Virtual prototyping with surface reconstruction and freeform geometric modeling using level-set method

More and more products with complex geometries are being designed and manufactured by computer aided design (CAD) and rapid prototyping (RP) technologies. Freeform surface is a geometrical feature widely used in modern products like car bodies, airfoils and turbine blades as well as in aesthetic artifacts. How to efficiently design and generate digital prototypes with freeform surfaces is an important issue in CAD. This paper presents the development of a Virtual Sculpting system and addresses the issues of surface reconstruction from dexel data structures and freeform geometric modeling using the level-set method from distance field structure. Our virtual sculpting method is based on the metaphor of carving a solid block into a 3D freeform object using a 3D haptic input device integrated with the computer visualization. This dissertation presents the result of the study and consists primarily of four papers --Abstract, page iv

An Investigation of Standard Test Part Design for Additive Manufacturing

This paper investigated the efficiency of the standard test part feature designs. Standard geometrical dimensioning and tolerancing criteria were analyzed for their efficiency in representing process characteristics of additive manufacturing (AM) processes. The design efficiency of the standard test part proposed by NIST was evaluated, and based on the analysis, the part was redesigned. A minimum characteristic set method was proposed to be used for future development of standard test part design. In addition, it was suggested that feature dimensional effect as well as feature orientations are both critical to geometrical quality evaluations, and therefore should be included in the feature design of the standard test part.Mechanical Engineerin

Thermophysical Phenomena in Metal Additive Manufacturing by Selective Laser Melting: Fundamentals, Modeling, Simulation and Experimentation

Among the many additive manufacturing (AM) processes for metallic materials, selective laser melting (SLM) is arguably the most versatile in terms of its potential to realize complex geometries along with tailored microstructure. However, the complexity of the SLM process, and the need for predictive relation of powder and process parameters to the part properties, demands further development of computational and experimental methods. This review addresses the fundamental physical phenomena of SLM, with a special emphasis on the associated thermal behavior. Simulation and experimental methods are discussed according to three primary categories. First, macroscopic approaches aim to answer questions at the component level and consider for example the determination of residual stresses or dimensional distortion effects prevalent in SLM. Second, mesoscopic approaches focus on the detection of defects such as excessive surface roughness, residual porosity or inclusions that occur at the mesoscopic length scale of individual powder particles. Third, microscopic approaches investigate the metallurgical microstructure evolution resulting from the high temperature gradients and extreme heating and cooling rates induced by the SLM process. Consideration of physical phenomena on all of these three length scales is mandatory to establish the understanding needed to realize high part quality in many applications, and to fully exploit the potential of SLM and related metal AM processes

Sketch-based character prototyping by deformation

Master'sMASTER OF SCIENC

Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review

Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed

Interactive Feature Selection and Visualization for Large Observational Data

Data can create enormous values in both scientific and industrial fields, especially for access to new knowledge and inspiration of innovation. As the massive increases in computing power, data storage capacity, as well as capability of data generation and collection, the scientific research communities are confronting with a transformation of exploiting the advanced uses of the large-scale, complex, and high-resolution data sets in situation awareness and decision-making projects. To comprehensively analyze the big data problems requires the analyses aiming at various aspects which involves of effective selections of static and time-varying feature patterns that fulfills the interests of domain users. To fully utilize the benefits of the ever-growing size of data and computing power in real applications, we proposed a general feature analysis pipeline and an integrated system that is general, scalable, and reliable for interactive feature selection and visualization of large observational data for situation awareness. The great challenge tackled in this dissertation was about how to effectively identify and select meaningful features in a complex feature space. Our research efforts mainly included three aspects: 1. Enable domain users to better define their interests of analysis; 2. Accelerate the process of feature selection; 3. Comprehensively present the intermediate and final analysis results in a visualized way. For static feature selection, we developed a series of quantitative metrics that related the user interest with the spatio-temporal characteristics of features. For timevarying feature selection, we proposed the concept of generalized feature set and used a generalized time-varying feature to describe the selection interest. Additionally, we provided a scalable system framework that manages both data processing and interactive visualization, and effectively exploits the computation and analysis resources. The methods and the system design together actualized interactive feature selections from two representative large observational data sets with large spatial and temporal resolutions respectively. The final results supported the endeavors in applications of big data analysis regarding combining the statistical methods with high performance computing techniques to visualize real events interactively