TY - JOUR
T1 - Intelligent systems for volumetric feature recognition from CAD mesh models
AU - Hase, Vaibhav J.
AU - Bhalerao, Yogesh J.
AU - Verma, Saurabh
AU - Patil, G. J. Vikhe
PY - 2020/1/24
Y1 - 2020/1/24
N2 - This paper presents an intelligent technique to recognise the volumetric features from CAD mesh models based on hybrid mesh segmentation. The hybrid approach is an intelligent blending of facet-based, vertex based, rule-based, and artificial neural network (ANN)-based techniques. Comparing with existing state-of-the-art approaches, the proposed approach does not depend on attributes like curvature, minimum feature dimension, number of clusters, number of cutting planes, the orientation of model and thickness of the slice to extract volumetric features. ANN-based intelligent threshold prediction makes hybrid mesh segmentation automatic. The proposed technique automatically extracts volumetric features like blends and intersecting holes along with their geometric parameters. The proposed approach has been extensively tested on various benchmark test cases. The proposed approach outperforms the existing techniques favourably and found to be robust and consistent with coverage of more than 95% in addressing volumetric features.
AB - This paper presents an intelligent technique to recognise the volumetric features from CAD mesh models based on hybrid mesh segmentation. The hybrid approach is an intelligent blending of facet-based, vertex based, rule-based, and artificial neural network (ANN)-based techniques. Comparing with existing state-of-the-art approaches, the proposed approach does not depend on attributes like curvature, minimum feature dimension, number of clusters, number of cutting planes, the orientation of model and thickness of the slice to extract volumetric features. ANN-based intelligent threshold prediction makes hybrid mesh segmentation automatic. The proposed technique automatically extracts volumetric features like blends and intersecting holes along with their geometric parameters. The proposed approach has been extensively tested on various benchmark test cases. The proposed approach outperforms the existing techniques favourably and found to be robust and consistent with coverage of more than 95% in addressing volumetric features.
KW - CAD mesh model
KW - CMM
KW - Hybrid mesh segmentation
KW - Volumetric feature recognition
UR - http://www.scopus.com/inward/record.url?scp=85078847449&partnerID=8YFLogxK
U2 - 10.1504/IJIE.2020.104661
DO - 10.1504/IJIE.2020.104661
M3 - Article
VL - 7
SP - 267
EP - 278
JO - International Journal of Intelligent Enterprise
JF - International Journal of Intelligent Enterprise
SN - 1745-3232
IS - 1/2/3
ER -