  In this research a theoretical approach is proposed to evaluate sound
transmission loss (STL) of a sandwich panel with tubular cells core (TCC) in
the in-plane orientation. Afterwards, the core geometric parameters are
optimized by implementing the Non-dominated Sorting Genetic Algorithm II
(NSGA-II) to maximize the STL through the sandwich panel while minimizing the
total mass and height of the panel. The sound transmission response of the
sandwich panel is parameterized in terms of four independent geometric
parameters of the core, which are: number of tubular cells in the length and
height directions of the core, thickness of the tubular cell wall and radii of
the tubular cells. It is assumed that every tubular cell has a similar
thickness while their radius in each row differs. Using the Abaqus scripting
interface, a structural acoustic finite element model of the sandwich panel
with tubular cell core is generated based on the specified parametric
parameters. The study designs are obtained within a frequency range of 50Hz to
350Hz, which includes the first few resonance regions of the sandwich panel. In
order to decrease the computational cost, a machine learning technique based on
gradient boosting decision tress (GBDT) is employed to predict the STL of the
sandwich panel. Findings reveal that the GBDT model is successfully trained.
Data comparison shows that the discrepancies between the GBRT model and the FE
model are confined to a margin of -3.09% to 2.7% in 95% of instances. The
training is comprised of 500 examples, and an extra 150 samples are employed
for testing to guarantee the reliability of the outcomes. The optimized panel
using tubular cells demonstrates 52% and 23% reduction in the mass and height
of the panel, respectively, when compared to the reference panel with the same
area under the STL vs. frequency curve.
