CN105300326A - Method and device for quantitative determination of paint surface flatness - Google Patents
Method and device for quantitative determination of paint surface flatness Download PDFInfo
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Abstract
The invention discloses a method and a device for quantitative determination of paint surface flatness. The method comprises the following steps: S1, collecting low-coherence light interference spectra of positions on a paint surface at equal intervals one point after another, and obtaining a low-coherence light interference spectrum matrix of the positions of the paint surface; S2, calculating the phase difference [Delta]phi between the low-coherence light interference spectra of any two adjacent positions on the paint surface; S3, calculating a depth distance difference [Delta]z of the two adjacent positions according to the phase difference [Delta]phi; and S4, calculating the integral of the depth distance difference [Delta]z and obtaining the flatness quantitative distribution of the paint surface. The invention provides a brand new technology for automobile paint surface flatness determination, achieves quantitative determination of paint surface flatness, thus helps people understand the flatness situation of the paint surface more directly and concretely and find the cause of ups and downs of the paint surface, and provides clearer guidance for modification and coating techniques.
Description
Technical Field
The invention relates to a method and a device for quantitatively detecting paint surface flatness, and belongs to the technical field of material surface flatness detection.
Background
In the automotive industry, the color, gloss and surface texture of body paints affect the visual appearance of people, and even when the gloss of a coating film is high, the appearance is affected by the degree of surface waviness, and this effect is called "orange peel", which can also be defined as "wavy texture of high gloss surface". Orange peel in painted bodies can give the coating surface a visual appearance such as mottling.
At present, for detecting the flatness of the surface of paint, an orange peel instrument is generally adopted in the coating industry to measure the orange peel condition of the surface of a material. However, the method can only carry out qualitative detection on the flatness of the paint surface, detect whether the paint surface has fluctuation or not, and cannot carry out quantitative detection on the flatness of the paint surface.
Dawn swallow et al disclose a utility model patent entitled "optical prism lamp" (refer to chinese patent publication No. CN2903685Y), which utilizes a transparent prism to detect paint surface defects of a product, and utilizes the action of the prism and parallel stripes under the irradiation of an optical prism lamp, so that the light on the paint surface shows irregular changes, and surface defects such as unevenness, color difference, scratches, pores, orange peel and the like are detected. On the basis of the technology, Cao Zehua et al disclose a paint surface quality detection device (refer to Chinese patent publication No. CN201876427U), which comprises a detection plate and a plurality of detection windows with different areas, thereby solving the problems of long paint surface detection time and the like and saving the labor intensity of detection personnel. However, the above techniques can only perform qualitative detection on the flatness of the paint surface, and cannot perform quantitative detection.
Disclosure of Invention
The invention aims to provide a method and a device for quantitatively detecting the flatness of a paint surface, which can effectively solve the problems existing in the prior art, particularly the problems that the prior art can only qualitatively detect the flatness of the paint surface and cannot realize quantitative detection.
In order to solve the technical problems, the invention adopts the following technical scheme: a quantitative detection method for paint surface flatness comprises the following steps:
s1, collecting the low coherent light interference spectrums of all the positions on the surface of the paint point by point at equal intervals to obtain low coherent light interference spectrum matrixes of all the positions on the surface of the paint;
s2, calculating the phase difference between the interference spectrums of the low coherent light at the adjacent positions of the paint surface
S3, according to the phase differenceCalculating the depth distance difference delta z of each adjacent position;
and S4, integrating the depth distance difference delta z to obtain the flatness quantitative distribution of the paint surface.
Preferably, the step of calculating the phase difference between the interference spectra of the low coherent light at the adjacent positions on the paint surface in the step S2 specifically includes the following steps: carrying out Fourier transform on the low coherent light interference spectra at adjacent positions to obtain corresponding complex exponential functions; dividing the complex exponential function to obtain the complex exponential function; wherein,
the low coherence light interference spectrum at position 1 is:
the low coherence light interference spectrum at position 2 is:in the formula I1(k)、I2(k) Light intensity signals at position 1 and position 2, respectively, S (k) is light source spectrum, ERAmplitude of reference light for entering the line camera, ESFor the detection light amplitude entering the line camera, k is the wave number,λ is the central wavelength, n is the refractive index of air, z1For the optical path difference that is the depth information,in order to be the initial phase position,is the phase difference resulting from the difference in depth distance from position 1 to position 2 (i.e., the projection of the displacement distance between position 1 to position 2 on the vertical axis).
Preferably, in step S3, the phase difference is determined according toThe depth distance difference Δ z of each adjacent position is calculated by:
therefore, the flatness data of the paint surface of the automobile can be obtained more intuitively and accurately.
In the foregoing method for quantitatively detecting the flatness of the paint surface, assuming that the depth at the position 1 on the paint surface is zero, the depth distance at the position m is:
wherein, Δ ziThe depth distance difference between the (i + 1) th position and the (i) th position is obtained, so that the flatness condition of the paint surface of the automobile can be quantitatively detected.
The device for quantitatively detecting the flatness of the surface of the paint, which realizes the method, comprises the following steps: the system comprises a broadband low-coherence light source, a circulator, a coupler, a lens A, a grating, a lens B, a linear array camera, a computer, a reference system and a detection system, wherein light emitted by the broadband low-coherence light source is divided into two paths after passing through the circulator and the coupler, one path of light is used as detection light and enters the detection system to be focused on the surface of paint, and the other path of light is used as reference light and enters the reference system; the backward scattering light reflected by the paint surface and the reference light reflected by the reference system enter the lens A to be collimated and then irradiate the grating after passing through the coupler and the circulator, the interference spectrum is imaged on the linear array camera through the lens B, and the linear array camera records the interference spectrum and transmits the interference spectrum to the computer for processing.
Preferably, the reference system comprises: the reference light is collimated by the C lens and then focused on the reference mirror by the D lens.
Preferably, the detection system comprises: the detection system realizes two-dimensional scanning of the surface of the painted orange peel through vibration of the X vibration mirror and the Y vibration mirror.
Compared with the prior art, the invention provides a brand-new detection technology for detecting the flatness of the paint surface of the automobile, and realizes quantitative detection of the flatness of the paint surface, so that people can be helped to know the flatness condition of the paint surface more intuitively and specifically, people can be helped to find the reason of the fluctuation problem of the paint surface better, and more definite guidance is provided for modifying the coating process. In addition, the detection system provided by the invention is provided with the X-vibration mirror and the Y-vibration mirror, so that the detection system can be driven by light, different positions of the paint surface of the automobile can be scanned, and the flatness data of the paint surface can be obtained.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic view of the structural connection of the device of the present invention.
Reference numerals: the system comprises a 1-broadband low-coherence light source, a 2-circulator, a 3-coupler, a 5-A lens, a 6-grating, a 7-B lens, an 8-line camera, a 9-computer, a 10-reference system, an 11-detection system, a 12-C lens, a 13-D lens, a 14-reference mirror, a 15-E lens, a 16-X galvanometer, a 17-Y galvanometer and an 18-F lens.
The invention is further described with reference to the following figures and detailed description.
Detailed Description
The embodiment of the invention comprises the following steps: a method for quantitatively detecting the flatness of a paint surface is shown in figure 1 and comprises the following steps:
s1, collecting the low coherent light interference spectrums of all the positions on the surface of the paint point by point at equal intervals to obtain low coherent light interference spectrum matrixes of all the positions on the surface of the paint;
s2, calculating the phase difference between the interference spectrums of the low coherent light at the adjacent positions of the paint surfaceThe method specifically comprises the following steps: carrying out Fourier transform on the low coherent light interference spectra at adjacent positions to obtain corresponding complex exponential functions; dividing the complex exponential function to obtain the complex exponential function; wherein,
the low coherence light interference spectrum at position 1 is:
the low coherence light interference spectrum at position 2 is:in the formula I1(k)、I2(k) Light intensity signals at position 1 and position 2, respectively, S (k) is light source spectrum, ERAmplitude of reference light for entering the line camera, ESFor the detection light amplitude entering the line camera, k is the wave number,λ is the central wavelength, n is the refractive index of air, z1For the optical path difference that is the depth information,in order to be the initial phase position,is the phase difference resulting from the difference in depth distance from position 1 to position 2;
s3, according to the phase differenceCalculating a depth distance difference Δ z of each neighboring position:
s4, integrating the depth distance difference delta z to obtain the flatness quantitative distribution of the paint surface;
assuming that the depth at position 1 of the paint surface is zero, the depth distance at position m is:
wherein, Δ ziIs the difference in depth distance between the i +1 th position and the i th position.
The device for quantitatively detecting the flatness of the surface of the paint, as shown in fig. 2, for implementing the method comprises: the system comprises a broadband low-coherence light source 1, a circulator 2, a coupler 3, a lens A5, a grating 6, a lens B7, a linear array camera 8, a computer 9, a reference system 10 and a detection system 11, wherein light emitted by the broadband low-coherence light source 1 is divided into two paths after passing through the coupler 3 via the circulator 2, one path of light enters the detection system 11 as detection light and is focused on the surface of paint, and the other path of light enters the reference system 10 as reference light; the backward scattering light reflected by the paint surface and the reference light reflected by the reference system enter the A lens 5 after passing through the coupler 3 and the circulator 2 and are collimated and then irradiate the grating 6, the interference spectrum is imaged on the linear array camera 8 through the B lens 7, and the linear array camera 8 records the interference spectrum and transmits the interference spectrum to the computer 9 for processing. The reference system 10 comprises: the reference light is collimated by the C lens 12 and then focused on the reference mirror 14 through the D lens 13. The detection system 11 comprises: the detection light is collimated by the E lens 15, then reflected by the X vibrating mirror 16 and the Y vibrating mirror 17, and finally focused on the paint surface through the F lens 18.
The reference system 10 and the detection system 11 in the present embodiment can also be implemented by other structures in the prior art.
The working principle of one embodiment of the invention is as follows: light emitted by the broadband low-coherence light source 1 firstly enters an A port of a circulator 2, and then is divided into two paths after passing through a coupler 3 after coming out of a B port of the circulator 2, wherein one path is used as detection light and enters a detection system 11 to be focused on the surface of paint, and the other path is used as reference light and enters a reference system 10; the backward scattering light reflected by the paint surface and the reference light reflected by the reference system 10 enter the coupler 3, then enter the port B of the circulator 2, exit from the port C of the circulator 2, are collimated by the lens A5 and then irradiate the grating 6, the spectrum of the grating is imaged on the line camera 8 through the lens B7, and the line camera 8 records the interference spectrum and transmits the interference spectrum to the computer 9 for processing.
The reference system 10 is for providing a reference optical signal, and includes: the reference light is converted into collimated parallel light in space through the C lens 12, and then is focused on the reference mirror 14 through the D lens 13, and the light reflected by the reference mirror 14 returns to the coupler 3 along the light path.
The detection system 11 comprises: the detection light is firstly changed into collimated light through the E lens 15, then deflected through the X vibrating mirror 16 and the Y vibrating mirror 17, and finally focused on the surface of the painted orange peel through the F lens 18, and the light scattered back through the surface of the painted orange peel returns to the coupler 3 along the light path. The detection system realizes two-dimensional scanning of the surface of the painted orange peel through the vibration of the X-ray galvanometer 16 and the Y-ray galvanometer 17.
During the measurement, the spectral data are acquired line by means of the line camera 8, assuming that a first scan is performed at position 1 and a second scan is performed at position 2 of the paint surface, then
The low coherence light interference spectrum at position 1 is:
the low coherence light interference spectrum at position 2 is:to I1(k) And I2(k) After Fourier transform, the corresponding complex exponential function is obtainedAndF1and F2Is obtained after the divisionThat is, the phase difference between the interference spectra at the position 1 and the position 2 can be obtainedAccording to said phase differenceThat is, the difference Δ z between the depth distances at position 1 and position 2:
in the formula I1(k)、I2(k) Light intensity signals at position 1 and position 2, respectively, S (k) is light source spectrum, ERAmplitude of reference light for entering the line camera, ESFor the detection light amplitude entering the line camera, k is the wave number,λ is the central wavelength, n is the refractive index of air, z1For the optical path difference that is the depth information,in order to be the initial phase position,is the phase difference resulting from the difference in depth distance from position 1 to position 2.
Integrating the depth distance difference delta z to obtain the flatness quantitative distribution of the paint surface:
wherein, the depth distance difference Δ z between two adjacent points can be represented as:
Δz21＝z2-z1
Δz32＝z3-z2
……
Δzmm-1＝zm-zm-1
suppose z1I.e., the depth at position 1 is zero, 0, then the depth distance at position m may be expressed as,
z1＝0
z2＝z1+Δz21＝Δz21
z3＝z2+Δz32＝Δz21+Δz32
z4＝z3+Δz3＝Δz21+Δz32+Δz43
……
zm＝zm-1+Δzm-1＝Δz21+Δz32+…+Δzmm-1
where Δ z can be obtained by the calculation method in step S3, it can be obtained that the depth distance based on the zero point at position m is:
in the formula,. DELTA.ziIs the difference between the depth distances at the i +1 th position and the i th position
The flatness of the paint surface can be observed according to the obtained curve, and quantitative detection of the flatness of the paint surface is realized.
Claims (7)
1. A quantitative detection method for paint surface flatness is characterized by comprising the following steps:
s1, collecting the low coherent light interference spectrums of all the positions on the surface of the paint point by point at equal intervals to obtain low coherent light interference spectrum matrixes of all the positions on the surface of the paint;
s2, calculating the phase difference between the interference spectrums of the low coherent light at the adjacent positions of the paint surface
S3, according to the phase differenceCalculating the depth distance difference delta z of each adjacent position;
and S4, integrating the depth distance difference delta z to obtain the flatness quantitative distribution of the paint surface.
2. The method for quantitatively detecting the flatness of the paint surface according to claim 1, wherein the step of calculating the phase difference between the interference spectra of the low coherent light at the adjacent positions of the paint surface in the step S2 specifically comprises the steps of: carrying out Fourier transform on the low coherent light interference spectra at adjacent positions to obtain corresponding complex exponential functions; dividing the complex exponential function to obtain the complex exponential function; wherein,
the low coherence light interference spectrum at position 1 is:
the low coherence light interference spectrum at position 2 is:in the formula I1(k)、I2(k) Light intensity signals at position 1 and position 2, respectively, S (k) is light source spectrum, ERAmplitude of reference light for entering the line camera, ESFor the detection light amplitude entering the line camera, k is the wave number,λ is the central wavelength, n is the refractive index of air, z1For the optical path difference that is the depth information,in order to be the initial phase position,is the phase difference resulting from the difference in depth distance from position 1 to position 2.
3. The method for quantitatively detecting the flatness of a painted surface according to claim 2, wherein in step S3, said phase difference is used as a basisThe depth distance difference Δ z of each adjacent position is calculated by:
4. the method for quantitatively detecting the flatness of the paint surface according to claim 3, wherein assuming that the depth at the position 1 of the paint surface is zero, the depth distance at the position m is:
wherein, Δ ziIs the difference in depth distance between the i +1 th position and the i th position.
5. The device for quantitatively detecting the flatness of the surface of paint for realizing the method of claims 1 to 4 is characterized by comprising the following steps: the system comprises a broadband low-coherence light source (1), a circulator (2), a coupler (3), a lens A (5), a grating (6), a lens B (7), a linear array camera (8), a computer (9), a reference system (10) and a detection system (11), wherein light emitted by the broadband low-coherence light source (1) is divided into two paths after passing through the coupler (3) via the circulator (2), one path of light enters the detection system (11) as detection light and is focused on the surface of paint, and the other path of light enters the reference system (10) as reference light; the backscattered light reflected by the paint surface and the reference light reflected by the reference system pass through the coupler (3) and the circulator (2), enter the lens A (5) for collimation and then irradiate the grating (6), the interference spectrum is imaged on the linear array camera (8) through the lens B (7), and the linear array camera (8) records the interference spectrum and transmits the interference spectrum to the computer (9) for processing.
6. The apparatus for quantitatively detecting the flatness of a painted surface according to claim 5, characterized in that the reference system (10) comprises: the device comprises a C lens (12), a D lens (13) and a reference mirror (14), wherein the reference light is collimated by the C lens (12) and then focused on the reference mirror (14) through the D lens (13).
7. The apparatus for quantitatively detecting the flatness of a painted surface according to claim 5 or 6, characterized in that the detection system (11) comprises: the device comprises an E lens (15), an X galvanometer (16), a Y galvanometer (17) and an F lens (18), wherein the detection light is collimated by the E lens (15), then reflected by the X galvanometer (16) and the Y galvanometer (17), and finally focused on the surface of the paint through the F lens (18).
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106289118A (en) * | 2016-09-06 | 2017-01-04 | 乐视控股（北京）有限公司 | The detection method of surface smoothness, device and terminal |
| CN107121058A (en) * | 2016-02-25 | 2017-09-01 | 株式会社三丰 | Measuring method and process of measurement |
| CN107144235A (en) * | 2017-05-03 | 2017-09-08 | 佛山科学技术学院 | A kind of article surface Shape measure method and device |
| CN107869973A (en) * | 2017-12-11 | 2018-04-03 | 湖南太子化工涂料有限公司 | A kind of aqueous paint surface quality detection method |
| CN110361099A (en) * | 2019-07-17 | 2019-10-22 | 东北大学 | A kind of spectral domain low-coherent light interference optical path difference demodulation method |
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| CN114308590A (en) * | 2021-12-28 | 2022-04-12 | 惠州深赛尔化工有限公司 | Coating method of water-based building material color steel finish paint |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101652625A (en) * | 2007-02-21 | 2010-02-17 | 康宁股份有限公司 | Apparatus for measuring defects in a glass sheet |
| CN101672628A (en) * | 2009-10-12 | 2010-03-17 | 中国兵器工业第二〇五研究所 | Aspheric optical element surface shape detection device |
| CN201876427U (en) * | 2010-11-08 | 2011-06-22 | 浙江吉利汽车研究院有限公司 | Paint surface quality detecting device |
| CN103727901A (en) * | 2014-01-14 | 2014-04-16 | 中国科学院长春光学精密机械与物理研究所 | Wavelength phase-shifting method based inter-planar parallelism detection method |
| CN104296698A (en) * | 2014-10-27 | 2015-01-21 | 广州飞拓优视光电科技有限公司 | Method for measuring evenness of optical surface with ultrahigh precision |
| JP2015148604A (en) * | 2014-01-10 | 2015-08-20 | Ntn株式会社 | Height detection device, coating device, and height detection method |
-
2015
- 2015-11-30 CN CN201510850078.7A patent/CN105300326B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101652625A (en) * | 2007-02-21 | 2010-02-17 | 康宁股份有限公司 | Apparatus for measuring defects in a glass sheet |
| CN101672628A (en) * | 2009-10-12 | 2010-03-17 | 中国兵器工业第二〇五研究所 | Aspheric optical element surface shape detection device |
| CN201876427U (en) * | 2010-11-08 | 2011-06-22 | 浙江吉利汽车研究院有限公司 | Paint surface quality detecting device |
| JP2015148604A (en) * | 2014-01-10 | 2015-08-20 | Ntn株式会社 | Height detection device, coating device, and height detection method |
| CN103727901A (en) * | 2014-01-14 | 2014-04-16 | 中国科学院长春光学精密机械与物理研究所 | Wavelength phase-shifting method based inter-planar parallelism detection method |
| CN104296698A (en) * | 2014-10-27 | 2015-01-21 | 广州飞拓优视光电科技有限公司 | Method for measuring evenness of optical surface with ultrahigh precision |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107121058A (en) * | 2016-02-25 | 2017-09-01 | 株式会社三丰 | Measuring method and process of measurement |
| CN107121058B (en) * | 2016-02-25 | 2020-09-15 | 株式会社三丰 | Measuring method |
| CN106289118A (en) * | 2016-09-06 | 2017-01-04 | 乐视控股（北京）有限公司 | The detection method of surface smoothness, device and terminal |
| CN107144235A (en) * | 2017-05-03 | 2017-09-08 | 佛山科学技术学院 | A kind of article surface Shape measure method and device |
| CN107869973A (en) * | 2017-12-11 | 2018-04-03 | 湖南太子化工涂料有限公司 | A kind of aqueous paint surface quality detection method |
| CN110361099A (en) * | 2019-07-17 | 2019-10-22 | 东北大学 | A kind of spectral domain low-coherent light interference optical path difference demodulation method |
| CN110361099B (en) * | 2019-07-17 | 2020-08-25 | 东北大学 | Spectral domain low-coherence light interference optical path difference demodulation method |
| CN114213895A (en) * | 2021-12-10 | 2022-03-22 | 惠州深赛尔化工有限公司 | Process quality control method of water-based household appliance color steel primer coating |
| CN114308590A (en) * | 2021-12-28 | 2022-04-12 | 惠州深赛尔化工有限公司 | Coating method of water-based building material color steel finish paint |
| CN115164786A (en) * | 2022-08-11 | 2022-10-11 | 深圳市深赛尔股份有限公司 | Paint surface flatness detection method and equipment for environment-friendly water-based coil steel coating |
| CN115164786B (en) * | 2022-08-11 | 2023-03-10 | 深圳市深赛尔股份有限公司 | Paint surface flatness detection method and equipment for environment-friendly water-based coil steel coating |
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