Automated manual assembly station using computer vision
This project aims to design an intelligent, data-driven workstation that efficiently measures operators' performance, assists operators in enhancing productivity and improving quality control, and addresses long-standing challenges within traditional manual assembly stations. This innovative...
| Main Author: | |
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/6630/ http://eprints.utar.edu.my/6630/1/fyp_CS_2024_CSJ.pdf |
| _version_ | 1848886730821730304 |
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| author | Ch'ng, Shin Joe |
| author_facet | Ch'ng, Shin Joe |
| author_sort | Ch'ng, Shin Joe |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | This project aims to design an intelligent, data-driven workstation that efficiently
measures operators' performance, assists operators in enhancing productivity and
improving quality control, and addresses long-standing challenges within traditional
manual assembly stations. This innovative technology is intended to replace outdated
proprietary systems and paper-based processes, which provide little room for
innovation and flexibility. This system includes sensors, open-source software, and
computer vision to transform the assembly process. This project implements an
integrated quality inspection model based on real-time picture data for immediate
fault detection to streamline processes and remove roadblocks. This workstation's
implementation of a unique QR code-based triggering event mechanism is a novel
feature. This inventive method allows the system to decode QR code values to
identify and initiate particular assembly tasks, bringing a new level of accuracy and
efficiency to the assembly process. This innovative workstation's release has the
potential to change the manufacturing industry completely. It's not only more
affordable for startups, but it also positively impacts overall excellence by increasing
quality standards, efficiency, and adaptability in a constantly changing industry. This
project introduces a dynamic assembly process, advocates open-source architectures,
and seamlessly integrates quality assurance throughout the assembly workflow to
accomplish this goal. |
| first_indexed | 2025-11-15T19:43:09Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6630 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:43:09Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-66302024-10-23T05:48:04Z Automated manual assembly station using computer vision Ch'ng, Shin Joe T Technology (General) TD Environmental technology. Sanitary engineering This project aims to design an intelligent, data-driven workstation that efficiently measures operators' performance, assists operators in enhancing productivity and improving quality control, and addresses long-standing challenges within traditional manual assembly stations. This innovative technology is intended to replace outdated proprietary systems and paper-based processes, which provide little room for innovation and flexibility. This system includes sensors, open-source software, and computer vision to transform the assembly process. This project implements an integrated quality inspection model based on real-time picture data for immediate fault detection to streamline processes and remove roadblocks. This workstation's implementation of a unique QR code-based triggering event mechanism is a novel feature. This inventive method allows the system to decode QR code values to identify and initiate particular assembly tasks, bringing a new level of accuracy and efficiency to the assembly process. This innovative workstation's release has the potential to change the manufacturing industry completely. It's not only more affordable for startups, but it also positively impacts overall excellence by increasing quality standards, efficiency, and adaptability in a constantly changing industry. This project introduces a dynamic assembly process, advocates open-source architectures, and seamlessly integrates quality assurance throughout the assembly workflow to accomplish this goal. 2024-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6630/1/fyp_CS_2024_CSJ.pdf Ch'ng, Shin Joe (2024) Automated manual assembly station using computer vision. Final Year Project, UTAR. http://eprints.utar.edu.my/6630/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Ch'ng, Shin Joe Automated manual assembly station using computer vision |
| title | Automated manual assembly station using computer vision |
| title_full | Automated manual assembly station using computer vision |
| title_fullStr | Automated manual assembly station using computer vision |
| title_full_unstemmed | Automated manual assembly station using computer vision |
| title_short | Automated manual assembly station using computer vision |
| title_sort | automated manual assembly station using computer vision |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6630/ http://eprints.utar.edu.my/6630/1/fyp_CS_2024_CSJ.pdf |