A view-based 3D CAD model reuse framework enabling product lifecycle reuse. (January 2019)
- Record Type:
- Journal Article
- Title:
- A view-based 3D CAD model reuse framework enabling product lifecycle reuse. (January 2019)
- Main Title:
- A view-based 3D CAD model reuse framework enabling product lifecycle reuse
- Authors:
- Zhang, Chao
Zhou, Guanghui - Abstract:
- Highlights: Proposing a view-based 3D CAD model reuse framework for product lifecycle reuse. Views refer to 2D projections of a 3D CAD model from multiple fixed viewpoints. Defining the problem of 3D CAD model retrieval as a view recognition problem. Using a deep learning approach for facilitating the view-based 3D CAD model reuse. Abstract: 3D CAD models have great significances for product lifecycle reuse as each model aggregates abundant knowledge in a vivid 3D CAD model and enables engineers to reuse the existing mature designs from a high-level perspective. The effective reuse of the pre-existing 3D CAD models could greatly save time and cost in new product development. Consequently, this paper proposes a novel view-based 3D CAD model reuse framework, which supports the effective reuse of 3D CAD models throughout the new product lifecycle by a deep learning approach. In this framework, each 3D CAD model is first represented by a series of orthogonal two-dimensional views, which contain rich spatial information for differentiating 3D CAD models. Then, we define the problem of model retrieval as a view recognition problem, where a deep residual network (ResNet) is successfully trained to facilitate the view-based 3D CAD model retrieval. With the learned ResNet, engineers could take the understandable views of a model that represent their query intents as input and acquire the relevant 3D CAD models for product lifecycle reuse. Finally, the typical application scenarioHighlights: Proposing a view-based 3D CAD model reuse framework for product lifecycle reuse. Views refer to 2D projections of a 3D CAD model from multiple fixed viewpoints. Defining the problem of 3D CAD model retrieval as a view recognition problem. Using a deep learning approach for facilitating the view-based 3D CAD model reuse. Abstract: 3D CAD models have great significances for product lifecycle reuse as each model aggregates abundant knowledge in a vivid 3D CAD model and enables engineers to reuse the existing mature designs from a high-level perspective. The effective reuse of the pre-existing 3D CAD models could greatly save time and cost in new product development. Consequently, this paper proposes a novel view-based 3D CAD model reuse framework, which supports the effective reuse of 3D CAD models throughout the new product lifecycle by a deep learning approach. In this framework, each 3D CAD model is first represented by a series of orthogonal two-dimensional views, which contain rich spatial information for differentiating 3D CAD models. Then, we define the problem of model retrieval as a view recognition problem, where a deep residual network (ResNet) is successfully trained to facilitate the view-based 3D CAD model retrieval. With the learned ResNet, engineers could take the understandable views of a model that represent their query intents as input and acquire the relevant 3D CAD models for product lifecycle reuse. Finally, the typical application scenario demonstrates the feasibility of the proposed framework, and the evaluation experiments show the superiorities of ResNet used in this framework. … (more)
- Is Part Of:
- Advances in engineering software. Volume 127(2019)
- Journal:
- Advances in engineering software
- Issue:
- Volume 127(2019)
- Issue Display:
- Volume 127, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 127
- Issue:
- 2019
- Issue Sort Value:
- 2019-0127-2019-0000
- Page Start:
- 82
- Page End:
- 89
- Publication Date:
- 2019-01
- Subjects:
- Product lifecycle reuse -- View-based approach -- Deep learning -- Residual network -- Model retrieval
Computer-aided engineering -- Periodicals
Engineering -- Computer programs -- Periodicals
Engineering -- Software -- Periodicals
Periodicals
620.0028553 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09659978 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.advengsoft.2018.09.001 ↗
- Languages:
- English
- ISSNs:
- 0965-9978
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0705.450000
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British Library HMNTS - ELD Digital store - Ingest File:
- 9006.xml