Three-dimensional Automated Optical Inspection (AOI) with machine learning approach
Electronic products are widely used in various applications to enhance the quality of life. Companies in the electronic industry are in competition to first introduce the newest technology into the market first to sustain the high global demand for electronic components. Hence, the electronic produc...
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| Format: | Final Year Project / Dissertation / Thesis |
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2022
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| Online Access: | http://eprints.utar.edu.my/4814/ http://eprints.utar.edu.my/4814/1/fyp_EE_LSS_2022.pdf |
| _version_ | 1848886250317021184 |
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| author | Lim, Sin Shian |
| author_facet | Lim, Sin Shian |
| author_sort | Lim, Sin Shian |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | Electronic products are widely used in various applications to enhance the quality of life. Companies in the electronic industry are in competition to first introduce the newest technology into the market first to sustain the high global demand for electronic components. Hence, the electronic products must be brought into market with shortest possible time but without compromising reliability. Therefore, Automated Optical Inspection (AOI) has been introduced into the manufacturing lines to replace the human visual inspection which takes a longer inspection time and unable to sustain high-volume requirements. AOI is a monitoring tool that helps to detect and identify failures in printed circuit boards assemblies (PCBA). An AOI system consists of cameras to scan and capture images of PCBA under the case of sufficient lightning and magnification provided. These captured images were then processed using software for further identification of defects and then provide the PASS/FAIL analysis results. In this project, background studies on previous final year projects and recent research papers investigating on AOI concepts, defects analysis, and machine learning (ML) were done, then further improvements are proposed and implemented. The concept of AOI is implemented with the following features: automated scanning, 3D inspection, defects identification, defects classification, and accuracy improvement through ML approach. The AOI implemented consists of two main structures which are hardware and software configurations. In the hardware configuration, four systems are illumination, camera, magnification, and motion system. In the software configuration, four systems are data collection, data analysation, data classification and data tabulation. All these systems are discussed in terms of equipment selections and considerations. |
| first_indexed | 2025-11-15T19:35:30Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-4814 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:35:30Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-48142022-12-29T10:27:39Z Three-dimensional Automated Optical Inspection (AOI) with machine learning approach Lim, Sin Shian T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Electronic products are widely used in various applications to enhance the quality of life. Companies in the electronic industry are in competition to first introduce the newest technology into the market first to sustain the high global demand for electronic components. Hence, the electronic products must be brought into market with shortest possible time but without compromising reliability. Therefore, Automated Optical Inspection (AOI) has been introduced into the manufacturing lines to replace the human visual inspection which takes a longer inspection time and unable to sustain high-volume requirements. AOI is a monitoring tool that helps to detect and identify failures in printed circuit boards assemblies (PCBA). An AOI system consists of cameras to scan and capture images of PCBA under the case of sufficient lightning and magnification provided. These captured images were then processed using software for further identification of defects and then provide the PASS/FAIL analysis results. In this project, background studies on previous final year projects and recent research papers investigating on AOI concepts, defects analysis, and machine learning (ML) were done, then further improvements are proposed and implemented. The concept of AOI is implemented with the following features: automated scanning, 3D inspection, defects identification, defects classification, and accuracy improvement through ML approach. The AOI implemented consists of two main structures which are hardware and software configurations. In the hardware configuration, four systems are illumination, camera, magnification, and motion system. In the software configuration, four systems are data collection, data analysation, data classification and data tabulation. All these systems are discussed in terms of equipment selections and considerations. 2022-10 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4814/1/fyp_EE_LSS_2022.pdf Lim, Sin Shian (2022) Three-dimensional Automated Optical Inspection (AOI) with machine learning approach. Final Year Project, UTAR. http://eprints.utar.edu.my/4814/ |
| spellingShingle | T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Lim, Sin Shian Three-dimensional Automated Optical Inspection (AOI) with machine learning approach |
| title | Three-dimensional Automated Optical Inspection (AOI) with machine learning approach
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| title_full | Three-dimensional Automated Optical Inspection (AOI) with machine learning approach
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| title_fullStr | Three-dimensional Automated Optical Inspection (AOI) with machine learning approach
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| title_full_unstemmed | Three-dimensional Automated Optical Inspection (AOI) with machine learning approach
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| title_short | Three-dimensional Automated Optical Inspection (AOI) with machine learning approach
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| title_sort | three-dimensional automated optical inspection (aoi) with machine learning approach |
| topic | T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utar.edu.my/4814/ http://eprints.utar.edu.my/4814/1/fyp_EE_LSS_2022.pdf |