TWM606206U - Intelligent device for optimizing parameters automatically - Google Patents

Abstract

An intelligent device for automatically optimizing parameters includes an optical device, a visual detection device, an artificial intelligence device and a parameter adjustment device. The artificial intelligence device is provided with a parameter setter, a true and false defect number determiner, and a processor, and the processor is connected to the parameter setter and the true and false defect number determiner. The parameter adjustment device is equipped with an automatic adjuster. The visual inspection device inputs the Nth inspection position of a product to be tested to the visual inspection device. The artificial intelligence device sets the value of the algorithm detection type as the first parameter value through the parameter setter according to the Nth inspection position of the product to be tested, and judges the true and false defects of the first parameter value through the true and false defect number determiner Quantity.

Description

Intelligent device that automatically optimizes parameters

A parameter setting of a visual inspection device, especially an intelligent device for automatically optimizing parameters.

The quality and appearance inspection of various components on the circuit board in electronic products is an important step in the manufacturing and inspection process of the circuit board. In order to make the inspection more accurate, most of the visual inspection devices are currently used to inspect by imaging. The existing inspection The method is to input the standard inspection value of the circuit board (circuit board layout) into the visual inspection device, allowing the operator of the visual inspection device to adjust the standard inspection value based on their accumulated experience, but this approach is quite reliable Low, it often happens that qualified products are judged as defective products. At this time, the operator must adjust the standard test value again so that other qualified products can pass the test. This method requires labor even for quite experienced operators. It takes time to repeat the test to adjust the appropriate standard detection value, reduce false defects, and catch true defects.

This creation proposes an intelligent device that automatically optimizes parameters. The intelligent device that automatically optimizes parameters includes an optical device, a visual inspection device, an artificial intelligence device, and a parameter adjustment device. The visual inspection device is connected to the optical device, the artificial intelligence device is connected to the visual inspection device and the parameter adjustment device is connected to the artificial intelligence device. The artificial intelligence device is provided with a parameter setter, a true and false defect number determiner, and a processor, and the processor is connected to the parameter setter and the true and false defect number determiner. The parameter adjustment device is provided with an automatic adjuster, and the automatic adjuster is connected to the processor. The visual inspection device inputs the Nth inspection position of the product to be tested to the visual inspection device. The artificial intelligence device sets the value of the algorithm detection type as the first parameter value through the parameter setter according to the Nth inspection position of the product to be tested, and judges the true and false defects of the first parameter value through the true and false defect number determiner Quantity. The parameter adjustment device automatically adjusts the value of the algorithm detection type through the automatic adjuster to the next parameter value. The artificial intelligence device uses the processor to find the value corresponding to the algorithm detection type with the largest number of true defects and the smallest number of false defects as the most Best parameter value, where N is a positive integer greater than one.

In an embodiment of this creation, the algorithm detection type is contour, position coordinate, color or brightness, and the value of the algorithm detection type is the contour value, position coordinate value, color value, or brightness value.

In an embodiment of this creation, the automatic adjuster adjusts the value of the type detected by the automatic adjustment algorithm in the range of the lower limit to the upper limit in the step of the next parameter value.

In an embodiment of this creation, in the step of judging whether the value of the algorithm detection type is adjusted by the processor, the value of the algorithm detection type is determined to be within the range of the lower limit and the upper limit. Adjustment.

In an embodiment of this creation, the value of each algorithm detection category corresponds to a set of data about the number of true defects and the number of false defects.

In order to achieve the above-mentioned purpose, the intelligent device that automatically optimizes the parameters of this creation can adjust the appropriate standard detection value, so that the false defects can be reduced, and the real defects can be caught.

The following detailed descriptions are given by specific examples, and it will be easier to understand the purpose, technical content, characteristics and effects of this creation.

In order to solve the problems of quality and appearance inspection of various components on existing circuit boards, the creators have gone through years of research and development to improve the shortcomings of existing products. The follow-up will introduce in detail how this creation uses a smart device that automatically optimizes parameters. To achieve the most efficient functional requirements.

Please refer to the first figure. The first figure is a flowchart of the parameter setting method of the visual inspection device created. As shown in Figure 1, the parameter setting method 100 of the visual inspection device includes the following steps: input the Nth inspection position of the product to be tested to the visual inspection device (step S110); set the calculation according to the Nth inspection position of the product to be tested The value of the method detection type is used as the first parameter value (step S120); the number of true defects and the number of false defects of the first parameter value are judged (step S130); the value of the detection type of the algorithm is automatically adjusted to the next parameter value (step S140) ; Calculate the number of true defects and the number of false defects in step D (step S150); determine whether the value of the algorithm detection type is adjusted (step S160); find the algorithm detection type corresponding to the largest number of true defects and the least number of false defects The value is used as the optimal parameter value (step S170), where N is a positive integer greater than one.

It should be noted that the detection types of the above algorithms are contour, position coordinates, color or brightness, but they are not limited to these four. In addition, the value of the algorithm detection type is the appearance contour value, the position coordinate value, the color value or the brightness value. In addition, in the step where the value of the detection type of the automatic adjustment algorithm is the next parameter value (i.e., step S140), the adjustment is performed within the range of the lower limit value to the upper limit value (gradual increase) or the upper limit value is lowered Adjust within the range of the limit (gradual downward adjustment). In the step of judging whether the value of the algorithm detection type has been adjusted (that is, step S160), to determine whether the value of the algorithm detection type has been adjusted within the range of the lower limit and the upper limit, the value adjustment is performed The spacing can be set by the designer. The value of each algorithm detection category corresponds to a set of data about the number of true defects and the number of false defects. The above-mentioned upper limit and lower limit are set by the designer.

In detail, please refer to the first to third figures at the same time. The second figure is a block diagram of the intelligent device that automatically optimizes parameters. The third picture is a schematic diagram of the product under test created by this author. The intelligent device 200 for automatically optimizing parameters using the parameter setting method of the visual inspection device includes a visual inspection device 220, an optical device 230, an artificial intelligence device 240, and a parameter adjustment device 250. The artificial intelligence device 240 is provided with a parameter setter 242, a true and false defect number determiner 244, and a processor 246. The parameter adjustment device 250 has an automatic adjuster 252. The visual inspection device 220 is connected to the optical device 230, and the artificial intelligence device 240 is connected to the visual inspection device 220. The parameter adjustment device 250 is connected to the artificial intelligence device 240. The processor 246 is connected to the parameter setter 242 and the true and false defect number determiner 244. The automatic adjuster 252 is connected to the processor 246. The optical device 220 is used to photograph the product 210 to be tested. The artificial intelligence device 240 uses the parameter setter 242 to set the value of the algorithm detection type as the first parameter value according to the Nth inspection position of the product to be tested 210 as the first parameter value, and uses the true and false defect number determiner 244 to determine the true defect of the first parameter value Quantity and the number of fake defects. The parameter adjustment device 250 automatically adjusts the value of the algorithm detection type through the automatic adjuster 252 to the next parameter value. The artificial intelligence device 240 uses the processor 246 to find the algorithm detection corresponding to the largest number of true defects and the smallest number of false defects. The value of the category is taken as the optimal parameter value, where N is a positive integer greater than one. The solution proposed by this creation is aimed at the quality and appearance inspection of various components on existing circuit boards. The working mechanism is described in detail as follows.

The optical device 230 will take a picture of a product 210 to be tested, and then will inspect multiple inspection positions (W, X, Y, and Z) of the product 210 to be tested. Here, four inspection positions (equivalent to N=1) To 4) as an example, but not limited to this. First, the first inspection position W is inspected, and the first inspection position of the product 210 to be tested (here, the inspection position W) is input to the visual inspection device 220. After that, the visual inspection device 220 sets the value of the algorithm detection type as a first parameter value according to the first inspection position W of the product to be tested 210, where the value of the algorithm detection type inspected by the first inspection position W is outside Type contour value, position coordinate value, color value or brightness value. Here it is assumed that the algorithm detection type of the first test is brightness and the value of the algorithm detection type of brightness is set as the first parameter value. Next, the artificial intelligence device 240 is used to check the first inspection position W to determine the number of true defects and the number of false defects of the first parameter value. For example, when inspecting the brightness at the first inspection position W, the number of first true defects obtained is 8 and the number of first false defects is 15. Afterwards, using the feedback correction mechanism, the parameter adjustment device 250 is used to automatically adjust the value of the algorithm detection type to obtain the next brightness parameter value (here, the brightness value is taken as an example), and then the visual detection device 220 is used to calculate the The number of true defects and the number of false defects of another brightness value in one inspection position W, for example, the number of true defects is 12 and the number of false defects is 3 at this time. Next, the artificial intelligence device 240 is used to perform re-judgment and calculation to judge whether the value of the algorithm detection type of the first inspection position W (the parameter value adjusted here is the brightness value) has been adjusted, that is, the judgment calculation Whether the value of the method detection category has been adjusted numerically within the range of the lower limit and the upper limit.

If the artificial intelligence device 240 determines that the adjustment has not been completed, it will continue to automatically adjust the value of the algorithm detection type through the parameter adjustment device 250 and proceed to the subsequent steps. If the artificial intelligence device 240 determines that the adjustment has been completed, the artificial intelligence device 240 finds the value corresponding to the algorithm detection type with the largest number of true flaws and the smallest number of false flaws as an optimal parameter value. In the above working mechanism, the value of each algorithm detection category corresponds to a set of data about the number of true defects and the number of false defects. For example, in this embodiment, the parameter value to be adjusted at the first inspection position is the brightness value, and the adjustment is performed 15 times in total. After the artificial intelligence device 240 summarizes the calculation, it is found that the second data is relatively optimized data, so the second The second data is the best parameter value of brightness.

After the brightness optimization is finished, at the same first inspection position W, the intelligent device 200 that automatically optimizes parameters will start to adjust the algorithm detection type, such as the outline, position coordinates, or color. Through the above mechanisms to detect, set the initial value, feedback and adjust the value of the algorithm detection type, summarize and find the relatively optimized parameter value, a relatively optimized set of data will be obtained. That is to say, the value of the detection type of the algorithm is adjusted according to the above-mentioned mechanism to obtain the best value of the contour, the best value of the position coordinate, or the best value of the color. In this way, the related optimization operation of the first inspection position W of the product 210 to be tested is completed.

Next, inspection will be carried out for the second inspection position X. The second inspection position (here, inspection position X) of the product 210 to be tested is input to the visual inspection device 220. The subsequent working mechanism is as described above, and the rest of the inspection positions Y and X are also the same. Therefore, this creation is to optimize the value of the algorithm detection type at each inspection position in sequence. The optimization standard is to correspond to the algorithm detection type with the largest number of true flaws and the smallest number of false flaws. The value is the best parameter value.

In summary, the intelligent device that automatically optimizes the parameters of the visual inspection device created by this creation can adjust the appropriate standard inspection value, reduce false defects, and catch true defects.

Only the above are only preferred embodiments of this creation, and are not used to limit the scope of implementation of this creation. Therefore, all equivalent changes or modifications made in accordance with the characteristics and spirit of the application scope of this creation shall be included in the scope of patent application of this creation.

100: Parameter setting method of visual inspection device S110, S120, S130, S140, S150, S160, S170: steps 200: Smart device that automatically optimizes parameters 210: product to be tested 220: Visual inspection device 230: optical device 240: Artificial Intelligence Device 242: Parameter Setter 244: True and False Defects Number Judge 246: processor 250: Parameter adjustment device 252: automatic adjuster W, X, Y, Z: inspection position

The first picture is the flow chart of the parameter setting method of the visual inspection device created for this creation The second figure is a block diagram of the intelligent device that automatically optimizes parameters created by this author. The third picture is a schematic diagram of the product under test created by this author.

200: Smart device that automatically optimizes parameters

210: product to be tested

220: Visual inspection device

230: optical device

240: Artificial Intelligence Device

242: Parameter Setter

244: True and False Defects Number Judge

246: processor

250: Parameter adjustment device

252: automatic adjuster

Claims (5)

An intelligent device for automatically optimizing parameters, including: An optical device; A visual inspection device connected to the optical device, and input an Nth inspection position of a product to be tested to the visual inspection device; An artificial intelligence device is connected to the visual inspection device, the artificial intelligence device is provided with a parameter setter, a true and false defect number determiner, and a processor, and the processor is connected to the parameter setter and the true and false defect number determination The artificial intelligence device sets an algorithm detection type value as a first parameter value through the parameter setter according to the Nth inspection position of the product to be tested, and determines the first parameter value through the true and false defect number determiner The number of true defects and the number of false defects of a parameter value; and A parameter adjustment device connected to the artificial intelligence device, the parameter adjustment device is provided with an automatic adjuster, the automatic adjuster is connected to the processor, the parameter adjustment device automatically adjusts the value of the algorithm detection type through the automatic adjuster Is the next parameter value, where the artificial intelligence device uses the processor to find the value corresponding to the algorithm detection type with the largest number of true flaws and the smallest number of false flaws as an optimal parameter value, where N is a positive integer greater than one . The intelligent device for automatically optimizing parameters as described in claim 1, wherein the algorithm detection type is contour, position coordinate, color or brightness, and the value of the algorithm detection type is the contour value, position coordinate value, Color value or brightness value. The intelligent device for automatically optimizing parameters as described in claim 1, wherein the automatic adjuster automatically adjusts the value of the algorithm detection type to the next parameter value, which is in the range from a lower limit to an upper limit Adjustment. The intelligent device for automatically optimizing parameters according to claim 2, wherein the processor is determining whether the value of the algorithm detection type is adjusted to determine whether the value of the algorithm detection type is within the lower limit and the Numerical adjustments have been made within the upper limit range. The intelligent device for automatically optimizing parameters as described in claim 1, wherein each value of the algorithm detection category corresponds to a set of data about the number of true defects and the number of false defects.

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