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  1. No Access

    Article

    Retrieval of CAD part models complying with design specification using a relational design rule-embedded BOM

    Reusing existing CAD part models in product development can significantly shorten design time and reduce costs. However, due to the extensive number of existing CAD part models within the Product Data Manageme...

    Omin Kwon, Changmo Yeo, Duhwan Mun in The International Journal of Advanced Manu… (2024)

  2. No Access

    Article

    Machining Feature Recognition Using Descriptors with Range Constraints for Mechanical 3D Models

    In machining feature recognition, geometric elements generated in a three-dimensional computer-aided design model are identified. This technique is used in manufacturability evaluation, process planning, and t...

    Seungeun Lim, Changmo Yeo, Fazhi He in International Journal of Precision Enginee… (2023)

  3. Article

    Open Access

    Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems

    Recently, studies applying deep learning technology to recognize the machining feature of three-dimensional (3D) computer-aided design (CAD) models are increasing. Since the direct utilization of boundary repr...

    Changmo Yeo, Byung Chul Kim, Sanguk Cheon, **won Lee, Duhwan Mun in Scientific Reports (2021)

  4. No Access

    Article

    A method of generating depth images for view-based shape retrieval of 3D CAD models from partial point clouds

    Laser scanners can easily acquire the geometric data of physical environments in the form of point clouds. Industrial 3D reconstruction processes generally recognize objects from point clouds, which should inc...

    Hyungki Kim, Changmo Yeo, Moohyun Cha, Duhwan Mun in Multimedia Tools and Applications (2021)

  5. No Access

    Article

    Deep learning applications in an industrial process plant: repository of segmented point clouds for pipework components

    The success of deep learning technology depends on the availability of adequate amounts of data for training deep neural networks. Many repositories of general two- (2D) and three-dimensional (3D) data are ava...

    Changmo Yeo, Seyoon Kim, Hyungki Kim, Siro Kim, Duhwan Mun in JMST Advances (2020)