About
Dr
Mandana Kariminejad
Postdoctoral Researcher
ATU
Dr. Mandana Kariminejad is a postdoctoral researcher at I-Form, based at Atlantic Technological University (ATU) Sligo. She earned her PhD in Mechatronic Engineering from ATU in 2024, where her research focused on optimising and implementing real-time control strategies for the injection moulding process to reduce cycle time while maintaining precise dimensional tolerances. Her academic journey began at Shiraz University in Iran, where she obtained a Bachelor's degree in Mechanical Engineering. She then completed a Master’s degree in Mechanical Engineering with a focus on Control and Dynamic Systems at K.N. Toosi University of Technology in Tehran. Currently, Dr. Kariminejad’s research at I-Form focuses on advancing data-driven modelling and control techniques for inkjet 3D printing. Her work aims to enhance the precision and efficiency of fabricating Na/K-ion batteries—an innovative and sustainable alternative to traditional Li-ion batteries.
Technical Summary
Mandana’s research is centred on developing advanced data-driven control and modelling approaches to optimise inkjet 3D printing for the fabrication of flexible sodium/potassium-ion (Na/K-ion) batteries. These batteries provide a sustainable and environmentally friendly alternative to traditional lithium-ion batteries, addressing critical global energy and environmental challenges. The objective of her work is to optimise the printing process to ensure high-quality battery performance while minimising material waste enhancing overall efficiency.
The primary challenge lies in the precise control of inkjet 3D printing, which is inherently sensitive to variations in material properties, environmental conditions, and process dynamics. Small deviations in droplet formation, deposition accuracy, and curing processes can significantly impact the battery’s performance.
To address these challenges, the project explores state-of-the-art controllers and modelling techniques capable of real-time detection and correction of process deviations. To overcome the limitations of experimental data generation, computational models are integrated into the controller and optimisation development. Dynamic Mode Decomposition (DMD) is used to extract low-order models from complex Computational Fluid Dynamics (CFD) simulations, capturing critical process dynamics efficiently. These models support advanced control techniques, including Koopman operator-based control enabling adaptive and data-efficient optimisation of the printing process. By enhancing the reliability and efficiency of Na/K-ion battery fabrication, this research contributes to the advancement of sustainable energy solutions with real-world impact, making innovation in both advanced manufacturing and renewable energy technologies.