The geometric quality (taper), recast layer, and processing efficiency of micro-holes are important issues in femtosecond laser trepan drilling (LTD). Although one-step drilling based on low-power femtosecond LTD may be an ideal drilling method, its disadvantages such as processing time and taper quality still need to be improved. To address these issues, machine learning was successfully applied to establish an accurate predictive model for the femtosecond LTD process. Based on the machine learning model, the femtosecond LTD results for a given parameter set were quickly and accurately obtained, avoiding a large number of complex experiments and characterization requirements. Subsequently, through the combination of our established optimal machine learning predictive model and a high-throughput genetic algorithm, optimized solutions were quickly and successfully designed for a wide range of process spaces. Finally, the reliability of the optimized process was verified by experiments. The combination of machine learning and a high-throughput optimization algorithm provided an efficient and low-cost solution for the optimization of complex laser processing technology.

微孔的几何质量（锥度）、重铸层和加工效率是飞秒激光环钻（LTD）的重要问题。尽管基于低功率飞秒LTD的一步钻孔可能是一种理想的钻孔方法，但其加工时间和锥度质量等缺点仍有待改进。为了解决这些问题，成功地应用机器学习为飞秒 LTD 过程建立准确的预测模型。基于机器学习模型，快速准确地获得给定参数集的飞秒LTD结果，避免了大量复杂的实验和表征要求。随后，通过我们建立的最优机器学习预测模型和高通量遗传算法的结合，优化的解决方案被快速成功地设计成适用于广泛的工艺空间。最后通过实验验证了优化过程的可靠性。机器学习与高通量优化算法的结合，为复杂激光加工技术的优化提供了高效、低成本的解决方案。