Lidé na fakultě
Dr. Afdhal Afdhal

2024, Materials & Design, 244, p. 1-13), ISSN 1873-4197

In this paper, a set of hexachiral auxetic structural designs with near zero Poisson's ratio (ZPR) characteristics is discovered via the combination of machine learning and experimentally validated finite element simulation. An active learning-enhanced Gaussian process model is utilized to generate multiple designs with near-ZPR properties and discover the boundary of the positive and negative Poisson's ratio. The results show that active learning successfully constructs a probabilistic estimation of the ZPR boundary. A comprehensive analysis of the identified ZPR contour is performed to extract crucial design insights. The findings indicate that the near-ZPR characteristic can be attained through various combinations of geometric parameters. This offers users the flexibility to select the configuration that best aligns with their specific requirements. Additionally, an investigation of the various ZPR configurations that have been discovered is carried out to understand the mechanism that yields near-ZPR property. One discovered near-ZPR design was subsequently fabricated using 3D printing for validation purposes. The experimental outcomes demonstrated a good agreement with the numerical predictions, underscoring the effectiveness of the active learning strategy in uncovering designs that closely approach ZPR conditions.

Článek v periodiku excerpovaném SCI Expanded

2023, Materials & Design, 232, p. 1-13), ISSN 1873-4197

A design exploration of hexachiral structures using explainable machine learning (ML) is performed in this work. The hexachiral structures are fabricated using resin via vat photopolymerization (VPP). The ML model is used to build the function that explains the association between Poisson’s ratio and the hexachiral design parameters. The data set for ML model construction is first collected by using the Halton sequence and simulated using the finite element method (FEM). To validate the data set, the results obtained from the FEM simulation are compared with those obtained from the compression test. The Gaussian Process Regression (GPR) models for Poisson’s ratio and porosity are constructed to extract important design insight. A Global Sensitivity Analysis (GSA) and Shapley Additive Explanations (SHAP) are used to analyze the sensitivity of the porosity and Poisson’s ratio to the hexachiral design parameters. GSA result shows that the strut’s thickness is the most decisive parameter that affects the Poisson’s ratio. The application of SHAP also reveals that the relationship between the strut thickness and Poisson’s ratio is nonlinear. Finally, the minimum Poisson’s ratio value is achieved by design with minimum strut thickness, minimum node radius, and maximum strut length.

Článek v periodiku excerpovaném SCI Expanded