  The control of the velocity of a high-speed laser-induced microjet is crucial
in applications such as needle-free injection. Previous studies have indicated
that the jet velocity is heavily influenced by the volumes of secondary
cavitation bubbles generated through laser absorption. However, there has been
a lack of investigation of the relationship between the positions of cavitation
bubbles and the jet velocity. In this study, we investigate the effects of
cavitation bubbles on the jet velocity of laser-induced microjets extracted
using explainable artificial intelligence (XAI). An XAI is used to classify the
jet velocity from images of cavitation bubbles and to extract features from the
images through visualization of the classification process. For this purpose,
we run 1000 experiments and collect the corresponding images. The XAI model,
which is a feedforward neural network (FNN), is trained to classify the jet
velocity from the images of cavitation bubbles. After achieving a high
classification accuracy, we analyze the classification process of the FNN. The
predictions of the FNN, when considering the cavitation positions, show a
higher correlation with the jet velocity than the results considering only
cavitation volumes. Further investigation suggested that cavitation that occurs
closer to the laser focus position has a higher acceleration effect. These
results suggest that the velocity of a high-speed microjet is also affected by
the cavitation position.
