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

Abstract 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 representation (B-rep) models as input data for neural networks in terms of data struc...

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Autores principales:	Changmo Yeo, Byung Chul Kim, Sanguk Cheon, Jinwon Lee, Duhwan Mun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/557a425a39c54308ac5e8ef538e14b62
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spelling	oai:doaj.org-article:557a425a39c54308ac5e8ef538e14b622021-11-14T12:18:04ZMachining feature recognition based on deep neural networks to support tight integration with 3D CAD systems10.1038/s41598-021-01313-32045-2322https://doaj.org/article/557a425a39c54308ac5e8ef538e14b622021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01313-3https://doaj.org/toc/2045-2322Abstract 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 representation (B-rep) models as input data for neural networks in terms of data structure is difficult, B-rep models are generally converted into a voxel, mesh, or point cloud model and used as inputs for neural networks for the application of 3D models to deep learning. However, the model’s resolution decreases during the format conversion of 3D models, causing the loss of some features or difficulties in identifying areas of the converted model corresponding to a specific face of the B-rep model. To solve these problems, this study proposes a method enabling tight integration of a 3D CAD system with a deep neural network using feature descriptors as inputs to neural networks for recognizing machining features. Feature descriptor denotes an explicit representation of the main property items of a face. We constructed 2236 data to train and evaluate the deep neural network. Of these, 1430 were used for training the deep neural network, and 358 were used for validation. And 448 were used to evaluate the performance of the trained deep neural network. In addition, we conducted an experiment to recognize a total of 17 types (16 types of machining features and a non-feature) from the B-rep model, and the types for all 75 test cases were successfully recognized.Changmo YeoByung Chul KimSanguk CheonJinwon LeeDuhwan MunNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Changmo Yeo Byung Chul Kim Sanguk Cheon Jinwon Lee Duhwan Mun Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems
description	Abstract 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 representation (B-rep) models as input data for neural networks in terms of data structure is difficult, B-rep models are generally converted into a voxel, mesh, or point cloud model and used as inputs for neural networks for the application of 3D models to deep learning. However, the model’s resolution decreases during the format conversion of 3D models, causing the loss of some features or difficulties in identifying areas of the converted model corresponding to a specific face of the B-rep model. To solve these problems, this study proposes a method enabling tight integration of a 3D CAD system with a deep neural network using feature descriptors as inputs to neural networks for recognizing machining features. Feature descriptor denotes an explicit representation of the main property items of a face. We constructed 2236 data to train and evaluate the deep neural network. Of these, 1430 were used for training the deep neural network, and 358 were used for validation. And 448 were used to evaluate the performance of the trained deep neural network. In addition, we conducted an experiment to recognize a total of 17 types (16 types of machining features and a non-feature) from the B-rep model, and the types for all 75 test cases were successfully recognized.
format	article
author	Changmo Yeo Byung Chul Kim Sanguk Cheon Jinwon Lee Duhwan Mun
author_facet	Changmo Yeo Byung Chul Kim Sanguk Cheon Jinwon Lee Duhwan Mun
author_sort	Changmo Yeo
title	Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems
title_short	Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems
title_full	Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems
title_fullStr	Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems
title_full_unstemmed	Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems
title_sort	machining feature recognition based on deep neural networks to support tight integration with 3d cad systems
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/557a425a39c54308ac5e8ef538e14b62
work_keys_str_mv	AT changmoyeo machiningfeaturerecognitionbasedondeepneuralnetworkstosupporttightintegrationwith3dcadsystems AT byungchulkim machiningfeaturerecognitionbasedondeepneuralnetworkstosupporttightintegrationwith3dcadsystems AT sangukcheon machiningfeaturerecognitionbasedondeepneuralnetworkstosupporttightintegrationwith3dcadsystems AT jinwonlee machiningfeaturerecognitionbasedondeepneuralnetworkstosupporttightintegrationwith3dcadsystems AT duhwanmun machiningfeaturerecognitionbasedondeepneuralnetworkstosupporttightintegrationwith3dcadsystems
_version_	1718429287520927744

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

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