CN106552998B - The method of estimation of laser index carving technological parameter and laser index carving method - Google Patents
The method of estimation of laser index carving technological parameter and laser index carving method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 115
- 238000012360 testing method Methods 0.000 claims abstract description 70
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 claims abstract description 63
- 229910052782 aluminium Inorganic materials 0.000 claims abstract description 63
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- 238000005286 illumination Methods 0.000 description 4
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- 238000003723 Smelting Methods 0.000 description 1
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- 238000002679 ablation Methods 0.000 description 1
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Abstract
The present invention relates to laser index carving field more particularly to a kind of methods of estimation for identifying required laser index carving technological parameter in aluminium ingot surface marking aluminium ingot using laser.To improve marking effect and marking efficiency, the present invention proposes a kind of method of estimation of laser index carving technological parameter：It the horizontal orthogonal test of multiple factors such as carries out on marking aluminium ingot is treated and records multigroup test data；Establish the computation model equation of the gray value of image G of marking image：bi(i=0,1,2,3), b4、b5、......、b9、b10For fitting coefficient；According to etc. the test data fitting of horizontal orthogonal test of multiple factors obtain the value of fitting coefficient；Laser index carving technological parameter is obtained according to the estimations of object function G ＜ 150.Can laser index carving technological parameter be estimated according to the gray value of image that marking needs using the method for estimation of the laser index carving technological parameter and can be directly used for laser index carving, improve the automatic identification rate of laser index carving effect, efficiency and marking image.
Description
Technical Field
The invention relates to the field of laser marking, in particular to a method for estimating laser marking process parameters required by marking an aluminum ingot mark on the surface of an aluminum ingot by using laser and a method for marking the aluminum ingot mark on the surface of the aluminum ingot by using the laser.
Background
Before the aluminum ingot is manufactured and delivered from a factory, an aluminum ingot mark is marked on the aluminum ingot, and the aluminum ingot mark generally records relevant information such as production units, production dates, test information, smelting furnace numbers, grade information, weight information and the like of the aluminum ingot in the form of characters, bar codes and/or two-dimensional codes.
At present, the aluminum ingot mark is usually made by adhering a paper label printed with the aluminum ingot mark on the aluminum ingot or by marking the aluminum ingot mark on the surface of the aluminum ingot by using an inkjet marking, pneumatic marking or laser marking method. Wherein, to be printed with the paper label of aluminium ingot sign and paste on the aluminium ingot, waste time and energy, it is with high costs, and the paper label is easy impaired in aluminium ingot transportation, storage process, influence discernment. The automatic identification of the aluminum ingot mark obtained by ink-jet marking and pneumatic marking is poor and inconvenient. When the laser marking method is adopted for marking the aluminum ingot mark on the surface of the aluminum ingot, the laser marking process parameters need to be selected and adjusted according to the marking requirements, but the laser marking process parameters are difficult to select due to uneven surface of the aluminum ingot and complex marking background images, the marking effect is poor, the marking efficiency is low, and further the automatic identification rate of the marked aluminum ingot mark, especially the one-dimensional code and the two-dimensional code in the aluminum ingot mark is low.
Disclosure of Invention
In order to improve the marking effect and the marking efficiency, the invention provides an estimation method of laser marking process parameters, which comprises the following steps:
step 1: performing an equal-level multi-factor orthogonal test on an aluminum ingot to be marked, adjusting laser marking process parameters to obtain image gray values of different marked images in the test process, and recording a plurality of groups of test data, wherein the test data comprises values of laser marking process parameter focused laser beam diameter d, laser machine Q frequency f, laser scanning speed v, laser power p and filling line spacing s and image gray values of corresponding marked images;
step 2: establishing a calculation model equation of the image gray value of the marked image:
wherein,
g denotes the image gray value of the marking image,
bi(i＝0、1、2、3)、b4、b5、......、b9、b10is a fitting coefficient;
and step 3: fitting according to the test data of the equi-level multi-factor orthogonal test in the step 1 to obtain the value of a fitting coefficient in a calculation model equation of the image gray value;
and 4, step 4: and estimating to obtain the values of laser spot overlapping times n, focused laser beam diameter d, laser machine Q frequency f, laser scanning speed v, laser single pulse energy e and laser power p of laser marking process parameters according to the target function G < 150.
The laser marking process parameter estimation method can estimate the laser marking process parameter according to the image gray value required by marking, and the matching degree between the marked image gray value measured value obtained by marking the laser marking process parameter, namely the marked image gray actual value, and the marked image gray estimated value obtained by estimating the laser marking process parameter is higher. That is to say, the gray value of the marking image obtained by directly carrying out laser marking by utilizing the estimated laser marking process parameters is closer to the image gray value of the expected marking image, so the estimated laser marking process parameters can be directly used for laser marking, and the laser marking effect and the marking efficiency are improved. In addition, when the target function is used for estimating the laser marking process parameters, the specific value of the target function G can be set according to the automatic identification requirement, so that the automatic identification rate of the marked image can be improved.
Preferably, in the step 3, an immune clone algorithm is adopted to fit to obtain the value of the fitting coefficient in the calculation model equation of the image gray value. Further, when fitting, the affinity optimization objective function is adopted as
Wherein,
n is the number of groups of test data acquired when the equi-level multifactor orthogonal test is carried out in the step 1,
Gmjthe gray scale estimation value of the marked image obtained by the jth group of experiments in the affinity calculation is obtained,
Gcjthe measured value of the image gray scale of the marked image obtained by the jth group of experiments in the first affinity calculation is obtained.
Therefore, the accuracy of the fitting speed and the fitting coefficient can be further improved, and the estimation precision of the laser marking process parameters can be further improved.
The invention also provides a laser marking method, which comprises the following steps:
s1, ablating a light color marking background area on the surface of the aluminum ingot to be marked, wherein the gray value variance sigma of the light color marking background area2Less than or equal to 50, and the image gray mean value M is more than or equal to 150;
s2, estimating laser marking process parameters for marking aluminum ingot marks in the light color marking background area by using the method for estimating the laser marking process parameters as claimed in any one of claims 1 to 3;
and S3, marking the aluminum ingot mark in the light-color marking background area by using the laser marking process parameters estimated in the step S2.
Preferably, in the step S1, the filling line spacing S in the laser ablation process parameter ranges from 0.01mm to 0.2mm, the laser scanning speed v ranges from 500 mm/S to 1000mm/S, the laser power p ranges from 10W to 20W, and the Q frequency f of the laser machine ranges from 50 kHz to 100 kHz.
Preferably, in the step S3, when the aluminum ingot mark is marked in the light-color marking background area, the filling line interval S in the laser marking process parameter has a value range of 0.01-0.1mm, the laser scanning speed v has a value range of 100-200mm/S, the laser power p has a value range of 12-20W, and the frequency f of the laser Q has a value range of 20-28 kHz.
The laser marking method is adopted to mark the aluminum ingot mark on the surface of the aluminum ingot, the estimated laser marking process parameters are directly adopted to mark the aluminum ingot mark in the light color marking background area, the laser marking process parameters are not required to be adjusted repeatedly, and the marking efficiency of the aluminum ingot mark is improved; and setting an objective function for estimating laser marking process parameters according to the automatic identification requirement, thereby improving the automatic identification rate of the aluminum ingot mark obtained by marking.
Drawings
FIG. 1 is a laser marking image obtained by performing a laser marking test to verify the influence of laser spot overlapping times n, laser single pulse energy e and filling line spacing s on a laser marking effect;
FIG. 2 is a graph showing a variation curve of an image gray level value of a marked image obtained by a single-factor variation test with the laser spot overlapping times n as a variation factor;
FIG. 3 is a graph showing a variation of gray level of an image of a marked image obtained by a single-factor variation test using laser single-pulse energy e as a variation factor;
FIG. 4 is a graph showing a variation of gray level of an image of a marked image obtained by a single-factor variation test using the space s between the filling lines as a variation factor;
FIG. 5 is a graph of the marking effect obtained from an equi-level multifactor orthogonal test;
FIG. 6 is a graph of the marking effect obtained by the verification test;
fig. 7 is a comparison result between the gray scale estimated value of the marking image and the gray scale measured value of the marking image obtained by the verification test.
Detailed Description
The following describes in detail the method for estimating the laser marking process parameters and the method for marking aluminum ingot marks on the surface of an aluminum ingot by using the laser marking process parameters estimated by the estimating method, with reference to the accompanying drawings.
According to the laser ablation principle, the energy acquired by the laser marking surface depends on the overlapping times n of laser spots and the energy e of a single laser pulse. The laser spot overlapping times n refers to the maximum accumulated ablation times of the inner area in the process that the laser pulse light spot moves along the linear direction in the laser marking process, and when the laser scanning speed v is low and the Q frequency f is high, the laser spot overlapping times n is high, otherwise, the laser spot overlapping times n are opposite, and can be expressed asLaser single pulse energy e refers to the energy contained in each laser pulse, and is positively correlated with laser power P, negatively correlated with Q frequency f, and can be expressed asIt can be known from the above reasoning that, when laser marking is performed, when the laser spot overlapping number n is the same as the laser single pulse energy e, the laser marking inputs the same to the material energy, and the marking images obtained by marking have the same effect, that is, the gray values of the marking images are the same. To verify that the above reasoning is accurateThe inventors performed a scale verification test on the surface of an aluminum block of a material chemical composition of al99.7 shown in table 1 using a pulse fiber laser whose focused laser beam diameter is d0And other marking process parameters are shown in table 2.
TABLE 1 Al99.7 materials chemical composition (mass fraction)
| Al | Fe | Si | Cu | Ca | Mg | Zn |
| 99.75％ | 0.17％ | 0.05％ | 0％ | 0.02％ | 0％ | 0.01％ |
TABLE 2 laser marking process parameters
Where s is the space between the fill lines, which represents the space between the scan lines when the laser ablates the surface pattern. The laser marking image obtained by marking on the surface of the aluminum block with the grade of al99.7 by using the laser marking process parameters shown in table 2 is shown in fig. 1. As can be seen from the corresponding table 2, the laser marking process parameters for the four groups of marking verification tests in each row in fig. 1 have the same laser spot overlapping times n, laser single pulse energy e and filling line spacing s; the laser marking process parameters for the marking verification tests in the 2 nd row and the 3 rd row have the same laser spot overlapping times n, the same laser single pulse energy e and different filling line intervals s; the laser marking process parameters for the marking verification tests in the 4 th row and the 5 th row have the same laser spot overlapping times n, the same laser single pulse energy e and different filling line intervals s; the laser marking process parameters for the marking verification tests in the 2 nd row and the 4 th row have the same filling line spacing s, different laser spot overlapping times n and laser single pulse energy e; the laser marking process parameters for the marking verification tests in the 3 rd row and the 5 th row have the same filling line spacing s, different laser spot overlapping times n and different laser single pulse energy e. Therefore, in addition to the laser spot overlapping times n and the laser single pulse energy e, the value of the filling line spacing s also influences the image gray value of the laser marking image obtained by marking.
In order to respectively study the influence of the laser spot overlapping times n, the laser single-pulse energy e and the filling line spacing s on the image gray value of a laser marking image obtained by laser marking, the inventor adopts a pulse optical fiber laser to perform a single-factor change laser marking test on the surfaces of a plurality of aluminum blocks with the specifications of 150mm multiplied by 10mm and the brands of all Al99.7, and the focused laser beam of the pulse optical fiber laser for the test has the diameter of 0.05mm, the focal length of 20cm, the maximum output power of 20W, the wavelength of 1064nm and the pulse width of 100 nm.
Firstly, three groups of single-factor change laser marking tests are carried out by taking the overlapping times n of laser spots as change factors, the values of laser single pulse energy e in the three groups of single-factor change laser marking tests are 0.5mJ, 0.7mJ and 0.9mJ in sequence, the values of filling line spacing s are 0.1mm, the test marking specification is a square figure of 8mm multiplied by 8mm, a scanner capable of avoiding the influence of illumination on gray scale values is selected to obtain the image gray scale values of marked images, and the change curves of the three image gray scale values obtained by the three groups of single-factor change laser marking tests are shown in figure 2. As can be seen from fig. 2, at the initial positions of the three image gray value change curves, the image gray values are distributed unequally, but as the value of the laser spot overlapping times n increases, the three image gray value change curves all have an overall descending trend, and the descending speed is faster as the laser single pulse energy e is larger, and when the laser spot overlapping times n is between 5 times and 15 times, the marking image obtains the lowest image gray value; after the laser spot overlapping times n reach a certain number, the image gray value of the marked image gradually rises and tends to be consistent; after the overlapping times n of the laser spots exceed 15 times, the gray value of the image of the marked image changes slowly and tends to be consistent.
Secondly, four groups of single-factor change tests are carried out by taking the laser single-pulse energy e as a change factor, the values of the laser spot overlapping times n in the four groups of single-factor change laser marking tests are sequentially 2, 6, 10 and 15, the values of the filling line spacing s are all 0.1mm, the test marking specification is a square figure with 8mm multiplied by 8mm, a scanner capable of avoiding the influence of illumination on the gray scale value is selected to obtain the image gray scale value of the marked image, and the change curves of the four image gray scale values obtained in the four groups of single-factor change laser marking tests are shown in figure 3. As can be seen from fig. 3, the image gray values of the marking images obtained by the four sets of single-factor-change laser marking tests all have a descending trend along with the increase of the laser single-pulse energy e, and the image gray values have different descending speeds under different laser spot overlapping times n, when the laser spot overlapping times are 2, the image gray value descending speed is slower, and when the laser spot overlapping times n are increased, the image gray value descending speed is faster, for example, when the laser spot overlapping times are 10 and 6; when the laser spot overlapping number n reaches a certain number, the image gray value tends to a certain value, for example, when the laser spot overlapping number is 15. In addition, when the laser single pulse energy e is less than 0.3, the laser marking trace is not obvious and the image is not clear because the energy is too small.
And finally, carrying out four groups of single-factor change tests by taking the space s between the filling lines as a change factor, wherein the values of the overlapping times n of the laser spots in the four groups of single-factor change laser marking tests are 4, 20, 3 and 10 in sequence, the value of the laser single-pulse energy e is 0.3mJ, 0.6mJ and 0.6mJ in sequence, the standard of the test marking is a square figure with the size of 8mm multiplied by 8mm, a scanner capable of avoiding the influence of illumination on the gray value is selected to obtain the image gray value of the marked image, and the change curves of the four image gray values obtained by the four groups of single-factor change laser marking tests are shown in figure 4. As can be seen from fig. 4, the image gray values of the marking images obtained by the four groups of single-factor change laser marking tests gradually increase with the increase of the space s between the filling lines, and when the space s between the filling lines changes from 0.01mm to 0.03mm, the image gray values increase obviously; when the distance s between the filling lines is larger than 0.03mm, the amplitude of the gray value of the image is small and tends to be stable; when the distance s between the filling lines exceeds 0.1mm, the heat generated by the interaction between the laser and the surface of the aluminum block cannot influence the surface of the aluminum block between the filling lines, the gray value of the surface of the aluminum block hardly changes, and a clear marking image cannot be obtained. In addition, the smaller the filling line spacing s is, the lower the laser marking efficiency is, so that the value of the filling line spacing s is generally greater than 0.5mm when laser marking is performed.
Through the three single-factor change test analyses, the value ranges of the laser spot overlapping times n, the laser single-pulse energy e and the filling line spacing s used when the pulse fiber laser is used for laser marking on the surface of an aluminum block with the mark of Al99.7 can be obtained. However, when the number of laser spot overlaps n, the laser single pulse energy e, and the space between filling lines s are respectively selected to be different values, the image gray values of the marked images obtained by the pulse fiber laser through laser marking on the surface of the aluminum block are different, that is, the color depths of the marked images are different, and when the image gray values of the marked images are closer to the gray values of the marked background areas, the automatic identification rate of the marked images, especially the bar codes and/or the two-dimensional codes in the marked images is easily reduced.
In order to improve the automatic identification rate of a marked image obtained by laser marking on the surface of an aluminum ingot by using a pulse optical fiber laser, the inventor establishes a calculation model equation of an image gray value of the marked image according to the test result, so that the values of laser marking process parameters, namely the Q frequency f of the laser machine, the laser scanning speed v and the laser power p, required by the pulse optical fiber laser for laser marking on the surface of the aluminum ingot can be obtained according to the calculation model of the image gray value of the marked image.
Firstly, according to the corresponding relationship between the laser spot overlapping times n and the image gray scale value of the marked image shown in fig. 2, the image gray scale value G of the marked image, which is only affected by the laser spot overlapping times n, is subjected to1The computational model equation of (a) is fit to:
G1＝a0+a1n+a2n2+a3n3(1)
namely, it is
Wherein, aiFor the fitting coefficient, i is 0,1, 2, 3.
Secondly, according to the corresponding relationship between the laser single pulse energy e and the image gray scale value of the marking image shown in FIG. 3, the image gray scale value G 'of the marking image only affected by the laser single pulse energy e'2The computational model equation of (a) is fit to:
wherein, a41And a5Are fitting coefficients.
Then, according to the correspondence between the filler line pitch s and the image gradation value of the marking image shown in fig. 4, the image gradation value G 'to the marking image to be affected only by the filler line pitch s'3The computational model equation of (a) is fit to:
wherein, a42And a8Are fitting coefficients.
In pair G1、G'2And G'3When the calculation model equation is fitted, an immune clone algorithm can be preferably adopted for fitting to obtain a value of a fitting coefficient. To improve goodness of fit of the immune cloning algorithm, G'2And G'3Deformation to G2And G3Wherein
because when a6＝a9＝0，a7＝a101'2＝G2，G'3＝G3Therefore, G 'is obtained by using the immune clone algorithm'2、G'3、G2And G3At the time of the optimal solution of (2), G'2The optimum value is included in G2Of G'3The optimum value of (A) is included in G3Therefore, a calculation model equation of the image gray value G of the marked image can be obtained:
G＝G1×G2×G3(7)
wherein, a4＝a41+a42
That is to say that the first and second electrodes,
the diameter d of the focused laser beam of the pulse fiber laser is an unadjustable technological parameter, and therefore, the temperature of the molten steel is controlled,
wherein,
bi＝aidi，i＝0、1、2、3，
b4＝a4、b5＝a5、b6＝a6、b7＝a7、b8＝a8、b9＝a9、b10＝a10。
the adjustable laser marking process parameters and levels of the pulse fiber laser are set as shown in table 3 according to the value ranges of the laser spot overlapping times n, the laser single pulse energy e and the filling line spacing s obtained by the single-factor test and by combining the physical meanings of the laser spot overlapping times n and the laser single pulse energy e.
TABLE 3 Adjustable laser marking Process parameters and levels
The method comprises the steps of designing an equal-level multi-factor orthogonal test according to table 3, obtaining 25 groups of laser marking process parameters in total, obtaining 25 marking images obtained by marking through the 25 groups of laser marking process parameters as shown in fig. 5, obtaining image gray scale measurement values of the marking images through a scanner capable of avoiding the influence of illumination on gray scale values, and obtaining the laser marking process parameters and the image gray scale measurement values of the corresponding marking images as shown in table 4.
TABLE 4 Experimental data for equi-level multifactor orthogonal experiments
| Group number | s(mm) | v(mm/s) | P(w) | f(kHz) | e(mJ) | n | Image gray scale measurement |
| 1 | 0.06 | 100 | 10 | 20 | 0.5 | 10 | 128 |
| 2 | 0.07 | 200 | 12 | 22 | 0.55 | 5.5 | 154 |
| 3 | 0.08 | 300 | 14 | 24 | 0.58 | 4 | 169 |
| 4 | 0.09 | 400 | 16 | 26 | 0.62 | 3.25 | 172 |
| 5 | 0.10 | 500 | 18 | 28 | 0.64 | 2.8 | 176 |
| 6 | 0.07 | 300 | 16 | 28 | 0.571 | 4.67 | 162 |
| 7 | 0.08 | 400 | 18 | 20 | 0.9 | 2.5 | 170 |
| 8 | 0.09 | 500 | 10 | 22 | 0.45 | 2.2 | 198 |
| 9 | 0.10 | 100 | 12 | 24 | 0.5 | 12 | 145 |
| 10 | 0.06 | 200 | 14 | 26 | 0.54 | 6.5 | 143 |
| 11 | 0.08 | 500 | 12 | 26 | 0.46 | 2.6 | 196 |
| 12 | 0.09 | 100 | 14 | 28 | 0.5 | 14 | 142 |
| 13 | 0.10 | 200 | 16 | 20 | 0.8 | 5 | 148 |
| 14 | 0.06 | 300 | 18 | 22 | 0.82 | 3.67 | 150 |
| 15 | 0.07 | 400 | 10 | 24 | 0.42 | 3 | 201 |
| 16 | 0.09 | 200 | 18 | 24 | 0.75 | 6 | 139 |
| 17 | 0.10 | 300 | 10 | 26 | 0.38 | 4.33 | 186 |
| 18 | 0.06 | 400 | 12 | 28 | 0.43 | 3.5 | 186 |
| 19 | 0.07 | 500 | 14 | 20 | 0.7 | 2 | 184 |
| 20 | 0.08 | 100 | 16 | 22 | 0.73 | 11 | 134 |
| 21 | 0.10 | 400 | 14 | 22 | 0.67 | 2.75 | 174 |
| 22 | 0.06 | 500 | 16 | 24 | 0.67 | 2.4 | 169 |
| 23 | 0.07 | 100 | 18 | 26 | 0.69 | 13 | 137 |
| 24 | 0.08 | 200 | 10 | 28 | 0.36 | 7 | 170 |
| 25 | 0.09 | 300 | 12 | 20 | 0.6 | 3.33 | 168 |
According to the test data of the above-mentioned equal level multifactor orthogonal test, there is no obvious regularity between the laser marking process parameter and the image gray value of the marked image, the laser marking process parameter corresponding to the marked image in the neighborhood of the image gray value of the marked image is not simply distributed in the neighborhood of the laser marking process parameter corresponding to the image gray value of the marked image, but distributed in the neighborhood and set of the laser marking process parameters corresponding to the image gray values of a plurality of marked images, such as the 1 st group, the 12 th group, the 16 th group, the 20 th group and the 23 rd group of tests, the laser marking process parameter for the test is the combination of the laser marking process parameters with different values selected in the range of power P of 10-18W, Q frequency f of 20-28kHz, filling line spacing s of 0.06-0.09 mm, scanning speed v of 100-200mm/s, and calculating to obtain laser single pulse energy e and laser spot overlapping times n with different values, and marking a marked image with similar image gray value on the surface of the aluminum block after combining the laser marking process parameters with different values. However, the marking images with similar gray values of the images can not be marked after the laser marking process parameter combinations selected randomly within the value ranges, and the marking images with larger difference of the gray values of the images can be marked by different laser marking process parameter combinations. According to the analysis, the equal-level multi-factor orthogonal test shows the corresponding relation between different laser marking process parameter combinations and the image gray values of marked images obtained by marking: a plurality of groups of different laser marking process parameters are combined to mark marking images with similar image gray values; and marking images with larger difference of image gray values can be marked after different laser marking process parameters are combined. Therefore, the selected equivalent level multi-factor orthogonal test data has strong representativeness and can be used as reasonable data to fit the fitting coefficient in the calculation model equation of the image gray value G of the marked image.
Fitting according to the equi-level multi-factor orthogonal test data in Table 4 to obtain fitting coefficient b in regression calculated model equation of image gray value G of marked imagei(i＝0、1、2、3)、b4、b5、b6、b7、b8、b9And b10And in the fitting process, the fitting target is to minimize the value of the error sum between the image gray scale measured value and the image gray scale predicted value of the marked image.
In order to improve the fitting speed and the fitting accuracy, the immune clone algorithm is preferably adopted as the fitting algorithm for fitting. In the fitting process, the affinity optimization objective function is adopted as follows:
wherein,
n is the number of sets of test data collected during the horizontal multifactor orthogonal test, and in table 4, N is 25,
Gmjis affinity at one timeCalculating the gray scale estimated value of the marking image obtained by the jth group of experiments,
Gcjthe measured value of the image gray scale of the marked image obtained by the jth group of experiments in the first affinity calculation is obtained.
Fitting coefficient b in the calculation model equation of the image gray value G of the marked image by using the test data of the equal level multi-factor orthogonal test in the table 40、b1、b2、b3、b4、b5、b6、b7、b8、b9And b10Then, the calculation model equation of the image gray value G of the obtained marking image is as follows:
to avoid the fitting coefficients from fitting being unreliable, a complex coefficient R is used below2And the goodness of fit of a calculation model equation of the image gray value G of the marked image to the measured data set and the reliability of the calculation model equation are measured by the variance analysis F value, and the result is as follows:
wherein,
rss is the sum of squares of residuals between the image gray scale measurement value and the image gray scale estimation value of the marked image,
tss is the sum of squares of the total deviation between the image gray scale measured value and the image gray scale estimated value of the marked image;
wherein,
ess is the sum of the squares of the regression,
h is the degree of freedom of the regression sum of squares,
w-h-1 is the degree of freedom of the residual sum of squares.
From the above results, it is clear that R is present2The goodness of fit is better when the value is 0.97 and is close to 1; as can be seen from the F table, F is 51.45, F0.05(10,15) is 2.54, and the reliability of the calculation model equation for the image gray-scale value G of the marked image is high.
In order to deepen the color depth of the marked image and improve the automatic identification rate of the marked image, the inventor sets the image gray value G of the marked image marked by laser to be less than 150. That is, the objective function when the laser marking process parameters are estimated using the calculation model equation of the image gray value G of the marking image is G < 150. Of course, in the actual marking process, the user can select the specific value of the target function G according to the automatic identification requirement.
In order to verify the effectiveness of the estimation method of the laser marking process parameters, 25 groups of laser marking process parameters shown in table 5 are utilized to perform a laser marking verification test on the surface of an aluminum block with the designation of Al99.7, the marked image is shown in fig. 6, and the image gray scale measurement values of the marked image obtained by marking with different laser marking process parameters are shown in table 5.
Table 5 test data of the laser marking verification test
Meanwhile, an image gray scale estimation value of a marked image obtained by performing laser marking on the surface of an aluminum block with the designation of al99.7 by using 25 groups of laser marking process parameters shown in table 5 can be sequentially calculated by using a formula (11), and a comparison result of the image gray scale estimation value and an image gray scale measurement value of the marked image obtained by a laser marking verification test is shown in fig. 7. As can be seen from fig. 7, the image gray scale estimation value of the marking image estimated by the estimation method of the laser marking process parameter of the present invention has a high coincidence degree with the image gray scale measurement value of the marking image obtained by the laser marking verification test.
The laser marking method for marking aluminum ingot marks on an aluminum ingot to be marked by the laser marking process parameter estimation method is explained in detail below, and the laser marking method comprises the following steps:
s1, ablating a light color marking background area on the surface of the aluminum ingot to be marked by using the pulse fiber laser, wherein the gray value variance sigma of the light color marking background area2Less than or equal to 50, and the image gray mean value M is more than or equal to 150. Preferably, when a light-colored marking background area is ablated on the surface of an aluminum ingot to be marked, the value range of the filling line spacing s in the laser ablation process parameters is 0.01-0.2mm, the value range of the laser scanning speed v is 500-1000mm/s, the value range of the laser power p is 10-20W, and the value range of the Q frequency f of the laser machine is 50-100 kHz.
S2, estimating laser marking process parameters required for marking aluminum ingot marks in the light color marking background area by using the estimation method of the laser marking process parameters;
and S3, marking the aluminum ingot mark in the light color marking background area by using the pulse fiber laser, wherein the laser marking process parameters for marking are the laser marking process parameters estimated in the step S2. Preferably, when marking aluminum ingot marks in the light color marking background area, the range of the filling line spacing s in the laser marking process parameters is 0.01-0.1mm, the range of the laser scanning speed v is 100-200mm/s, the range of the laser power p is 12-20W, and the range of the Q frequency f of the laser machine is 20-28 kHz.
Claims (6)
1. A method for estimating laser marking process parameters is characterized by comprising the following steps:
step 1: performing an equal-level multi-factor orthogonal test on an aluminum ingot to be marked, adjusting laser marking process parameters to obtain image gray values of different marked images in the test process, and recording a plurality of groups of test data, wherein the test data comprises values of laser marking process parameter focused laser beam diameter d, laser machine Q frequency f, laser scanning speed v, laser power p and filling line spacing s and image gray values of corresponding marked images;
step 2: establishing a calculation model equation of the image gray value of the marked image:
wherein,
g denotes the image gray value of the marking image,
bi(i＝0、1、2、3)、b4、b5、......、b9、b10is a fitting coefficient;
and step 3: fitting according to the test data of the equi-level multi-factor orthogonal test recorded in the step 1 to obtain a value of a fitting coefficient in a calculation model equation of the image gray value;
and 4, step 4: and estimating to obtain the values of the laser marking process parameter focused laser beam diameter d, the laser machine Q frequency f, the laser scanning speed v and the laser power p according to the target function G < 150.
2. The method of claim 1, wherein in step 3, the values of the fitting coefficients in the computational model equation of the image gray-scale values are fitted using an immune-clonal algorithm.
3. The method of claim 2, wherein the fitting is performed using an affinity optimization objective function of
Wherein,
n is the number of groups of test data acquired during the equi-level multi-factor orthogonal test in the step 1, GmjThe gray scale estimation value of the marked image obtained by the jth group of experiments in the affinity calculation is obtained,
Gcjimage gray scale measurement of marking image obtained from jth group of experiments in one time of affinity calculationMagnitude.
4. A laser marking method is characterized by comprising the following steps:
s1, ablating a light color marking background area on the surface of the aluminum ingot to be marked, wherein the gray value variance sigma of the light color marking background area2Less than or equal to 50, and the image gray mean value M is more than or equal to 150;
s2, estimating laser marking process parameters for marking aluminum ingot marks in the light color marking background area by using the method for estimating the laser marking process parameters as claimed in any one of claims 1 to 3;
and S3, marking the aluminum ingot mark in the light-color marking background area by using the laser marking process parameters estimated in the step S2.
5. The laser marking method as claimed in claim 4, wherein in step S1, the filling line spacing S in the laser ablation process parameters ranges from 0.01 to 0.2mm, the laser scanning speed v ranges from 500 mm/S to 1000mm/S, the laser power p ranges from 10W to 20W, and the Q frequency f of the laser machine ranges from 50 kHz to 100 kHz.
6. The laser marking method as claimed in claim 4, wherein in the step S3, when the aluminum ingot mark is marked in the light-colored marking background area, the filling line spacing S in the laser marking process parameters ranges from 0.01 to 0.1mm, the laser scanning speed v ranges from 100 to 200mm/S, the laser power p ranges from 12 to 20W, and the Q frequency f of the laser machine ranges from 20 to 28 kHz.
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| CN108406124B (en) * | 2018-03-28 | 2020-10-02 | 歌尔股份有限公司 | Laser marking method and system |
| CN108672941A (en) * | 2018-05-03 | 2018-10-19 | 合肥江航飞机装备有限公司 | The method of laser scoring Quick Response Code |
| CN114054971B (en) * | 2022-01-10 | 2022-07-12 | 武汉华工激光工程有限责任公司 | Automatic real-time GV value detection and compensation method and system |
| CN116432841A (en) * | 2023-04-03 | 2023-07-14 | 东莞理工学院 | Optimal coloring parameter determination method and system for metal laser coloring |
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