Bayesian optimization of single-pulse laser drilling using advanced image processing

A significant challenge in laser drilling is the optimization of process parameters and drilling strategies to achieve high-quality holes. This is further complicated by the fact that quality assessment is a manual and time-consuming task. This paper presents a methodology designed to significantly reduce the manual effort required in optimizing parameters for single-pulse laser drilling of 0.3 mm thick stainless steel. The objective is to precisely drill holes with an entry diameter of 70 µm and an exit diameter of 20 µm, achieving high roundness. The features of the drilled holes were extracted automatically from the raw data. The outcomes were compared against manual measurements. Results indicate that the mean deviations between automated and manual measurements for both inlet and outlet diameters are less than 1.5 µm. Based on the results of the feature extraction, we employed a Bayesian optimization algorithm to efficiently explore the parameter space without the need for incorporating expert knowledge. The approach rapidly identified optimal drilling parameters after only a few iterations, significantly expediting the optimization process and considerably reducing manual labor.