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Digital technology is increasingly becoming a part of life and culture in society, and it must be consciously designed for the long-term benefit of humanity. Today, information systems are designed to do more than fulfill human duties or complete tasks. A widely adopted approach is a system design that focuses on the positive aspects of human-technology interaction. Positive computing is a design paradigm gaining traction because it emphasizes the importance of well-being as a bold goal to be implemented in system design. In this dissertation, technology design is part of an intergenerational environment aiming to facilitate information sharing regarding global startup innovation. Nevertheless, much of the research focuses on how technology can be used to facilitate intergenerational collaboration. On the other hand, very little is known about how technology can be "positively" designed to promote intergenerational innovation. Therefore, this dissertation applied Design Science Research (DSR) to inform and guide the creation of design principles through the lens of positive computing. The study results provide a holistic picture of the numerous barriers, well-being factors, competing concerns, and competencies that have been encountered in the context of intergenerational innovation and their implications. This dissertation is presented as a cumulative dissertation, answering three research questions divided into seven studies, consisting of nine articles.
Optimization of Encircling Eddy Current Sensors for Online Monitoring of Hot Rolled Round Steel Bars
(2014)
Modern manufacturing industries are continually working on quality enhancements for the hot rolling process of round products. One method for improving the finalisation of the rods is the implementation of an automatic size control system. As a result of these trends over the last few years, there has been an increasing demand for more accurate online measurements. Thus the reason for the research performed for this thesis. A particular challenge throughout this research was dealing with the temperature changes (up to 1200°C) from the in- and output of the fervent rolling stocks, and the effect this temperature changes had on the sensors. Furthermore, there is also high demand for developing fast and practical electronic measuring equipment, capable of measuring during high transport velocities (up to 120 m/s). The eddy current principle is just one of the very few methods available which can with-stand such harsh industrial environments. In fact, eddy current sensors are already being integrated into online monitoring tasks for hot rolling processes. The measurement uncertainty, however, is still considerably large for process control purposes. One reason for this lies within the ability for eddy current detectors to receive signals influenced by outward forces, i.e. forces dependent on its location, its geometry, the outside temperature and the material properties of a particular target. Thus the current accuracy for a cross-sectional area measurement, for example, is no higher than 1%. As a result, this thesis investigates the magnitude of all individual influential factors on the eddy current detectors, using model-based analysis techniques. The analytical model provides a solution for all rotationally symmetrical targets and the FEA model covers all of the other influencing parameters in a more time consuming manner. This thesis then provides different methods which are developed to separate the cross-sectional area measurement of a rod from all of the other influencing parameters. In addition, a material tracking approach for round products is developed. Two different kinds of prototypes, capable of measuring approximately 466 Tons of red-hot steel rods during the production process, are finally introduced in this thesis. The usefulness of the eddy current principle is validated by the provided field test results. The count accuracy for the identification of 2876 bars was found to be 99.93%, and the average measurement accuracy for the cross-sectional area experiments was reduced to ± 0.29 % when including all of the findings.