Cathal Hoare is a postdoctoral researcher in the Data Management and Analytics area of I-Form and is based in UCD. Mr Hoare is a graduate of Computer Science at University College Cork. He has worked for several companies, including multinationals and start-ups, working on data management solutions in domains ranging from telecoms to commercial analytics. On returning to academia, Mr Hoare has worked on both SFI and Horizon 2020 funded projects, working to build both data integration and high volume high velocity data management solutions.
Research Interests (Lay Summary)
Additive manufacturing is dependent on a wide variety of data sources, including those that describe the design of a part to be printed, configuration of a print process, management of the build and quality assessment information. This data is produced quickly and in large volumes. The data is used for a variety of purposes, including real time anomaly detection and post print quality analysis. Mr Hoare’s research interests include developing software systems to collect, manage and provide these data in a timely and secure manner.
Technical Summary
Additive manufacturing processes both consume and produces high volume, high velocity heterogeneous data, including designs, materials description, hardware configurations, and process data. These data must be collected, managed and served to a inform a variety of processes, including anomaly detection for print issues such as porosity. In turn, the results of such analysis are presented to operators in a variety of forms, including novel visualisations, or through the use of recommendation on how to remedy a discovered anomaly. This work investigates how to homogenize the heterogeneous data sources by producing a unified data schema. A highly performant software architecture that can ingest and serve these data in near real time will also be developed with a view to using state of the art data streams based access mechanisms; this architecture will ensure that analysis tools will be able to respond to anomalous states quickly, informing operators and allowing remedial actions to be taken, and so provide large savings in terms of time, energy and materials.
Expertise
Data Analytics, Image Processing, Multimedia Data Mining