Automated image-based parameter optimization for single-pulse laser drilling

A significant challenge in laser drilling is the optimization of process parameters and drilling strategies to achieve highquality 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 0.3mm thick stainless steel. The objective is to precisely drill holes with an entry diameter of 70μm and an exit diameter of 20 μm, achieving high roundness. The  features of the drilled holes were extracted automatically from the raw data using a combined approach that utilizes deep learning and image processing techniques. 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 one  micrometer. 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.