DCU Expo 2023 Final Year Projects

35 27. An Investigation on the Role of Processing Parameters on Corrosion Resistance of AdditivelyManufacturedMetal Parts, Ti64 and Ni-rich Nitinol, for Biomedical Applications AdditiveManufacturing progress in the last decade has made the fabrication of 3D constructs a possibility. Niti and Ti64 alloys have prompted considerable interest in all kinds of industries including, biomedical, automotive, machines and tooling, and aerospace. However, due to the instantaneous melting and re-solidification that occurs during additive manufacturing, the metal powder is vulnerable to severe alterations that could result in the evaporation of some alloying components. This project investigates the corrosion resistance of laser-based powder bed fusion parts concerning the processing parameters in which they were formed. Class Biomedical Engineering (Year 5) Project Area AdditiveManufacturing, Biomedical Engineering, Materials Testing Project Technology Excel/VB, Ganry EchemAnalysis Software, SEM visual software Student Name(s) Katelyn Gallagher Email katelyn.gallagher29@mail.dcu.ie Supervisor Dr Muhannad Obeidi 28. Fog Climatology andNowcasting at Dublin Airport This project aims to improve fog nowcasting capabilities at Dublin Airport usingmachine learning and analysis-derived insights. Numerous models were developed to predict, at 1 hour lead time, the presence of fog at Dublin Airport. This aids Air Traffic Controllers in planning aircraft operations and safely managing aircraft flow. Several models were compared with the aimof outperforming Harmonie-Arome (model currently in use) at fog nowcasting at Dublin airport, using its performance as a benchmark. Class Data Science Project Area Artificial Intelligence, Data Analytics, DataMining, Sensor Data, Statistical Analysis, Meteorology Project Technology Python, Open SSL, Machine Learning, Google Cloud Platform Student Name(s) Julita Swiatek  |  Joseph Oluwasanya Email julita.swiatek2@mail.dcu.ie  |  joseph.oluwasanya2@mail.dcu.ie Supervisor Prof TomasWard 29. SLEN – Sign Language to English Translator SLEN is amobile app to assist users in translating sign language to English. Users will be able to use the app to record themselves communicating in sign language or upload an existing video. After this, the video will be sent to our server to be translated. Upon being translated, the server will send the translated English text back to the user. Class Computer Applications Project Area Android, Artificial Intelligence, Image/Video Processing, Mobile App Project Technology Python, Machine Learning Student Name(s) Nicholas Jelovina  |  Aleksandrs Radcenko Email nicholas.jelovina2@mail.dcu.ie   |  aleksandrs.radcenko2@mail.dcu.ie Supervisor Dr David Sinclair