@inbook{fa43e841d2e14068aab80ded96ce3a86,
title = "Intelligent Threshold Prediction for Hybrid Mesh Segmentation Through Artificial Neural Network",
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.",
author = "Hase, {Vaibhav J.} and Bhalerao, {Yogesh J.} and Patil, {G. J. Vikhe} and Nagarkar, {Mahesh P.}",
year = "2020",
doi = "10.1007/978-981-32-9515-5_83",
language = "English",
isbn = "978-981-32-9514-8",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "889--899",
editor = "Brijesh Iyer and P.S. Deshpande and S.C. Sharma and Ulhas Shiurkar",
booktitle = "Computing in Engineering and Technology",
address = "Germany",
}