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Hase, Vaibhav J., Bhalerao, Yogesh J. 
ORCID: https://orcid.org/0000-0002-0743-8633, Patil, G. J. Vikhe and Nagarkar, Mahesh P.
(2020)
Intelligent Threshold Prediction for Hybrid Mesh Segmentation Through Artificial Neural Network.
In:
Computing in Engineering and Technology.
Advances in Intelligent Systems and Computing
.
Springer, pp. 889-899.
ISBN 978-981-32-9514-8
Abstract
Accurate and reliable Area deviation factor (threshold) is one of the decisive factors in hybrid mesh segmentation. Inadequate threshold leads to under-segmentation or over-segmentation. Setting the optimal threshold is a difficult task for a layman. This proposed method, automatically predicts the threshold using artificial neural networks (ANN). ANN predicts the threshold by considering mesh quality of Computer-Aided Design (CAD) mesh model as input feature vectors. Extensive testing on benchmark test cases validates ANN prediction model, and based on Levenberg-Marquardt back propagation (LM-BP) improves the accuracy and stability of prediction. The efficacy of the approach is quantified by measuring coverage. The ANN predicts the threshold elegantly using LM-BP algorithm with coverage for hybrid mesh segmentation greater than 95%. The novelty of the proposed method lies in the “mesh quality”-based threshold prediction through ANN. The predicted threshold finds application in automatic feature recognition from CAD mesh model using hybrid mesh segmentation.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Engineering (former - to 2024) |
| UEA Research Groups: | Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 14 Jan 2020 06:19 |
| Last Modified: | 07 Nov 2024 12:49 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/73625 |
| DOI: | 10.1007/978-981-32-9515-5_83 |
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