DEMOCRATISING DESIGN through SURROGATE MODEL CONVOLUTIONAL NEURAL NETWORKS of COMPUTER AIDED DESIGN REPOSITORIES

J. Gopsill; S. Jennings

doi:10.1017/dsd.2020.93

DEMOCRATISING DESIGN through SURROGATE MODEL CONVOLUTIONAL NEURAL NETWORKS of COMPUTER AIDED DESIGN REPOSITORIES

J. Gopsill, S. Jennings

Department of Mechanical Engineering

Research output: Contribution to conference › Conference Paper › peer-review

3 Citations (Scopus)

Abstract

The capability to manufacture at home is continually increasing with technologies, such as 3D printing. However, the ability to design products suitable for manufacture and use remains a highly-skilled and knowledge intensive activity. This has led to 'content creators' providing vast repositories of manufacturable products for society, however challenges remain in the search & retrieval of models. This paper presents the surrogate model convolutional neural networks approach to search and retrieve CAD models by mapping them directly to their real-world photographed counterparts.

Original language	English
Pages	1285-1294
Number of pages	10
DOIs	https://doi.org/10.1017/dsd.2020.93
Publication status	Published - 2020
Event	16th International Design Conference, DESIGN 2020 - Virtual, Online Duration: 26 Oct 2020 → 29 Oct 2020

Conference

Conference	16th International Design Conference, DESIGN 2020
City	Virtual, Online
Period	26/10/20 → 29/10/20

Bibliographical note

Funding Information:
The work reported in this paper has been undertaken as part of the UKRI NCC (EP/R513556/1) and EPSRC Trans-disciplinary Engineering (EP/R013179/1) grants.

Publisher Copyright:
© The Author(s), 2020. Published by Cambridge University Press.

Keywords

3D printing
computer-aided design (CAD)
convolutional neural networks
design informatics
surrogate models

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title = "DEMOCRATISING DESIGN through SURROGATE MODEL CONVOLUTIONAL NEURAL NETWORKS of COMPUTER AIDED DESIGN REPOSITORIES",

abstract = "The capability to manufacture at home is continually increasing with technologies, such as 3D printing. However, the ability to design products suitable for manufacture and use remains a highly-skilled and knowledge intensive activity. This has led to 'content creators' providing vast repositories of manufacturable products for society, however challenges remain in the search & retrieval of models. This paper presents the surrogate model convolutional neural networks approach to search and retrieve CAD models by mapping them directly to their real-world photographed counterparts.",

keywords = "3D printing, computer-aided design (CAD), convolutional neural networks, design informatics, surrogate models",

author = "J. Gopsill and S. Jennings",

note = "Funding Information: The work reported in this paper has been undertaken as part of the UKRI NCC (EP/R513556/1) and EPSRC Trans-disciplinary Engineering (EP/R013179/1) grants. Publisher Copyright: {\textcopyright} The Author(s), 2020. Published by Cambridge University Press.; 16th International Design Conference, DESIGN 2020 ; Conference date: 26-10-2020 Through 29-10-2020",

year = "2020",

doi = "10.1017/dsd.2020.93",

language = "English",

pages = "1285--1294",

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AU - Gopsill, J.

AU - Jennings, S.

N1 - Funding Information: The work reported in this paper has been undertaken as part of the UKRI NCC (EP/R513556/1) and EPSRC Trans-disciplinary Engineering (EP/R013179/1) grants. Publisher Copyright: © The Author(s), 2020. Published by Cambridge University Press.

PY - 2020

Y1 - 2020

N2 - The capability to manufacture at home is continually increasing with technologies, such as 3D printing. However, the ability to design products suitable for manufacture and use remains a highly-skilled and knowledge intensive activity. This has led to 'content creators' providing vast repositories of manufacturable products for society, however challenges remain in the search & retrieval of models. This paper presents the surrogate model convolutional neural networks approach to search and retrieve CAD models by mapping them directly to their real-world photographed counterparts.

AB - The capability to manufacture at home is continually increasing with technologies, such as 3D printing. However, the ability to design products suitable for manufacture and use remains a highly-skilled and knowledge intensive activity. This has led to 'content creators' providing vast repositories of manufacturable products for society, however challenges remain in the search & retrieval of models. This paper presents the surrogate model convolutional neural networks approach to search and retrieve CAD models by mapping them directly to their real-world photographed counterparts.

KW - 3D printing

KW - computer-aided design (CAD)

KW - convolutional neural networks

KW - design informatics

KW - surrogate models

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DO - 10.1017/dsd.2020.93

M3 - Conference Paper

AN - SCOPUS:85107875289

SP - 1285

EP - 1294

T2 - 16th International Design Conference, DESIGN 2020

Y2 - 26 October 2020 through 29 October 2020

ER -

DEMOCRATISING DESIGN through SURROGATE MODEL CONVOLUTIONAL NEURAL NETWORKS of COMPUTER AIDED DESIGN REPOSITORIES

Abstract

Conference

Bibliographical note

Keywords

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