CN105300326A - Method and device for quantitative determination of paint surface flatness - Google Patents

Abstract

The invention discloses a method and a device for quantitatively detecting the flatness of a paint surface. The method includes the following steps: S1, collecting the low-coherent light interference spectrum of each position on the paint surface at equal intervals point by point, and obtaining the low-coherence light of each position on the paint surface Interference spectrum matrix; S2, calculate the phase difference between the low-coherence light interference spectra of each adjacent position on the paint surface S3, according to the phase difference Calculating the depth distance difference Δz of each adjacent position; S4, integrating the depth distance difference Δz to obtain the quantitative distribution of the flatness of the paint surface. The invention provides a brand-new detection technology for detecting the flatness of the paint surface of automobiles, realizes the quantitative detection of the flatness of the paint surface, thereby helping people to understand the flatness of the paint surface more intuitively and concretely, and can also be more accurate. Good to help people find the reason for the undulation of the paint surface, and provide more clear guidance for modifying the painting process.

Description

Quantitative detection method and device for paint surface roughness

technical field

The invention relates to a method and a device for quantitatively detecting the flatness of a paint surface, belonging to the technical field of detection of the flatness of a material surface.

Background technique

In the automotive industry, the color, gloss, and surface structure of body paint affect people's visual effects. Even a high-gloss coating film will have its appearance affected by surface fluctuations. This effect is called "Orange peel", orange peel can also be defined as "the wavy structure of a high-gloss surface". Orange peel on painted bodies can create visual appearances such as mottled surfaces.

At present, for the detection of the flatness of the paint surface, the coating industry generally uses an orange peel meter to measure the orange peel condition of the material surface. However, this method can only qualitatively detect the flatness of the painted surface, detect whether there are undulations on the painted surface, and cannot quantitatively detect the flatness of the painted surface.

Chen Xiaoyan and others disclosed a utility model patent named "Optical Prism Lamp" (refer to Chinese Patent Announcement No. CN2903685Y). Under the influence of prisms and parallel stripes, the light on the paint surface will show irregular changes, so that surface defects such as unevenness, color difference, scratches, pores, and orange peel can be detected. On the basis of this technology, Cao Zenghua and others disclosed a "paint surface quality detection device" (refer to Chinese patent announcement number CN201876427U), which includes a detection board and several detection windows with different areas, thereby solving the problem of paint surface detection. Long time and other issues, saving the labor intensity of testing personnel. But above-mentioned these technologies still all can only carry out qualitative detection to the flatness situation of paint surface, can't realize quantitative detection.

Contents of the invention

The object of the present invention is to provide a method and device for quantitatively detecting the flatness of the paint surface, which can effectively solve the problems in the prior art, especially the prior art can only perform qualitative detection on the flatness of the paint surface, and cannot Achieving quantitative detection problems.

In order to solve the above-mentioned technical problems, the present invention adopts the following technical scheme: a quantitative detection method for paint surface roughness, comprising the following steps:

S1, collect the low-coherent light interference spectrum of each position on the paint surface point by point at equal intervals, and obtain the low-coherence light interference spectrum matrix of each position on the paint surface;

S2, calculate the phase difference between the low-coherence light interference spectra of each adjacent position on the paint surface

S3, according to the phase difference Calculate the depth distance difference Δz of each adjacent position;

S4, integrating the depth distance difference Δz to obtain the quantitative distribution of the flatness of the paint surface.

Preferably, the calculation of the phase difference between the low-coherent light interference spectra of adjacent positions on the paint surface described in step S2 specifically includes the following steps: performing Fourier transform on the low-coherent light interference spectra of adjacent positions to obtain the corresponding complex Exponential function; The complex exponential function is divided to get final product; Wherein,

The interference spectrum of low-coherence light at position 1 is:

The interference spectrum of low-coherence light at position 2 is: In the formula, I ₁ (k) and I ₂ (k) are the light intensity signals at position 1 and position 2 respectively, S(k) is the light source spectrum, E _R is the reference light amplitude entering the line scan camera, E _S is the The detection light amplitude of the line scan camera, k is the wave number, λ is the central wavelength, n is the refractive index of air, z ₁ is the optical path difference or depth information, is the initial phase, is the phase difference generated by the difference in the depth distance from position 1 to position 2 (that is, the projection of the displacement distance between position 1 and position 2 on the vertical axis).

Preferably, in step S3, according to the phase difference The depth distance difference Δz of each adjacent position is calculated in the following way:

Therefore, the flatness data of the automobile paint surface can be obtained more intuitively and accurately.

In the foregoing quantitative detection method for paint surface roughness, assuming that the depth at position 1 of the paint surface is zero, then the depth distance at position m is:

${\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;z</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

Among them, Δz _i is the depth distance difference between the i+1th place and the ith place, so that quantitative detection of the flatness of the automobile paint surface can be realized.

The paint surface roughness quantitative detection device realizing the foregoing method comprises: a broadband low-coherence light source, a circulator, a coupler, an A lens, a grating, a B lens, a line-scan camera, a computer, a reference system and a detection system, and a broadband low-coherence light source. The emitted light is divided into two paths after passing through the circulator and the coupler, one of which enters the detection system as the detection light and focuses on the paint surface, and the other enters the reference system as the reference light; the backscattered light reflected by the paint surface and the reference light The reference light reflected by the system, after passing through the coupler and circulator, enters the A lens for collimation and then irradiates the grating. The interference spectrum is imaged on the line array camera through the B lens. The line array camera records the interference spectrum and transmits it to the computer for further processing. deal with.

Preferably, the reference system includes: a C lens, a D lens and a reference mirror, the reference light is collimated by the C lens, and then focused on the reference mirror by the D lens.

Preferably, the detection system includes: an E lens, an X vibrating mirror, a Y vibrating mirror and an F lens; The lens focuses on the paint surface, and the detection system realizes the two-dimensional scanning of the paint orange peel surface through the vibration of the X vibrating mirror and the Y vibrating mirror.

Compared with the prior art, the present invention provides a brand-new detection technology for detecting the flatness of the automobile paint surface, and realizes the quantitative detection of the flatness of the paint surface, thereby helping people to understand the paint surface more intuitively and concretely. The flatness situation can also better help people find the cause of the undulation problem on the paint surface, and provide clearer guidance for modifying the painting process. In addition, the detection system in the present invention can make the light move by setting the X vibrating mirror and the Y vibrating mirror, so as to scan different positions on the painted surface of the car, and then obtain the flatness data of the painted surface.

Description of drawings

Fig. 1 is method flowchart of the present invention;

Fig. 2 is a schematic diagram of the device structure connection of the present invention.

Reference signs: 1-broadband low-coherence light source, 2-circulator, 3-coupler, 5-A lens, 6-grating, 7-B lens, 8-line array camera, 9-computer, 10-reference system, 11-detection system, 12-C lens, 13-D lens, 14-reference mirror, 15-E lens, 16-X vibration mirror, 17-Y vibration mirror, 18-F lens.

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

detailed description

Embodiments of the present invention: a quantitative detection method for paint surface roughness, as shown in Figure 1, comprises the following steps:

S1, collect the low-coherent light interference spectrum of each position on the paint surface point by point at equal intervals, and obtain the low-coherence light interference spectrum matrix of each position on the paint surface;

S2, calculate the phase difference between the low-coherence light interference spectra of each adjacent position on the paint surface It specifically includes the following steps: performing Fourier transform on the low-coherent light interference spectrum at adjacent positions to obtain a corresponding complex exponential function; dividing the complex exponential function to obtain; wherein,

The interference spectrum of low-coherence light at position 1 is:

The interference spectrum of low-coherence light at position 2 is: In the formula, I ₁ (k) and I ₂ (k) are the light intensity signals at position 1 and position 2 respectively, S(k) is the light source spectrum, E _R is the reference light amplitude entering the line scan camera, E _S is the The detection light amplitude of the line scan camera, k is the wave number, λ is the central wavelength, n is the refractive index of air, z ₁ is the optical path difference or depth information, is the initial phase, is the phase difference generated by the depth distance difference from position 1 to position 2;

S3, according to the phase difference Calculate the depth distance difference Δz of each adjacent position:

S4, integrating the depth distance difference Δz to obtain the quantitative distribution of the flatness of the paint surface;

Assuming that the depth at position 1 of the paint surface is zero, the depth distance at position m is:

${\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;z</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

Among them, Δz _i is the depth distance difference between the i+1th location and the ith location.

The paint surface roughness quantitative detection device that realizes the above method, as shown in Figure 2, includes: a broadband low-coherence light source 1, a circulator 2, a coupler 3, an A lens 5, a grating 6, a B lens 7, a line scan camera 8, The computer 9, the reference system 10 and the detection system 11, the light emitted by the broadband low-coherence light source 1 is divided into two paths after passing through the circulator 2 and then the coupler 3, one of which enters the detection system 11 as the detection light and focuses on the paint surface, and the other One path 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, after the coupler 3 and the circulator 2, enter the A lens 5 for collimation and then irradiate the grating 6, which The interference spectrum is imaged on the line scan camera 8 through the B lens 7, and the line scan camera 8 records the interference spectrum and sends it to the computer 9 for processing. The reference system 10 includes: a C lens 12 , a D lens 13 and a reference mirror 14 . The reference light is collimated by the C lens 12 and then focused on the reference mirror 14 by the D lens 13 . Described detection system 11 comprises: E lens 15, X vibrating mirror 16, Y vibrating mirror 17 and F lens 18, after described detection light is collimated by E lens 15, then through X vibrating mirror 16, Y vibrating mirror 17 The reflection is finally focused on the paint surface through the F lens 18.

The reference system 10 and the detection system 11 in this embodiment can also be realized by using other structures in the prior art.

The working principle of an embodiment of the present invention: the light emitted by the broadband low-coherence light source 1 first enters the A port of the circulator 2, and after coming out from the B port of the circulator 2, it is divided into two paths after passing through the coupler 3, and one path is used as After the detection light enters the detection system 11, it is focused on the paint surface, and the other path enters the reference system 10 as a reference light; the backscattered light reflected by the paint surface and the reference light reflected by the reference system 10 enter the coupler 3, and then enter the The B port of the circulator 2 exits from the C port of the circulator 2, and after being collimated by the A lens 5, it irradiates the grating 6, and its spectrum is imaged on the line camera 8 by the B lens 7, and the line camera 8 records the interference spectrum, and It is sent to the computer 9 for processing.

The reference system 10 is used to provide a reference light signal, including: a C lens 12, a D lens 13 and a reference mirror 14. The reference light is first converted into collimated parallel light in space by the C lens 12, and then passed through the D lens 12. The lens 13 focuses on the reference mirror 14 , and the light reflected by the reference mirror 14 returns to the coupler 3 along this optical path.

Described detection system 11 comprises: E lens 15, X vibrating mirror 16, Y vibrating mirror 17 and F lens 18, described probing light first becomes collimated light through E lens 15, then passes X vibrating mirror 16, Y The vibrating mirror 17 deflects, and finally focuses on the paint orange peel surface through the F lens 18, and the light scattered by the paint orange peel surface returns to the coupler 3 along this optical path. The detection system realizes the two-dimensional scanning of the paint orange peel surface through the vibration of the X vibrating mirror 16 and the Y vibrating mirror 17 .

During the measurement, the spectral data is collected line by line by the line array camera 8, assuming that the first scan is performed at position 1 of the paint surface, and the second scan is performed at position 2, then

The interference spectrum of low-coherence light at position 1 is:

The interference spectrum of low-coherence light at position 2 is: After performing Fourier transform on I ₁ (k) and I ₂ (k), the corresponding complex exponential function is obtained and F ₁ is divided by F ₂ to get The phase difference of the interference spectrum at position 1 and position 2 can be obtained According to the phase difference That is, the depth distance difference Δz between position 1 and position 2 is obtained:

In the formula, I ₁ (k) and I ₂ (k) are the light intensity signals at position 1 and position 2 respectively, S(k) is the light source spectrum, E _R is the reference light amplitude entering the line scan camera, E _S is the The detection light amplitude of the line scan camera, k is the wave number, λ is the central wavelength, n is the refractive index of air, z ₁ is the optical path difference or depth information, is the initial phase, is the phase difference generated by the depth distance difference from position 1 to position 2.

Integrate the depth distance difference Δz to obtain the quantitative distribution of the flatness of the paint surface:

Among them, the depth distance difference Δz between two adjacent points can be expressed as:

Δz ₂₁ =z ₂ -z ₁

Δz ₃₂ =z ₃ -z ₂

...

Δz _mm-1 =z _m -z _m-1

Assuming that z ₁ =0, that is, the depth at position 1 is zero, then the depth distance at position m can be expressed as,

z ₁ =0

z ₂ =z ₁ +Δz ₂₁ =Δz ₂₁

z ₃ =z ₂ +Δz ₃₂ =Δz ₂₁ +Δz ₃₂

z ₄ =z ₃ +Δz ₃ =Δz ₂₁ +Δz ₃₂ +Δz ₄₃

...

z _m =z _m-1 +Δz _m-1 =Δz ₂₁ +Δz ₃₂ +...+Δz _mm-1

Among them, Δz can be obtained by the calculation method in step S3, so it can be obtained that the depth distance based on the zero point at position m is:

${\mathrm{<span\; class="notranslate">\; z</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;z</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

In the formula, Δz _i is the depth distance difference between the i+1th place and the ith place

The flatness of the paint surface can be observed from the obtained curve, and the 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, E_RAmplitude of reference light for entering the line camera, E_SFor 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:

$z_m=\underset{i=1}{\overset{i=m-1}{\&Sigma;}}{\mathrm{\&Delta;z}}_i$

wherein, Δ z_iIs 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).