School of Mechanical Engineering at Dublin City University (DCU), Dublin, IrelandAdditive manufacturing; Laser processing; Materials engineering; Near net shape forming; Data capture, Analysis and control; AI for process and part design
Prof. Dermot Brabazon holds a Full Professorship of Materials Science and Engineering in the School of Mechanical Engineering at Dublin City University (DCU). He is Director of the DCU Institute of Advanced Processing Technology, Director for the Centre for Doctoral Training in Advanced Metallic Systems (RoI), and Deputy-Director of I-Form, the Research Ireland centre focused on the development of manufacturing technologies. From 1995 to 2000 he worked with Materials Ireland, a state materials science research centre. From 2000 he was appointed as a lecturer at Dublin City University, promoted to Senior Lecturer in 2006, Deputy Head of School in 2007, Associate Dean for Research in 2009, Professor in 2014, and Full Professor in 2020. In recognition of his academic achievements and contributions to development of engineering technologies, Dermot was conferred with the President’s Award for Research in 2009, appointed a Fellow of the Institute of Mechanical Engineering in 2015, received Invent Commercialization awards in 2015, 2017, 2018, 2022, and the International AMPT Gold Medal in 2018 for lifetime achievements in materials processing research and education. Dermot is Editorial Board Member for the journals of Advances in Materials and Processing Technologies, Nanomanufacturing and Nanometrology, Metals, Materials, the International Journal of Material Forming, and Editor in Chief for the Elsevier Materials Reference Works Encyclopaedia. He was appointed in 2010 and is currently a member of the Board of Directors of ESAFORM (EU Material Forming Society). His research focuses on materials and processing technologies, including Additive Manufacturing, Near Net Shape Forming, Laser Processing and AI for process control and part design. These overlapping activities are focused toward the development of advanced materials science and engineering knowledge to enable improved product and production, capability and quality, for the benefit of companies and the broader society.
Artificial Intelligence is being used more and more for the analysis of the datasets that are produced from the Additive Manufacturing Process. These data sets include IR, acoustic, eddy current, oxygen, composition and part density data. Often fundamental models can be produced which predict the melt pool width and height, the part density, pore formation, thermal fields, resulting microstructure, and part mechanical properties. While these analytical and FEA models can give a good understanding of the process. However, in many situations they are too slow for real time quality or close loop process control. Surrogate models (also called metamodels) are commonly being used instead for these predictions where faster analysis is required. Where the analytical and FEA models are validated within defined bounds, they can be used to generate a lot of process input and output simulation data. This data can be used in conjunction with the experimental data to train the AI models and thereby make them available for more real time process quality prediction or control.
Original research articles and reviews that fall within this topic area are welcomed. In this special issue and in the context of additive manufacturing, articles will be accepted which cover one of the following areas:
- the generation of experimental data that can be used for AI model development;
- the generation of model data that can be used for AI model development;
- the use of experimental, modelling or combination of data sets for the generation of AI models;
- the analysis of AI models for the prediction of process quality and/or control.
Prediction of wall geometry for cold-metal-transfer-based wire-arc additive manufacturing
Layer porosity in powder-bed fusion prediction using regression machine learning models and time-series features