An advanced auto-inspection system for micro-router collapse
Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Perng, Der-Baau [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2009 |
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| Übergeordnetes Werk: |
Enthalten in: Machine vision and applications - Springer-Verlag, 1988, 21(2009), 6 vom: 12. Sept., Seite 811-824 |
|---|---|
| Übergeordnetes Werk: |
volume:21 ; year:2009 ; number:6 ; day:12 ; month:09 ; pages:811-824 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00138-009-0221-z |
|---|
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OLC2074625081 |
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| 520 | |a Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. | ||
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10.1007/s00138-009-0221-z doi (DE-627)OLC2074625081 (DE-He213)s00138-009-0221-z-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Perng, Der-Baau verfasserin aut An advanced auto-inspection system for micro-router collapse 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2009 Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. Micro-router Machine vision Auto-optical inspection Chen, Yen-Chung aut Enthalten in Machine vision and applications Springer-Verlag, 1988 21(2009), 6 vom: 12. Sept., Seite 811-824 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:21 year:2009 number:6 day:12 month:09 pages:811-824 https://doi.org/10.1007/s00138-009-0221-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_70 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 21 2009 6 12 09 811-824 |
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10.1007/s00138-009-0221-z doi (DE-627)OLC2074625081 (DE-He213)s00138-009-0221-z-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Perng, Der-Baau verfasserin aut An advanced auto-inspection system for micro-router collapse 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2009 Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. Micro-router Machine vision Auto-optical inspection Chen, Yen-Chung aut Enthalten in Machine vision and applications Springer-Verlag, 1988 21(2009), 6 vom: 12. Sept., Seite 811-824 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:21 year:2009 number:6 day:12 month:09 pages:811-824 https://doi.org/10.1007/s00138-009-0221-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_70 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 21 2009 6 12 09 811-824 |
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10.1007/s00138-009-0221-z doi (DE-627)OLC2074625081 (DE-He213)s00138-009-0221-z-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Perng, Der-Baau verfasserin aut An advanced auto-inspection system for micro-router collapse 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2009 Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. Micro-router Machine vision Auto-optical inspection Chen, Yen-Chung aut Enthalten in Machine vision and applications Springer-Verlag, 1988 21(2009), 6 vom: 12. Sept., Seite 811-824 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:21 year:2009 number:6 day:12 month:09 pages:811-824 https://doi.org/10.1007/s00138-009-0221-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_70 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 21 2009 6 12 09 811-824 |
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10.1007/s00138-009-0221-z doi (DE-627)OLC2074625081 (DE-He213)s00138-009-0221-z-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Perng, Der-Baau verfasserin aut An advanced auto-inspection system for micro-router collapse 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2009 Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. Micro-router Machine vision Auto-optical inspection Chen, Yen-Chung aut Enthalten in Machine vision and applications Springer-Verlag, 1988 21(2009), 6 vom: 12. Sept., Seite 811-824 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:21 year:2009 number:6 day:12 month:09 pages:811-824 https://doi.org/10.1007/s00138-009-0221-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_70 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 21 2009 6 12 09 811-824 |
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10.1007/s00138-009-0221-z doi (DE-627)OLC2074625081 (DE-He213)s00138-009-0221-z-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Perng, Der-Baau verfasserin aut An advanced auto-inspection system for micro-router collapse 2009 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2009 Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. Micro-router Machine vision Auto-optical inspection Chen, Yen-Chung aut Enthalten in Machine vision and applications Springer-Verlag, 1988 21(2009), 6 vom: 12. Sept., Seite 811-824 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:21 year:2009 number:6 day:12 month:09 pages:811-824 https://doi.org/10.1007/s00138-009-0221-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_70 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 21 2009 6 12 09 811-824 |
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An advanced auto-inspection system for micro-router collapse |
| abstract |
Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. © Springer-Verlag 2009 |
| abstractGer |
Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. © Springer-Verlag 2009 |
| abstract_unstemmed |
Abstract In this paper, we propose an auto-optical inspection (AOI) system that can inspect micro-router (router) collapse automatically. The router is a tool used to cut a printed circuit board (PCB). A few types of defects could occur in the routers and cause unexpected damage to the PCBs. Among these defects, collapse is the most critical defect that must be detected. Currently, router manufacturing companies rely on human inspectors to control the router quality. We first extract the silhouette edges and associated features (peaks and valleys) of a router’s silhouette image by computer vision technique. Then, these silhouette edges and associated features are used to reconstruct a set of 2D isograms that correspond to the router surface. Finally, a pattern recognition method is devised to identify and classify some features of the pattern in the 2D isograms. In this study, two types of routers with different diameters are used for inspection experiments. There are 15 routers of each type. The experimental results reveal that the proposed AOI system can robustly and successfully detect the collapse of diamond-patterned routers with different sizes. The successful detection rate is above 96%. The proposed AOI system can assist in determining the quality of the routers. © Springer-Verlag 2009 |
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| container_issue |
6 |
| title_short |
An advanced auto-inspection system for micro-router collapse |
| url |
https://doi.org/10.1007/s00138-009-0221-z |
| remote_bool |
false |
| author2 |
Chen, Yen-Chung |
| author2Str |
Chen, Yen-Chung |
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| doi_str |
10.1007/s00138-009-0221-z |
| up_date |
2024-07-03T22:53:02.566Z |
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7.3985195 |