Surface roughness measurement sensing device, measurement method and system

Abstract

This invention discloses a surface roughness measurement sensing device, method, and system. The surface roughness measurement sensing device includes: a preset light source, a polarization grating, a reflector, a polarization camera, and a data processing module. The preset light source emits an incident light beam; the polarization grating splits the incident beam into two orthogonal circularly polarized beams; the reflector performs transverse shearing interference on the two circularly polarized beams; the polarization camera simultaneously captures four phase-shifted interferograms from four polarizer arrays in different directions; and the data processing module calculates surface roughness parameters from the interferograms. This invention directly calculates surface roughness from a single interferogram, achieving fast, non-contact, high-precision surface roughness measurement with strong resistance to environmental interference, making it suitable for rapid quality inspection in industrial settings.

Description

Technical Field

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

[0002] Surface roughness is an important parameter for measuring the surface quality of a product and is widely used in manufacturing, materials science, and optical engineering. Traditional contact measurement methods (such as stylus profilometers) offer high accuracy but suffer from drawbacks such as slow measurement speed and potential surface damage. Optical measurement techniques (such as phase-shifting interferometers and confocal microscopes) can achieve non-contact measurement, but they typically require axial scanning, leading to system complexity and sensitivity to environmental vibrations.

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

[0004] The purpose of this invention is to provide a surface roughness measurement sensing device, measurement method and system. The described sensing device, by combining statistical machine learning technology and partial integration method, can quickly and accurately measure surface roughness parameters, 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, comprising the following steps:

[0006] The system includes a preset light source, a polarization grating, a reflector, a polarization camera, and a data processing module.

[0007] The preset light source is used to emit an incident light beam;

[0008] The polarization grating is used to split the incident beam into two orthogonal circularly polarized beams;

[0009] The reflector is used to perform transverse shearing interference on two circularly polarized beams.

[0010] A polarization camera is used to simultaneously capture four phase-shifted interferograms of polarizer arrays in four different directions.

[0011] The data processing module is used to calculate surface roughness parameters from the interferogram.

[0012] In this scheme, the preset light source includes an LED lamp with a center wavelength of 940 nanometers, and the imaging optical element in the polarization camera is a 10x near-infrared objective lens.

[0013] In this solution, the data processing module uses statistical machine learning methods or partial integration methods to calculate the surface roughness parameters from the transverse gradient map.

[0014] A second aspect of the present invention also provides a surface roughness measurement method, applied to a surface roughness measurement sensing device as described in any of the preceding claims, wherein the method comprises the following steps:

[0015] After calibrating and measuring the flatness of the mirror surface, begin the measurement.

[0016] The transverse gradient map was obtained based on four phase-shifted interferograms captured by a polarization camera;

[0017] Interferogram phase is extracted based on the transverse gradient map, and surface roughness parameters are calculated.

[0018] In this scheme, the polarization grating is split to obtain two orthogonal circularly polarized beams, including a right-handed circularly polarized beam and a left-handed circularly polarized beam.

[0019] Right-hand circularly polarized light The electric field is represented as:

[0020] ;

[0021] Left-handed circularly polarized light The electric field is represented as:

[0022] ;

[0023] Where k is the wave number, Let x and y be the wavefront of the surface to be measured, x and y be the spatial coordinates, and s be the transverse shear distance. It is the imaginary unit.

[0024] In this scheme, the step of extracting the interferogram phase based on the transverse gradient map and calculating the surface roughness parameters specifically includes:

[0025] The transverse gradient of the wavefront is extracted from the four interferograms using the following formula:

[0026] ;

[0027] in, , , , These represent the intensities of the four interferograms. The gradient is the horizontal gradient, and k is the wave number.

[0028] The surface roughness parameters are calculated using statistical machine learning methods or partial integration methods in conjunction with the lateral gradient.

[0029] A third aspect of the present invention also provides a surface roughness measurement system, including a memory and a processor, wherein the memory includes a surface roughness measurement method program, and the surface roughness measurement method program, when executed by the processor, performs the following steps:

[0030] After calibrating and measuring the flatness of the mirror surface, begin the measurement.

[0031] The transverse gradient map was obtained based on four phase-shifted interferograms captured by a polarization camera;

[0032] Interferogram phase is extracted based on the transverse gradient map, and surface roughness parameters are calculated.

[0033] In this scheme, the polarization grating is split to obtain two orthogonal circularly polarized beams, including a right-handed circularly polarized beam and a left-handed circularly polarized beam.

[0034] Right-hand circularly polarized light The electric field is represented as:

[0035] ;

[0036] Left-handed circularly polarized light The electric field is represented as:

[0037] ;

[0038] Where k is the wave number, Let x and y be the wavefront of the surface to be measured, x and y be the spatial coordinates, and s be the transverse shear distance. It is the imaginary unit.

[0039] In this scheme, the step of extracting the interferogram phase based on the transverse gradient map and calculating the surface roughness parameters specifically includes:

[0040] The transverse gradient of the wavefront is extracted from the four interferograms using the following formula:

[0041] ;

[0042] in, , , , These represent the intensities of the four interferograms. The gradient is the horizontal gradient, and k is the wave number.

[0043] The surface roughness parameters are calculated using statistical machine learning methods or partial integration methods in conjunction with the lateral gradient.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium comprising a surface roughness measurement method program for a machine, wherein when executed by a processor, the surface roughness measurement method program implements the steps of a surface roughness measurement method as described in any of the preceding claims.

[0045] This invention discloses a surface roughness measurement sensing device, measurement method, and system. It directly calculates the surface roughness using a single-shot interferogram, which is fast, achieves non-contact, high-precision surface roughness measurement, has strong resistance to environmental interference, and is suitable for rapid quality inspection in industrial settings. Attached Figure Description

[0046] Figure 1 A flowchart of a surface roughness measurement method according to the present invention is shown;

[0047] Figure 2 A structural diagram of a surface roughness measurement sensing device according to the present invention is shown;

[0048] Figure 3 The phase deviation interferogram of a surface roughness measurement method according to the present invention is shown;

[0049] Figure 4 The gradient diagram of the surface to be measured in the surface roughness measurement method of the present invention is shown;

[0050] Figure 5 A block diagram of a surface roughness measurement system according to the present invention is shown. Detailed Implementation

[0051] To better understand the above-mentioned objectives, 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 embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0053] Figure 1 A flowchart of a surface roughness measurement method according to this application is shown.

[0054] like Figure 1 As shown, this application discloses a surface roughness measurement method, including the following steps:

[0055] S102, After calibrating and measuring the flatness error of the mirror surface, begin the measurement;

[0056] S104, obtain the lateral gradient map based on four phase-shifted interferograms captured by the polarization camera;

[0057] S106, based on the transverse gradient map, extracts the phase of the interferogram and calculates the surface roughness parameters.

[0058] It should be noted that, in this embodiment, the surface roughness measurement described is applied to a surface roughness measurement sensing device, specifically, as shown in... Figure 2 As 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. The preset light source emits an incident light beam; the polarization grating splits the incident light beam into two orthogonal circularly polarized beams; the reflector performs transverse shearing interference on the two circularly polarized beams; the polarization camera simultaneously captures four phase-shifted interferograms from four polarizer arrays in four different directions; and the data processing module calculates surface roughness parameters from the interferograms.

[0059] Furthermore, in this embodiment, specifically, the preset light source, in actual use, can be an LED lamp with a center wavelength of "940" nanometers to emit an incident beam. Further, the polarization grating (PG) is used to split the incident beam into two orthogonal circularly polarized beams, namely right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP). The reflector is used to perform lateral shearing interference on the two circularly polarized beams, thereby extracting surface gradient information using their phase difference. Correspondingly, the imaging optical element in the polarization camera (PCMOS) is a 10x near-infrared objective lens. Correspondingly, the data processing module uses statistical machine learning methods or partial integration methods to calculate the surface roughness parameters from the lateral gradient map. The statistical machine learning method generates a large number of random rough surfaces and calculates the statistical characteristics of their lateral gradient maps, such as the root mean square gradient roughness. Spatial correlation coefficient and establish surface roughness With root mean square gradient roughness The statistical relationship model between the two can be used to directly calculate the surface roughness parameters from the measured gradient map; while for the partial integration method, the surface profile is partially reconstructed by accumulating the height difference in the transverse gradient map, and the roughness parameters are calculated from it.

[0060] Furthermore, specifically, the root mean square gradient roughness is used to describe the statistical fluctuation of the transverse gradient of the surface wavefront, and is calculated as follows:

[0061] ;

[0062] in, The root mean square gradient roughness, Let x and y be the wavefront of the surface to be measured in the j-th sampling, and x and y be the spatial coordinates. Let N be the gradient mean and N be the number of sampling points.

[0063] Furthermore, root mean square roughness The root mean square roughness can be obtained from the transverse gradient plot. Spatial correlation coefficient calculate:

[0064] ;

[0065] in, The spatial correlation coefficient between the two shear surfaces.

[0066] According to an embodiment of the present invention, the polarization grating splits to obtain two orthogonal circularly polarized beams, including right-handed circularly polarized light and left-handed circularly polarized light, wherein,

[0067] Right-hand circularly polarized light The electric field is represented as:

[0068] ;

[0069] Left-handed circularly polarized light The electric field is represented as:

[0070] ;

[0071] Where k is the wave number, Let x and y be the wavefront of the surface to be measured, x and y be the spatial coordinates, and s be the transverse shear distance. It is the imaginary unit.

[0072] It should be noted that, in this embodiment, the above embodiment describes the polarization grating (PG) used to split the incident beam into two orthogonal circularly polarized beams, namely right-hand circularly polarized light (RHCP) and left-hand circularly polarized light (LHCP), and the reflector used to perform transverse shearing interference on the two circularly polarized beams. Accordingly, each polarized beam has its own electric field. Therefore, the right-hand circularly polarized light obtained after transverse shearing interference... The electric field is represented as: The resulting left-handed circularly polarized light The electric field is represented as: .

[0073] According to an embodiment of the present invention, the step of extracting the interferogram phase based on the transverse gradient map and calculating the surface roughness parameters specifically includes:

[0074] The transverse gradient of the wavefront is extracted from the four interferograms using the following formula:

[0075] ;

[0076] in, , , , These represent the intensities of the four interferograms. The gradient is the horizontal gradient, and k is the wave number.

[0077] The surface roughness parameters are calculated using statistical machine learning methods or partial integration methods in conjunction with the lateral gradient.

[0078] It should be noted that, in this embodiment, the polarization camera described in the above embodiments is specifically used to image four different directions (the angles correspond to 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10, 10, 11, 12, 13, 18, , and Four phase-shifted interferograms were simultaneously captured using a polarizer array, such as... Figure 3 As shown, the diagram displays phase deviation interferograms, where the four interferograms correspond to the following: , , , Then, the lateral gradient is calculated based on the formula for calculating the lateral gradient, wherein the gradient map of the surface to be measured is as follows: Figure 4 As shown.

[0079] Figure 5A block diagram of a surface roughness measurement system according to the present invention is shown.

[0080] like Figure 5 As shown, this invention discloses a surface roughness measurement system, including a memory and a processor. The memory includes a surface roughness measurement method program, which, when executed by the processor, performs the following steps:

[0081] After calibrating and measuring the flatness of the mirror surface, begin the measurement.

[0082] The transverse gradient map was obtained based on four phase-shifted interferograms captured by a polarization camera;

[0083] Interferogram phase is extracted based on the transverse gradient map, and surface roughness parameters are calculated.

[0084] It should be noted that, in this embodiment, the surface roughness measurement described is applied to a surface roughness measurement sensing device, specifically, as shown in... Figure 2 As 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. The preset light source emits an incident light beam; the polarization grating splits the incident light beam into two orthogonal circularly polarized beams; the reflector performs transverse shearing interference on the two circularly polarized beams; the polarization camera simultaneously captures four phase-shifted interferograms from four polarizer arrays in four different directions; and the data processing module calculates surface roughness parameters from the interferograms.

[0085] Furthermore, in this embodiment, specifically, the preset light source, in actual use, can be an LED lamp with a center wavelength of "940" nanometers to emit an incident beam. Further, the polarization grating (PG) is used to split the incident beam into two orthogonal circularly polarized beams, namely right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP). The reflector is used to perform lateral shearing interference on the two circularly polarized beams, thereby extracting surface gradient information using their phase difference. Correspondingly, the imaging optical element in the polarization camera (PCMOS) is a 10x near-infrared objective lens. Correspondingly, the data processing module uses statistical machine learning methods or partial integration methods to calculate the surface roughness parameters from the lateral gradient map. The statistical machine learning method generates a large number of random rough surfaces and calculates the statistical characteristics of their lateral gradient maps, such as the root mean square gradient roughness. Spatial correlation coefficient and establish surface roughness With root mean square gradient roughness The statistical relationship model between the two can be used to directly calculate the surface roughness parameters from the measured gradient map; while for the partial integration method, the surface profile is partially reconstructed by accumulating the height difference in the transverse gradient map, and the roughness parameters are calculated from it.

[0086] Furthermore, specifically, the root mean square gradient roughness is used to describe the statistical fluctuation of the transverse gradient of the surface wavefront, and is calculated as follows:

[0087] ;

[0088] in, The root mean square gradient roughness, Let x and y be the wavefront of the surface to be measured in the j-th sampling, and x and y be the spatial coordinates. Let N be the gradient mean and N be the number of sampling points.

[0089] Furthermore, root mean square roughness The root mean square roughness can be obtained from the transverse gradient plot. Spatial correlation coefficient calculate:

[0090] ;

[0091] in, The spatial correlation coefficient between the two shear surfaces.

[0092] According to an embodiment of the present invention, the polarization grating splits to obtain two orthogonal circularly polarized beams, including right-handed circularly polarized light and left-handed circularly polarized light, wherein,

[0093] Right-hand circularly polarized light The electric field is represented as:

[0094] ;

[0095] Left-handed circularly polarized light The electric field is represented as:

[0096] ;

[0097] Where k is the wave number, Let x and y be the wavefront of the surface to be measured, x and y be the spatial coordinates, and s be the transverse shear distance. It is the imaginary unit.

[0098] It should be noted that, in this embodiment, the above embodiment describes the polarization grating (PG) used to split the incident beam into two orthogonal circularly polarized beams, namely right-hand circularly polarized light (RHCP) and left-hand circularly polarized light (LHCP), and the reflector used to perform transverse shearing interference on the two circularly polarized beams. Accordingly, each polarized beam has its own electric field. Therefore, the right-hand circularly polarized light obtained after transverse shearing interference... The electric field is represented as: The resulting left-handed circularly polarized light The electric field is represented as: .

[0099] According to an embodiment of the present invention, the step of extracting the interferogram phase based on the transverse gradient map and calculating the surface roughness parameters specifically includes:

[0100] The transverse gradient of the wavefront is extracted from the four interferograms using the following formula:

[0101] ;

[0102] in, , , , These represent the intensities of the four interferograms. The gradient is the horizontal gradient, and k is the wave number.

[0103] The surface roughness parameters are calculated using statistical machine learning methods or partial integration methods in conjunction with the lateral gradient.

[0104] It should be noted that, in this embodiment, the polarization camera described in the above embodiments is specifically used to image four different directions (the angles correspond to 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10, 10, 11, 12, 13, 18, , and Four phase-shifted interferograms were simultaneously captured using a polarizer array, such as... Figure 3 As shown, the diagram displays phase deviation interferograms, where the four interferograms correspond to the following: , , , Then, the lateral gradient is calculated based on the formula for calculating the lateral gradient, wherein the gradient map of the surface to be measured is as follows: Figure 4 As shown, the red text Represented as the horizontal axis The spacing.

[0105] A third aspect of the present invention provides a computer-readable storage medium including a surface roughness measurement method program, which, when executed by a processor, implements the steps of a surface roughness measurement method as described in any of the preceding claims.

[0106] This invention discloses a surface roughness measurement sensing device, measurement method, and system. It directly calculates the surface roughness using a single-shot interferogram, which is fast, achieves non-contact, high-precision surface roughness measurement, has strong resistance to environmental interference, and is suitable for rapid quality inspection in industrial settings.

[0107] In the several embodiments provided in this 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 units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0108] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0109] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0110] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for measuring surface roughness, characterized in that, This invention relates to a surface roughness measurement sensing device, the device comprising a preset light source, a polarization grating, a reflector, a polarization camera, and a data processing module. The preset light source is used to emit an incident light beam, including LED lights; The polarization grating is used to split the incident beam into two orthogonal circularly polarized beams; The reflector is used to perform transverse shearing interference on two circularly polarized beams. A polarization camera is used to simultaneously capture four phase-shifted interferograms from an array of polarizers in four different directions. The imaging optical element in the polarization camera is a 10x near-infrared objective lens. The data processing module is used to calculate surface roughness parameters from the interferogram, and to calculate the surface roughness parameters from the transverse gradient map using statistical machine learning methods or partial integration methods. The method is characterized by comprising the following steps: After calibrating and measuring the flatness of the mirror surface, begin the measurement. The transverse gradient map was obtained based on four phase-shifted interferograms captured by a polarization camera; Interferogram phase is extracted based on transverse gradient map and surface roughness parameters are calculated; The polarization grating splits into two orthogonal circularly polarized beams: a right-handed circularly polarized beam and a left-handed circularly polarized beam. Right-hand circularly polarized light The electric field is represented as: ； Left-handed circularly polarized light The electric field is represented as: ； Where k is the wave number, Let x and y be the wavefront of the surface to be measured, x and y be the spatial coordinates, and s be the transverse shear distance. The imaginary unit; Specifically, the step of extracting the interferogram phase based on the transverse gradient map and calculating the surface roughness parameters includes: The transverse gradient of the wavefront is extracted from the four interferograms using the following formula: ； in, , , , These represent the intensities of the four interferograms. The gradient is the horizontal gradient, and k is the wave number. The surface roughness parameters are calculated using statistical machine learning methods or partial integration methods in conjunction with the lateral gradient.

2. A surface roughness measurement system, characterized in that, The system includes a memory and a processor. The memory contains a surface roughness measurement method program, which, when executed by the processor, performs the following steps: After calibrating and measuring the flatness of the mirror surface, begin the measurement. The transverse gradient map was obtained based on four phase-shifted interferograms captured by a polarization camera; Interferogram phase is extracted based on transverse gradient map and surface roughness parameters are calculated; The polarization grating splits into two orthogonal circularly polarized beams: a right-handed circularly polarized beam and a left-handed circularly polarized beam. Right-hand circularly polarized light The electric field is represented as: ； Left-handed circularly polarized light The electric field is represented as: ； Where k is the wave number, Let x and y be the wavefront of the surface to be measured, x and y be the spatial coordinates, and s be the transverse shear distance. The imaginary unit; Specifically, the step of extracting the interferogram phase based on the transverse gradient map and calculating the surface roughness parameters includes: The transverse gradient of the wavefront is extracted from the four interferograms using the following formula: ； in, , , , These represent the intensities of the four interferograms. The gradient is the horizontal gradient, and k is the wave number. The surface roughness parameters are calculated using statistical machine learning methods or partial integration methods in conjunction with the lateral gradient.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a surface roughness measurement method program, which, when executed by a processor, implements the steps of the surface roughness measurement method as described in claim 1.

Images