Surface roughness measurement sensing device and measurement method and system

Abstract

The invention discloses a surface roughness measurement sensing device and a measurement method and system, the surface roughness measurement sensing device comprises a preset light source, a polarization grating, a reflector, a polarization camera and a data processing module, the preset light source is used for emitting an incident light beam; the polarization grating is used for splitting an incident light beam into two orthogonal circularly polarized light beams; the reflecting mirror is used for carrying out transverse shearing interference on the two circularly polarized light beams; the polarization camera is used for simultaneously capturing the polaroid arrays in four different directions to obtain four phase shift interferograms; and the data processing module is used for calculating a surface roughness parameter from the interferogram. According to the method, direct calculation is carried out through the interferogram shot at a time, the speed is high, non-contact and high-precision surface roughness measurement is achieved, the environment interference resistance is high, and the method is suitable for rapid quality detection in industrial scenes.

Description

Technical Field

[0001] The present invention relates to the field of optical measurement technology, and more specifically, to a surface roughness measurement sensing device, measurement method and system. Background Art

[0002] Surface roughness is an important parameter for measuring the surface quality of products, and is widely used in fields such as manufacturing, materials science and optical engineering. Although traditional contact measurement methods (such as stylus profilometers) have high precision, they have the disadvantages of slow measurement speed and easy surface damage. Although optical measurement technologies (such as phase-shifting interferometers, confocal microscopes, etc.) can achieve non-contact measurement, they usually require axial scanning, resulting in a complex system and being sensitive to environmental vibrations.

[0003] Lateral Shearing Interferometry (LSI) can measure the lateral gradient of the wavefront without using a reference wave by laterally splitting the wavefront into two parts and making them interfere with each other. However, traditional LSI technology can only obtain the gradient map of the wavefront, cannot directly reconstruct the original surface profile, and the measurement accuracy is limited by environmental noise and system complexity. Summary of the Invention

[0004] The purpose of the present invention is to provide a surface roughness measurement sensing device, measurement method and system. The described sensing device can quickly and accurately measure surface roughness parameters by combining statistical machine learning technology and the partial integration method, and is suitable for real-time surface quality detection in industrial environments.

[0005] The first aspect of the present invention provides a surface roughness measurement sensing device, including the following steps: A preset light source, a polarization grating, a reflector, a polarization camera and a data processing module, wherein, The preset light source is used to emit an incident light beam; The polarization grating is used to split the incident light beam into two orthogonal circularly polarized light beams; The reflector is used to perform lateral shearing interference on the two circularly polarized light beams; The polarization camera is used to simultaneously capture four polarization plate arrays in four different directions to obtain four phase-shifted interference patterns; The data processing module is used to calculate surface roughness parameters from the interference patterns.

[0006] In this solution, the preset light source includes an LED lamp with a central wavelength of 940 nanometers, and the imaging optical element in the polarization camera is a 10-fold near-infrared objective lens.

[0007] In this solution, the data processing module calculates the surface roughness parameter from the lateral gradient map by using a statistical machine learning method or a partial integration method.

[0008] The second aspect of the present invention also provides a surface roughness measurement method, which is applied to a surface roughness measurement sensing device described in any one of the above, wherein the method includes the following steps: Start measuring after calibrating the flat mirror error. Obtain a lateral gradient map based on four phase-shifted interferograms captured by a polarization camera. Extract the interferogram phase based on the lateral gradient map and calculate the surface roughness parameter.

[0009] In this solution, the polarization grating splits into two orthogonal circularly polarized light beams, including a right-handed circularly polarized light and a left-handed circularly polarized light, wherein The right-handed circularly polarized light The electric field is expressed as: ; The left-handed circularly polarized light The electric field is expressed as: ; Where k is the wave number, Is the wavefront of the surface to be measured, x and y are spatial coordinates, s is the lateral shear distance, Is the imaginary unit.

[0010] In this solution, the step of extracting the interferogram phase based on the lateral gradient map and calculating the surface roughness parameter specifically includes: Extract the lateral gradient of the wavefront from four interferograms, and the formula is: ; Where , , , Are the intensities of the four interferograms respectively, Is the lateral gradient, and k is the wave number; Calculate the surface roughness parameter by using a statistical machine learning method or a partial integration method in combination with the lateral gradient.

[0011] The third aspect of the present invention also provides a surface roughness measurement system, including a memory and a processor. The memory includes a surface roughness measurement method program. When the surface roughness measurement method program is executed by the processor, the following steps are implemented: Start measuring after calibrating the flat mirror error. Obtain a lateral gradient map based on four phase-shifted interferograms captured by a polarization camera. Extract the phase of the interferogram based on the lateral gradient map and calculate the surface roughness parameters.

[0012] In this solution, the polarization grating splits into two orthogonal circularly polarized beams, including a right-handed circularly polarized light and a left-handed circularly polarized light. Among them, The right-handed circularly polarized light The electric field is expressed as: ; The left-handed circularly polarized light The electric field is expressed as: ; Among them, k is the wave number, is the wavefront of the surface to be measured, x and y are spatial coordinates, s is the lateral shear distance, is the imaginary unit.

[0013] In this solution, the method of extracting the interferogram phase based on the lateral gradient map and calculating the surface roughness parameters specifically includes: Extract the lateral gradient of the wavefront from four interferograms. The formula is: ; Among them, , , , are the intensities of the four interferograms respectively, is the lateral gradient, and k is the wave number; Combine the lateral gradient and use a statistical machine learning method or a partial integration method to calculate the surface roughness parameters.

[0014] The fourth aspect of the present invention provides a computer-readable storage medium, which includes a surface roughness measurement method program for a machine. When the surface roughness measurement method program is executed by a processor, the steps of a surface roughness measurement method as described in any one of the above are implemented.

[0015] A surface roughness measurement sensing device, measurement method and system disclosed by the present invention are directly calculated through a single-shot interferogram, with fast speed, realizing non-contact and high-precision surface roughness measurement, strong anti-environmental interference ability, and being suitable for rapid quality inspection in industrial scenarios. Brief Description of the Drawings

[0016] Figure 1 Shows a flowchart of a surface roughness measurement method of the present invention; Figure 2 Shows a structural diagram of a surface roughness measurement sensing device of the present invention; Figure 3Shows the phase deviation interference pattern of a surface roughness measurement method of the present invention; Figure 4 Shows the gradient map of the surface to be measured of a surface roughness measurement method of the present invention; Figure 5 Shows the block diagram of a surface roughness measurement system of the present invention. Detailed implementation manners

[0017] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0018] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0019] Figure 1 Shows the flowchart of a surface roughness measurement method of the present application.

[0020] As Figure 1 shown, the present application discloses a surface roughness measurement method, including the following steps: S102, start measurement after calibrating and measuring the flat mirror error; S104, obtain the transverse gradient map based on four phase-shifted interference patterns captured by the polarization camera; S106, extract the interference pattern phase from the transverse gradient map and calculate the surface roughness parameter.

[0021] It should be noted that in this embodiment, the described surface roughness measurement is applied to a surface roughness measurement sensing device. Specifically, as Figure 2 shown, the surface roughness measurement sensing device includes a preset light source, a polarization grating, a reflector, a polarization camera and a data processing module. Among them, the preset light source is used to emit an incident light beam; the polarization grating is used to split the incident light beam into two orthogonal circularly polarized light beams; the reflector is used to perform lateral shear interference on the two circularly polarized light beams; the polarization camera is used to simultaneously capture four polarization plate arrays in four different directions to obtain four phase-shifted interference patterns; the data processing module is used to calculate the surface roughness parameter from the interference pattern.

[0022] Further, in this embodiment, specifically, when the preset light source is actually used, an LED lamp with a central wavelength of "940" nanometers can be employed to emit an incident light beam. Further, the polarization grating (PG) is used to split the incident light beam into two orthogonal circularly polarized light beams, namely, right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP). The mirror is used to perform lateral shear interference on the two circularly polarized light beams, so that the surface gradient information can be extracted by using the phase difference between them. Correspondingly, the imaging optical element in the polarization camera (PCMOS) is a 10× near-infrared objective lens. Correspondingly, the data processing module uses a statistical machine learning method or a partial integration method to calculate the surface roughness parameter from the lateral gradient map. Among them, the statistical machine learning method generates a large number of random rough surfaces, calculates the statistical characteristics of their lateral gradient maps, such as root mean square gradient roughness and spatial correlation coefficient , and establishes a statistical relationship model between the surface roughness and the root mean square gradient roughness . Furthermore, the surface roughness parameter can be directly calculated from the measured gradient map by using this model; for the partial integration method, the height differences in the lateral gradient map are accumulated to partially reconstruct the surface profile, and the roughness parameter is calculated therefrom.

[0023] Further, specifically, the root mean square gradient roughness is used to describe the statistical fluctuation degree of the lateral gradient of the surface wavefront, and the calculation formula is as follows: ; wherein, is the root mean square gradient roughness, is the wavefront of the surface to be measured at the jth sampling, x and y are spatial coordinates, is the gradient mean value, and N is the number of sampling points.

[0024] Further, the root mean square roughness can be calculated through the root mean square roughness of the lateral gradient map and the spatial correlation coefficient : ; wherein, is the spatial correlation coefficient of the two shear surfaces.

[0025] According to an embodiment of the present invention, splitting a polarization grating results in two orthogonal circularly polarized light beams, including a right-handed circularly polarized light and a left-handed circularly polarized light, where the right-handed circularly polarized light has an electric field expressed as: ; the left-handed circularly polarized light has an electric field expressed as: ; where k is the wave number, is the wavefront of the surface to be measured, x and y are spatial coordinates, s is the lateral shear distance, is the imaginary unit.

[0026] It should be noted that in this embodiment, the above embodiment illustrates that the polarization grating (Polarization Grating, PG) is used to split an incident light beam into two orthogonal circularly polarized light beams, namely a right-handed circularly polarized light (Right-Hand Circular Polarization, RHCP) and a left-handed circularly polarized light (Left-Hand Circular Polarization, LHCP), and the mirror is used to perform lateral shear interference on the two circularly polarized light beams. Correspondingly, each polarized light has its own electric field. Therefore, after performing lateral shear interference, the electric field of the right-handed circularly polarized light is expressed as: ; and the electric field of the obtained left-handed circularly polarized light is expressed as: .

[0027] According to an embodiment of the present invention, extracting the interference pattern phase based on the lateral gradient map and calculating the surface roughness parameter specifically includes: Extracting the lateral gradient of the wavefront from four interference patterns, and the formula is: ; where , , , are the intensities of the four interference patterns respectively, is the lateral gradient, and k is the wave number; Combining the lateral gradient, use a statistical machine learning method or a partial integration method to calculate the surface roughness parameter.

[0028] It should be noted that in this embodiment, the above embodiment illustrates that the polarization camera is specifically used for four different directions (the angles correspond to 0, , and Simultaneously capture four phase-shifted interferograms using a polarizer array of (), where, as Figure 3 shown, it is shown as a phase deviation interferogram, where the four interferograms respectively correspond to , , , , and then calculate the lateral gradient based on the calculation formula of the lateral gradient. The gradient map of the surface to be measured is as Figure 4 shown.

[0029] Figure 5 shows a block diagram of a surface roughness measurement system according to the present invention.

[0030] As Figure 5 shown, the present invention discloses a surface roughness measurement system, including a memory and a processor. The memory includes a surface roughness measurement method program. When the surface roughness measurement method program is executed by the processor, the following steps are implemented: Start measurement after calibrating and measuring the flat mirror error; Obtain a lateral gradient map based on four phase-shifted interferograms captured by a polarization camera; Extract the interferogram phase based on the lateral gradient map and calculate the surface roughness parameter.

[0031] It should be noted that in this embodiment, the surface roughness measurement described is applied to a surface roughness measurement sensing device. Specifically, as Figure 2 shown, the surface roughness measurement sensing device includes a preset light source, a polarization grating, a reflector, a polarization camera, and a data processing module. Among them, the preset light source is used to emit an incident light beam; the polarization grating is used to split the incident light beam into two orthogonal circularly polarized light beams; the reflector is used to perform lateral shear interference on the two circularly polarized light beams; the polarization camera is used to simultaneously capture four phase-shifted interferograms using a polarizer array in four different directions; the data processing module is used to calculate the surface roughness parameter from the interferogram.

[0032] Further, in this embodiment, specifically, when the preset light source is actually used, an LED lamp with a central wavelength of "940" nanometers can be adopted to emit an incident light beam. Further, the polarization grating (Polarization Grating, PG) is used to split the incident light beam into two orthogonal circularly polarized light beams, namely right-hand circularly polarized light (Right-Hand Circular Polarization, RHCP) and left-hand circularly polarized light (Left-Hand Circular Polarization, LHCP). The mirror is used to perform lateral shear interference on the two circularly polarized light beams, so that the surface gradient information can be extracted by using the phase difference between the two. Correspondingly, the imaging optical element in the polarization camera (Polarization Camera, PCMOS) is a 10x near-infrared objective lens. Correspondingly, the data processing module uses a statistical machine learning method or a partial integration method to calculate the surface roughness parameter from the lateral gradient map. Among them, the statistical machine learning method generates a large number of random rough surfaces, calculates the statistical characteristics of their lateral gradient maps, such as the root mean square gradient roughness and the spatial correlation coefficient , and establishes a statistical relationship model between the surface roughness and the root mean square gradient roughness . Furthermore, the surface roughness parameter can be directly calculated from the measured gradient map by using this model; for the partial integration method, the height differences in the lateral gradient map are accumulated to partially reconstruct the surface profile, and the roughness parameter is calculated therefrom.

[0033] Further, specifically, the root mean square gradient roughness is used to describe the statistical fluctuation degree of the lateral gradient of the surface wavefront, and the calculation formula is as follows: ; wherein, is the root mean square gradient roughness, is the wavefront of the surface to be measured at the jth sampling, x and y are spatial coordinates, is the gradient mean value, and N is the number of sampling points.

[0034] Further, the root mean square roughness can be calculated through the root mean square roughness of the lateral gradient map and the spatial correlation coefficient : ; wherein, is the spatial correlation coefficient of the two shear surfaces.

[0035] According to an embodiment of the present invention, splitting a polarization grating results in two orthogonal circularly polarized light beams, including a right-handed circularly polarized light and a left-handed circularly polarized light, where right-handed circularly polarized light the electric field of which is expressed as: ; left-handed circularly polarized light the electric field of which is expressed as: ; where k is the wave number, is the wavefront of the surface to be measured, x and y are spatial coordinates, s is the transverse shear distance, is the imaginary unit.

[0036] It should be noted that in this embodiment, the above embodiment illustrates that the polarization grating (Polarization Grating, PG) is used to split an incident light beam into two orthogonal circularly polarized light beams, namely right-handed circularly polarized light (Right-Hand Circular Polarization, RHCP) and left-handed circularly polarized light (Left-Hand Circular Polarization, LHCP), and the mirror is used to perform transverse shear interference on the two circularly polarized light beams. Accordingly, each polarized light has its own electric field. Therefore, after transverse shear interference, the electric field of the right-handed circularly polarized light is expressed as: ; and the electric field of the obtained left-handed circularly polarized light is expressed as: .

[0037] According to an embodiment of the present invention, extracting the phase of the interference pattern based on the transverse gradient map and calculating the surface roughness parameter specifically includes: extracting the transverse gradient of the wavefront from four interference patterns, and the formula is: ; where , , , are the intensities of the four interference patterns respectively, is the transverse gradient, and k is the wave number; combining the transverse gradient to calculate the surface roughness parameter by using a statistical machine learning method or a partial integration method.

[0038] It should be noted that in this embodiment, the above embodiment illustrates that the polarization camera is specifically used for four different directions (the angles correspond to 0, , and Simultaneously capture four phase-shifted interferograms using a polarizer array as shown in Figure 3 . The interferograms are shown as phase deviation interferograms. The four interferograms respectively correspond to , , , . Then, calculate the lateral gradient based on the calculation formula of the lateral gradient. The gradient map of the surface to be measured is shown in Figure 4 . The red font represents the spacing of the horizontal axis .

[0039] A third aspect of the present invention provides a computer-readable storage medium, which includes a surface roughness measurement method program. When the surface roughness measurement method program is executed by a processor, the steps of a surface roughness measurement method as described in any one of the above are implemented.

[0040] A surface roughness measurement sensing device, measurement method, and system disclosed by the present invention calculate directly from the interferogram captured in a single shot, with high speed, realize non-contact and high-precision surface roughness measurement, have strong anti-environmental interference ability, and are suitable for rapid quality inspection in industrial scenarios.

[0041] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0042] The units described as separate components above may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0043] In addition, in each embodiment of the present invention, the functional units can all be integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0044] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0045] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

Claims

1. A surface roughness measurement sensing device, characterized in that Comprising: A preset light source, a polarization grating, a reflector, a polarization camera, and a data processing module, wherein, The preset light source is used to emit an incident light beam; The polarization grating is used to split the incident light beam into two orthogonal circularly polarized light beams; The reflector is used to perform lateral shear interference on the two circularly polarized light beams; The polarization camera is used to simultaneously capture four polarization filter arrays in different directions to obtain four phase-shifted interference patterns; The data processing module is used to calculate the surface roughness parameter from the interference pattern.

2. The surface roughness measurement sensing device according to claim 1, characterized in that, The preset light source includes an LED lamp, and the imaging optical element in the polarization camera is a 10x near-infrared objective lens.

3. The surface roughness measurement sensing device according to claim 1, characterized in that The data processing module calculates the surface roughness parameter from the lateral gradient map by using a statistical machine learning method or a partial integration method.

4. A surface roughness measurement method, characterized in that, Applied to a surface roughness measurement sensing device according to any one of claims 1 to 3 above, wherein the method includes the following steps: Start measurement after calibrating and measuring the flat mirror error; Obtain a lateral gradient map based on the four phase-shifted interference patterns captured by the polarization camera; Extract the interference pattern phase based on the lateral gradient map and calculate the surface roughness parameter.

5. A surface roughness measurement method according to claim 4, characterized in that, The polarization grating splits to obtain two orthogonal circularly polarized light beams including a right-handed circularly polarized light and a left-handed circularly polarized light, wherein, Right-handed circularly polarized light The electric field of which is expressed as: ； Left-handed circularly polarized light The electric field of which is expressed as: ； where k is the wave number, is the wavefront of the surface to be measured, x and y are spatial coordinates, and s is the transverse shear distance, is the imaginary unit.

6. The surface roughness measurement method according to claim 5, wherein, The extracting the interference pattern phase based on the lateral gradient map and calculating the surface roughness parameter specifically includes: Extract the lateral gradient of the wavefront from the four interference patterns, and the formula is: ； Among them, , , , are the intensities of four interference patterns respectively, is the transverse gradient, and k is the wave number; Combine the lateral gradient and use a statistical machine learning method or a partial integration method to calculate the surface roughness parameter.

7. A surface roughness measurement system, characterized in that, Comprising a memory and a processor, wherein the memory includes a surface roughness measurement method program, and when the surface roughness measurement method program is executed by the processor, the following steps are implemented: Start measurement after calibrating and measuring the flat mirror error; Obtain a lateral gradient map based on the four phase-shifted interference patterns captured by the polarization camera; Extract the interference pattern phase based on the lateral gradient map and calculate the surface roughness parameter.

8. A surface roughness measurement system according to claim 7, characterized in that, The polarization grating splits to obtain two orthogonal circularly polarized light beams including a right-handed circularly polarized light and a left-handed circularly polarized light, wherein, Right-handed circularly polarized light The electric field of which is expressed as: ； Left-handed circularly polarized light The electric field of which is expressed as: ； where k is the wave number, is the wavefront of the surface to be measured, x and y are spatial coordinates, and s is the lateral shear distance, is the imaginary unit.

9. A surface roughness measurement system according to claim 8, characterized in that, The extracting the interference pattern phase based on the lateral gradient map and calculating the surface roughness parameter specifically includes: Extract the lateral gradient of the wavefront from the four interference patterns, and the formula is: ； Among them, , , , are the intensities of four interference patterns respectively, is the transverse gradient, and k is the wave number; Combine the lateral gradient and use a statistical machine learning method or a partial integration method to calculate the surface roughness parameter.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a surface roughness measurement method program, and when the surface roughness measurement method program is executed by a processor, the steps of a surface roughness measurement method according to any one of claims 4 to 6 are implemented.

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