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

Ultrashort Pulse Laser (USPL) ablation is a versatile manufacturing process, but predicting its outcomes across different materials often requires extensive and costly experimentation. This work 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. We generated a high-fidelity computational dataset using the LS-PLUME® simulator for Stainless Steel 316 (SS 316), and then complemented with targeted experimental studies on SS 316, Silicon (Si) and Aluminum (Al) to capture real-world deviations. A model pre-trained on the simulation data was successfully adapted to the experimental domain, effectively absorbing systematic deviations and extending its predictive capability to new materials with minimal experimental data. Our transfer learning framework bridged the simulation-to-experiment gap using minimal data, successfully fine-tuning a base model trained on 3075 samples with just 49 experimental points for Si and 46 for Al with mean percentage errors under 5%, thus demonstrating high data efficiency for industrial laser surface texturing. Furthermore, the application of explainable artificial intelligence revealed that the model predictions are more sensitive to peak fluence and the number of passes, with SS 316 exhibiting higher overall sensitivity to input parameter variations than Si and Al, thus providing actionable physical and process-level insight relevant for industrial optimization.