Data set for Optimisation and Experimental Validation of Near-Isotropic 3D Ordered Star Cell Auxetic Structures

This data is the finite element files, image files and ncorr settings used in the development and analysis of a set of experimental auxetic lattice structure unit cells.

This data set is a compilation of the information needed to re-create the findings in the study "Optimisation and Experimental Validation of Near-Isotropic 3D Ordered Star Cell Auxetic Structures". The file contains images and Ncorr files for the image analysis of the physical tests and Ansys and data files to run the FEA models for as tested cells, isotropy analysis and the optimisation and setup of the individual cells. The four parts of the data set are:

1) the images and n_corr session data used to assess the Poisson's ratio of the samples with image analysis;
2) the Finite Element models used during the optimisation of the base cells in various stages, with infinite simulation boundary conditions and the output values;
3) the flat angle form of the Finite Element models to allow re-creation of the isotropy tests for each lattice;
4) the as-tested FEA models to allow re-creation of the validation simulations for the physical testing process.

Keywords:
Auxetic, Meta-material, Lattice, Star, Petal, Isotropic
Subjects:
Materials sciences
Mechanical engineering

Cite this dataset as:
Rogers, B., 2023. Data set for Optimisation and Experimental Validation of Near-Isotropic 3D Ordered Star Cell Auxetic Structures. Bath: University of Bath Research Data Archive. Available from: https://doi.org/10.15125/BATH-01164.

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Data

Data_for_MandD_Submission.zip
application/zip (10GB)
Creative Commons: Attribution 4.0

Compiled data to allow replication of the study

Creators

Ben Rogers
University of Bath

Contributors

University of Bath
Rights Holder

Coverage

Collection date(s):

From 1 February 2021 to 17 December 2021

Documentation

Data collection method:

The four sections of the data have separate methodology: - The as-tested FEA are 3D models close to the physical test samples loaded with fixed boundary conditions to emulate the way the cells were loaded during testing and allow direct comparison. - The isotropy study is a global level cell model for each type of lattice, with a variable loading and boundary vector. - The optimisation study has the infinite grid simulation boundary conditions and includes the optimisation methods. Also included are the cell models based on literature which were not optimised in this study but were studied in comparison. The numerical methods and data that it produced are also included. - The images and Ncorr files used to assess them are also included to replicate physical testing. More details can be found in the paper itself.

Technical details and requirements:

The files require Matlab R2020b with Ncorr (https://ncorr.com/) and Ansys Workbench 2021R2.

Methodology link:

Rogers, B. A., Valentine, M. D.A., Lunt, A. J.G., Pegg, E. C., and Dhokia, V., 2023. Optimization and experimental validation of 3D near-isotropic auxetic structures. Materials & Design, 229, 111844. Available from: https://doi.org/10.1016/j.matdes.2023.111844.

Documentation Files

ReadMe.txt
text/plain (2kB)
Creative Commons: Attribution 4.0

A ReadMe file for the data set

Funders

Studentship

Publication details

Publication date: 12 April 2023
by: University of Bath

Version: 1

DOI: https://doi.org/10.15125/BATH-01164

URL for this record: https://researchdata.bath.ac.uk/id/eprint/1164

Related papers and books

Rogers, B. A., Valentine, M. D.A., Lunt, A. J.G., Pegg, E. C., and Dhokia, V., 2023. Optimization and experimental validation of 3D near-isotropic auxetic structures. Materials & Design, 229, 111844. Available from: https://doi.org/10.1016/j.matdes.2023.111844.

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Ben Rogers

Departments:

Faculty of Engineering & Design
Mechanical Engineering

Research Centres & Institutes
UKRI CDT in Accountable, Responsible and Transparent AI