Rearview mirror lens coating production method, system and equipment

Abstract

The invention relates to the technical field of automobile part production, in particular to a rearview mirror lens coating production method, system and equipment, and the method comprises the steps: sequentially depositing at least two functional film layers in the coating process, monitoring the thickness and uniformity of the film layers in real time, and generating a first film layer quality parameter set; based on the first film layer quality parameter set, predicting a deformation risk area of the lens in a subsequent calibration stage, and outputting a deformation risk level and a compensation suggestion parameter; according to the deformation risk grade and the compensation suggestion parameters, local driving force distribution of the deformable mold is adjusted in a self-adaptive mode, secondary forming processing is conducted on the lens, and actual shape deviation data are collected; and carrying out association analysis on the first film layer quality parameter set, the deformation risk level and the actual morphology deviation data, and dynamically optimizing a coating deposition path and a mold deformation compensation strategy for subsequent lens production. According to the invention, the problem of dynamic deformation of the rearview mirror lens caused by film layer stress accumulation in the film coating production process is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile component production, and in particular to a rearview mirror lens coating production method, system and equipment. Background Art

[0002] In the coating production of automotive rearview mirror lenses, the deposition process of multilayer functional films can cause internal stress accumulation due to differences in thermal expansion coefficients between the film and the substrate, as well as deposition stress. Existing technologies generally employ offline testing combined with static compensation strategies. This involves obtaining film thickness distribution data after coating is complete through optical interferometry or laser scanning, calculating theoretical deformation based on empirical formulas, and then applying a single pressure correction through the mold.

[0003] However, this method suffers from the problem of dynamic accumulation of film stress during the deposition process. Especially for aspheric lenses with complex curvatures, the superposition of multi-layer film stress will evolve in real time with the coating path and changes in ambient temperature and humidity, resulting in spatiotemporal asynchrony in stress release. Existing static compensation models are unable to capture this dynamic deformation process, resulting in delayed deformation prediction and irreversible deformation after stress release due to offline detection. Compensation accuracy is insufficient, as the empirical formula does not consider the coupling relationship between the coating path and deformation conduction, resulting in residual local ripples after mold correction. Process adaptability is poor, and the stress accumulation rate changes when environmental parameters fluctuate. Fixed compensation parameters lead to increased batch defect rates. These problems directly lead to optical distortion of lenses, increased calibration rework rates, and reduced production efficiency, becoming a bottleneck in the production of high-precision automotive-grade rearview mirrors.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a rearview mirror lens coating production method, system and equipment, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: A method for producing a rearview mirror lens coating, the method comprising: Depositing at least two functional film layers in sequence during the coating process, monitoring the film thickness and uniformity in real time, and generating a first film quality parameter set; Based on the first film quality parameter set, predict the deformation risk area of ​​the lens in the subsequent calibration stage, and output the deformation risk level and compensation recommended parameters; The deposited lens is transferred to a calibration station, and the local driving force distribution of the deformable mold is adaptively adjusted according to the deformation risk level and the compensation recommended parameters, the lens is subjected to secondary molding, and actual morphology deviation data is collected; The first film layer quality parameter set, the deformation risk level and the actual morphology deviation data are correlated and analyzed, the weight coefficient of the prediction model is updated, and the coating deposition path and mold deformation compensation strategy for subsequent lens production are dynamically optimized.

[0007] Furthermore, the deformation risk areas of the lens in the subsequent calibration stage are predicted, including: Acquire real-time vibration spectrum data and temperature gradient distribution data of the lens substrate during the coating process, and perform spatiotemporal alignment with the first film layer quality parameter set; Extracting the resonance peak frequency offset and the directional consistency coefficient of the temperature gradient distribution from the vibration spectrum data to generate a coupling stress index between the film layer and the lens substrate; Input the coupling stress index into a dynamic risk prediction model, combine it with the lens curvature constraint condition, and output the deformation risk level and the boundary coordinates of the risk area; Based on the boundary coordinates of the risk area, the deformation risk distribution is marked in the three-dimensionally reconstructed model of the lens substrate, and the coating deposition path is associated to identify the abnormal coating movement segment that causes the risk area.

[0008] Furthermore, marking the deformation risk distribution in the three-dimensionally reconstructed model of the lens substrate comprises: The high-frequency sensor array captures the structural deformation information of the lens substrate during the coating process and analyzes the characteristic frequency band of crack initiation in the information; Synchronously collecting topological change data of the optical interference fringes on the surface of the lens substrate and extracting the interference fringes distortion area; Matching the crack initiation characteristic frequency band with the interference fringe distortion region to identify the critical intersection region where deformation energy is concentratedly released; Based on the relative spatial relationship between the critical intersection area and the coating deposition path, the deformation risk conduction path is marked in the three-dimensional model, and the risk level is dynamically divided according to the angle between the path and the main axis of lens curvature.

[0009] Furthermore, matching the crack initiation characteristic frequency band with the interference fringe distortion region includes: Performing time-frequency energy focusing analysis on the crack initiation characteristic frequency band to extract the time-space coordinates corresponding to the energy peak; Mapping the space-time coordinates to the geometric center of the interference fringe distortion region, and calculating the coupling weight of the energy focusing coefficient and the fringe distortion density; generating a dynamic matching threshold based on the coupling weight, and screening out an overlapping area where both the energy focusing coefficient and the distortion density exceed the dynamic matching threshold as a critical intersection area; The deformation risk conduction path is fitted according to the geometric contour of the critical intersection area, and the risk level weight value is corrected in combination with the angle of the main axis of curvature of the lens.

[0010] Furthermore, the local driving force distribution of the deformable die is adaptively adjusted, including: Real-time collection of dot matrix pressure values ​​in the compressed area of ​​the lens, and simultaneous analysis of the diffusion direction of deformation ripples at the edge of the lens; Performing vector superposition of the lattice pressure value and the diffusion direction of the deformation ripple to generate a pressure compensation spectrum, wherein the pressure enhancement area in the pressure compensation spectrum corresponds to the ripple convergence direction, and the pressure release area corresponds to the ripple divergence direction; Adaptively matching the intensity coefficient of the pressure compensation spectrum according to the deformation risk level, wherein the intensity coefficient of the high-risk area increases according to a nonlinear gradient; The deformation actuator driving the mold applies force along the pressure compensation spectrum distribution, and captures the spectrum attenuation inside the lens in real time during the force application process.

[0011] Furthermore, generating a pressure compensation spectrum includes: Decomposing the lattice pressure value into a normal pressure component and a tangential friction vector, and constructing a pressure action direction topology; Analyze the energy density distribution in the diffusion direction of the deformation ripples and divide them into a high-energy region where the ripples converge and a low-energy region where the ripples diverge; Calculating dynamic weight factors of high-energy zones and low-energy zones based on the pressure action direction topology and the energy density distribution, wherein the weight factor of the high-energy zone is positively correlated with the deformation risk level; fusing the normal pressure component and the tangential friction vector according to the dynamic weight factor to generate an initial pressure compensation value; The initial pressure compensation value is subjected to path tracing optimization, the compensation gradient is extended along the ripple diffusion direction, and the compensation intensity coefficient is superimposed in the region where the lens curvature suddenly changes, so as to form the continuous and smooth pressure compensation spectrum.

[0012] Furthermore, the coating deposition path and mold deformation compensation strategy for subsequent lens production are dynamically optimized, including: Correlating and analyzing the first film layer quality parameter set, the deformation risk level, and the actual morphology deviation data to generate a coating path adjustment plan; According to the real-time parameters of the production environment, screen the solution combination that meets the process constraints, and give priority to the coating path adjustment solution with the highest coating uniformity; During the next lens coating process, the movement trajectory of the coating equipment is dynamically adjusted according to the selected scheme, and the compensation parameters of the mold deformation compensation strategy are pre-set; The deformation characteristics of the lens after the coating adjustment are monitored in real time, the calibration deviation results are compared, and the coating deposition path and the compensation parameters are continuously optimized and adjusted.

[0013] Furthermore, the first film layer quality parameter set, the deformation risk level and the actual morphology deviation data are correlated and analyzed, including: Aligning the spatiotemporal coordinates of the coating deposition path with the spatial distribution of the deformation risk area point by point, and establishing a mapping table of corresponding relationships between coating parameters and deformation positions; Analyzing the thickness variation trend of the first film layer quality parameter set, dynamically matching it with the actual topography deviation data, and quantifying the effect of film layer stress on deformation; Performing graded correction on the action intensity according to the deformation risk level to generate an intensity distribution map of the deformation risk conduction path; Based on the intensity distribution map, the intersection area of ​​the deformation risk conduction path is located, marked as a key deformation control node, and associated with the corresponding trajectory segment in the coating deposition path.

[0014] A rearview mirror lens coating production system, the system comprising: a quality detection module, which deposits at least two functional film layers in sequence during the coating process, monitors the film thickness and uniformity in real time, and generates a first film layer quality parameter set; The risk recommendation module predicts the deformation risk area of ​​the lens in the subsequent calibration stage based on the first film quality parameter set, and outputs the deformation risk level and compensation recommendation parameters; The calibration and correction module transfers the deposited lens to the calibration station. Based on the deformation risk level and the recommended compensation parameters, it adaptively adjusts the local driving force distribution of the deformable mold, performs secondary molding on the lens, and collects actual shape deviation data. The dynamic optimization module correlates and analyzes the first film layer quality parameter set, deformation risk level, and actual morphology deviation data, updates the weight coefficient of the prediction model, and dynamically optimizes the coating deposition path and mold deformation compensation strategy for subsequent lens production.

[0015] A rearview mirror lens coating production device is used to implement the rearview mirror lens coating production method.

[0016] The technical solution of the present invention can achieve the following technical effects: It effectively solves the problem of dynamic deformation of rearview mirror lenses caused by film stress accumulation during the coating production process, as well as the resulting lens quality defects and calibration deviations.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 The figure is a flow chart of a rearview mirror lens coating production method; Figure 2 Schematic diagram of the process for predicting deformation risk areas; Figure 3 Schematic diagram of the structure for adjusting the local driving force distribution; Figure 4 Schematic diagram of the process flow for optimizing the coating deposition path and deformation compensation strategy. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Embodiment 1; like Figure 1 As shown, the present application provides a method for producing a rearview mirror lens coating, the method comprising: S10: sequentially depositing at least two functional film layers during the coating process, monitoring the film layer thickness and uniformity in real time, and generating a first film layer quality parameter set; S20: Based on the first film layer quality parameter set, predict the deformation risk area of ​​the lens in the subsequent calibration stage, and output the deformation risk level and compensation recommended parameters; S30: The deposited lens is transferred to a calibration station. Based on the deformation risk level and the recommended compensation parameters, the local driving force distribution of the deformable mold is adaptively adjusted to perform a secondary molding process on the lens, and actual shape deviation data is collected. S40: Correlation analysis is performed on the first film layer quality parameter set, deformation risk level, and actual morphology deviation data, the weight coefficient of the prediction model is updated, and the coating deposition path and mold deformation compensation strategy for subsequent lens production are dynamically optimized.

[0023] Specifically, first, in a suitable vacuum coating equipment, film layers of different materials are selectively applied, and at least two functional film layers are preferably deposited in sequence. These film layers generally include anti-reflection films, anti-reflection films or other functional film layers. Each film layer is deposited with an optimized thickness to ensure that the functional effect of the film layer is maximized. In this process, high-precision film layer monitoring equipment is used to measure the thickness of each film layer and its distribution uniformity on the lens surface in real time, thereby generating a first film layer quality parameter set. The first film layer quality parameter set not only records the thickness change of the film layer during the deposition process, but also records the specific conditions and film layer properties of each deposition in real time, and identifies potential film layer quality problems through a preset algorithm to ensure the accuracy of the entire coating process; next, based on the first film layer quality parameter set, software analysis is used to predict the risk areas of lens deformation that may occur in the subsequent calibration stage and divide the possible deformation of the lens in the future into regions, and provide clear compensation recommendation parameters. The risk level will be classified according to the unevenness or defect information of the film layer to help calibrate the system. The system undergoes subsequent processing, and then the lens is transferred to the calibration station. At this stage, the local driving force distribution of the mold is adaptively adjusted using the known deformation risk level and compensation recommended parameters. By precisely controlling the force distribution changes in the mold, the lens can be secondary molded in the local area, effectively reducing or eliminating the deformation effects introduced by the previous coating. At the same time, after the calibration process is completed, the sensor collects the actual morphology deviation data of the lens to help identify the effect of secondary molding. Finally, the first film layer quality parameter set, deformation risk level and actual morphology deviation data are correlated and analyzed to form new feedback data. The weight coefficient of the prediction model is continuously updated through the machine learning algorithm to more accurately predict and optimize the coating deposition path and mold deformation compensation strategy of the subsequent lens production. This feedback loop ensures that the production process can cope with the ever-changing process parameters and external environmental factors, improves the consistency and performance stability of the product, and through continuous optimization and adjustment, better utilizes resources, reduces material waste, and improves production efficiency and product quality.

[0024] The technical solution of the present invention effectively solves the problem of dynamic deformation of rearview mirror lenses caused by film stress accumulation during the coating production process, as well as the lens quality defects and calibration deviation problems derived therefrom.

[0025] Further, if Figure 2 As shown, the deformation risk areas of the lens in the subsequent calibration stage are predicted, including: Acquire real-time vibration spectrum data and temperature gradient distribution data of the lens substrate during the coating process, and align them with the first film layer quality parameter set in time and space; Extract the resonance peak frequency offset and the directional consistency coefficient of the temperature gradient distribution from the vibration spectrum data to generate the coupling stress index between the film layer and the lens substrate; The coupling stress index is input into the dynamic risk prediction model, and combined with the lens curvature constraint condition, the deformation risk level and the boundary coordinates of the risk area are output; Based on the boundary coordinates of the risk area, the deformation risk distribution is marked in the 3D reconstructed model of the lens substrate, and the coating deposition path is associated to identify the abnormal coating movement segments that lead to the risk area.

[0026] As a preferred embodiment of the above, the real-time vibration spectrum data of the lens substrate during the coating process refers to the frequency changes caused by physical vibrations on the surface of the lens substrate during the production process. These vibrations may be caused by mechanical movement or environmental factors, and their spectrum analysis can reveal the dynamic response of the substrate; the temperature gradient distribution data refers to the temperature difference on the surface of the substrate at different times during the coating process. This difference reflects the transfer and distribution of heat energy on the substrate; for ease of implementation, the production equipment is equipped with two sensors, one for capturing real-time vibration signals and converting them into spectrum data, and the other for recording temperature changes on the surface of the substrate. Through high-speed data transmission and processing, these data are synchronously collected and aligned in time and space with the quality parameters of the first film layer. This alignment is the basis of analysis, allowing us to identify potential stress and deformation risks from the source; the extraction of resonance peak frequency offset is the core step in analyzing the vibration spectrum. In this process, the change in resonance frequency is identified by comparing the spectrum data of the substrate under different deposition conditions. Usually, this step can be effectively achieved by using fast Fourier transform technology; the directional consistency coefficient is used to reflect whether the distribution direction of the temperature gradient is consistent with expectations. A key analysis of temperature data involves calculating the directional consistency coefficient by comparing the direction of temperature flow in space to reveal any abnormal temperature distribution. Next, the coupled stress index is calculated by de-integrating the resonant frequency change and directional consistency. This stress index reflects the physical interaction between the film and the substrate during film deposition, and this indicator can effectively predict the deformation trend of the substrate. The application of a dynamic risk prediction model is a core component of the embodiment. Based on training data and lens curvature constraints, the risk prediction model is designed to output the deformation risk level and the boundary coordinates of the risk area. The accuracy of the model relies on the accumulation of empirical data and the optimized integration of machine learning algorithms. After repeated verification, the model can accurately predict high-risk areas and provide real-time guidance for the production process. During the 3D reconstruction process, advanced modeling software is used to fully reproduce the lens substrate model. The risk area boundary coordinates are marked on the model, which can intuitively display the deformation risk distribution. Combined with the actual production path, the analysis of abnormal coating motion segments that lead to risk areas requires in-depth analysis of the coating equipment operation and process parameters. This technical implementation can help identify potential process anomalies and provide corrective suggestions.

[0027] Specifically, the deformation risk distribution is marked in the 3D reconstructed lens substrate model, including: The high-frequency sensor array captures the structural deformation information of the lens substrate during the coating process and analyzes the characteristic frequency band of crack initiation in the information; Synchronously collect topological change data of the optical interference fringes on the surface of the lens substrate and extract the interference fringes distortion area; Match the crack initiation characteristic frequency band with the interference fringe distortion area to identify the critical intersection area where deformation energy is concentratedly released; Based on the relative spatial relationship between the critical intersection area and the coating deposition path, the deformation risk conduction path is marked in the 3D model, and the risk level is dynamically divided according to the angle between the path and the main axis of lens curvature.

[0028] As a preferred embodiment of the above, first, the structural deformation information of the lens substrate during the coating process is captured by a high-frequency sensor array. The high-frequency sensor array is composed of multiple precision sensors and can continuously monitor the tiny deformation of the substrate during the coating process. These sensors are arranged in key areas of the coating equipment and can record the dynamic response of the substrate in real time. The structural deformation information is specific spectral data, with particular attention to the characteristic frequency bands of crack initiation. These frequency bands usually reflect signs of weakening of the substrate at stress concentration locations and indicate possible deformation areas. Secondly, the topological change data of the optical interference fringes on the surface of the lens substrate are synchronously collected. Optical interference technology is widely used in lens production. By observing the interference fringes, the surface changes of the substrate can be intuitively obtained. In this embodiment, a high-precision camera and an optical interferometer are used to record the fringes changes in the coating process in real time, and the distortion areas therein are extracted. These distortion areas reflect uneven film deposition or changes in the microstructure of the substrate surface. Next, the crack initiation characteristic frequency bands are matched with the interference fringes distortion areas to identify the areas where deformation energy is concentratedly released. The critical intersection area matching process utilizes data analysis software to perform pattern recognition and spatial comparison by integrating spectral data with optical data, thereby accurately locating the impact area of ​​concentrated energy release. This process is a key step in this implementation plan and ensures that potential deformation risk points can be identified in advance. Based on the relative spatial relationship between the critical intersection area and the coating deposition path, the deformation risk transmission path is marked in the three-dimensional model. This process uses computer-aided design software to fuse the three-dimensional model of the lens substrate with the real-time data to generate detailed risk transmission path maps. These path maps not only show the distribution of deformation risks, but also provide spatial guidance information to help the production team directly intervene in abnormal areas. To further refine the risk assessment, the risk level is dynamically divided by analyzing the angle between these paths and the main axis of lens curvature. The risk level division is based on the size of the angle between the path and the main axis of curvature and combined with historical production data, and dynamically adjusted using an empirical model. This classification method can more accurately locate the risk level and guide the subsequent production links to take appropriate corrective measures.

[0029] Furthermore, the crack initiation characteristic frequency band is matched with the interference fringe distortion area, including: Perform time-frequency energy focusing analysis on the characteristic frequency band of crack initiation and extract the time and space coordinates corresponding to the energy peak; Map the space-time coordinates to the geometric center of the interference fringe distortion area, and calculate the coupling weight of the energy focusing coefficient and the fringe distortion density; A dynamic matching threshold is generated based on the coupling weight, and the overlapping area where both the energy focusing coefficient and the distortion density exceed the dynamic matching threshold is selected as the critical intersection area; The deformation risk conduction path is fitted according to the geometric contour of the critical intersection area, and the risk level weight value is corrected in combination with the angle of the main axis of the lens curvature.

[0030] As a preferred embodiment of the above, first, a time-frequency energy focusing analysis is performed on the characteristic frequency band of crack initiation. The purpose of this analysis is to identify the energy concentration point in the deformation process. Time-frequency analysis techniques, such as wavelet transform, are used to refine the time accuracy of the spectrum analysis to ensure that the energy concentration event is accurately located on the time axis. In this work, the time-space coordinates corresponding to the energy peak are extracted to provide a detailed description of the time and space of the crack initiation. Through this method, the corresponding vibration characteristics of the lens substrate during the coating process can be accurately determined; next, the obtained time-space coordinates are mapped to the geometric center of the interference fringe distortion area, the energy focusing coefficient is solved, and the coupling weight with the fringe distortion density is calculated. The energy data at the corresponding time point of each frequency band is compared with the density of the fringe distortion position, and the coupling weight representing the mutual influence and overlap between the two is calculated. The coupling weight depends on the degree of energy accumulation in a specific area and the degree of fringe distortion in that area; based on the calculated coupling weight, a Dynamic matching thresholds are used to screen overlapping areas with both high energy focusing coefficients and fringe distortion densities. These areas are identified as potential critical intersection areas. In actual operations, the screening process can be made more accurate by dynamically adjusting the thresholds. This selection ensures that only areas with real high deformation risk are identified. Subsequently, the deformation risk conduction path is fitted based on the geometric contours of the critical intersection area. This step involves reconstructing the deformation path in three-dimensional space by analyzing the stress concentration and its propagation trend in the area, usually with the help of three-dimensional space modeling tools. The final conduction path obtained provides a visual early warning for the production process. In addition, the angle between the above-mentioned conduction path and the principal axis of the lens curvature is taken into consideration to correct the risk level weight value. The size of the angle directly affects the sensitivity of the path to deformation risk. Therefore, the adjustment process needs to be based on the support of historical data and empirical models, and the risk level is refined and standardized through regularization methods. In this way, the risk assessment is more targeted and practical.

[0031] Further, if Figure 3 As shown in Figure 1, the local driving force distribution of the deformable mold is adaptively adjusted, including: Real-time collection of dot matrix pressure values ​​in the compressed area of ​​the lens, and simultaneous analysis of the diffusion direction of deformation ripples at the edge of the lens; The lattice pressure value and the diffusion direction of the deformation ripple are vector-superimposed to generate a pressure compensation spectrum. The pressure enhancement area in the pressure compensation spectrum corresponds to the ripple convergence direction, and the pressure release area corresponds to the ripple divergence direction. Adaptively match the intensity coefficient of the pressure compensation spectrum according to the deformation risk level, where the intensity coefficient of the high-risk area increases according to a nonlinear gradient; The deformation actuator that drives the mold applies force along the pressure compensation spectrum distribution and captures the spectrum attenuation inside the lens in real time during the force application process.

[0032] As a preferred embodiment of the above, first, a high-precision pressure sensor network is used in the implementation scheme to collect real-time data on the pressure area of ​​the lens. These sensors are distributed in the form of a dot matrix on the mold surface and can continuously record the pressure changes on the lens substrate during the coating process. In addition to direct pressure measurement, the sensor is also used in conjunction with an edge capture instrument to analyze the diffusion direction of the deformation ripples on the edge of the lens. Deformation ripples are usually significant features that occur on the surface of the substrate under pressure changes. Through directional analysis, we can understand how stress propagates on the surface of the substrate; next, the collected dot matrix pressure values ​​and the deformation ripple diffusion direction obtained by analysis are vector-superimposed to generate a pressure compensation spectrum. The pressure compensation spectrum is calculated in real time, and its purpose is to coordinate the energy distribution applied to the substrate. Specifically, the pressure enhancement area in the pressure compensation spectrum corresponds to the ripple convergence direction to increase the concentration of the force; and the pressure release area corresponds to the ripple divergence direction. , to reduce excessive stress concentration; according to the deformation risk level of the lens, different strength coefficients are set for different risk areas through the risk analysis model. In high-risk areas, a nonlinear gradient increasing strategy is adopted to make the force application more precise and in line with the requirements of dynamic changes. According to real-time data feedback, the fineness of force application is adjusted at different production stages to achieve the goal of adaptive adjustment; finally, the deformation actuator that drives the mold applies force accurately according to the distribution of the pressure compensation spectrum. The deformation actuator includes multiple micro-drives, which work together through computer control, so that each unit can be fine-tuned according to the assigned pressure spectrum. At the same time, the spectrum attenuation monitoring is integrated during the implementation process, which can capture the spectrum changes and attenuation caused by the force inside the lens, thereby providing real-time feedback on the force effect. This feedback mechanism ensures the effectiveness of the pressure compensation strategy and can be used for further optimization and adjustment of the mold.

[0033] More specifically, a pressure-compensated spectrum is generated, including: Decompose the lattice pressure value into the normal pressure component and the tangential friction vector, and construct the pressure action direction topology; Analyze the energy density distribution in the diffusion direction of deformation ripples and divide them into high-energy areas where ripples converge and low-energy areas where ripples diverge; Based on the topology of the pressure direction and the energy density distribution, the dynamic weight factors of the high-energy and low-energy zones are calculated. The weight factor of the high-energy zone is positively correlated with the deformation risk level. The normal pressure component and the tangential friction vector are fused according to the dynamic weight factor to generate the initial pressure compensation value; The initial pressure compensation value is optimized by path tracking, the compensation gradient is extended along the ripple diffusion direction, and the compensation intensity coefficient is superimposed in the region where the lens curvature changes suddenly, forming a continuous and smooth pressure compensation spectrum.

[0034] As a preferred embodiment of the above, first, the dot matrix pressure value generated by the pressure on the lens is decomposed to clarify its influence. The pressure value is decomposed into a normal pressure component and a tangential friction vector. The former reflects the effect of the external force on the normal direction of the lens surface, while the latter is manifested as friction parallel to the surface. In order to better understand the actual direction of pressure, these component data are used to construct a pressure action direction topology map, which is an important basis for comprehensive analysis and regulation of pressure. On this basis, the energy density distribution in the direction of diffusion of the deformation ripples is analyzed, so that production personnel can better manage the influence of pressure concentration on the substrate. By finely zoning the ripple diffusion, high-energy areas where ripples converge and low-energy areas where ripples diverge can be divided. The high-energy area usually shows higher internal stress during the deformation process, while the low-energy area is relatively stable. Therefore, the clear distinction between these two areas has guiding significance for subsequent pressure regulation. Further, based on the constructed pressure action direction topology and energy density distribution, the high-energy area and the low-energy area are dynamically calculated. The weight factor of the energy zone. This step introduces a dynamic weight factor to adjust the component intensity. The weight factor of the high-energy zone is positively correlated with the deformation risk level, that is, as the deformation risk increases, the force weight of the high-energy zone increases significantly. This relative change supports the enhanced application of pressure in high-risk areas. According to the adjustment result of the dynamic weight factor, the normal pressure component is fused with the tangential friction vector to generate an initial pressure compensation value. In the process of realizing pressure compensation, this initial value is used as the basic setting, and the final compensation spectrum is obtained through further optimization. The optimization process adopts a path tracking scheme to optimize the initial pressure compensation value, extend the compensation gradient in the direction of ripple diffusion, and superimpose the compensation intensity coefficient in the area where the curvature of the lens changes suddenly. This superposition method ensures that the compensation spectrum is also continuous and effective in areas with drastic changes. Through continuous correction and adjustment, a continuous and smooth pressure compensation spectrum is formed to cover the entire area that needs to be regulated, thereby ensuring that the pressure on the lens surface is evenly distributed.

[0035] Further, if Figure 4 As shown, the coating deposition path and mold deformation compensation strategy for subsequent lens production are dynamically optimized, including: Correlate and analyze the first film layer quality parameter set, deformation risk level, and actual morphology deviation data to generate a coating path adjustment plan; Based on the real-time parameters of the production environment, screen the solution combination that meets the process constraints and give priority to the coating path adjustment solution with the highest coating uniformity; During the next lens coating process, the movement trajectory of the coating equipment is dynamically adjusted according to the selected scheme, and the compensation parameters of the mold deformation compensation strategy are pre-set; Monitor the lens deformation characteristics after coating adjustment in real time, compare the calibration deviation results, and continuously optimize and adjust the coating deposition path and compensation parameters.

[0036] As a preferred embodiment of the above, first, in the implementation plan, a correlation analysis is performed on the first film layer quality parameter set, the deformation risk level and the actual morphology deviation data. The first film layer quality parameter set includes important indicators such as film thickness, uniformity and adhesion. The deformation risk level is derived from the aforementioned dynamic risk prediction model, and the actual morphology deviation data is obtained from the lens surface measurement. These data are comprehensively compared by advanced data analysis software to generate a set of coating path adjustment schemes. Through statistical analysis and machine learning methods, the best matching coating path can be found to optimize the applied film layer. When selecting the coating path adjustment scheme, the real-time parameters of the production environment, including temperature, humidity and equipment status, must also be considered. These parameters are fed back in real time. Through multi-condition screening, a combination of schemes that meet the process constraints is selected, and the adjustment scheme with the highest coating uniformity is given priority to ensure that the produced lenses reach the best quality. By calculating the path optimization algorithm, it is possible to The optimal path is selected from multiple candidate solutions; in the next lens coating process, the movement trajectory of the coating equipment is dynamically adjusted. The movement trajectory of the coating equipment is adjusted through pre-set control parameters, which are automatically adjusted according to the optimization plan. The equipment can adjust its movement in real time according to the predetermined path to ensure the uniformity of the coating. At the same time, the mold deformation compensation strategy is also pre-set. The compensation parameters of the compensation strategy include the magnitude, direction and action time of the compensation force. These parameters are applied to the deformable mold in real time to achieve precise pressure compensation; real-time monitoring of the lens deformation characteristics after coating adjustment is a key step. Through high-precision sensors, the lens deformation data during and after coating is captured, and the actual test results are compared with the expected calibration deviation. The comparison of this deviation can determine the adaptability of the coating path and compensation parameters. If a large deviation is found, immediate feedback is given, and the coating deposition path and compensation parameters are automatically optimized and adjusted to optimize subsequent production links.

[0037] Furthermore, the first film layer quality parameter set, deformation risk level, and actual morphology deviation data are correlated and analyzed, including: Align the spatiotemporal coordinates of the coating deposition path with the spatial distribution of the deformation risk area point by point, and establish a mapping table of the corresponding relationship between coating parameters and deformation positions; Analyze the thickness variation trend of the first film layer quality parameter set, dynamically match it with the actual morphology deviation data, and quantify the effect of film layer stress on deformation; The action intensity is graded and corrected according to the deformation risk level to generate the intensity distribution map of the deformation risk transmission path; Based on the intensity distribution map, the intersection area of ​​the deformation risk conduction path is located, marked as the key deformation control node, and associated with the corresponding trajectory segment in the coating deposition path.

[0038] As a preferred embodiment of the above, first, in order to implement the preferred steps of the solution, the spatiotemporal coordinates of the coating deposition path are aligned point by point with the spatial distribution of the deformation risk area. This step uses precision positioning to correspond each spatial coordinate in the coating process to the identified deformation risk area. At the same time, a detailed relationship mapping table is established using data management tools. This table shows the correspondence between each coating parameter and the deformation position for subsequent analysis and optimization adjustment; then, the thickness change trend of the first film layer quality parameter concentration is analyzed, and dynamic matching is performed with the actual morphology deviation data. The thickness change trend is a key factor in identifying the time effect of the film layer. By analyzing its changes, the intensity of the film layer stress on the deformation can be quantified. This process uses a dynamic data matching algorithm to compare different test results to ensure the correlation between the identified film layer stress and deformation. This quantitative analysis is Through detailed data comparison and mathematical model support, the foundation is laid for generating the adjustment strategy for the next stage; according to the deformation risk level, the above-mentioned action intensity is graded and corrected. At this stage, an intensity distribution map of the deformation risk conduction path is generated. The map shows the stress propagation path of different intensity areas through a graded risk model, and provides an accurate visualization tool to help identify high-incidence stress areas and their propagation trends. This work is crucial, so as to specifically locate the pressure enhancement and release areas and guide the subsequent implementation of the compensation strategy; next, based on the intensity distribution map, the intersection area of ​​the deformation risk conduction path is located. The intersection area is the key area for stress concentration and release, and is marked as a key deformation control node. By comparing the morphology of the intersection area with historical data, the area of ​​significant change is determined, and the corresponding trajectory segments in the coating deposition path are identified. These trajectory segments become important adjustment targets in the subsequent production optimization process.

[0039] Embodiment 2; Based on the same inventive concept as the rearview mirror lens coating production method in the aforementioned embodiment, the present invention also provides a rearview mirror lens coating production system, the system comprising: a quality detection module, which deposits at least two functional film layers in sequence during the coating process, monitors the film thickness and uniformity in real time, and generates a first film layer quality parameter set; The risk recommendation module predicts the deformation risk area of ​​the lens in the subsequent calibration stage based on the first film quality parameter set, and outputs the deformation risk level and compensation recommendation parameters; The calibration and correction module transfers the deposited lens to the calibration station. Based on the deformation risk level and the recommended compensation parameters, it adaptively adjusts the local driving force distribution of the deformable mold, performs secondary molding on the lens, and collects actual shape deviation data. The dynamic optimization module correlates and analyzes the first film layer quality parameter set, deformation risk level, and actual morphology deviation data, updates the weight coefficient of the prediction model, and dynamically optimizes the coating deposition path and mold deformation compensation strategy for subsequent lens production.

[0040] The above-mentioned adjustment system in the present invention can effectively realize a rearview mirror lens coating production method, and the technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0041] Embodiment 3; Based on the same inventive concept as the rearview mirror lens coating production method in the aforementioned embodiment, the present invention also provides a rearview mirror lens coating production device for implementing a rearview mirror lens coating production method.

[0042] The above-mentioned equipment in the present invention can effectively realize a rearview mirror lens coating production method, and the technical effects that can be achieved are as described in the above embodiments and will not be repeated here.

[0043] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A method for producing a rearview mirror lens coating, characterized in that: The method comprises: Depositing at least two functional film layers in sequence during the coating process, monitoring the film thickness and uniformity in real time, and generating a first film quality parameter set; Based on the first film quality parameter set, predict the deformation risk area of ​​the lens in the subsequent calibration stage, and output the deformation risk level and compensation recommended parameters; The deposited lens is transferred to a calibration station, and the local driving force distribution of the deformable mold is adaptively adjusted according to the deformation risk level and the compensation recommended parameters, the lens is subjected to secondary molding, and actual morphology deviation data is collected; The first film layer quality parameter set, the deformation risk level and the actual morphology deviation data are correlated and analyzed, the weight coefficient of the prediction model is updated, and the coating deposition path and mold deformation compensation strategy for subsequent lens production are dynamically optimized.

2. The rearview mirror lens coating production method according to claim 1, characterized in that: Predicting risk areas of lens deformation during subsequent calibration phases, including: Acquire real-time vibration spectrum data and temperature gradient distribution data of the lens substrate during the coating process, and perform spatiotemporal alignment with the first film layer quality parameter set; Extracting the resonance peak frequency offset and the directional consistency coefficient of the temperature gradient distribution from the vibration spectrum data to generate a coupling stress index between the film layer and the lens substrate; Input the coupling stress index into a dynamic risk prediction model, combine it with the lens curvature constraint condition, and output the deformation risk level and the boundary coordinates of the risk area; Based on the boundary coordinates of the risk area, the deformation risk distribution is marked in the three-dimensionally reconstructed model of the lens substrate, and the coating deposition path is associated to identify the abnormal coating movement segment that causes the risk area.

3. The rearview mirror lens coating production method according to claim 2, characterized in that: Marking a deformation risk distribution in the three-dimensionally reconstructed model of the lens substrate comprises: The high-frequency sensor array captures the structural deformation information of the lens substrate during the coating process and analyzes the characteristic frequency band of crack initiation in the information; Synchronously collecting topological change data of the optical interference fringes on the surface of the lens substrate and extracting the interference fringes distortion area; Matching the crack initiation characteristic frequency band with the interference fringe distortion region to identify the critical intersection region where deformation energy is concentratedly released; Based on the relative spatial relationship between the critical intersection area and the coating deposition path, the deformation risk conduction path is marked in the three-dimensional model, and the risk level is dynamically divided according to the angle between the path and the main axis of lens curvature.

4. The rearview mirror lens coating production method according to claim 3, characterized in that: Matching the crack initiation characteristic frequency band with the interference fringe distortion region includes: Performing time-frequency energy focusing analysis on the crack initiation characteristic frequency band to extract the time-space coordinates corresponding to the energy peak; Mapping the space-time coordinates to the geometric center of the interference fringe distortion region, and calculating the coupling weight of the energy focusing coefficient and the fringe distortion density; generating a dynamic matching threshold based on the coupling weight, and screening out an overlapping area where both the energy focusing coefficient and the distortion density exceed the dynamic matching threshold as a critical intersection area; The deformation risk conduction path is fitted according to the geometric contour of the critical intersection area, and the risk level weight value is corrected in combination with the angle of the main axis of curvature of the lens.

5. The rearview mirror lens coating production method according to claim 1, characterized in that: Adaptively adjust the local driving force distribution of the deformable die, including: Real-time collection of dot matrix pressure values ​​in the compressed area of ​​the lens, and simultaneous analysis of the diffusion direction of deformation ripples at the edge of the lens; Performing vector superposition of the lattice pressure value and the diffusion direction of the deformation ripple to generate a pressure compensation spectrum, wherein the pressure enhancement area in the pressure compensation spectrum corresponds to the ripple convergence direction, and the pressure release area corresponds to the ripple divergence direction; Adaptively matching the intensity coefficient of the pressure compensation spectrum according to the deformation risk level, wherein the intensity coefficient of the high-risk area increases according to a nonlinear gradient; The deformation actuator driving the mold applies force along the pressure compensation spectrum distribution, and captures the spectrum attenuation inside the lens in real time during the force application process.

6. The rearview mirror lens coating production method according to claim 5, characterized in that: Generates pressure-compensated spectra, including: Decomposing the lattice pressure value into a normal pressure component and a tangential friction vector, and constructing a pressure action direction topology; Analyze the energy density distribution in the diffusion direction of the deformation ripples and divide them into a high-energy region where the ripples converge and a low-energy region where the ripples diverge; Calculating dynamic weight factors of high-energy zones and low-energy zones based on the pressure action direction topology and the energy density distribution, wherein the weight factor of the high-energy zone is positively correlated with the deformation risk level; fusing the normal pressure component and the tangential friction vector according to the dynamic weight factor to generate an initial pressure compensation value; The initial pressure compensation value is subjected to path tracing optimization, the compensation gradient is extended along the ripple diffusion direction, and the compensation intensity coefficient is superimposed in the region where the lens curvature suddenly changes, so as to form the continuous and smooth pressure compensation spectrum.

7. The rearview mirror lens coating production method according to claim 1, characterized in that: Dynamically optimize the coating deposition path and mold deformation compensation strategy for subsequent lens production, including: Correlating and analyzing the first film layer quality parameter set, the deformation risk level, and the actual morphology deviation data to generate a coating path adjustment plan; According to the real-time parameters of the production environment, screen the solution combination that meets the process constraints, and give priority to the coating path adjustment solution with the highest coating uniformity; During the next lens coating process, the movement trajectory of the coating equipment is dynamically adjusted according to the selected scheme, and the compensation parameters of the mold deformation compensation strategy are pre-set; The deformation characteristics of the lens after the coating adjustment are monitored in real time, the calibration deviation results are compared, and the coating deposition path and the compensation parameters are continuously optimized and adjusted.

8. The rearview mirror lens coating production method according to claim 7, characterized in that: Correlating and analyzing the first film layer quality parameter set, the deformation risk level, and the actual morphology deviation data includes: Aligning the spatiotemporal coordinates of the coating deposition path with the spatial distribution of the deformation risk area point by point, and establishing a mapping table of corresponding relationships between coating parameters and deformation positions; Analyzing the thickness variation trend of the first film layer quality parameter set, dynamically matching it with the actual topography deviation data, and quantifying the effect of film layer stress on deformation; Performing graded correction on the action intensity according to the deformation risk level to generate an intensity distribution map of the deformation risk conduction path; Based on the intensity distribution map, the intersection area of ​​the deformation risk conduction path is located, marked as a key deformation control node, and associated with the corresponding trajectory segment in the coating deposition path.

9. A rearview mirror lens coating production system, characterized in that: The system comprises: a quality detection module, which deposits at least two functional film layers in sequence during the coating process, monitors the film thickness and uniformity in real time, and generates a first film layer quality parameter set; The risk recommendation module predicts the deformation risk area of ​​the lens in the subsequent calibration stage based on the first film quality parameter set, and outputs the deformation risk level and compensation recommendation parameters; The calibration and correction module transfers the deposited lens to the calibration station. Based on the deformation risk level and the recommended compensation parameters, it adaptively adjusts the local driving force distribution of the deformable mold, performs secondary molding on the lens, and collects actual shape deviation data. The dynamic optimization module correlates and analyzes the first film layer quality parameter set, deformation risk level, and actual morphology deviation data, updates the weight coefficient of the prediction model, and dynamically optimizes the coating deposition path and mold deformation compensation strategy for subsequent lens production.

10. A rearview mirror lens coating production equipment, characterized in that: Used to implement a rearview mirror lens coating production method as described in claims 1-8.

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