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Evaluation of 3D Printer Accuracy in Producing Fractal Structure

Overview of attention for article published in Journal of Oleo Science, January 2017
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  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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Title
Evaluation of 3D Printer Accuracy in Producing Fractal Structure
Published in
Journal of Oleo Science, January 2017
DOI 10.5650/jos.ess16151
Pubmed ID
Authors

Kana Kikegawa, Kyuuichirou Takamatsu, Masaru Kawakami, Hidemitsu Furukawa, Hiroyuki Mayama, Yoshimune Nonomura

Abstract

Hierarchical structures, also known as fractal structures, exhibit advantageous material properties, such as water- and oil-repellency as well as other useful optical characteristics, owing to its self-similarity. Various methods have been developed for producing hierarchical geometrical structures. Recently, fractal structures have been manufactured using a 3D printing technique that involves computer-aided design data. In this study, we confirmed the accuracy of geometrical structures when Koch curve-like fractal structures with zero to three generations were printed using a 3D printer. The fractal dimension was analyzed using a box-counting method. This analysis indicated that the fractal dimension of the third generation hierarchical structure was approximately the same as that of the ideal Koch curve. These findings demonstrate that the design and production of fractal structures can be controlled using a 3D printer. Although the interior angle deviated from the ideal value, the side length could be precisely controlled.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 18%
Student > Master 2 18%
Student > Bachelor 1 9%
Lecturer > Senior Lecturer 1 9%
Student > Doctoral Student 1 9%
Other 1 9%
Unknown 3 27%
Readers by discipline Count As %
Engineering 3 27%
Biochemistry, Genetics and Molecular Biology 1 9%
Chemistry 1 9%
Social Sciences 1 9%
Unknown 5 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 March 2018.
All research outputs
#14,801,390
of 22,962,258 outputs
Outputs from Journal of Oleo Science
#337
of 652 outputs
Outputs of similar age
#240,284
of 421,050 outputs
Outputs of similar age from Journal of Oleo Science
#17
of 39 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 652 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,050 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.