New developments at the field of factory information systems and resource allocation solutions are constantly taken into practice within the field of manufacturing and production. Customers are turning their vision for more customized products and requesting further monitoring possibilities for the product itself, for its manufacturing and for its delivery. Similar paradigm change is taking place within the companies’ departments and between the clusters of manufacturing stakeholders. Modern cloud based tools are providing the means for gaining these objectives. Technology evolved from parallel, grid and distributed computing; at present cited as Cloud computing is one key future paradigm in factory and production automation. Regardless of the terminology still settling, in multiple occasions cloud computing is used term when referring to cloud services or cloud resources. Cloud technology is further-more understood as resources located outside individual entities premises. These resources are pieces of functionalities for gaining overall performance of the designed system and so worth such an architectural style is referred as Resource-Oriented Architecture (ROA). Most prominent connection method for combining the resources is a communication via REST (Representational State Transfer) based interfaces. When comping cloud resources with internet connected devices technology, Internet-of-Things (IoT) and furthermore IoT Dashboards for creating user interfaces, substantial benefits can be gained. These benefits include shorter lead-time for user interface development, process data gathering and production monitoring at higher abstract level.This Master’s Thesis takes a study for modern cloud computing resources and IoT Dashboards technologies for gaining process monitoring capabilities able to be used in the field of university research. During the thesis work, an alternative user group is kept in mind. Deploying similar methods for private production companies manufacturing environments. Additionally, field of Additive Manufacturing (AM) and one of its sub-category Direct Energy Deposition Method (DED) is detailed for gaining comprehension over the process monitoring needs, laying in the questioned manufacturing method. Finally, an implementation is developed for monitoring Tampere University of Technology Direct Energy Deposition method manufacturing environment research cell process both in real-time and gathering the process data for later reviewing. These functionalities are gained by harnessing cloud based infrastructures and resources.
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