Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The logical allocation of tasks in engineering offices currently relies heavily on the experience and intuition of project managers. In large scale projects the complexity of the task allocation procedure exceeds the capacity of human intuition, and a systematic technique is required to aid project managers in assigning tasks to individuals. In this project such a systematic technique is modelled and implemented using the Java programming language. An equation was developed to calculate an individual’s workload, and used in conjunction with other criteria to intelligently and systematically select an optimal individual to complete engineering tasks. The software solution is network-based, and also aims to aid project managers in various managerial duties. / AFRIKAANSE OPSOMMING: Die logiese toekenning van ingenieurstake steun tans swaar op die ervaring en aanvoeling van projek bestuurders. In grootskaalse projekte is die kompleksiteit van die taak toekenningsproses veel groter as die kapasiteit van menslike intuïsie. Dus word ‘n sistematiese proses wat projek-bestuurders kan help met die toeken van take aan individue vereis. In hierdie projek is so 'n sistematiese tegniek ontwikkel en geïmplementeer met behulp van die Java-programmeringstaal. 'n Vergelyking is ontwikkel om 'n individu se werklading te bereken en is in samewerking met ander kriteria gebruik om take intelligent en sistematies toe te ken. Die sagteware is network en databasis-gebaseerd en kan ook gebruik word om projek-bestuurders te help met verskeie bestuurspligte.

Product visualization in AR/VR applications requires a largely manual process of data preparation. Previous publications focus on error-free triangulation or transformation of product structure data and display attributes for AR/VR applications. This paper focuses on the preparation of the required geometry data. In this context, a significant reduction in effort can be achieved through automation. The steps of geometry preparation are identified and examined concerning their automation potential. In addition, possible couplings of sub-steps are discussed. Based on these explanations, a structure for the geometry preparation process is proposed. With this structured preparation process, it becomes possible to consider the available computing power of the target platform during the geometry preparation. The number of objects to be rendered, the tessellation quality, and the level of detail (LOD) can be controlled by the automated choice of transformation parameters. Through this approach, tedious preparation tasks and iterative performance optimization can be avoided in the future, which also simplifies the integration of AR/VR applications into product development and use. A software tool is presented in which partial steps of the automatic preparation are already implemented. After an analysis of the product structure of a CAD file, the transformation is executed for each component. Functions implemented so far allow, for example, the selection of assemblies and parts based on filter options, the transformation of geometries in batch mode, the removal of certain details, and the creation of UV maps. Flexibility, transformation quality, and timesavings are described and discussed.