The keyhole phenomenon is widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time has been a challenging task. In situ characterization of keyhole dynamic behavior using a synchrotron X-ray is complicated and expensive. Current simulations are hindered by their poor accuracies in predicting keyhole depths due to the lack of real-time laser absorptance data. Here, we develop a machine learning-aided simulation method that allows us to accurately predict keyhole depth over a wide range of processing parameters. Based on titanium and aluminum alloys, two commonly used engineering materials as examples, we achieve an accuracy with an error margin of 10 %, surpassing those simulated using other existing models (with an error margin in a range of 50-200 %). Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

翻译：小孔现象在激光材料加工中广泛存在，包括激光焊接、重熔、熔覆、钻孔和增材制造。小孔诱发的缺陷（主要是气孔）会显著影响最终产品的性能，阻碍这些激光技术的广泛应用。这些气孔的形成通常与小孔的动态行为相关。迄今为止，精确表征和预测小孔特征（尤其是小孔深度随时间的变化）仍是一项具有挑战性的任务。使用同步辐射X射线对小孔动态行为进行原位表征既复杂又昂贵。由于缺乏实时激光吸收率数据，当前模拟方法在预测小孔深度方面精度不足。在此，我们开发了一种机器学习辅助的模拟方法，能够在广泛的工艺参数范围内精确预测小孔深度。以钛合金和铝合金两种常用工程材料为例，我们实现了误差在10%以内的预测精度，优于现有其他模型（误差范围为50-200%）。该机器学习辅助模拟方法成本低廉且易于推广至多种材料，为消除或减少各类激光材料加工技术中的缺陷开辟了新途径。