Dataset for “Processing feature-mapping topology optimisation designs towards feature-based CAD processing”



Dataset for paper entitled “Processing feature-mapping topology optimisation designs towards feature-based CAD processing”.

Abstract for paper:
Feature-mapping (FM) optimisation frameworks have received much attention for structural topology optimisation with explicit geometric parameters. The corresponding paper to this dataset presents a methodology for constructing parametric feature-based CAD models from designs generated using Moving Morphable Components (MMC). Emphasis is placed on constructing feature-based CAD models that conform to conventional modelling practices, where individual parameterised features are modelled using feature templates and united through Boolean union operations. This involves the use of algorithms to facilitate feature clean-up and identify connections between features. The progression through several examples demonstrates how the developed algorithms can realise a feature-based CAD model from the results of an FM optimisation. Integration with a commercial CAD system provides a wide range of modelling capabilities to the designer for downstream design tasks.

Dataset information:
The provided dataset includes a range of MATLAB code/scripts (.m / .rtf) created to test and validate the algorithms described in the corresponding publication. Also included is a .csv file containing data to recreate the cantilever beam example demonstrated in the manuscript, along with the key input parameters. Additional information is provided in the included readme file.

The provided code may be used to generate feature-based models from the outputs of a feature-mapping optimisation where linear, constant-width features were used in the optimisation. The output of the code is a refined list of features and their geometric parameters that are more suitable for constructing a feature-based CAD model. The features may be created in CAD software using an extruded feature template. The dataset was finalised in August 2023.
Date made available08 Aug 2023
PublisherQueen's University Belfast
Date of data productionAug 2023

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