Rotary optical element inner wall surface shape detection method and device

Abstract

The invention provides a rotary optical element inner wall surface shape detection method and device, and belongs to the technical field of precision optical manufacturing. The method comprises the following steps: acquiring a reflected light spot image formed on a curtain by the inner wall of a to-be-measured optical element under the irradiation of a collimated light beam; carrying out denoising and gray scale correction processing on the collected light spot image to obtain an actual light spot image; executing pixel-by-pixel ray tracing based on the actual light spot image, and reconstructing to obtain an initial surface shape of the inner wall to be measured; establishing a simulation light path by taking the initial surface shape as an optical interface, and outputting a theoretical light spot diagram; the theoretical light spot image is compared with the actual light spot image, difference pixels are recognized, and the surface shape is tracked and corrected again; and iteratively executing a correction process until a convergence condition is met, and taking the final surface shape data as a measurement result. According to the invention, non-contact and high-precision detection of the surface shape of the inner wall of the rotary optical element is realized, surface damage is effectively avoided, and the method is suitable for surface shape reconstruction and error evaluation of a complex curved surface.

Description

Technical Field

[0001] This invention belongs to the field of precision optical manufacturing technology, specifically relating to a method and apparatus for detecting the surface shape of the inner wall of a rotary optical element. Background Technology

[0002] In the manufacturing and testing of optical components, the accuracy of the inner wall surface shape is one of the key factors determining the overall performance of the optical system. Traditional contact testing methods easily cause scratches or contamination to the surface of optical components, thus affecting their performance and service life. Although existing non-contact testing technologies avoid such damage to some extent, they still suffer from problems such as complex operating procedures and poor adaptability to complex curved surfaces. Especially for rotary optical components with complex curved surfaces, existing methods often fail to achieve comprehensive and accurate surface shape evaluation. Therefore, there is an urgent need to propose a non-destructive testing scheme for the inner wall surface shape of rotary optical components, so as to achieve effective evaluation of the complex curved inner wall surface shape without damaging the component surface, thereby making up for the shortcomings of existing technologies in terms of practicality and non-destructive testing requirements. Summary of the Invention

[0003] This invention aims to provide a method and apparatus for detecting the surface shape of the inner wall of a rotary optical element, achieving high-precision, non-destructive, and non-contact surface shape reconstruction, particularly suitable for detecting inner wall curved surfaces with complex geometries. Its core lies in constructing a controllable and adjustable optical path system to acquire high-quality images of reflected light spots from the inner wall, and inverting the surface shape state of the surface under test based on ray tracing technology, thereby achieving accurate measurement of the surface shape through iterative optimization.

[0004] The technical solution adopted in this invention is:

[0005] A rotary optical element inner wall surface shape detection device includes an optical platform, a reflector lifting mechanism, a bracket, an optical element, a screen fixing mechanism, a screen, an XY two-degree-of-freedom guide rail, an image sensor, an optical element clamping mechanism, a reflector, and a light source; the reflector lifting mechanism is used to lift the reflector mounting bracket and change the angle between the reflector and the substrate; the screen fixing mechanism is used to clamp and fix the screen; the XY two-degree-of-freedom guide rail is used to fix the image sensor and change its position; the optical element clamping mechanism is used to clamp and position the optical element.

[0006] A method for detecting the inner wall surface shape of a rotary optical element includes the following steps:

[0007] S1. Place the support on the optical platform; mount the optical element to be tested on the optical element clamping mechanism in the test state, and turn on the light source;

[0008] S2. Raise the reflector and move the screen to create a clear light spot pattern on the screen;

[0009] S3. Adjust the position and focal length of the image sensor to ensure that it can capture a clear light spot pattern; wherein, the position includes at least translation in the X-axis and Y-axis directions;

[0010] S4. Use a black matte material to wrap the light path to absorb ambient stray light, and control the image sensor to collect and record the light spot pattern on the screen;

[0011] S5. The acquired light spot image is denoised, and the denoised image is used as the actual light spot image.

[0012] S6. Based on the actual light spot image, perform pixel-by-pixel ray tracing calculation to determine the coordinates of the intersection point between each tracing ray and the inner wall surface of the optical element under test, and reconstruct the initial surface shape of the surface under test based on the set of intersection point coordinates.

[0013] S7. Using the initial surface shape as the optical interface, establish a simulated optical path, perform optical simulation, and output a theoretical light spot diagram;

[0014] S8. Compare the theoretical spot image with the actual spot image, identify pixels whose grayscale value difference exceeds a set threshold, and re-trace the light on these pixels to correct the surface shape of the surface to be tested, and use the corrected surface shape as the new initial surface shape.

[0015] S9. Repeat steps S7 to S8 until the difference between the theoretical spot image and the acquired spot image meets the preset convergence condition, then stop the iteration and take the surface data obtained from the last update as the final surface measurement result of the optical element under test.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. This invention constructs a controllable and adjustable optical path system, utilizing a screen to receive macroscopic light spots formed by reflections from the inner wall of the optical element under test, which are then acquired by an image sensor. This completely avoids surface scratches or contamination that may be caused by traditional contact probes or sensors. Simultaneously, the employed "measurement-modeling-comparison-correction" iterative algorithm reconstructs the surface shape from the light spot image through ray tracing and continuously compares and corrects it with the theoretical light spot generated by optical simulation until convergence. This effectively overcomes the accuracy issues of existing non-contact technologies caused by model simplification or environmental interference, ultimately achieving accurate and non-destructive reconstruction of the inner wall surface shape, including complex curved surfaces.

[0018] 2. This invention integrates multiple anti-interference measures: it employs a high dynamic range image sensor to capture rich information, uses matte black material to wrap the optical path to absorb stray light, and introduces the Monte Carlo method to simulate the scattering characteristics of the light source in ray tracing. These designs effectively suppress environmental interference, and combined with the self-correction mechanism of the iterative algorithm, this method can maintain the accuracy and repeatability of measurements even on complex curved surfaces and under complex working conditions.

[0019] 3. The detection logic of this invention does not rely on prior assumptions about the shape of the surface to be measured, and can directly deduce various complex inner wall shapes from the light spot image. This feature effectively overcomes the limitation of poor adaptability of traditional methods and broadens the application range of high-precision optical detection. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structure of the present invention;

[0021] Figure 2 It is an image of the inner wall light spot of a complex curved surface of revolution obtained by the method of the present invention;

[0022] Figure 3 It is a fitted surface image of a complex surface of revolution obtained by the method of the present invention;

[0023] Figure 4 This is a flowchart of the invention;

[0024] The components include: 1. Optical platform; 2. Reflector lifting mechanism; 3. Support; 4. Optical element; 5. Curtain fixing mechanism; 6. Curtain; 7. XY two-degree-of-freedom guide rail; 8. Image sensor; 9. Optical element clamping mechanism; 10. Reflector; 11. Light source. Detailed Implementation

[0025] To better understand the purpose, structure, and function of this invention, the embodiments of this invention will be described in further detail below with reference to the accompanying drawings.

[0026] like Figure 1 As shown, the present invention provides a rotary optical element inner wall surface shape detection device, including an optical platform 1; a reflector lifting mechanism 2; a bracket 3; an optical element 4; a screen fixing mechanism 5; a screen 6; an XY two-degree-of-freedom guide rail 7; an image sensor 8; an optical element clamping mechanism 9; a reflector 10; and a light source 11. The reflector lifting mechanism 2 is used to lift the reflector mounting bracket and change the angle between the reflector 10 and the substrate; the screen fixing mechanism 5 is used to clamp and fix the screen 6; the XY two-degree-of-freedom guide rail 7 is used to fix the image sensor 8 and change its position; and the optical element clamping mechanism 9 is used to clamp and position the optical element 4.

[0027] like Figure 1As shown, bracket 3 is securely installed on vibration isolation optical platform 1 to ensure the basic stability of the entire system and avoid external vibration interference.

[0028] The optical element clamping mechanism 9 is installed at a designated position on the bracket 3 and fixes the optical element 4 within the mechanism. This mechanism should be capable of adjusting for different radii. The optical element clamping mechanism 9 is finely adjusted using a high-precision indicator or laser alignment instrument to ensure that the central axis of the optical element 4 is strictly coaxial with the theoretical axis of the circular through hole on the bracket 3.

[0029] The reflector lifting mechanism 2 is fixed above the base plate of the bracket 3. The reflector 10 is mounted on the mounting bracket of the reflector lifting mechanism 2. The initial position should be such that the reflector 10 is approximately below the entrance of the optical element 4.

[0030] The curtain fixing mechanism 5 should be able to reliably clamp the curtain 6 and ensure that the surface of the curtain 6 is flat. Place it above the optical element 4 and initially adjust its position so that it is roughly aligned with the outlet of the optical element 4.

[0031] The image sensor 8 is mounted on the XY two-degree-of-freedom guide rail 7, and the guide rail system is fixed above the screen 6.

[0032] Wrap the entire optical path area with black matte cloth or blackened mechanical parts to absorb stray light to the maximum extent and improve the signal-to-noise ratio.

[0033] like Figure 4 As shown, the present invention provides a method for detecting the inner wall surface shape of a rotary optical element, comprising the following steps:

[0034] S1. Install optical element 4, turn on light source 11, and preheat until the light intensity and mode are stable.

[0035] S2. Slowly operate the reflector lifting mechanism 2 to finely adjust the pitch angle and height of the reflector 10, so that the collimated beam emitted by the light source 11, after being reflected by the reflector 10, can accurately and orthogonally fill the entrance aperture of the optical element 4. This process can be aided by temporarily placing a piece of white paper at the entrance of the optical element 4 to observe the light spot.

[0036] Observe the light spot formed on screen 6. By fine-tuning the position and tension of screen 6, as well as the angle of reflector 10, the goal is to form a macroscopic light spot pattern on screen 6 with clear boundaries, smooth brightness gradients, and no severe distortion or splitting. The quality of this light spot directly affects the accuracy of subsequent surface reconstruction.

[0037] S3. Using computer control, the XY two-degree-of-freedom guide rail 7 is moved to position the image sensor 8 so that its optical axis is perpendicular to the surface of the screen 6 and aligned with the energy center of the light spot. Then, the focal length and aperture of the image sensor 8 are adjusted via a motorized interface, and observed in real-time on a monitor until the clearest, most appropriately bright, and unexposed light spot image is obtained. The exposure time, gain, and other parameters of the image sensor 8 are then set to fully utilize its dynamic range.

[0038] S4. After the optical path is stabilized and the parameters of image sensor 8 are optimized, the image sensor 8 is triggered by the computer to acquire a single frame or an average of multiple high dynamic range images, such as... Figure 2 As shown. The image is saved in 16-bit TIFF format to retain the most grayscale information. Simultaneously, a dark-field image was acquired under completely dark conditions for subsequent image processing.

[0039] S5. Read the acquired raw spot image and dark field image. Subtract the corresponding pixel value (Gdark) of the dark field image from the gray value (Graw) of each pixel in the raw image to eliminate the effects of dark current and fixed pattern noise.

[0040] A three-level discrete wavelet transform was performed on the image after background subtraction. The high-frequency subbands obtained from the decomposition were then shrunk using the BayesShrink soft-thresholding method. This method adaptively determines the threshold based on the statistical characteristics of the coefficients within the subband, outperforming the fixed-threshold method and effectively suppressing noise while better preserving image edge information. After processing, an inverse wavelet transform was performed to reconstruct the image, yielding the denoised actual spot image.

[0041] S6. Ray tracing for initial surface reconstruction.

[0042] S61. The program reads the grayscale values ​​of the actual spot image after denoising. The grayscale value of each pixel (G = Gray - Gdark) is converted into a relative photon number weight (W). The calculation formula is: W = (G * Gain) / QE, where Gain is the system gain of the image sensor 8 (ADU / e-), and QE is the quantum efficiency (e- / photon). This weight is used to determine the number of rays traversed from that pixel region in subsequent Monte Carlo simulations.

[0043] S62. Establish a virtual optical model, including the positions of the light source 11, the reflector 10, and the curtain 6 with known parameters, as well as an initial assumed inner wall surface shape to be measured.

[0044] Starting from the center of each pixel on the screen 6, trace back a main ray towards the reflector 10, and calculate its reflection point on the reflector 10.

[0045] To simulate the non-ideal nature of the real light source 11, a random directional perturbation is assigned to each trailing principal ray. This perturbation follows a three-dimensional Gaussian distribution with a mean of zero and a standard deviation of σ. The standard deviation σ can be set according to the actual half-angle divergence angle of the light source 11. Using the Monte Carlo method, a large number of sub-rays with different random perturbations are generated for each pixel.

[0046] Tracing these sub-rays, after being reflected by mirror 10, they form an "illumination point cloud" on the inner wall surface to be measured. According to the law of conservation of light energy, the center of the density distribution of the illumination point cloud is the most likely intersection point between the ray corresponding to that pixel and the inner wall surface.

[0047] Repeat this process for all pixels to obtain a set of three-dimensional intersection coordinates covering the entire measured area.

[0048] S63. For example Figure 3 As shown, using the three-dimensional point cloud data obtained above, the uniform rational B-spline NURBS fitting algorithm is used to reconstruct the surface and generate the initial surface shape.

[0049] S7. Using the initial surface shape obtained in S6 as the new optical interface, input it into optical simulation software that is completely equivalent to the physical device. Keeping all other optical parameters unchanged, simulate the process of light rays starting from the light source 11, being reflected by the mirror 10, being reflected again on the initial surface shape, and finally reaching the screen 6. By statistically analyzing the light intensity distribution on the screen 6, a theoretical light spot diagram is generated.

[0050] S8. Register the theoretical spot image with the actual spot image, and then calculate the gray-level difference ΔI pixel by pixel. Set a difference threshold, identify all pixels whose ΔI exceeds the threshold, and mark them as "difference pixels".

[0051] Determine if the difference pixels meet the convergence condition. The convergence condition can be set as follows: the proportion of "difference pixels" to the total number of pixels is lower than a preset value. If the convergence condition is met, the surface shape at this point is the final surface shape measurement result. If the convergence condition is not met, continue to step S9.

[0052] S9. Correct the surface data and perform iterative comparison.

[0053] S91. For the "difference pixels" mentioned in S8, this indicates that the initial surface shape has an error in the region corresponding to that pixel. For the incident rays corresponding to these pixels, initiate a more refined tracing calculation. Increase the number of sampled rays in the Monte Carlo simulation, or use a more complex scattering model to more accurately determine their actual intersection coordinates on the inner wall.

[0054] S92. Merge the high-precision intersection coordinates obtained in S91 with the initial intersection coordinates of pixels whose grayscale differences do not exceed the threshold to form a new, more accurate 3D point cloud dataset. Then, use the surface fitting algorithm again to reconstruct the corrected surface shape based on this new dataset.

[0055] S93. Using the corrected surface shape as the new initial surface shape, repeat S7 and S8. The iteration stops when any convergence condition is met, and the surface shape at this point is the final surface shape measurement result.

[0056] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A device for detecting the inner wall surface shape of a rotary optical element, characterized in that: The system includes an optical platform (1), a reflector lifting mechanism (2), a bracket (3), optical elements (4), a screen fixing mechanism (5), a screen (6), an XY two-degree-of-freedom guide rail (7), an image sensor (8), an optical element clamping mechanism (9), a reflector (10), and a light source (11). The reflector lifting mechanism (2) is used to lift the reflector mounting bracket and change the angle between the reflector and the substrate. The screen fixing mechanism (5) is used to clamp and fix the screen (6). The XY two-degree-of-freedom guide rail (7) is used to fix the image sensor (8) and change its position. The optical element clamping mechanism (9) is used to clamp and position the optical element (4).

2. The device for detecting the inner wall surface shape of a rotary optical element according to claim 1, characterized in that: The bracket (3) is an integrally formed structure, including a base plate and a middle plate from top to bottom; the reflector lifting mechanism (2) is installed on the base plate; a circular through hole is provided in the middle of the middle plate, and the outer ring of the optical element clamping mechanism (9) is arranged on the middle plate.

3. The device for detecting the inner wall surface shape of a rotary optical element according to claim 1, characterized in that: The image sensor (8) is a high dynamic range image sensor with a grayscale resolution of not less than 16 bits.

4. The device for detecting the inner wall surface shape of a rotary optical element according to claim 1, characterized in that: The optical element clamping mechanism (9) is a double-layer ring structure coaxially arranged with the circular through hole.

5. A method for detecting the inner wall surface shape of a rotary optical element, characterized in that, The device for detecting the inner wall surface shape of a rotary optical element as described in claim 1 includes the following steps: S1. Place the bracket (3) on the optical platform (1); install the optical element to be tested (4) on the optical element clamping mechanism (9) in the test state, and turn on the light source (11); S2. Raise the reflector (10) and move the screen (6) to form a clear light spot pattern on the screen (6); S3. Adjust the position and focal length of the image sensor (8) to ensure that it can capture a clear light spot pattern; wherein the position includes at least translation in the X-axis and Y-axis directions; S4. Use black matte material to wrap the light path to absorb ambient stray light, and control the image sensor (8) to collect and record the light spot pattern on the screen; S5. Denoise the acquired light spot image and use the denoised image as the actual light spot image; S6. Based on the actual light spot image, perform pixel-by-pixel ray tracing calculation to determine the coordinates of the intersection point between each tracing ray and the inner wall surface of the optical element under test, and reconstruct the initial surface shape of the surface under test based on the set of intersection point coordinates. S7. Using the initial surface shape as the optical interface, establish a simulated optical path, perform optical simulation, and output a theoretical light spot diagram; S8. Compare the theoretical spot image with the actual spot image, identify pixels whose grayscale value difference exceeds a set threshold, and re-trace the light on these pixels to correct the surface shape of the surface to be tested, and use the corrected surface shape as the new initial surface shape. S9. Repeat steps S7 to S8 until the difference between the theoretical spot image and the acquired spot image meets the preset convergence condition, then stop the iteration and take the surface data obtained from the last update as the final surface measurement result of the optical element under test.

6. The method for detecting the inner wall surface shape of a rotary optical element according to claim 5, characterized in that: To implement S6, the following steps are included: S61. Light Quantity Allocation Step: Based on the grayscale value of each pixel in the grayscale image, estimate the number of photons incident on that pixel, and use the number of photons as a weight to determine the amount of light to be allocated to that pixel; S62. Ray Direction Simulation Steps: Simulate the random outgoing direction for each ray of light.

7. The method for detecting the inner wall surface shape of a rotary optical element according to claim 5, characterized in that: In step S7, the simulated optical path established is optically equivalent to the actual illumination conditions constructed in S1 to S4.

8. The method for detecting the inner wall surface shape of a rotary optical element according to claim 5, characterized in that: In step S8, the grayscale difference threshold is determined based on the surface accuracy requirements of the optical element under test; in step S9, the preset convergence is determined based on the surface accuracy requirements of the optical element under test.

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