Evaluation, optimization and prediction of the transverse shear modulus of biomimetic 3D printed sandwich core

Abstract

In this study, the evaluation, optimization and prediction of the transverse shear modulus of a novel biomimetic 3D sandwich core based on the microstructure of bamboo structure are investigated. The TSM of the biomimetic 3D printed core is evaluated through experimental and numerical analysis based on an alternative dynamic method. The finite element analysis results are validated with experimental results for the TSM of the biomimetic 3D printed core. Further, artificial neural network (ANN) and particle swarm optimization are used to predict and optimize the TSM of the biomimetic sandwich core structure. Optimization problems are formulated by considering cell wall thickness, cell length, and corner radius to maximize and minimize the TSM of the biomimetic 3D core. ANN is used to develop the fitness function, and further PSO is used to solve to obtain the optimal TSM of the core structure. It is shown that the optimization yields the maximum and minimum of the TSM with identical optimal design parameters like cell thickness, cell length and corner radius for the 3D printed core.

Keywords:

• Artificial neural network
• particle swarm optimization
• biomimetic core
• transverse shear modulus
• finite element analysis
• optimization
• mechanical testing

Authors’ contributions

Muthukumaran Gunasegeran: conceptualization and methodology and investigation. Edwin Sudhagar P: original draft preparation—content writing and visualization and supervision.

Acknowledgements

We thank VIT faculties, Additive Manufacturing Laboratory, Noise Vibration and Harshness laboratory, and VIT Vellore, Tamil Nadu, India, for conducting the research.