Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤종헌 | - |
dc.date.accessioned | 2022-03-28T05:47:47Z | - |
dc.date.available | 2022-03-28T05:47:47Z | - |
dc.date.issued | 2021-12 | - |
dc.identifier.citation | JOURNAL OF MANUFACTURING SYSTEMS, Page. 1-28 | en_US |
dc.identifier.issn | 0278-6125 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0278612521002089 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/169463 | - |
dc.description.abstract | Automating stages for deformable objects in the production line, in which assembling a wire harness into a predefined position is a complex task owing to the specialized characteristics of the objects. Besides a few automatized systems proposed in the other studies to implement this task under simplified setup conditions, a significant portion of this process remains to be completed manually in industrial environments. To construct an automatic wire harness assembly system, the development of a method that can automatically detect the wire harness profile in a 3D environment and, consequently, guide robot arms to implement assembly tasks is indispensable. Therefore, this study presents an approach that satisfies this requirement, which not only proposes a deep learning-based system to detect the wire profile, but also improves the accuracy of the detected results through a correction method according to the depth values of contiguous areas. The verification of the approach in a robot system that highlights its usefulness and practicality demonstrates the potential of the proposed method to replace people and consequently, reduce labour costs in factory environments. | en_US |
dc.description.sponsorship | This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE), Korea, under the “Digital Manufacturing Platform (DigiMaP)” (reference number N0002598) supervised by the Korea Institute for Advancement of Technology (KIAT). The author Prof. Jonghun Yoon received research funding from the National Research Foundation of Korea (NRF), grant funded by the Korea Government (MSIT) (No. 2019R1A2C4070160). | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER SCI LTD | en_US |
dc.subject | Automation system | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Machine vision | en_US |
dc.subject | Wire harness assembly | en_US |
dc.title | A novel vision-based method for 3D profile extraction of wire harness in robotized assembly process | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.jmsy.2021.10.003 | - |
dc.relation.page | 1-28 | - |
dc.relation.journal | JOURNAL OF MANUFACTURING SYSTEMS | - |
dc.contributor.googleauthor | Nguyen, Thong Phi | - |
dc.contributor.googleauthor | Yoon, Jonghun | - |
dc.relation.code | 2021000616 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DEPARTMENT OF MECHANICAL ENGINEERING | - |
dc.identifier.pid | jyoon | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.