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Intelligent Threshold Prediction for Hybrid Mesh Segmentation Through Artificial Neural Network

  • Vaibhav J. HaseEmail author
  • Yogesh J. Bhalerao
  • G. J. Vikhe Patil
  • Mahesh P. Nagarkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1025)

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.

Keywords

Artificial neural network CAD mesh model Feature recognition Hybrid mesh segmentation Threshold prediction 

Notes

Acknowledgements

Authors are grateful to Dr. V. D. Wakchaure and Dr. P. J. Pawar for their constructive, thoughtful suggestions that helped to improve the manuscript.

References

  1. 1.
    Sunil, V.B., Pande, S.S.: Automatic recognition of features from freeform surface CAD models. Comput. Aided Des. 40(4), 502–517 (2008)CrossRefGoogle Scholar
  2. 2.
    Zhang, J., Li, Y.: Region segmentation and shape characterisation for tessellated CAD models. Int. J. Comput. Integr. Manuf. 29(8), 907–915 (2016)CrossRefGoogle Scholar
  3. 3.
    Corney, J., Hayes, C., Sundararajan, V., Wright, P.: The CAD/CAM interface: a 25-year retrospective. J. Comput. Inf. Sci. Eng. 5(3), 188–197 (2005)CrossRefGoogle Scholar
  4. 4.
    Wang, J., Yu, Z.: Surface feature based mesh segmentation. Comput. Graph. 35(3), 661–667 (2011)CrossRefGoogle Scholar
  5. 5.
    Xú, S., Anwer, N., Mehdi-Souzani, C., Harik, R., Qiao, L.: STEP-NC based reverse engineering of in-process model of NC simulation. Int. J. Adv. Manuf. Technol. 86(9–12), 3267–3288 (2016)CrossRefGoogle Scholar
  6. 6.
    Hase, V.J., Bhalerao, Y.J., Verma, S., Jadhav, S., Vikhe Patil, G.J.: Complex hole recognition from CAD mesh models. Int. J. Manag. Technol. Eng. 8(IX), 1102–1119 (2018)Google Scholar
  7. 7.
    Hase, V.J., Bhalerao, Y.J., Verma, S., Wakchaure, V.D., Vikhe, G.J.: Intelligent threshold prediction in hybrid mesh segmentation using machine learning classifiers. Int. J. Manag. Technol. Eng. 8(IX), 1426–1442 (2018)Google Scholar
  8. 8.
    Ye, Z., Kim, M.K.: Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: case study of a shopping mall in China. Sustain. Cities Soc. 42, 176–183 (2018)Google Scholar
  9. 9.
    Chatterjee, S., Datta, B., Sen, S., Dey, N., Debnath, N.C.: Rainfall prediction using hybrid neural network approach. In: 2nd International Conference on Recent Advances in Signal Processing, pp. 67–72. Telecommunications and Computing, SIGTELCOM, Janua (2018)Google Scholar
  10. 10.
    Shebani, A., Iwnicki, S.: Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear 406–407, 173–184 (2018)CrossRefGoogle Scholar
  11. 11.
    Tanyildizi, H.: Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine. Adv. Civil Eng. 2018, 1–10 (2018)CrossRefGoogle Scholar
  12. 12.
    Rahman, A., Zhang, X.: Prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger through artificial neural network technique. Int. J. Heat Mass Transf. 124, 1088–1096 (2018)CrossRefGoogle Scholar
  13. 13.
    Rafe Biswas, M.A., Robinson, M.D., Fumo, N.: Prediction of residential building energy consumption: a neural network approach. Energy 117, 84–92 (2016)CrossRefGoogle Scholar
  14. 14.
    Porte, P., Isaac, R.K., Kiran, K., Mahilang, S.: Groundwater level prediction using artificial neural network model. Int. J. Curr. Microbiol. Appl. Sci. 7(02), 2947–2954 (2018)CrossRefGoogle Scholar
  15. 15.
    Kanat, Z.E., Özdil, N.: Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content. J. Text. Inst. 109(9), 1247–1253 (2018)CrossRefGoogle Scholar
  16. 16.
    Drouillet, C., Karandikar, J., Nath, C., Journeaux, A.C., Mansori, M., Kurfess, T.: Tool life predictions in milling using spindle power with the neural network technique. J. Manuf. Process. 22, 161–168 (2016)CrossRefGoogle Scholar
  17. 17.
    Esfandani, M.A., Nematzadeh, H.: Prediction of air pollution in Tehran based on evolutionary models. Indian J. Sci. Technol. 8(35) (2015)Google Scholar
  18. 18.
    Muraleedharan, L.P., Kannan, S.S., Karve, A., Muthuganapathy, R.: Random cutting plane approach for identifying volumetric features in a CAD mesh model. Comput. Graph. 70, 51–61 (2018)CrossRefGoogle Scholar
  19. 19.
    Kim, H.S., Choi, H.K., Lee, K.H.: Feature detection of triangular meshes based on tensor voting theory. Comput. Aided Des. 41(1), 47–58 (2009)CrossRefGoogle Scholar
  20. 20.
    Qin, F., Li, L., Gao, S., Yang, X., Chen, X.: A deep learning approach to the classification of 3D CAD models. J. Zhejiang Univ. Sci. C 15(2), 91–106 (2014)CrossRefGoogle Scholar
  21. 21.
    National Design Repository, Drexel University. http://edge.cs.drexel.edu/repository/. Last Accessed 05 Aug 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringAVCOESangamnerIndia
  2. 2.Mechanical Engineering, MIT Academy of EngineeringAlandi, PuneIndia
  3. 3.Department of Mechanical EngineeringSCSMCOEAhmednagarIndia

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