Smart laser microhole drilling: data-driven compensation and predictive process optimisation

Laser microhole drilling often relies on empirical methods due to beam imperfections, complex process parameters, and limited metrology. We present a structured, data-driven approach to improve process control and repeatability. Three case studies are discussed: (i) dynamic power modulation to compensate for thermal effects during high-speed drilling with >120W ultrafast lasers, (ii) correction of hole distortion in thick ceramics using human-in-the-loop Bayesian optimisation, and (iii) predictive modelling of laser parameters by training machine learning models on historical data for “first-time-right” machining. These strategies reduce reliance on expert intuition for optimisation, improving hole size consistency and shape fidelity. Our results show that intelligent compensation can enhance the performance of imperfect laser systems, offering a cost-effective alternative to more advanced hardware.