

AI-Driven Laser Micro-Machining: An Automated Workstation for Precision Microfluidic Fabrication
Their work centres around the development of a compact, low-cost laser micro-machining system designed for rapid prototyping of microfluidic devices.
This system integrates artificial intelligence, specifically artificial neural networks (ANNs), to autonomously optimise process parameters for laser ablation on various materials such as PMMA.
The DCU researchers, alongside Prof Markus Helfert from Maynooth University, played a central role in designing and implementing the closed-loop control and automation framework.
Their system combines FPGA-based hardware control with LabVIEW software to enable real-time alignment, focusing, and laser firing with sub-micrometre precision.
Through experimental validation, the researchers demonstrated improved accuracy and repeatability in creating microstructures, especially micro-channels for lab-on-a-chip applications.
This innovation represents a significant advancement in accessible, intelligent microfabrication tools for academic and industrial research.
You can access the full article here: https://ieeexplore.ieee.org/document/10849947