About
Danial Pazoki
Postgraduate Researcher
UCD
Danial Pazoki is a PhD student at University College Dublin (UCD). He obtained his B.Sc. in Electrical Engineering, with a focus on control engineering, from the Department of Automation at the Petroleum University of Technology in 2020. His bachelor's project focused on CSTR temperature control using a neural network controller. He earned his M.Sc. in Electrical Engineering-Control from the Faculty of Electrical Engineering at K. N. Toosi University of Technology in 2023. His Master’s thesis focused on the nonlinear control design of pressure fluctuations based on a heave disturbance observer in an offshore managed pressure drilling system. His research interests include control systems, system identification, machine learning, optimisation, additive manufacturing (AM), environmental sustainability, and estimation.
Technical Summary
AM has been widely used in both industry and academia, exhibiting the advantages of flexibility and the ability to produce on demand with reduced cost, time and waste. This research project seeks to shed light on the contribution of AM in the transition towards the objective of sustainable manufacturing. An AM process (e.g. PBF-LB) and products with various geometries will be analysed, in lab and industrial settings. Based on this analysis, the researcher will develop innovative digital tools using control systems for achieving optimal process control signals and outputs. The research will include the use of blockchain and/or Distributed Ledger Technology (DLT) for in-process data storage and sharing as well as the application of Artificial intelligence (AI) algorithms to measure sustainability in AM processes and products. The resulting assessment aims to provide a feedback mechanism to the AM process design parameters such as spot size, hatch spacing, layer thickness, and production batch sizes to save energy and resource consumption.
The overall outcome of the project is envisaged to be a software and hardware (cyber-physical) architecture design and validation based on generated sample and experimental data aiming to achieve optimal sustainability level in a given AM process. The designed digital system will work on existing sample and experimental datasets and will perform the design of experiment-based process output optimisation, minimising the number of prints, resources and energy consumptions.