TY - JOUR
T1 - Imagery creation based on autonomic system for finite element by using fully convolutional network
AU - Miyaki, Gentaro
AU - Tanaka, Komei
AU - Hasegawa, Hiroshi
N1 - Publisher Copyright:
© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing.
PY - 2020
Y1 - 2020
N2 - For applications in Industry 4.0, a system that can analyze the deformation and stress of a target object from an image acquired by a smartphone or tablet is proposed in this paper. The process for the proposed system is more convenient for users than creating a computer-aided design model. The target objects include bridges and plant equipment, and the proposed process aims to facilitate maintenance inspections. To acquire results from this system, a fully convolutional network is employed to extract the target object from the obtained image, and density-based topology optimization is applied to produce a finite element model, which is imported to commercial software. Artificial intelligence image processing is adopted to generate the output of the finite element model for the target object. In this work, numerical examples demonstrate that the final model for the target object is accurate and appropriate for finite element deformation and stress analysis.
AB - For applications in Industry 4.0, a system that can analyze the deformation and stress of a target object from an image acquired by a smartphone or tablet is proposed in this paper. The process for the proposed system is more convenient for users than creating a computer-aided design model. The target objects include bridges and plant equipment, and the proposed process aims to facilitate maintenance inspections. To acquire results from this system, a fully convolutional network is employed to extract the target object from the obtained image, and density-based topology optimization is applied to produce a finite element model, which is imported to commercial software. Artificial intelligence image processing is adopted to generate the output of the finite element model for the target object. In this work, numerical examples demonstrate that the final model for the target object is accurate and appropriate for finite element deformation and stress analysis.
KW - Autonomic Generation
KW - Finite Element Model
KW - Fully Convolutional Network
KW - Image Processing
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U2 - 10.1016/j.promfg.2020.02.059
DO - 10.1016/j.promfg.2020.02.059
M3 - Conference article
AN - SCOPUS:85084214045
SN - 2351-9789
VL - 42
SP - 383
EP - 386
JO - Procedia Manufacturing
JF - Procedia Manufacturing
T2 - 1st International Conference on Industry 4.0 and Smart Manufacturing, ISM 2019
Y2 - 20 November 2019 through 22 November 2019
ER -