  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.
