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Data · dataset · 2023

Post-processed and normalized data sets for the data processing, analysis, and evaluation methods for co-design of coreless filament-wound structures

Listed in DaRUS

Post-processed and normalized data sets for specimens S2-0, S2-1, S2-2, S2-4, S2-8 and S2-9, used in Figure 14 of the publication: "Data processing, analysis, and evaluation methods for co-design of coreless filament-wound building systems", in the Journal of Computational Design and Engineering.

Description

The data allows the comparison of different geometrical, fabrication and structural parameters per segment of each specimen.

The raw data was obtained during the robotic fabrication and mechanical testing of specimens S1, S2 and S3 for the publication "Computational co-design framework for coreless wound fibre-polymer composite structures. Journal of Computational Design and Engineering 9(2), 310-32", and the complete raw data is published in the data set "Object model data sets of the case study specimens for the computational co-design framework for coreless wound fibre-polymer composite structures (V1)".

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To extend the research, 6 specimens of the series S2 were chosen for further postprocessing. A representative number per segment was calculated for each data set. The fabrication data, which originally is produced per layer wound, is either accumulated or averaged for the total of layers in one segment.

While for the geometrical or structural data, the average or maximum number of all bar elements in one segment was chosen. These decisions were taken to find representative values based on the experience of the researchers, and it is described in the data set. Finally, by normalizing all data with respect to the 6 specimens and all segments, the data can be analyzed in the same format, making compatible the comparison of geometrical, structural and fabrication data to find interrelations and possible reasons for the failure of the specimens during the mechanical test.

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Engineering
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Engineering
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Automotive engineering 73%
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