Visual Analysis of Defects

Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersIn 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.17

Bladder runner:visual analytics for the exploration of RT-induced bladder toxicity in a cohort study

\u3cp\u3eWe present the Bladder Runner, a novel tool to enable detailed visual exploration and analysis of the impact of bladder shape variation on the accuracy of dose delivery, during the course of prostate cancer radiotherapy (RT). Our tool enables the investigation of individual patients and cohorts through the entire treatment process, and it can give indications of RT-induced complications for the patient. In prostate cancer RT treatment, despite the design of an initial plan prior to dose administration, bladder toxicity remains very common. The main reason is that the dose is delivered in multiple fractions over a period of weeks, during which, the anatomical variation of the bladder – due to differences in urinary filling – causes deviations between planned and delivered doses. Clinical researchers want to correlate bladder shape variations to dose deviations and toxicity risk through cohort studies, to understand which specific bladder shape characteristics are more prone to side effects. This is currently done with Dose-Volume Histograms (DVHs), which provide limited, qualitative insight. The effect of bladder variation on dose delivery and the resulting toxicity cannot be currently examined with the DVHs. To address this need, we designed and implemented the Bladder Runner, which incorporates visualization strategies in a highly interactive environment with multiple linked views. Individual patients can be explored and analyzed through the entire treatment period, while inter-patient and temporal exploration, analysis and comparison are also supported. We demonstrate the applicability of our presented tool with a usage scenario, employing a dataset of 29 patients followed through the course of the treatment, across 13 time points. We conducted an evaluation with three clinical researchers working on the investigation of RT-induced bladder toxicity. All participants agreed that Bladder Runner provides better understanding and new opportunities for the exploration and analysis of the involved cohort data.\u3c/p\u3