Dissertationen (eigene und begutachtete):

A. Amirkhanov:
"Visual Analysis of Defects";
Betreuer/in(nen), Begutachter/in(nen): E. Gröller, G. Mistelbauer, Ch. Heinzl; Visual Computing and Human-Centered Technology, 2021; Rigorosum: 18.10.2021.

Kurzfassung englisch:
In everyday life, we use many objects on which we rely and expect them to work correctly. We use phones to communicate with friends, bicycles to commute, payment cards to buy groceries. However, due to defects, these objects may fail at some time, leading to adverse outcomes. Modern industry continually improves the quality of outputs (e.g., products and services) and ensures that they meet their specifications. A common quality management strategy is the defect analysis used to identify and control outputs that do not conform to their specifications. Traditional defect analysis methods are often manual and, therefore, time-consuming procedures. To build more efficient solutions, defect analysis increasingly employs visual analytics techniques. These techniques automatize and enhance the up-to-now manual analysis steps and support new visual approaches for defect representations that resolve existing defects without introducing new ones. In this dissertation, visual analytics techniques applied to defect analysis are referred to as visual analysis of defects. Being a rapidly developing area, the domain of visual analysis of defects is still missing a formalized basis. This dissertation presents and discusses a workflow for the visual analysis of defects based on the plan-do-check-act cycle of continual improvement. The workflow consists of four steps: defect prevention, control of defective outputs, performance evaluation, and improvement. During the defect prevention step, domain experts plan the design and development processes to ensure that intended results can be achieved while forecasting risks and opportunities. During the control of defective outputs step, domain experts implement the processes and control defects arising throughout these processes. During the performance evaluation step, domain experts ensure that defective outputs are identified by measuring the object's characteristics. During the improvement step, domain experts explore possible actions that improve the object quality. This dissertation presents four solutions that advance the visual analysis of defects at the four distinct steps of the workflow. The first solution corresponds to the defect prevention step and provides a preview of dental treatment. It helps dental technicians to identify the most suitable treatment option and avoid cases when patients are unsatisfied with the results due to poor denture aesthetics. The second solution corresponds to the control of defective outputs step and supports dental technicians in designing aesthetic and functional dentures. The approach provides immediate visual feedback on a change in the denture design, which helps to evaluate how the change affects aesthetics. The third solution corresponds to the performance evaluation step and supports material engineers in investigating the damage mechanism in composite materials. First, the system captures and measures various defects such as matrix fracture, fiber/matrix debonding, fiber pull-out, and fiber fracture. Later, users analyze these defects using several interactive visualization techniques. The fourth solution corresponds to the improvement step and visualizes 4D dynamical systems describing various phenomena. The solution enables the 4D representation of dynamical systems and allows the 4D representation to seamlessly transition into, familiar to the user, lower-dimensional plots.

Elektronische Version der Publikation:

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.