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A performance assessment of a developed mesh-generating algorithm: A computer-aided design modelling process to support progression within additive manufacturing

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conference contribution
posted on 2018-04-05, 08:32 authored by James A. Gardner, Guy Bingham, Abby PatersonAbby Paterson
Developments in Additive Manufacturing (AM) technologies and associated processes have increased the limits of attainable design complexity, yet Computer-Aided Design (CAD) tools that may utilise these new potentials have not advanced as rapidly. Research detailed in this paper addresses aspects of automatic geometric design that may support the generation of high resolution, functional, three-dimensional (3D) textures. A 3D CAD model of a surface with complex curvature was modelled to be systematically populated with points using a developed mesh-generation algorithm. Following the successful generation of a mesh the distances between points were analysed, throughout the process, to measure the performance of the algorithm. Equidistance between points was achieved and, if the algorithm was used as intended, would provide an essential foundation for successive texture application processes with minimal manual input required.

History

School

  • Design

Published in

Rapid Design, Prototyping and Manufacture Conference

Citation

GARDNER, J.A., BINGHAM, G.A. and PATERSON, A., 2017. A performance assessment of a developed mesh-generating algorithm: A computer-aided design modelling process to support progression within additive manufacturing. Presented at the 15th Conference on Rapid Design, Prototyping and Manufacture (RDPM 2017), Newcastle, UK, 27th-28th April 2017.

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2017-03-01

Publication date

2017

Notes

This is a conference paper.

ISBN

9781527221536

Language

  • en

Location

Lancaster University

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