters that can fully describe the location and shape of the surfaces. The Feed-Forward Neural Network (FFNN) is trained rigorously with the given dataset. Once it is validated and proven effective, the trained FFNN can quickly produce the corresponding surface parameters when specific system parameter combinations are provided. As a result, it serves as an optimal starting point for subsequent optimizations, significantly reducing the amount of manual effort required during the design process. This novel framework represents a step forward in the fusion of advanced machine learning techniques with optical design principles. By automating and streamlining the design process, this framework sets the stage for a new era in the creation of high-performance optical systems, paving the way for future advancements in space exploration, astronomy, and various other domains.This work explores the development of an innovative Neural Network-based framework to automate the design of freeform off-axis three-mirror imaging systems. These optical systems, consisting of three freeform optical components arranged in a non-collinear manner, have enormous potential in fields such as space exploration and astronomy, due to their compactness and superior imaging capabilities. Starting with a comprehensive overview of freeform optics, this thesis provides an in-depth explanation of the mathematical representations, fabrication, and metrology of freeform surfaces. The challenges of realizing complex optical systems are highlighted, emphasizing the need for efficient designs. Furthermore, we analyze the advantages of freeform off-axis three-mirror imaging systems in space exploration when compared to conventional designs, providing valuable context for the developed framework. In this thesis, we propose a methodology based on Neural Networks to generate effective starting points in the design process. The framework comprises several significant phases. To begin with, we identify the key parameters of the representative system which include the Field of View, F-number, and Entrance Pupil Diameter. Next, we establish the System Parameter Space (SPS) by taking into account the design requirements and the parameters involved in the system. Then, we create a dataset through systematic sampling within the SPS, using a system evolution approach to derive the corresponding surface parameters that can fully describe the location and shape of the surfaces. The Feed-Forward Neural Network (FFNN) is trained rigorously with the given dataset. Once it is validated and proven effective, the trained FFNN can quickly produce the corresponding surface parameters when specific system parameter combinations are provided. As a result, it serves as an optimal starting point for subsequent optimizations, significantly reducing the amount of manual effort required during the design process. This novel framework represents a step forward in the fusion of advanced machine learning techniques with optical design principles. By automating and streamlining the design process, this framework sets the stage for a new era in the creation of high-performance optical systems, paving the way for future advancements in space exploration, astronomy, and various other domains

Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

A feature-based reverse engineering system using artificial neural networks

Reverse Engineering (RE) is the process of reconstructing CAD models from scanned data of a physical part acquired using 3D scanners. RE has attracted a great deal of research interest over the last decade. However, a review of the literature reveals that most research work have focused on creation of free form surfaces from point cloud data. Representing geometry in terms of surface patches is adequate to represent positional information, but can not capture any of the higher level structure of the part. Reconstructing solid models is of importance since the resulting solid models can be directly imported into commercial solid modellers for various manufacturing activities such as process planning, integral property computation, assembly analysis, and other applications. This research discusses the novel methodology of extracting geometric features directly from a data set of 3D scanned points, which utilises the concepts of artificial neural networks (ANNs). In order to design and develop a generic feature-based RE system for prismatic parts, the following five main tasks were investigated. (1) point data processing algorithms; (2) edge detection strategies; (3) a feature recogniser using ANNs; (4) a feature extraction module; (5) a CAD model exchanger into other CAD/CAM systems via IGES. A key feature of this research is the incorporation of ANN in feature recognition. The use of ANN approach has enabled the development of a flexible feature-based RE methodology that can be trained to deal with new features. ANNs require parallel input patterns. In this research, four geometric attributes extracted from a point set are input to the ANN module for feature recognition: chain codes, convex/concave, circular/rectangular and open/closed attribute. Recognising each feature requires the determination of these attributes. New and robust algorithms are developed for determining these attributes for each of the features. This feature-based approach currently focuses on solving the feature recognition problem based on 2.5D shapes such as block pocket, step, slot, hole, and boss, which are common and crucial in mechanical engineering products. This approach is validated using a set of industrial components. The test results show that the strategy for recognising features is reliable

Doctor of Philosophy

dissertationWhile boundary representations, such as nonuniform rational B-spline (NURBS) surfaces, have traditionally well served the needs of the modeling community, they have not seen widespread adoption among the wider engineering discipline. There is a common perception that NURBS are slow to evaluate and complex to implement. Whereas computer-aided design commonly deals with surfaces, the engineering community must deal with materials that have thickness. Traditional visualization techniques have avoided NURBS, and there has been little cross-talk between the rich spline approximation community and the larger engineering field. Recently there has been a strong desire to marry the modeling and analysis phases of the iterative design cycle, be it in car design, turbulent flow simulation around an airfoil, or lighting design. Research has demonstrated that employing a single representation throughout the cycle has key advantages. Furthermore, novel manufacturing techniques employing heterogeneous materials require the introduction of volumetric modeling representations. There is little question that fields such as scientific visualization and mechanical engineering could benefit from the powerful approximation properties of splines. In this dissertation, we remove several hurdles to the application of NURBS to problems in engineering and demonstrate how their unique properties can be leveraged to solve problems of interest

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed