CN102346154A - Generation method of automatic optical detecting model diagram - Google Patents
Generation method of automatic optical detecting model diagram Download PDFInfo
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- CN102346154A CN102346154A CN2010102437043A CN201010243704A CN102346154A CN 102346154 A CN102346154 A CN 102346154A CN 2010102437043 A CN2010102437043 A CN 2010102437043A CN 201010243704 A CN201010243704 A CN 201010243704A CN 102346154 A CN102346154 A CN 102346154A
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Abstract
The invention provides a generation method of an automatic optical detecting model diagram, which is preformed on the basis of a first optical detecting program, wherein the first optical detecting program can generate a first detecting model diagram for detecting a first model, and the method is used for generating an optical detecting model diagram of a second model similar to the first model. The method comprises the steps: generating the first detecting model diagram for detecting the first model by the first optical detection program; comparing a part position data table of the second model with a part position data table of the first model to obtain a different part data table; modifying the different parts on the first detecting model diagram according to the different part data table to generate a second detecting model diagram; and generating a second optical detecting program of the second model according to the second detecting model diagram to automatically and optically detect the second model. Therefore, an optical detecting program programming time of the second model is reduced.
Description
[technical field]
The present invention provides a kind of automated optical detection model map generalization method of the map generalization of automated optical detection model method, particularly a kind of printed-wiring board (PWB).
[background technology]
PCB (Printed Circuie Board) is the abbreviation of printed wiring board, and SMT is exactly surface installation technique (surface mounting technology) (abbreviation of Surface Mounted Technology).And automatic visual inspection (AOI, Automated Optical Inspection) to be utilization high-speed, high precision visual processes technology detect automatically that various differences mount mistake and weld defects on the pcb board.The scope of pcb board can be from the thin space high-density plate to the low-density larger size panel, and online detection scheme can be provided, to enhance productivity and welding quality.
Through using AOI as the instrument that reduces defective, assembling technology procedure search and eliminate mistake in early days, to realize good process control.The early detection defective will be avoided bad plate is delivered to assembling stage subsequently, and AOI will reduce repair cost will avoid scrapping unmendable circuit board.Present detection mode is an installation and measuring program in the AOI machine; And this trace routine is through generating the mode of a kind of detection model figure; This detection model chart display has each part, and pcb board and this detection model figure that desire is detected do contrast to find out the mistake on this pcb board.
And the production of pcb board can be passed through usually: EVT (Engineering Verification Test) engineering verification; DVT (Design Verification Test) design verification; The checking of DMT (Design Maturity Test) degree of ripeness; The checking of MVT (Mass Verification Test) volume production.Or be the trace routine of the pcb board of existing a kind of machine, and need to detect a kind of similar machine.These processes only can be to increase perhaps to delete some parts usually, or the layout of pcb board is done some changes.Way according to prior art is to use the part position tables of data to write a program.
Yet another similar machine of optical detection program will write to(for) the automated optical trace routine that a kind of machine is arranged need use the part position tables of data, and coding is consuming time many.
[summary of the invention]
Therefore; The present invention is necessary to provide a kind of automated optical detection model map generalization method in fact; Utilize this method; Has a kind of automated optical trace routine of machine; And the optical detection program that will write another similar machine; Need not to use again the part position tables of data, thereby reduce the programming time.
For reaching above purpose; The present invention provides a kind of automated optical detection model map generalization method; This method is on the basis with one first optical detection program, to carry out; This first optical detection program can generate first a detection model figure who is used to detect first machine; This method is used for the optical detection model map generalization of second machine similar with first machine, and this method comprises the following steps:
The first optical detection program can generate first a detection model figure who is used to detect first machine;
The part position tables of data of second machine and the part position tables of data of first machine are done and are relatively drawn a difference part data table;
These difference parts are made amendment to generate one second detection model figure on the first detection model figure according to above-mentioned difference part data table.
Preferably, above-mentioned steps: on the first detection model figure, these difference parts are made amendment to generate one second detection model figure according to above-mentioned difference part data table; After it, can also comprise a step: the second optical detection program according to this second detection model figure generates one second machine, detect in order to the automated optical that carries out second machine.
Preferably, above-mentioned steps: on the first detection model figure, these difference parts are made amendment to generate one second detection model figure according to above-mentioned difference part data table; This step can also be specially step: calculate each difference part nearest part position on the first detection model figure, thereby on the first detection model figure, make amendment each difference part and generate the second detection model figure.
Compared to prior art; Utilize this automated optical detection model map generalization method, have the first automated optical trace routine of first machine, and the optical detection program that will write one second machine; Need not to use again the part position tables of data, thereby reduce the programming time.
[description of drawings]
Fig. 1 is the method flow diagram of automated optical detection model map generalization method of the present invention one preferred embodiment.
[embodiment]
See also Fig. 1, Fig. 1 illustrates the method flow diagram into automated optical detection model map generalization method of the present invention one preferred embodiment.
The present invention provides a kind of automated optical detection model map generalization method; This method is on the basis with one first optical detection program, to carry out; This first optical detection program can generate first a detection model figure who is used to detect first machine; This method is used for the optical detection model map generalization of second machine similar with first machine; In this preferred embodiment, this method comprises the following steps:
The first optical detection program can generate first a detection model figure (step 100) who is used to detect first machine;
The part position tables of data of second machine and the part position tables of data of first machine are done and are relatively drawn a difference part data table (step 101);
These difference parts are made amendment to generate one second detection model figure (step 102) on the first detection model figure according to above-mentioned difference part data table.
In addition, in this preferred embodiment, above-mentioned steps: on the first detection model figure, these difference parts are made amendment to generate one second detection model figure (step 102) according to above-mentioned difference part data table; After it, can also comprise a step: the second optical detection program according to this second detection model figure generates one second machine, detect (step 103) in order to the automated optical that carries out second machine.
Certainly, above-mentioned steps: on the first detection model figure, these difference parts are made amendment to generate one second detection model figure (step 101) according to above-mentioned difference part data table; This step can also be specially step: calculate each difference part nearest part position on the first detection model figure, thereby on the first detection model figure, make amendment each difference part and generate the second detection model figure.
Data in the above-mentioned part position tables of data comprise the item of part, coordinate, and angle can also comprise item number.
Compared to prior art; Utilize this automated optical detection model map generalization method, have the first automated optical trace routine of first machine, and the optical detection program that will write one second machine; Need not to use again the part position tables of data, thereby reduce the programming time.
Claims (6)
1. automated optical detection model map generalization method; This method is on the basis with one first optical detection program, to carry out; This first optical detection program can generate first a detection model figure who is used to detect first machine; This method is used for the optical detection model map generalization of second machine similar with first machine; It is characterized in that this method comprises the following steps:
The first optical detection program can generate first a detection model figure who is used to detect first machine;
The part position tables of data of second machine and the part position tables of data of first machine are done and are relatively drawn a difference part data table;
These difference parts are made amendment to generate one second detection model figure on the first detection model figure according to above-mentioned difference part data table.
2. automated optical detection model map generalization method according to claim 1 is characterized in that above-mentioned steps: these difference parts are made amendment to generate one second detection model figure on the first detection model figure according to above-mentioned difference part data table; After it, can also comprise a step: the second optical detection program according to this second detection model figure generates one second machine, detect in order to the automated optical that carries out second machine.
3. automated optical detection model map generalization method according to claim 1 is characterized in that above-mentioned steps: these difference parts are made amendment to generate one second detection model figure on the first detection model figure according to above-mentioned difference part data table; This step can also be specially step: calculate each difference part nearest part position on the first detection model figure, thereby on the first detection model figure, make amendment each difference part and generate the second detection model figure.
4. automated optical detection model map generalization method according to claim 2 is characterized in that above-mentioned steps: these difference parts are made amendment to generate one second detection model figure on the first detection model figure according to above-mentioned difference part data table; This step can also be specially step: calculate each difference part nearest part position on the first detection model figure, thereby on the first detection model figure, make amendment each difference part and generate the second detection model figure.
5. according to claim 1 or 2 or 3 or 4 described automated optical detection model map generalization methods, it is characterized in that the data in the above-mentioned part position tables of data comprise the item of part, coordinate, angle.
6. automated optical detection model map generalization method according to claim 5 is characterized in that the data in the above-mentioned part position tables of data also comprise the item number of part.
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| Application Number | Priority Date | Filing Date | Title |
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| CN2010102437043A CN102346154A (en) | 2010-08-03 | 2010-08-03 | Generation method of automatic optical detecting model diagram |
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| CN2010102437043A CN102346154A (en) | 2010-08-03 | 2010-08-03 | Generation method of automatic optical detecting model diagram |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086104A (en) * | 2018-08-10 | 2018-12-25 | 武汉精测电子集团股份有限公司 | The multi-thread body multistation parameter management method of AOI and system |
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| CN1430768A (en) * | 2000-05-24 | 2003-07-16 | Atg试验体系两合公司 | Method and device for examining pre-determined area of printed circuit board |
| CN1869667A (en) * | 2006-06-08 | 2006-11-29 | 李贤伟 | Profile analysing method for investigating defect of printed circuit board |
| US20070064998A1 (en) * | 2005-09-22 | 2007-03-22 | Advanced Mask Inspection Technology Inc. | Pattern inspection apparatus, pattern inspection method, and inspection sample |
| JP2008185514A (en) * | 2007-01-31 | 2008-08-14 | Oki Electric Ind Co Ltd | Substrate visual inspection apparatus |
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- 2010-08-03 CN CN2010102437043A patent/CN102346154A/en active Pending
Patent Citations (6)
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| US6400839B1 (en) * | 1998-04-24 | 2002-06-04 | Nec Corporation | Reticle inspecting apparatus capable of shortening an inspecting time |
| US20010055415A1 (en) * | 1999-12-16 | 2001-12-27 | Nec Corporation | Pattern inspection method and pattern inspection device |
| CN1430768A (en) * | 2000-05-24 | 2003-07-16 | Atg试验体系两合公司 | Method and device for examining pre-determined area of printed circuit board |
| US20070064998A1 (en) * | 2005-09-22 | 2007-03-22 | Advanced Mask Inspection Technology Inc. | Pattern inspection apparatus, pattern inspection method, and inspection sample |
| CN1869667A (en) * | 2006-06-08 | 2006-11-29 | 李贤伟 | Profile analysing method for investigating defect of printed circuit board |
| JP2008185514A (en) * | 2007-01-31 | 2008-08-14 | Oki Electric Ind Co Ltd | Substrate visual inspection apparatus |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086104A (en) * | 2018-08-10 | 2018-12-25 | 武汉精测电子集团股份有限公司 | The multi-thread body multistation parameter management method of AOI and system |
| CN109086104B (en) * | 2018-08-10 | 2021-08-13 | 武汉精测电子集团股份有限公司 | AOI multi-line multi-station parameter management method and system |
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Application publication date: 20120208 |