Intelligent Threshold Prediction for Hybrid Mesh Segmentation Through Artificial Neural Network

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

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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
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Depositing User: LivePure Connector
Date Deposited: 14 Jan 2020 06:19
Last Modified: 01 Oct 2022 08:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/73625
DOI: 10.1007/978-981-32-9515-5_83

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