QUALITY AND PRODUCTIVITY IMPROVEMENTS IN ADDITIVE MANUFACTURING

Additive manufacturing (AM) is a relatively new manufacturing technology compared to the traditional manufacturing methods. Even though AM processes have many advantages, they also have a series of challenges that need to be addressed to adapt this technology for a wide range of applications and mass production. AM faces a number of challenges, including the absence of methods/models for determining whether AM is the best manufacturing process for a given part. The first study of this thesis proposes a framework for choosing specific AM processes by considering the complexity level of a part. It has been proven that the method works effectively through numerical experiments. Optimization of process parameters through expensive and time-consuming experiments is another issue with AM. To address this issue, an empirical model is presented in the second study to optimize parameters for minimizing building costs through maximizing the trade-off between productivity and quality. The proposed model proves to be effective in reducing building costs at any quality level. The results indicate that process parameters can be optimized quickly and accurately, as compared to the time-consuming and expensive experimental methods. Another limitation of AM is the lack of capability to use multiple materials, which is a concern when adapting this technology to mass production. To address this issue, a new scheduling model with considering multi-material types is introduced in the third study. Based on the numerical results, the proposed model can provide optimal sequence by maximizing the trade-off between tardiness and material switching cost

Scene Segmentation and Object Classification for Place Recognition

This dissertation tries to solve the place recognition and loop closing problem in a way similar to human visual system. First, a novel image segmentation algorithm is developed. The image segmentation algorithm is based on a Perceptual Organization model, which allows the image segmentation algorithm to ‘perceive’ the special structural relations among the constituent parts of an unknown object and hence to group them together without object-specific knowledge. Then a new object recognition method is developed. Based on the fairly accurate segmentations generated by the image segmentation algorithm, an informative object description that includes not only the appearance (colors and textures), but also the parts layout and shape information is built. Then a novel feature selection algorithm is developed. The feature selection method can select a subset of features that best describes the characteristics of an object class. Classifiers trained with the selected features can classify objects with high accuracy. In next step, a subset of the salient objects in a scene is selected as landmark objects to label the place. The landmark objects are highly distinctive and widely visible. Each landmark object is represented by a list of SIFT descriptors extracted from the object surface. This object representation allows us to reliably recognize an object under certain viewpoint changes. To achieve efficient scene-matching, an indexing structure is developed. Both texture feature and color feature of objects are used as indexing features. The texture feature and the color feature are viewpoint-invariant and hence can be used to effectively find the candidate objects with similar surface characteristics to a query object. Experimental results show that the object-based place recognition and loop detection method can efficiently recognize a place in a large complex outdoor environment

Pattern Formation and Stress Propagation in Confined Colloidal Flows

Particulate solutions exhibit many interesting and varied behaviours when driven out of equilibrium. Not least of which is their ability to form elaborate and intricate patterns when subject to gravity driven flow in the confined space between a substrate and the fluid-air interface of a thin film. The present work presents results of investigations into some of the key physical pro- cesses within the fluid, that are thought to lead to the formation of patterns. These were performed using a range of simplified models and numerical simulations. The cen- tral theme of the work is a simplified two fluid model of the particle-laden fluid itself, the results of which reveal a novel pattern formation process, entirely distinct from the conventional instability driven process normally associated with patterning. This process involves the decay of fluctuations in the particle volume fraction in one direction while fluctuations in the other persist. Ultimately, however, it was found, using both simulations and analytical stability analysis, that the physical processes encompassed by this simple model are not sufficient to increase the intensity of the patterns. As well as considering additions to the model; two more, in depth, studies of physical processes at the microscopic level, thought to be potentially important to the formation of patterns, were also carried out. These consisted of the formulation of a simple, analytical, constitutive relation and a particle scale simulation including full many body hydrody- namic interactions. These highlighted the importance of memory effects and, long range, hydrodynamic interactions as potentially important processes by which band patterns may grow and increase in intensity. The whole issue of patterning on a surface also leads to the question of how these, two dimensional, patterns should be characterised and, to this end, a number of novel methods for calculating the complexity are also discussed