Scientific Publications

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Adapting Laser Ablation Models from Simulation to Experiment: A Transfer Learning Approach for Stainless Steel, Silicon and Aluminium

This paper provides a machine learning framework that leverages transfer learning to bridge the gap between simulation and experimental data, enabling accurate prediction of material behavior during USPL ablation under data-scarce conditions.

AI-Enhanced Laser Manufacturing: A Social Life Cycle Perspective From the White Goods Industry

This paper investigates how the transition from conventional manufacturing processes to AI‑enhanced laser manufacturing can influence the social dimension of sustainability in the white goods sector. Going beyond traditional assessments of cost and environmental impact, the research examines potential effects on workers, consumers, local communities, value chains and society as a whole.

Extending social life cycle assessment to capture technological change: A case study in AI-driven laser manufacturing for the aerospace sector

This paper explores the transition from conventional aerospace manufacturing processes towards AI-driven laser manufacturing, investigating the social implications of technological change. A key innovation of the paper is an enhanced Social Life Cycle Assessment (S-LCA) approach capable of better capturing the impacts of technological transitions within organisations, even when supply chains remain largely unchanged.

Polarization maintaining fiber beam delivery
for direct laser interference patterning

This paper introduces a pioneering method of delivering high-power picosecond laser pulses through microstructured optical fibers. This innovative approach enhances precision and flexibility in laser applications. Demonstrating its potential, the team successfully created interference patterns on stainless steel, paving the way for large-scale industrial applications in surface micromachining, robotics, and more.

Virtual Prototyping of a Novel Manipulator
for Efficient Laser Processing of Complex Large Parts

This study introduces a next-generation robotic manipulator designed for advanced 3D laser surface treatment of large industrial parts. Designed with a stiff and vibration damping mechanical architecture with direct drive motors, the system minimizes motion accuracy errors, enabling high-precision motion and greater responsiveness for complex manufacturing tasks.

Synthetic Data for AI-Powered Ultrafast
Laser Based Micro-structuring Method Description

This publication presents a combined approach using synthetic data and machine learning methods to predict femtosecond laser surface texturing outcomes on stainless steel 316L. Synthetic data were generated through LS-Plume® (LASEA) simulators to feed AI models, addressing the challenges of costly and time-consuming experimental data collection. Various regression models were evaluated, demonstrating that the synergy between simulation tools and AI can effectively accelerate the development of predictive models for laser micro-structuring.