  Laser machining is a highly flexible non-contact manufacturing technique that
has been employed widely across academia and industry. Due to nonlinear
interactions between light and matter, simulation methods are extremely
crucial, as they help enhance the machining quality by offering comprehension
of the inter-relationships between the laser processing parameters. On the
other hand, experimental processing parameter optimization recommends a
systematic, and consequently time-consuming, investigation over the available
processing parameter space. An intelligent strategy is to employ machine
learning (ML) techniques to capture the relationship between picosecond laser
machining parameters for finding proper parameter combinations to create the
desired cuts on industrial-grade alumina ceramic with deep, smooth and
defect-free patterns. Laser parameters such as beam amplitude and frequency,
scanner passing speed and the number of passes over the surface, as well as the
vertical distance of the scanner from the sample surface, are used for
predicting the depth, top width, and bottom width of the engraved channels
using ML models. Owing to the complex correlation between laser parameters, it
is shown that Neural Networks (NN) are the most efficient in predicting the
outputs. Equipped with an ML model that captures the interconnection between
laser parameters and the engraved channel dimensions, one can predict the
required input parameters to achieve a target channel geometry. This strategy
significantly reduces the cost and effort of experimental laser machining
during the development phase, without compromising accuracy or performance. The
developed techniques can be applied to a wide range of ceramic laser machining
processes.
