Int. J. Simul. Multidisci. Des. Optim.
Volume 13, 2022
Computation Challenges for engineering problems
|Number of page(s)||9|
|Published online||23 December 2022|
Feature recognition and machine learning in finite element models through a clustering algorithm
School of Mechanical Engineering, VIT University, Chennai 600127, Tamilnadu, India
* e-mail: email@example.com
Accepted: 10 September 2022
The feature identification of the CAD model is a significant task in any CAD algorithm. Enormous computational time, huge memory allocation, lack of understanding in computational geometry, etc., are some of the complications faced while implementing the feature recognition algorithms. This paper represents a clustering algorithm procedure in finite element models, which is the predominant component in analysis methodology. This study performs the clustering of data groups through density-based clustering algorithms such as mean shift clustering and K-means clustering algorithm. In addition to that, experimental evaluation based on the structured algorithm procedure for identifying the features of CAD geometries is investigated. Finally, the study evaluates the performance of the proposed structured algorithm and its efficiency in terms of both computational time and computational memory.
Key words: Mean-shift clustering / k-mean clustering / clustering algorithm / centroid / index-based sorting
© S. Premkumar et al., Published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.