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Intelligent Systems for Volumetric Feature Recognition from CAD Mesh Models

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EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

This chapter presents an intelligent technique to recognize 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 machine learning-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. The 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 favorably and found to be robust and consistent with coverage of more than 95% in addressing volumetric features.

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Acknowledgments

This work is supported by Centre for Computational Technologies, Pune, India. We also appreciate the authors of the Attene et al. [10], Schnabel et al. [11], and Li et al. [12] for making their code publicly available. Authors are also grateful to Dr. Truc Le, Dr. Ye Duan, and Dr. V.S. Gadakh for helping us to compute percentage coverage.

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Authors and Affiliations

  1. SPPU, Department of Mechanical Engineering, Amrutvahini College of Engineering, Sangamner, India

    Vaibhav Hase & G. J. Vikhe

  2. Department of Mechanical Engineering, MIT Academy of Engineering, Pune, India

    Yogesh Bhalerao

  3. Centre for Computational Technologies (CCTech), Pune, India

    Saurabh Verma

Authors
  1. Vaibhav Hase
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  2. Yogesh Bhalerao
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  3. Saurabh Verma
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  4. G. J. Vikhe
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Corresponding author

Correspondence to Vaibhav Hase .

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Editors and Affiliations

  1. Department of Computer Science & Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Department of Computer Science & Engineering, Presidency University, Bengaluru, India

    Arulmurugan Ramu

  3. Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Sudha Mohanram

  4. Department of Electrical Engineering, Faculty of Engineering, University of Malaysia, Kuala Lumpur, Kuala Lumpur, Malaysia

    Chow Chee Onn

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Hase, V., Bhalerao, Y., Verma, S., Vikhe, G.J. (2020). Intelligent Systems for Volumetric Feature Recognition from CAD Mesh Models. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-19562-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19561-8

  • Online ISBN: 978-3-030-19562-5

  • eBook Packages: EngineeringEngineering (R0)

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