Mechanical properties and energy absorption performance of bio-inspired dual architecture phase lattice structures

Huang, Zhixin; Li, Bangzheng; Ma, Li; Liu, Ying

doi:10.1080/15376494.2022.2045654

Cited by 18 publications

(1 citation statement)

References 33 publications

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“…Since its behavior depends on the base material and the unit cell's design parameters, effective homogenous qualities, like an effective elastic modulus, can be used to describe it on a macroscopic scale. It is possible to span a variety of material properties, including stiffness, strength, torsion resistance, density, permeability, and thermal conductivity, among others, by varying the geometry of the unit cell while maintaining the base material [6][7][8][9][10][11]. The term "meta-materials" is now used to refer to all architected cellular materials because of this special capacity to customize the effective material properties.…”

Section: Introductionmentioning

confidence: 99%

Transport Equation based Physics Informed Neural Network to predict the Yield Strength of Architected Materials

Mishra

2023

Preprint

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In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation, the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.

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