Microneedles (MNs),
that is, a matrix of micrometer-scale needles,
have diverse applications in drug delivery, skincare therapy, and
health monitoring. MNs offer a minimally invasive alternative to hypodermic
needles, characterized by rapid and painless procedures, cost-effective
fabrication methods, and reduced tissue damage. This study explores
four MN designs, cone-shaped, tapered cone-shaped, pyramidal with
a square base, and pyramidal with a triangular-shaped base, and their
optimization based on predefined criteria. The workflow encompasses
three loading conditions: compressive load during insertion, critical
buckling load, and bending loading resulting from incorrect insertion.
Geometric parameters such as base radius/width, tip radius/width,
height, and tapered angle tip influence the output criteria, namely,
total deformation, critical buckling loads, factor of safety (FOS),
and bending stress. The comprehensive framework employing a design
of experiment approach within the ANSYS workbench toolbox establishes
a mathematical model and a response surface fitting model. The resulting
regression model, sensitivity chart, and response curve are used to
create a multiobjective optimization problem that helps achieve an
optimized MN geometrical design across the introduced four shapes,
integrating machine learning (ML) techniques. This study contributes
valuable insights into a potential ML-augmented optimization framework
for MNs via needle designs to stay durable for various physiologically
relevant conditions.