Planning of optimal toolpath, cutter orientation, and feed rate for 5-axis Computer Numerical Control (CNC) machining of curved surfaces using a flat-end mill is a challenging task, although the approach has a great potential for much improved machining efficiency and surface quality of the finished part. This research combines and introduces several key enabling techniques for curved surface machining using 5-axis milling and a flat end cutter to achieve maximum machining efficiency and best surface quality, and to overcome some of the key drawbacks of 5-axis milling machine and flat end cutter use. First, this work proposes an optimal toolpath generation method by machining the curved surface patch-by-patch, considering surface normal variations using a fuzzy clustering technique. This method allows faster CNC machining with reduced slow angular motion of tool rotational axes and reduces sharp cutter orientation changes. The optimal number of surface patches or surface point clusters is determined by minimizing the two rotation motions and simplifying the toolpaths. Secondly, an optimal tool orientation generation method based on the combination of the surface normal method for convex curved surfaces and Euler-Meusnier Sphere (EMS) method for concave curved surfaces without surface gouge in machining has been introduced to achieve the maximum machining efficiency and surface quality. The surface normal based cutter orientation planning method is used to obtain the closest curvature match and longest cutting edge; and the EMS method is applied to obtain the closest curvature match and to avoid local gouging by matching the largest cutter Euler-Meusnier sphere with the smallest Euler-Meusnier sphere of the machined surface at each cutter contact (CC) point. For surfaces with saddle shapes, selection of one of these two tool orientation determination methods is based on the direction of the CNC toolpath relative to the change of surface curvature. A Non-uniform rational basis spline (NURBS) surface with concave, convex, and saddle features is used to demonstrate these newly introduced methods. Thirdly, the tool based and the Tri-dexel workpiece based methods of chip volume and cutting force predictions for flat-end mills in 5-axis CNC machining have been explored for feed rate optimization to achieve the maximum material removal rate. A new approach called local parallel slice method which extends the Alpha Shape method - only for chip geometry and removal volume prediction has been introduced to predict instant cutting forces for dynamic feed rate optimization. The Tri-dexel workpiece model is created to get undeformed chip geometry, chip volume, and cutting forces by determining the intersections of the tool envelope and continuously updating the workpiece during machining. The comparison of these two approaches is made and several machining experiments are conducted to verify the simulation results. At last, the chip ploughing effects that become a more serious problem in micro-machining due to chip thickness not always being larger than the tool edge radius are also considered. It is a challenging task to avoid ploughing effects in micro-milling. A new model of 3D chip geometry is thus developed to calculate chip thickness and ploughing volume in micro 5-axis flat-end milling by considering the minimum chip thickness effects. The research forms the foundation of optimal toolpath, cutter orientation, cutting forces/volume calculations, and ploughing effects in 5-axis CNC machining of curved surfaces using a flat-end mill for further research and direct manufacturing applications. / Graduate / 0548 / luoshan@uvic.ca

Aujourd’hui, sur le marché, on peut trouver une vaste gamme de produits différents ou des formes variées d’un même produit et ce grand assortiment fatigue les clients. Il est clair que la décision des clients d’acheter un produit dépend de l'aspect esthétique de la forme du produit et de l’affection émotionnelle. Par conséquent, il est très important de comprendre les propriétés esthétiques et de les adopter dans la conception du produit, dès le début. L'objectif de cette thèse est de proposer un cadre générique pour la cartographie des propriétés esthétiques des formes gauches en 3D en façon d'être en mesure d’extraire des règles de classification esthétiques et des propriétés géométriques associées. L'élément clé du cadre proposé est l'application des méthodologies de l’Exploration des données (Data Mining) et des Techniques d’apprentissage automatiques (Machine Learning Techniques) dans la cartographie des propriétés esthétiques des formes. L'application du cadre est d'étudier s’il y a une opinion commune pour la planéité perçu de la part des concepteurs non-professionnels. Le but de ce cadre n'est pas seulement d’établir une structure pour repérer des propriétés esthétiques des formes gauches, mais aussi pour être utilisé comme un chemin guidé pour l’identification d’une cartographie entre les sémantiques et les formes gauches différentes. L'objectif à long terme de ce travail est de définir une méthodologie pour intégrer efficacement le concept de l’Ingénierie affective (c.à.d. Affective Engineering) dans le design industriel. / Today on the market we can find a large variety of different products and differentshapes of the same product and this great choice overwhelms the customers. It is evident that the aesthetic appearance of the product shape and its emotional affection will lead the customers to the decision for buying the product. Therefore, it is very important to understand the aesthetic proper-ties and to adopt them in the early product design phases. The objective of this thesis is to propose a generic framework for mapping aesthetic properties to 3D freeform shapes, so as to be able to extract aesthetic classification rules and associated geometric properties. The key element of the proposed framework is the application of the Data Mining (DM) methodology and Machine Learning Techniques (MLTs) in the mapping of aesthetic properties to the shapes. The application of the framework is to investigate whether there is a common judgment for the flatness perceived from non-professional designers. The aim of the framework is not only to establish a structure for mapping aesthetic properties to free-form shapes, but also to be used as a guided path for identifying a mapping between different semantics and free-form shapes. The long-term objective of this work is to define a methodology to efficiently integrate the concept of Affective Engineering in the Industrial Designing.

Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into environments previously thought to be too perilous for humans to operate in. Some robots can be used to extract potentially hazardous materials from sites inaccessible to humans, while others are being developed to aid humans with laborious tasks. A crucial aspect of all artificial systems is the manner in which they interact with their immediate surroundings. Developing such a deceivingly simply aspect has proven to be significantly challenging, as it not only entails the methods through which the system perceives its environment, but also its ability to perform critical tasks. These undertakings often involve the coordination of numerous subsystems, each performing its own complex duty. To complicate matters further, it is nowadays becoming increasingly important for these artificial systems to be able to perform their tasks in real-time. The task of object recognition is typically described as the process of retrieving the object in a database that is most similar to an unknown, or query, object. Pose estimation, on the other hand, involves estimating the position and orientation of an object in three-dimensional space, as seen from an observer’s viewpoint. These two tasks are regarded as vital to many computer vision techniques and and regularly serve as input to more complex perception algorithms. An approach is presented which regards the object recognition and pose estimation procedures as mutually dependent. The core idea is that dissimilar objects might appear similar when observed from certain viewpoints. A feature-based conceptualisation, which makes use of a database, is implemented and used to perform simultaneous object recognition and pose estimation. The design incorporates data compression techniques, originally suggested by the image-processing community, to facilitate fast processing of large databases. System performance is quantified primarily on object recognition, pose estimation and execution time characteristics. These aspects are investigated under ideal conditions by exploiting three-dimensional models of relevant objects. The performance of the system is also analysed for practical scenarios by acquiring input data from a structured light implementation, which resembles that obtained from many commercial range scanners. Practical experiments indicate that the system was capable of performing simultaneous object recognition and pose estimation in approximately 230 ms once a novel object has been sensed. An average object recognition accuracy of approximately 73% was achieved. The pose estimation results were reasonable but prompted further research. The results are comparable to what has been achieved using other suggested approaches such as Viewpoint Feature Histograms and Spin Images. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

The problem of face detection and recognition within a given database has become one of the important problems in computer vision. A simple approach for Face Detection in video is to run a learning based face detector every frame. But such an approach is computationally expensive and completely ignores the temporal continuity present in videos. Moreover the search space can be reduced by utilizing visual cues extracted based on the relevant task at hand(top down approach). Once detection is done next step is to perform a face recognition based on the available database. But the faces detected from face detect or output is neither aligned nor well cropped and is prone to scale change. We call such faces as free form faces. But the current existing algorithms on face recognition assume faces to be properly aligned and cropped, and having the same scale as the faces in the database, which is highly constrained. In this thesis, we propose an integrated detect-track framework for Multiview face detection in videos. We overcome the limitations of the frame based approaches, by utilizing the temporal continuity present in videos and also incorporating the top down information of the task. We model the problem based on the concept from Experiential sampling [2]. This consists of determining certain key positions which are relevant to the task(face detection). These key positions are referred to as attention samples and Multiview face detection is performed only at these locations. These statistical samples are estimated based on the visual cues, past experience and the temporal continuity and is modeled as a Bayesian filtering problem, which is solved using Particle Filters. In order to detect all views we use a tracker integrated with the detector and come out with a novel track termination algorithm using the concepts from Track Before Detect(TBD)[26]. Such an approach is computationally efficient and also results in lower false positive rate. We provide experiments showing the efficiency of the integrated detect-track approach over the multiview face detector approach without a tracker. For free form face recognition we propose to use the concept of Principal Geodesic Analysis(PGA) of the Covariance descriptors obtained from Gabor filters. This is similar to Principal Component Analysis in Euclidean spaces (Covariance descriptors lie on a Riemannian manifold). Such a descriptor is robust to alignment and scaling problems and also are of lower dimensions. We also employ sparse modeling technique for Face recognition task using these Covariance descriptor which are dimensionally reduced by transforming them on to a tangent space, which we call PGA feature. Further, we improve upon the recognition results of linear sparse modeling, by non-linear mapping of the PGA features by employing “Kernel Trick” for these sparse models. We show that the Kernelized sparse models using the PGA features are indeed very efficient for free form face recognition by testing on two standard databases namely AR and YaleB database.

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into environments previously thought to be too perilous for humans to operate in. Some robots can be used to extract potentially hazardous materials from sites inaccessible to humans, while others are being developed to aid humans with laborious tasks. A crucial aspect of all artificial systems is the manner in which they interact with their immediate surroundings. Developing such a deceivingly simply aspect has proven to be significantly challenging, as it not only entails the methods through which the system perceives its environment, but also its ability to perform critical tasks. These undertakings often involve the coordination of numerous subsystems, each performing its own complex duty. To complicate matters further, it is nowadays becoming increasingly important for these artificial systems to be able to perform their tasks in real-time. The task of object recognition is typically described as the process of retrieving the object in a database that is most similar to an unknown, or query, object. Pose estimation, on the other hand, involves estimating the position and orientation of an object in three-dimensional space, as seen from an observer’s viewpoint. These two tasks are regarded as vital to many computer vision techniques and regularly serve as input to more complex perception algorithms. An approach is presented which regards the object recognition and pose estimation procedures as mutually dependent. The core idea is that dissimilar objects might appear similar when observed from certain viewpoints. A feature-based conceptualisation, which makes use of a database, is implemented and used to perform simultaneous object recognition and pose estimation. The design incorporates data compression techniques, originally suggested by the image-processing community, to facilitate fast processing of large databases. System performance is quantified primarily on object recognition, pose estimation and execution time characteristics. These aspects are investigated under ideal conditions by exploiting three-dimensional models of relevant objects. The performance of the system is also analysed for practical scenarios by acquiring input data from a structured light implementation, which resembles that obtained from many commercial range scanners. Practical experiments indicate that the system was capable of performing simultaneous object recognition and pose estimation in approximately 230 ms once a novel object has been sensed. An average object recognition accuracy of approximately 73% was achieved. The pose estimation results were reasonable but prompted further research. The results are comparable to what has been achieved using other suggested approaches such as Viewpoint Feature Histograms and Spin Images. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

The experiments presented and analyzed in this dissertation concern two well-established phenomena in behavioral economics: that human decision makers hold biased beliefs about probability and that free-form communication between economic agents promotes cooperation far in excess of what standard theory predicts. First, Chapter 2 studies subjective probability, focusing on the well-established existence of both the Hot Hand and Gambler's Fallacies — the false expectation of positive and negative autocorrelation, respectively. Both biases are prevalent throughout a wide variety of real-world contexts; what causes a person to favor one over the other? We conduct an experiment in which we observe fully informed subjects switching between the Hot Hand and Gambler's Fallacies when predicting future outcomes of mathematically identical sequences. Subjects exhibit the Gambler's Fallacy when predicting single outcomes but favor the Hot Hand when asked explicitly to estimate probabilities. Connecting our results to existing theory suggests that very subtle changes in framing lead decision makers to employ substantially different approaches to form predictions. The remainder of this dissertation studies cheap talk communication between human subjects playing incentivised trust games. In Chapter 3, we study free-form communication using a dataset of over 1000 messages sent between participants in a laboratory Trust game. We employ Natural Language Processing to systematically generate meaningful partitions of the messages space which we can then examine with established regression approaches. Our investigation reveals features correlated with trust that have not previously been considered. Most notably, highly detailed, specific promises establish trust more effectively than other messages which signal the same intended action. Additionally, we observe that the most and least trusted messages in our dataset differ starkly in their quality. Highly trusted messages are longer, more detailed, and contain fewer grammatical errors whereas the least trusted messages tend to be brief and prone to errors. In Chapter 4, we examine whether the difference is message quality affects trust by acting as a signal of effort. We report the results of an experiment designed to test whether promises which require higher levels of effort result in greater trust from their recipients. We find that more costly promises lead recipients to trust more frequently. However, there is no corresponding, significant difference in the trustworthiness of their senders. Further, when asked their beliefs explicitly, recipients do not believe that higher cost promises are more likely to be trustworthy. This presents a potential challenge to our understanding of trust between economic decision makers. If effort increases trust without altering receivers' beliefs, receivers must be concerned with factors other than their own payoff maximization. We conclude by presenting a follow-up experiment where varying effort cost cannot convey the sender's intentions, however, the results are inconclusive. / Doctor of Philosophy / This dissertation presents three projects in which we examine how human decision makers' choices differ from those predicted by standard economic theory. The experiments we conduct cover two broad topics: the way humans estimate the probability of random events and how communication leads to greater cooperation between agents with potentially conflicting monetary interests. It is well established that humans often hold distorted beliefs about probability. Depending on the direction of their bias, these beliefs are consistent with either the Hot Hand or Gambler's Fallacy. In Chapter 2, we examine the factors which may cause people to change the direction of their bias. Subjects exhibit the Gambler's Fallacy when predicting single outcomes, but favor the Hot Hand when asked explicitly to estimate probabilities. Chapters 3 and 4 study cheap talk communication between decision makers — messages which carry with them no commitment mechanism. It is no surprise to the average person that communication may enhance cooperation and trust between people. Experimental economists have verified this intuition in laboratory experiments and found that free-form communication is particularly effective. However, the precise mechanism through which free-form communication enhances cooperation is unclear. In Chapter 3, we collect a large dataset of free-form messages transmitted between players of an investment game. We then employ Natural Language Processing tools, novel to the Economics laboratory, to parse the unstructured data and identify message features associated with changes in trust and trustworthiness. Chapter 4 continues to examine communication, investigating whether the effort required to a promise affects its perceived or actual trustworthiness. We find that higher effort promises lead to greater trust, but find no corresponding increase in trustworthiness.

Trois modules seront développés: Le module de détection a produit une analyse des problématiques figurations con, à savoir un ensemble de domaines où soit quelques nouveaux DDL ou des changements locaux dans les contraintes sont obligatoires. Le module de traitement permettra à la défi nition des mécanismes pour aider la décision sur modi cations. Le module de prédiction dire le degré de déformation en pré-analyser les caractéristiques des configurations de NURBS. / Three modules will be developed: The detection module has produced an analysis of problematic con figurations, i.e. a set of areas where either some new DOFs or some local changes in the constraints are mandatory. The treatment module will enable the defi nition of mechanisms to help the decision on modi cations. The prediction module will tell the degree of deformation by pre-analyzing the features of NURBS configurations.

Semantic interoperability (SI) of a product model refers to automatic exchange of meaning associated with the product data, among applications/domains throughout the product development cycle. In the product development cycle, several applications (engineering design, industrial design, manufacturing, supply chain, marketing, maintenance etc.) and different engineering domains (mechanical, electrical, electronic etc.) come into play making the ability to exchange product data with semantics very significant. With product development happening in multiple locations with multiple tools/systems, SI between these systems/domains becomes important. The thesis presents a feature-based framework for shape model to address these SI issues when exchanging shape models. Problem of exchanging semantics associated with shape model to support the product lifecycle has been identified and explained. Different types of semantic interoperability issues pertaining to the shape model have been identified and classified. Features in a shape model can be associated with volume addition/subtraction to/from base-solid, deformation/modification of base-sheet/base surface, forming of material of constant thickness. The DIFF model has been extended to represent, classify and extract Free-Form Surface Features (FFSFs) and deformation features in a part model. FFSFs refer to features that modify a free-form surface. Deformation features are created in constant thickness part models, for example, deformation of material (as in sheet-metal parts) or forming of material (as in injection molded parts with constant thickness), also referred to as constant thickness features. Volumetric features covered in the DIFF model have been extended to classify and represent volumetric features based on relative variations of cross-section and PathCurve. Shape feature ontology is described based on unified feature taxonomy with definitions and labels of features as defined in the extended DIFF model. Features definitions are used as intermediate and unambiguous representation for shape features. The feature ontology is used to capture semantics of shape features. The proposed ontology enables reasoning to handle semantic equivalences between feature labels, and is used to map shape features from a source to target applications. Reasoning framework for identification of semantically equivalent feature labels and representations for the feature being exchanged across multiple applications is presented and discussed. This reasoning framework is used to associate multiple construction paths for a feature and associate applicable meanings from the ontology. Interface is provided to select feature label for a target application from the list of labels which are semantically equivalent for the feature being exchanged/mapped. Parameters for the selected feature label can be mapped from the DIFF representation; the feature can then be represented/constructed in the target application using the feature label and mapped parameters. This work shows that product model with feature information (feature labels and representations), as understood by the target application, can be exchanged and maintained in such a way that multiple applications can use the product information as their understandable labels and representations. Finally, the thesis concludes by summarizing the main contributions and outlining the scope for future work.

In 3D modelling software, deformations are used to add, to remove, or to modify geometric features of existing 3D models to create new models with similar but slightly different details. Traditional techniques for deforming virtual 3D models require users to explicitly define control points and regions of interest (ROIs), and to define precisely how to deform ROIs using control points. The awkwardness of defining these factors in traditional 3D modelling software makes it difficult for people with limited experience of 3D modelling to deform existing 3D models as they expect. As applications which require virtual 3D model processing become more and more widespread, it becomes increasingly desirable to lower the "difficulty of use" threshold of 3D model deformations for users. This thesis argues that the user experience, in terms of intuitiveness and ease of use, of a user interface for deforming virtual 3D models, can be greatly enhanced by employing sketch-based 3D model deformation techniques, which require the minimal quantities of interactions, while keeping the plausibility of the results of deformations as well as the responsiveness of the algorithms, based on modern home grade computing devices. A prototype system for sketch-based 3D model deformations is developed and implemented to support this hypothesis, which allows the user to perform a deformation using a single deforming stroke, eliminating the need to explicitly select control points, the ROI and the deforming operation. GPU based accelerations have been employed to optimise the runtime performance of the system, so that the system is responsive enough for real-time interactions. The studies of the runtime performance and the usability of the prototype system are conducted to provide evidence to support the hypothesis.