Aspheric lens wavefront error high-precision detection method using adaptive optics

Abstract

The invention belongs to the field of aspheric lens detection, and particularly relates to an aspheric lens wavefront error high-precision detection method using adaptive optics, which comprises the following steps: S1, designing and preparing a computer-generated holographic element; s2, constructing an interference detection light path; s3, integrating the intelligent database and a feedback platform; s4, embedding detection nodes in the production process; s5, wavefront error acquisition and calculation; according to the method, adaptive optics and computer-generated holography technologies are embedded into key nodes of a production line, online and real-time wavefront error detection of the aspheric lens is achieved, and a traditional offline sampling inspection mode is replaced. Through a'detection-feedback-optimization 'closed loop, errors can be found and corrected in real time in the production process, unqualified products are prevented from continuing to flow into subsequent procedures, waste of materials and working hours is reduced, the product percent of pass and the production rhythm are remarkably improved, and consumed time of traditional detection and parameter allocation is shortened to the minute level from several hours.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aspheric lens detection, and particularly to an aspheric lens wavefront error high-precision detection method using adaptive optics. BACKGROUND

[0002] High-precision aspheric optical lenses have been widely used in high-end imaging, lithography, laser processing, and space optics, etc. due to their ability to effectively correct aberrations, simplify optical system structure, reduce weight, and improve imaging quality. With the continuous improvement of the performance requirements of optical systems, the surface shape precision of aspheric lenses has entered the nanometer or even sub-nanometer level, and its manufacturing and detection have become a core technical challenge in the field of precision optics.

[0003] Currently, the wavefront error detection of aspheric lenses mainly relies on interferometric measurement technology. Traditional detection methods usually use a standard spherical wave or a plane wave as a reference wavefront, and an compensator (such as a zero lens) is used to adapt to the aspheric surface. However, such methods have poor universality, a compensator is only suitable for a specific surface shape, and the adjustment is complex and costly. For free-form surfaces or high-steep aspheric surfaces, it is often difficult to find a suitable compensation scheme. Another commonly used method is contact profilometry, which has a high dynamic range, but has the disadvantages of easy damage to the mirror surface by measurement force, low efficiency, and inability to directly obtain full-aperture wavefront phase information.

[0004] In recent years, computer-generated holography (CGH) technology has been introduced into aspheric interferometric detection as a flexible digital wavefront modulator. By encoding a diffraction wavefront that matches the ideal aspheric surface, CGH provides the possibility for high-precision and versatile aspheric detection. However, existing CGH-based detection schemes still have significant limitations: Detection and production are disconnected: Existing technologies are mostly offline and spot-checking methods, and the detection process is independent of the production line. This leads to a lag in error discovery, making it impossible to prevent the continuous production of defective products, resulting in waste of materials and time, and making it difficult to trace the specific process steps where errors occur.

[0005] Balancing precision and efficiency: Although CGH detection has high precision in theory, in the traditional process, each batch of products needs to be detected and analyzed for several hours offline, which seriously slows down the production rhythm. For example, the total time for each batch in the traditional way can reach 21 hours, of which nearly half of the time is occupied by offline detection and subsequent parameter adjustment.

[0006] Lack of intelligent feedback and control: Existing detection systems usually only provide "qualified / unqualified" judgments or surface shape error maps, and are not deeply integrated with manufacturing execution systems (MES) or process parameter libraries. Even if errors are identified, manual analysis and machine adjustment by engineers are required, which takes a long time and is difficult to ensure consistency.

[0007] Isolation of error analysis: traditional detection focuses on a single indicator of the final surface error, lacking the ability to automatically classify and diagnose the root causes of errors (such as design, material, clamping, thermal deformation, etc.). The detection data is an "information island" and cannot be associated with the multi-source data in the production process (such as machine tool state, environmental parameters), and cannot achieve preventive control and overall optimization of the process chain. SUMMARY

[0008] The purpose of the present application is to solve the problems in the background art, and to provide a high-precision aspheric lens wavefront error detection method using adaptive optics.

[0009] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: The high-precision aspheric lens wavefront error detection method using adaptive optics comprises the following steps: S1, calculating the holographic element design and preparation; S2, constructing an interference detection light path; S3, integrating an intelligent database and a feedback platform; S4, embedding a detection node in the production process; S5, wavefront error acquisition and solution; S6, error intelligent identification and classification; S7, real-time feedback and process parameter optimization; S8, batch learning and continuous optimization; S9, multi-modal error fusion analysis and cross-process collaborative optimization system.

[0010] Preferably, in S1, the ideal wavefront phase distribution of the target aspheric lens is calculated according to the theoretical surface shape parameters of the target aspheric lens using optical design software; a corresponding calculation hologram is designed, the phase function is encoded, and a diffractive optical element is generated; the calculation hologram is prepared by high-precision photolithography or laser direct writing technology to ensure that the diffractive wavefront error is extremely small.

[0011] Preferably, in S2, the calculation hologram is placed in the reference light path or the test light path of the interferometer; the positions of the light source, the spatial filter, the imaging lens and the sensor are adjusted to form a stable interference light path; a standard plane mirror or a high-precision spherical mirror with a known surface shape is used for system calibration to calibrate the zero error.

[0012] Preferably, in S3, a data signal containing various error types and their thresholds is established; online detection control software is developed to realize the functions of interference image acquisition, phase solution, error analysis and result determination; the production line PLC or process parameter control system is connected to support real-time parameter adjustment instructions, and the error types include design error, deformation error, uniformity error and refraction error.

[0013] Preferably, in S4, an online detection station is arranged after the grinding and polishing key process; the lens to be tested is placed in the interferometer test arm, and the automatic alignment and leveling system is started.

[0014] Preferably, in S5, the light source generates an ideal aspheric wavefront after being modulated by the computer hologram, and irradiates the lens surface; the reflected light and the reference light interfere, and the interference fringes are collected by the image sensor; the phase distribution of the wavefront is calculated by the phase extraction algorithm ψactual(x, y), the calculated wavefront error Δψ(x, y) = ψactual- ψideal, and the RMS value is fitted.

[0015] Preferably, in S6, the system compares Δψ(x, y) with the error patterns in the intelligent database to automatically identify the error type; and outputs a detection report, including: the numerical value and determination result of design error, deformation error, uniformity, and refractive error; the synthetic error value; the final determination of “qualified” or “unqualified”.

[0016] Preferably, in S7, if the detection result is unqualified, the system automatically analyzes the main error source; through the feedback control platform, parameter adjustment instructions are sent to the corresponding process unit, including adjusting the polishing pressure, rotation speed, time; correcting the tool path or residence time distribution; after adjustment, continue production, and reevaluate at the next detection node to form a “detection-feedback-optimization” closed loop, and the process unit includes a polishing machine and a grinding machine.

[0017] Preferably, in S8, the system records the detection data and adjustment parameters of each batch of products; through machine learning algorithm, a “process parameter-error type” mapping model is gradually established to improve the initial parameter accuracy of subsequent batches; and the production system realizes self-adaptive evolution, gradually reducing the dependence on manual intervention.

[0018] Preferably, in S9, the temperature, vibration, and pressure multi-physical field data in the production process are synchronously collected, a “process parameter-environment parameter-optical error” coupling model is established, and through fusion analysis, the deep process root and environmental cause of the error are identified, realizing the leap from single optical detection to multi-modal manufacturing process diagnosis; based on the error traceability result, collaborative optimization is carried out; when it is detected that the deformation after polishing is caused by residual stress in the grinding process, the polishing parameters are adjusted, and at the same time, feedback is made to the grinding process to optimize the grinding trajectory and pressure distribution, realizing preventive correction of the previous process; for small-batch and multi-variety aspheric lens production, according to the real-time detection data and historical learning model, the optimal process chain configuration is dynamically recommended to realize flexible manufacturing and precision adaptive production process reconstruction.

[0019] The aspherical lens wavefront error high-precision detection method of the application has the beneficial effects that: The application embeds adaptive optics and computational holography technology into key nodes of the production line to realize online and real-time wavefront error detection of aspherical lenses, replacing the traditional offline sampling mode. Through the "detection-feedback-optimization" closed loop, errors can be found and corrected immediately during the production process, avoiding the flow of unqualified products into subsequent processes, reducing material and time waste, and significantly improving product qualification rate and production rhythm. The time-consuming of traditional detection and parameter adjustment is compressed from hours to minutes.

[0020] The computational holographic element is used to generate a diffraction wavefront matched with the ideal aspherical surface, and high-precision wavefront error detection can be realized without a physical compensator. This method supports fast acquisition of full-aperture phase information and is a non-contact measurement method, which avoids damage to the mirror surface and is especially suitable for complex optical elements such as free-form surfaces and high-steep aspherical surfaces that are difficult to detect by traditional methods.

[0021] The integrated intelligent database and machine learning algorithm can automatically identify error types (such as design, deformation, uniformity, and refraction error), and fuse and analyze multi-source information such as process parameters and environmental data to quickly locate the root cause of the error. Through real-time feedback control, adjustment instructions are automatically issued to process units such as polishing machines and grinding machines to reduce the dependence on manual experience and improve the consistency and response speed of process adjustment.

[0022] By recording the detection data and process parameters of each batch, a "process parameter-error type" prediction model can be gradually established, and the initial process settings can be optimized based on historical data. This learning mechanism enables the production line to have adaptive evolution capabilities, continuously accumulates process knowledge, and realizes continuous improvement of manufacturing precision and flexible configuration of the production process.

[0023] Through multi-modal error fusion analysis, the deep process root cause and environmental cause of the error can be traced, and the coordinated optimization of the previous and subsequent processes can be supported. For example, the grinding process parameters are optimized in reverse according to the polishing stage detection results to realize preventive correction of the previous process, thereby realizing systematic precision control and efficiency improvement at the process chain level. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The flowchart of the aspherical lens wavefront error high-precision detection method using adaptive optics proposed by the application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. Embodiment one

[0026] Referring to Figure 1 , the aspherical lens wavefront error high-precision detection method using adaptive optics comprises the following steps: S1, calculating holographic element design and preparation; S2, constructing an interference detection light path; S3, integrating an intelligent database and a feedback platform; S4, embedding a detection node in the production process; S5, wavefront error collection and solution; S6, error intelligent identification and classification; S7, real-time feedback and process parameter optimization; S8, batch learning and continuous optimization; S9, multi-modal error fusion analysis and cross-process collaborative optimization system.

[0027] In this embodiment, in S1, according to the theoretical surface shape parameters of the target aspherical lens, the optical design software is used to calculate the ideal wavefront phase distribution ϕ(x, y); the corresponding calculation hologram is designed, the phase function ψ(x, y)=(ψideal-ψreference) is encoded, and the diffractive optical element is generated; the calculation hologram is prepared through high-precision photoetching or laser direct writing technology, and the diffraction wavefront error is ensured to be extremely small.

[0028] In this embodiment, in S2, the calculation hologram is placed in the reference light path or the test light path of the interferometer; the positions of the light source, the spatial filter, the imaging lens and the sensor are adjusted to form a stable interference light path; a standard plane mirror or a high-precision spherical mirror with a known surface shape is used for system calibration to calibrate the zero position error.

[0029] In this embodiment, in S3, a data signal containing various error types and their thresholds is established; online detection control software is developed to realize interference image acquisition, phase solution, error analysis and result determination functions; the production line PLC or process parameter control system is connected to support real-time parameter adjustment instructions to be issued, and the error types include design error, deformation error, uniformity error and refraction error.

[0030] In this embodiment, in S4, an online detection station is set after the key processes of grinding and polishing; the lens to be tested is placed in the test arm of the interferometer, and the automatic alignment and leveling system is started.

[0031] In this embodiment, in S5, the light source generates an ideal aspherical wavefront after being modulated by the calculation hologram, and irradiates the lens surface; the reflected light and the reference light interfere, and the interference fringes are collected by the image sensor; the wavefront phase distribution ψactual(x, y) is calculated by a phase extraction algorithm, the wavefront error Δψ(x, y)=ψactual−ψideal is calculated, and the RMS value is fitted.

[0032] In this embodiment, in S6, the system compares Δψ(x, y) with error patterns in the intelligent database to automatically identify error types; and outputs a detection report, including: numerical values and determination results of design errors, deformation errors, uniformity, and refractive errors; synthetic error values; a final determination of “qualified” or “unqualified”.

[0033] In this embodiment, in S7, if the detection result is unqualified, the system automatically analyzes the main error source; and sends parameter adjustment instructions to the corresponding process unit through the feedback control platform, including adjusting the polishing pressure, rotation speed, and time; correcting the tool path or residence time distribution; and continuing production after adjustment and reevaluating at the next detection node to form a “detection-feedback-optimization” closed loop. The process unit includes a polishing machine and a grinding machine.

[0034] In this embodiment, in S8, the system records the detection data and adjustment parameters of each batch of products; and gradually establishes a “process parameter-error type” mapping model through a machine learning algorithm to improve the initial parameter accuracy of subsequent batches; and realizes adaptive evolution of the production system to gradually reduce the dependence on human intervention.

[0035] In this embodiment, in S9, multi-physical field data such as temperature, vibration, and pressure in the production process are synchronously collected to establish a “process parameter-environment parameter-optical error” coupling model. Through fusion analysis, the deep process root and environmental cause of error generation are identified to realize a leap from single optical detection to multi-modal manufacturing process diagnosis. Based on the error traceability result, collaborative optimization is performed. When it is detected that the deformation of the as-polished surface is caused by residual stress in the grinding process, the polishing parameters are adjusted, and at the same time, feedback is given to the grinding process in the reverse direction to optimize the grinding trajectory and pressure distribution, thereby realizing preventive correction in the previous process. For small-batch and multi-variety aspherical lens production, the optimal process chain configuration is dynamically recommended according to real-time detection data and historical learning models to realize flexible manufacturing and precision adaptive production process reconstruction. Embodiment Two

[0036] The difference between this embodiment and Embodiment One is that an aspherical lens wavefront error high-precision detection method based on adaptive optics is used, including the following steps: S1, holographic element design and preparation are calculated; S2, an interference detection light path is constructed; S3, an intelligent database and a feedback platform are integrated; S4, a detection node is embedded in the production process; S5, wavefront error collection and calculation are performed; S6, error intelligent identification and classification are performed; S7, Real-time feedback and process parameter optimization; S8, Batch learning and continuous optimization; S9, Multimodal error fusion analysis and cross-process collaborative optimization system; S10, Integration of environmental control and online calibration subsystems: Deploy a network of temperature, humidity, and vibration sensors around the detection light path and production equipment to monitor environmental disturbances in real time.

[0037] Establish a coupling model between environmental disturbance and wavefront error, and perform dynamic compensation during calculation.

[0038] Integrate an online calibration module, which triggers before each batch detection or at regular intervals, to quickly calibrate and deduct the interference system's own errors using the built-in standard mirror, ensuring the long-term stability of the detection reference. Example Three

[0039] The difference between this embodiment and Example One is that the aspheric lens wavefront error high-precision detection method of adaptive optics is used, including the following steps: S1, Calculate the holographic element design and preparation; S2, Build an interference detection light path; S3, Integrate an intelligent database and feedback platform; S4, Embed detection nodes in the production process; S5, Wavefront error collection and calculation; S6, Error intelligent identification and classification; S7, Real-time feedback and process parameter optimization; S8, Batch learning and continuous optimization; S9, Multimodal error fusion analysis and cross-process collaborative optimization system; S10, Real-time monitoring and abnormal self-diagnosis during detection: During the collection of interference images and the calculation of wavefront, the image contrast, fringe stability, and phase calculation residual process indicators are monitored simultaneously.

[0040] Threshold values for process indicators are set. When abnormalities occur (such as severe fringe jitter or sudden contrast drop), an alarm is automatically triggered, the detection is paused, and a diagnosis program is started to suggest possible causes (such as workpiece loosening, unstable light source, or excessive environmental vibration) and guide maintenance.

[0041] Test Example I. Test Purpose To verify whether the adaptive optics wavefront error detection method proposed in this paper (including intelligent feedback, environmental control, online calibration, and abnormal diagnosis) is superior to traditional interference detection methods in terms of detection accuracy, stability, efficiency, and process optimization capability.

[0042] II. Test samples Three aspherical lenses (aperture 50mm, F#=2, aspherical degree about 20μm) of the same specification from the same batch.

[0043] Number: L1, L2, L3.

[0044] III. Equipment and system configuration Group Detection system Key features Control group Traditional laser interferometer + zero compensation mirror Manual adjustment, no online feedback, no environmental compensation, manual interpretation Experimental group A Example one system (basic version) Including computer holography, intelligent database, real-time feedback, batch learning Experimental group B Example two system (+ environmental control and online calibration) Based on A, increase temperature and humidity vibration monitoring, dynamic error compensation, timing self-calibration Experimental group C Example three system (+ abnormal self-diagnosis) Based on B, increase process monitoring and abnormal diagnosis function IV. Test steps Under the same environment (temperature fluctuation ±0.5℃, vibration <10Hz), L1, L2, L3 were tested in turn.

[0045] Each group of systems was repeatedly tested 5 times, and the average value was taken as the result.

[0046] The following indicators were recorded: Detection time (from placement to report); Wavefront error RMS value (unit: nm); Repeatability (standard deviation of 5 detection RMS); Error type identification accuracy (compared with artificial precision recheck); System abnormal response (simulated vibration disturbance, light source instability, etc.); V. Test results and comparison data Table 1: Comparison of detection accuracy and repeatability Lens Detection system RMS average (nm) Repeatability (nm, σ) Detection time (s) L1 Traditional interferometer 52.3 4.2 180 Experimental group A (basic version) 51.8 1.5 90 Experimental group B (+ environment) 51.7 0.8 95 Experimental group C (full function) 51.6 0.7 100 L2 Traditional interferometer 48.9 3.8 175 Experimental group A 48.5 1.3 88 Experimental group B 48.3 0.6 93 Experimental group C 48.2 0.6 98 L3 Traditional interferometer 55.1 5.1 185 Experimental group A 54.6 1.6 92 Experimental group B 54.4 0.9 96 Experimental group C 54.3 0.8 102 Table 2: Error type intelligent identification accuracy Detection system Design error recognition accuracy Surface deformation recognition accuracy Uniformity error recognition accuracy Consistency rate of comprehensive judgment Traditional interferometer (manual) 85% 80% 75% 82% Experimental group A 94% 92% 88% 92% Experimental group B 96% 94% 91% 94% Experimental group C 97% 95% 92% 95% Table 3: Environmental disturbance and abnormal response test Test scene Traditional interferometer Experimental group B (environmental control) Experimental group C (abnormal diagnosis) Temperature sudden rise 1℃ RMS fluctuation +3.2nm Fluctuation +0.5nm (after compensation) Fluctuation +0.4nm, record warning Simulated vibration interference (20Hz) Striation blur, cannot detect Still detectable, RMS fluctuation +1.1nm Detection pause, prompt "vibration exceeds standard" Light source intensity drops 30% Low contrast, error increases Automatic gain adjustment, little impact Alarm "light source unstable", suggest checking Workpiece slight tilt (<0.1°) Need to manually re-level Automatic leveling, detection continues Automatic leveling and record tilt history VI. Effect verification For L3 (RMS out of tolerance), the experimental group A system feedback "uneven polishing pressure", after automatically adjusting the polishing machine parameters, the recheck RMS decreased from 54.3nm to 49.8nm.

[0047] The experimental group B found that temperature gradient caused surface deformation through the environmental coupling model, and after optimizing the workshop air conditioning layout, the batch average RMS decreased by about 8%.

[0048] The experimental group C automatically identified "image contrast drop" in one test, diagnosed as water stains on the lens surface, and recovered after cleaning, avoiding misjudgment.

[0049] VII. Conclusion Accuracy and stability: The method in this paper (especially experimental groups B / C) is significantly better than traditional interferometers in repeatability (σ<1nm) and environmental anti-interference.

[0050] Detection efficiency: automation and intelligent recognition shorten the detection time by about 50%.

[0051] Process closed-loop optimization: the system can accurately identify the source of error and feedback to adjust the process parameters to realize the "detection-feedback-optimization" closed loop.

[0052] System reliability: abnormal self-diagnosis function can avoid false detection caused by environmental or equipment abnormalities, and improve system robustness.

[0053] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art within the technical range disclosed by the present application, according to the technical scheme and the inventive concept of the present application, equivalent replacement or change, should be covered within the protection scope of the present application.

Claims

1. A high-precision detection method for wavefront error using an aspherical lens with adaptive optics, characterized in that, Includes the following steps: S1. Design and fabrication of computational holographic elements; S2. Construct the interferometric detection optical path; S3, integrating intelligent database and feedback platform; S4. Embed detection nodes during the production process; S5. Wavefront error acquisition and calculation; S6. Intelligent error identification and classification; S7. Real-time feedback and process parameter optimization; S8, batch learning and continuous optimization; S9, Multimodal Error Fusion Analysis and Cross-Process Collaborative Optimization System.

2. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 1, characterized in that, In step S1, based on the theoretical surface parameters of the target aspherical lens, the ideal wavefront phase distribution ϕ(x,y) is calculated using optical design software; a corresponding computational hologram is designed, and the phase function ψ(x,y) = (ψideal − ψreference) is encoded to generate diffractive optical elements; computational holograms are prepared by high-precision photolithography or laser direct writing technology to ensure that their diffraction wavefront error is extremely small.

3. The high-precision wavefront error detection method using an aspherical lens with adaptive optics according to claim 2, characterized in that, In step S2, the calculated hologram is placed in the reference or test optical path of the interferometer; the positions of the light source, spatial filter, imaging lens and sensor are adjusted to form a stable interference optical path; the system is calibrated using a standard plane mirror or a high-precision spherical mirror with a known surface shape to calibrate the zero-position error.

4. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 3, characterized in that, In step S3, a data information system containing various error types and their thresholds is established; online detection and control software is developed to realize the functions of interferometric image acquisition, phase calculation, error analysis and result determination. Connect to the production line PLC or process parameter control system, support the issuance of real-time parameter adjustment commands, and address error types including design error, deformation error, uniformity error, and refraction error.

5. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 4, characterized in that, In step S4, an online inspection station is set up after the key processes of grinding and polishing; the lens to be tested is placed on the interferometer test arm, and the automatic alignment and leveling system is started.

6. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 5, characterized in that, In step S5, the light source generates an ideal aspherical wavefront after being modulated by a computed hologram, which then illuminates the lens surface; the reflected light interferes with the reference light, and the interference fringes are captured by an image sensor; the wavefront phase distribution is calculated using a phase extraction algorithm. Given ψactual(x,y), calculate the wavefront error Δψ(x,y) = ψactual − ψideal, and fit the RMS value.

7. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 6, characterized in that, In step S6, the system compares Δψ(x,y) with the error patterns in the intelligent database and automatically identifies the error type. Output test report, including: The numerical values ​​and judgment results of design error, deformation error, uniformity, and refraction error; Synthetic error value; The final determination is "qualified" or "unqualified".

8. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 7, characterized in that, In S7, if the test result is unqualified, the system automatically analyzes the main sources of error; through the feedback control platform, it sends parameter adjustment instructions to the corresponding process unit, including adjusting the polishing pressure, speed, and time; correcting the tool path or dwell time distribution; after adjustment, production continues, and it is re-evaluated at the next test node, forming a "test-feedback-optimization" closed loop. The process unit includes polishing machine and grinding machine.

9. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 8, characterized in that, In step S8, the system records the testing data and adjustment parameters for each batch of products; through machine learning algorithms, it gradually establishes a "process parameter - error type" mapping model to improve the accuracy of initial parameters for subsequent batches; and realizes the adaptive evolution of the production system, gradually reducing the dependence on manual intervention.

10. The high-precision detection method for wavefront error using an aspherical lens with adaptive optics according to claim 9, characterized in that, In S9, multi-physical field data of temperature, vibration, and pressure during the production process are collected simultaneously to establish a coupled model of "process parameters - environmental parameters - optical errors". Through fusion analysis, the deep-seated process roots and environmental causes of errors are identified, realizing a leap from single optical detection to multi-modal manufacturing process diagnosis. Based on the error tracing results, collaborative optimization is carried out. When the deformation of the polished surface is detected due to residual stress in the grinding process, the polishing parameters are adjusted, and feedback is simultaneously sent back to the grinding process to optimize the grinding trajectory and pressure distribution, realizing preventive correction in the previous process. For the production of small batches and multiple varieties of aspherical lenses, the optimal process chain configuration is dynamically recommended based on real-time detection data and historical learning models, realizing flexible manufacturing and precision adaptive production process reconstruction.

Images