A dynamic multi-parameter process monitoring method for security camera lens production process

Abstract

The application discloses a kind of security camera lens production process dynamic multi-parameter process monitoring method, and the application relates to the technical field of multi-parameter process monitoring.The steps of the method include: collecting process parameters and corrugation characteristic data;Collect the optical properties and physical response data of the raw materials of the security camera lens;Discrete data processing, generate raw material standard data according to material type and performance;Collect real-time polishing data, calculate the polishing deviation factor vector;Calculate the weight by entropy weight method, get the polishing alarm correction index, alarm when the threshold value is exceeded and adjust the polishing parameters, get the modified geometric characteristic data;Collect coating stage data, get coating material response and processing environment data by double-dimension feature extraction method, calculate material response deviation index and processing environment disturbance index respectively;Get the coating process comprehensive deviation index by combining the two indexes.The application monitors the security camera lens production process in real time according to the coating process comprehensive deviation index.

Description

Technical Field

[0001] This invention relates to the field of multi-parameter process monitoring engineering, specifically a method for dynamic multi-parameter process monitoring in the production process of security camera lenses. Background Technology

[0002] The purpose of the dynamic multi-parameter process monitoring method for the production process of security camera lenses is to ensure the quality stability and consistency of security camera lenses during the production process through real-time monitoring and intelligent control, thereby improving production efficiency and product yield.

[0003] However, in the traditional lens manufacturing process, the parameter monitoring of key processes such as polishing and coating is isolated and cannot effectively link material properties and changes in the processing environment, resulting in reduced lens production efficiency and affecting image quality. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a dynamic multi-parameter process monitoring method for the production process of security camera lenses, which solves the problem that traditional methods cannot effectively correlate material properties with changes in the processing environment.

[0005] To achieve the above objectives, the present invention provides a method for dynamic multi-parameter process monitoring in the production process of security camera lenses, comprising the following steps:

[0006] Step S1: Collect optical property data and physical response data of raw material samples for security camera lenses;

[0007] Step S2: After standardizing the optical property data and physical response data, classify them according to material type and performance to generate raw material standard data;

[0008] Step S3: Collect real-time polishing data, which includes real-time geometric feature data and real-time polishing process parameters. Based on the raw material standard data and real-time polishing data, calculate the surface roughness deviation rate, polishing pressure deviation rate and thermal stress deviation rate. Combine the surface roughness deviation rate, polishing pressure deviation rate and thermal stress deviation rate to obtain the polishing deviation factor vector.

[0009] Step S4: The polishing deviation factor is weighted by the entropy weight method to obtain the polishing deviation factor weight. The polishing deviation factor vector and the polishing deviation factor weight are combined to calculate the polishing alarm correction index. When the polishing alarm correction coefficient exceeds the preset threshold, an alarm is immediately triggered and the real-time polishing process parameters are dynamically adjusted to obtain the corrected geometric feature data.

[0010] Step S5: Collect coating stage data, extract features from the coating stage data using a two-dimensional feature extraction method to obtain coating material response data and coating processing environment data, calculate the material response deviation index based on the coating material response data and raw material standard data, and calculate the processing environment disturbance index based on the coating processing environment data and the corrected geometric feature data.

[0011] Step S6: Combine the material response deviation index with the processing environment disturbance index to calculate the coating process comprehensive deviation index, and monitor the security camera lens production process in real time based on the coating process comprehensive deviation index.

[0012] Preferably, the collection of optical characteristic data and physical response data from security camera lens raw material samples includes:

[0013] Optical property testing:

[0014] Raw material samples were placed in a spectral transmission device, and refractive index data were recorded every 1°C or 5%RH under different humidity conditions. Five batches of each material were tested and the average value was taken to eliminate batch differences.

[0015] Physical response test:

[0016] Thermal expansion trend: The coefficient of thermal expansion is calculated by monitoring the length change of the sample during the heating process using a laser interferometer. :

[0017]

[0018] Where α is a physical quantity that measures the change in length of the material when the temperature changes, and ΔL is the change in sample length. The original length of the sample is ΔT, and the temperature change is ΔT.

[0019] Mechanical stability: An extended stress was applied to the sample using a stress tester until fracture, the stress-strain curve was recorded, and the elastic modulus was calculated. Yield strength .

[0020] Preferably, the method for generating raw material standard data includes:

[0021] Standardization process: Using 25℃, 50%RH, and no stress as the baseline conditions, all parameters are normalized to the [0,1] range to eliminate dimensional differences;

[0022] Classification labels and database: Labels are assigned based on material type and performance indicators;

[0023] Standardized data, labels, and test conditions are stored in a structured database to create a "digital profile" for each material, generating standard data for raw materials: material ID, label, and refractive index. Temperature variation curve, coefficient of thermal expansion Elastic modulus Yield strength .

[0024] Preferably, the calculation of the surface roughness deviation rate, polishing pressure deviation rate, and thermal stress deviation rate based on the raw material standard data and real-time polishing data includes:

[0025] Calculation of thermal stress deviation rate:

[0026] Measured edge deformation was obtained using a laser displacement meter. , compared with theoretical value By comparison, the thermal stress deviation rate is obtained:

[0027]

[0028] in, It is the thermal stress deviation rate, which reflects the degree of deviation in thermal stress release. It is the measured edge deformation. This is the theoretical value of edge deformation;

[0029] Calculation of polishing pressure deviation rate:

[0030] Based on the yield strength of the raw materials Determine the pressure safety threshold Calculate the measured pressure Deviation rate:

[0031]

[0032] in, This represents the deviation rate of polishing pressure, used to quantify the degree of deviation between the actual pressure and the safety threshold. It is the actual measured polishing pressure value, acquired in real time through a pressure sensor. This is the safe threshold for polishing pressure; exceeding this value may result in permanent deformation or damage to the material.

[0033] Surface roughness deviation rate calculation:

[0034] Obtain the measured surface roughness , and target value contrast:

[0035]

[0036] in, The deviation rate of surface roughness is used to quantify the degree of deviation between the actual roughness and the target value. It is the surface roughness value actually measured by a surface profilometer. This is a pre-set target surface roughness value based on process requirements. This formula is used to evaluate whether the surface roughness after polishing meets the process requirements. A large value indicates that the actual roughness deviates significantly from the target value, which may affect the adhesion and uniformity of subsequent coatings. In this case, the polishing parameters need to be adjusted to ensure that the surface quality meets the requirements and to provide stable substrate conditions for subsequent processes.

[0037] Preferably, the step of combining the surface roughness deviation rate, polishing pressure deviation rate, and thermal stress deviation rate to obtain the polishing deviation factor vector includes:

[0038] Based on thermal stress, pressure, and roughness deviation rate, a polishing deviation factor vector is defined to generate the following:

[0039] ), indicating the polishing deviation factor vector It is a vector that includes thermal stress, pressure, and roughness deviation rate. This form comprehensively expresses the deviation of multiple factors, providing a basis for subsequent analysis of the influence of each factor on the polishing process.

[0040] Preferably, the step of calculating the polishing deviation factor weights using the entropy weight method includes:

[0041] Organize data and build a data matrix ;

[0042] thermal stress deviation rate Polishing pressure deviation rate Surface roughness deviation rate Historical data is integrated into a matrix format to provide foundational data for subsequent calculations;

[0043] Standardized processing

[0044]

[0045] in, This represents the value of the i-th sample after standardization on the j-th indicator. For the first Thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate for each sample. and These are the minimum and maximum values ​​of the indicator, respectively. For each indicator... The data is standardized to eliminate the influence of units and mapped to... interval;

[0046] Calculate the entropy value:

[0047] ,

[0048] in, Indicates the calculation of the first Each sample in the indicator The proportion in the data reflects the data distribution; Information entropy formula, a metric Uncertainty The larger the indicator The more uniform the data distribution, the lower its importance to decision-making; where n is the number of indicators.

[0049] Calculate the weights:

[0050]

[0051] in, It is the polishing deviation factor weight. This represents the entropy value related to the j-th indicator. This represents the entropy value related to the k-th index, where k is the index used to traverse the three deviation factors: thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate. Calculation indicators The difference coefficient is then normalized to obtain the weights. This weight is based on the variability of the data itself, objectively reflecting the degree of influence of each deviation factor on the polishing process, and avoiding the bias of subjectively setting weights.

[0052] Preferably, the polishing alarm correction index is calculated by combining the polishing deviation factor vector and the polishing deviation factor weight. When the polishing alarm correction coefficient exceeds a preset threshold, an alarm is immediately triggered and the real-time polishing process parameters are dynamically adjusted. The corrected geometric feature data includes:

[0053] Polishing alarm correction index:

[0054] =

[0055] in, It is the polishing alarm correction index. The dynamic weights of thermal stress, polishing pressure, and surface roughness deviation rate at time t. Let be the deviation rate of the deviation factor j;

[0056] Correction strategy:

[0057] Red alert: Immediately halt polishing equipment and trigger audible and visual alarms. Simultaneously send a work order containing detailed deviation data, along with historical data curves, to the engineer's terminal for quick root cause identification.

[0058] Yellow alert: Employing a multi-parameter coupling adjustment strategy:

[0059] Temperature control:

[0060] -

[0061] in, This indicates the value after correcting for the polishing fluid temperature. This indicates the current measured temperature of the polishing fluid, which is the basic value for temperature adjustment. This is the thermal stress temperature regulation coefficient, used to quantify the impact of thermal deviation rate on temperature regulation. It is the thermal stress deviation rate. The larger, The larger the value, the more the amount is subtracted from the measured temperature, reflecting the dominant role of thermal deviation in temperature regulation. This is the pressure-temperature cross-influence coefficient, used to measure the indirect impact of pressure deviation rate on temperature. It is the polishing pressure deviation rate, pressure deviation rate. The larger, The larger the value, the more it is subtracted from the measured temperature to ensure that the pressure deviation can also participate in temperature regulation; The surface roughness adjustment coefficient is used to quantify the impact of the surface roughness deviation rate on surface roughness adjustment. It is the surface roughness deviation rate;

[0062] Pressure regulation:

[0063]

[0064] in, It is the corrected polishing pressure. This is the actual measured polishing pressure value. It is the thermal stress deviation rate. It is the polishing pressure deviation rate. This refers to the surface roughness deviation rate. By considering the thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate, the actual measured polishing pressure is corrected, thereby optimizing the polishing process and improving polishing quality.

[0065] After each adjustment, the corrected data is collected in real time. , It is the corrected measured edge deformation value, which is measured in real time using a ruler after the material has been treated by the corrected polishing process.

[0066] Preferably, the step of calculating the material response deviation index based on the coating material response data and raw material standard data includes:

[0067] Record With the collection Used to calculate the actual coefficient of thermal expansion:

[0068]

[0069] in, It is the actual measured coefficient of thermal expansion. This is the corrected measured edge deformation value. It is the original length of the material. The temperature rise of the substrate is used to assess the matching degree of material properties;

[0070] Material response deviation index formula:

[0071]

[0072] in, It is the material response deviation index, which comprehensively measures the degree of deviation between the substrate characteristics and the raw material benchmark value, and evaluates the matching degree of material characteristics with the coating process; , , These are the weights of the coefficient of thermal expansion, the elastic modulus, and the refractive index, respectively. Through orthogonal experiments, the degree of influence of each factor on the overall performance index can be determined, thereby determining... , , The weights; It is the actual measured coefficient of thermal expansion; The baseline value for the coefficient of thermal expansion; It is the actual measured elastic modulus; The baseline value for the modulus of elasticity; Actual measured refractive index It is the reference value for the refractive index;

[0073] Preferably, the step of calculating the processing environment disturbance index based on the coating processing environment data and the corrected geometric feature data includes:

[0074] Formula for processing environment disturbance index:

[0075]

[0076] in, It is the processing environment disturbance index, used to measure the stability of environmental parameters during the coating stage. The higher the index, the more unstable the environment. , , The weights of the evaporation rate, vacuum chamber pressure, and substrate temperature rise on the coating process are respectively represented. It is the actual measured evaporation rate, reflecting the film deposition rate; This is the set target value for the evaporation rate, which is the ideal value for the process; It is the allowable fluctuation threshold of the evaporation rate, used to define the range of deviation; The actual measured vacuum chamber pressure affects the purity of the film and the deposition quality. The goal is to set a target value for the vacuum chamber pressure, which is the ideal pressure for the process. It is the allowable fluctuation threshold of the vacuum chamber pressure; Base temperature rise value, It is the set base temperature rise value. It is the allowable fluctuation threshold of the base temperature rise value.

[0077] Preferably, the step of combining the material response deviation index with the processing environment disturbance index to calculate the comprehensive deviation index of the coating process includes:

[0078] By combining the material response deviation index with the processing environment disturbance index, the comprehensive deviation index of the coating process is calculated:

[0079]

[0080] in: It is the comprehensive deviation index of the coating process. For time Dynamically changing weighting factors are used to adjust the weight of the material response deviation index in the overall deviation index; medium term. hour, Adjustments will be made dynamically based on material stability in the later stages. The environmental correction factor is calculated in real time using data from equipment status and workshop temperature and humidity sensors. The material response deviation index reflects the degree of matching between the substrate properties and the standard data of the raw materials; The processing environment disturbance index measures the stability of environmental parameters such as evaporation rate, vacuum chamber pressure, and substrate temperature rise.

[0081] Beneficial effects

[0082] This invention provides a method for dynamic multi-parameter process monitoring in the production of security camera lenses, relating to the intelligent manufacturing equipment industry and deep learning technology. It has the following beneficial effects:

[0083] (1) The dynamic multi-parameter process monitoring method for the production of security camera lenses collects optical characteristic data and physical response data of raw material samples of security camera lenses. This data is then discretized to make it more standardized, and further categorized by material type and performance, and stored in a structured database. This standardized database provides a precise reference benchmark for subsequent processes such as polishing and coating. It not only ensures that raw material selection is based on scientific data but also facilitates effective traceability of material performance throughout the entire production process, improving the scientific rigor and reliability of production.

[0084] (2) The dynamic multi-parameter process monitoring method for the production process of security camera lenses calculates thermal stress, pressure, and roughness deviation rate in real time, and determines the weight of deviation factors by combining the entropy weight method to generate a polishing alarm correction index. When the threshold is exceeded, the polishing parameters are dynamically adjusted to achieve real-time optimization of the polishing process, effectively improve the surface accuracy and geometric feature consistency of the lens, and reduce the quality defect rate of the polishing process.

[0085] (3) The dynamic multi-parameter process monitoring method for the production process of security camera lenses calculates the material response deviation index and the processing environment disturbance index separately during the coating stage, and then obtains the comprehensive deviation index. Through multi-dimensional data correlation analysis, comprehensive real-time monitoring of the coating process is achieved, ensuring the stability of coating quality and overall lens performance. Attached Figure Description

[0086] Figure 1 This is a flowchart of a dynamic multi-parameter process monitoring method for the production process of security camera lenses proposed in this invention.

[0087] Figure 2 This is a flowchart illustrating the polishing alarm correction index obtained by a dynamic multi-parameter process monitoring method for the production process of security camera lenses proposed in this invention.

[0088] Figure 3 This invention presents a flowchart illustrating a dynamic multi-parameter process monitoring method for the production process of security camera lenses, used to obtain the comprehensive deviation index of the coating process. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] Please see Figure 1This invention provides a technical solution: a method for dynamic multi-parameter process monitoring in the production process of security camera lenses. Specifically, the method for dynamic multi-parameter process monitoring in the production process of security camera lenses is provided below. Please refer to [link / reference]. Figure 1 The method includes the following steps:

[0091] Step S1: Collect optical property data and physical response data of raw material samples for security camera lenses;

[0092] Equipment calibration and test environment setup

[0093] First, the spectral transmission device, laser interferometer system, and stress detector are calibrated:

[0094] The wavelength accuracy of the spectral transmission device is calibrated (e.g., the error is controlled within ±0.1nm), and the test band is set (e.g., visible light 400-760nm or infrared band).

[0095] The stress detector calibrates the pressure sensor (error <1%) and sets the loading rate (e.g., tensile stress increasing by 10N per second).

[0096] Laser interferometers are used to calibrate displacement resolution (e.g., down to the nanometer level, for measuring minute deformations caused by thermal expansion of materials).

[0097] Set up a temperature and humidity controlled laboratory and configure multiple test conditions:

[0098] Temperature gradient: 20℃, 25℃, 30℃ (simulating different production environment temperatures);

[0099] Humidity gradients: 30%RH, 50%RH, 70%RH (testing the effect of high humidity environment on optical plastics);

[0100] Stress types: tensile stress, compressive stress (0-100MPa, covering the common processing stress range).

[0101] Multi-parameter testing of raw materials

[0102] Optical property testing:

[0103] Raw material samples were placed in a spectral transmission device, and refractive index data were recorded every 1°C or 5%RH under different humidity conditions (e.g., the refractive index of a certain glass is 1.52 at 25°C and 50%RH, and rises to 1.53 at 30°C). Five batches of each material were tested and the average value was taken to exclude batch differences.

[0104] Physical response test:

[0105] Thermal expansion trend: The length change of the sample during the heating process (heating rate 5℃ / min) was monitored using a laser interferometer, and the coefficient of thermal expansion was calculated. :

[0106] The linear thermal expansion coefficient (α) is a physical quantity that measures the change in length of a material as temperature changes, and is defined as:

[0107]

[0108] Where α is a physical quantity that measures the change in length of the material when the temperature changes, and ΔL is the change in sample length (cm). ΔT is the original length of the sample (cm), and ΔT is the temperature change (°C). For example, a material with an original length of 10cm will elongate by 0.002cm after the temperature is increased by 10°C. );

[0109] Mechanical stability: An increasing stress is applied to the sample using a stress tester until fracture, the stress-strain curve is recorded, and the elastic modulus is calculated. (stress Force / Cross-sectional area, strain Deformation / Original Length); Yield Strength (The stress value at which a material exhibits a specified plastic strain, such as the stress corresponding to 0.2% plastic strain) This indicates that exceeding this stress level will lead to irreversible deformation.

[0110] Step S2: After standardizing the optical property data and physical response data, classify them according to material type and performance to generate raw material standard data;

[0111] Standardization process:

[0112] Using 25℃, 50%RH, and no stress as the baseline, all parameters are normalized to the [0,1] range (e.g., refractive index 1.52 is normalized to 0.8, and thermal expansion coefficient 2×10^-6 / ℃ is normalized to 0.6) to eliminate dimensional differences (avoid direct comparison between Pa and dimensionless refractive index).

[0113] Category tags and database:

[0114] Labels are assigned based on material type (e.g., "Optical Glass Type A" or "Optical Glass Type B") and performance indicators (e.g., α>1×10^-6 / ℃ is labeled "High Expansion", E>70GPa is labeled "High Rigidity"), for example, "Type B - High Expansion - Low Rigidity".

[0115] Standardized data, labels, and test conditions are stored in a structured database (such as MySQL) to create a "digital profile" for each material, generating standard data for the raw materials: material ID, label, and refractive index. Temperature variation curve, coefficient of thermal expansion Elastic modulus Yield strength .

[0116] Step S3: Collect real-time polishing data, which includes real-time geometric feature data and real-time polishing process parameters. Based on the raw material standard data and the real-time polishing data, calculate the surface roughness deviation rate, polishing pressure deviation rate and thermal stress deviation rate. Combine the surface roughness deviation rate, polishing pressure deviation rate and thermal stress deviation rate to obtain the polishing deviation factor vector.

[0117] Sensor deployment and data acquisition

[0118] Non-contact displacement sensors (such as laser displacement meters with an accuracy of ±1μm) and surface interferometers (for measuring surface roughness with nanometer-level resolution) are integrated inside the polishing equipment for real-time monitoring of changes in the lens surface.

[0119] Real-time geometric feature data:

[0120] A displacement sensor is mounted above the polishing disc to monitor the deformation of the lens edge contour. (e.g., "edge lifting" occurs);

[0121] The surface interferometer periodically scans the lens surface to generate surface roughness. (such as PV value, RMS value);

[0122] Synchronously collect equipment operating parameters (real-time polishing process parameters):

[0123] Polishing pressure (0.1-0.5MPa), polishing fluid temperature T (20-30℃);

[0124] Environmental parameters: real-time temperature and humidity (affecting the thermal response of materials).

[0125] Calculation of theoretical thermal stress release rate

[0126] Utilizing the coefficient of thermal expansion of raw materials Original length of material Material bonding with polishing fluid temperature change Calculate the theoretical value of edge deformation:

[0127]

[0128] in, This is the theoretical value of edge deformation; It is the coefficient of thermal expansion of the raw material. It is the original length of the material, reflecting the material's ability to resist deformation; The temperature change of the polishing fluid drives the generation of thermal stress; this formula quantifies the thermal stress deformation of the material caused by temperature, providing a benchmark for subsequent deviation analysis.

[0129] Calculation of thermal stress deviation rate:

[0130] Measured edge deformation was obtained using a laser displacement meter. , compared with theoretical value By comparison, the thermal stress deviation rate is obtained:

[0131]

[0132] in, It is the thermal stress deviation rate, which reflects the degree of deviation in thermal stress release. It is the measured edge deformation. It is the theoretical value of edge deformation.

[0133] Calculation of polishing pressure deviation rate:

[0134] Based on the yield strength of the raw materials Determine the pressure safety threshold Calculate the measured pressure Deviation rate:

[0135]

[0136] in, This represents the deviation rate of polishing pressure, used to quantify the degree of deviation between the actual pressure and the safety threshold. It is the actual measured polishing pressure value, acquired in real time through a pressure sensor. This is the safe threshold for polishing pressure; exceeding this value may result in permanent deformation or damage to the material.

[0137] Surface roughness deviation rate calculation:

[0138] Obtain the measured surface roughness , and target value contrast:

[0139]

[0140] in, The deviation rate of surface roughness is used to quantify the degree of deviation between the actual roughness and the target value. It is the surface roughness value actually measured by equipment such as a surface profilometer; This is a pre-set target surface roughness value based on process requirements. This formula is used to evaluate whether the surface roughness after polishing meets the process requirements. A large deviation indicates that the actual roughness significantly deviates from the target value, which may affect the adhesion and uniformity of subsequent coatings. In this case, polishing parameters (such as polishing disc speed and pressure) need to be adjusted to ensure that the surface quality meets the requirements and provides stable substrate conditions for subsequent processes.

[0141] Based on thermal stress, pressure, and roughness deviation rate, a polishing deviation factor vector is defined to generate the following:

[0142] ), indicating the polishing deviation factor vector It is a vector that includes thermal stress, pressure, and roughness deviation rate. This form comprehensively expresses the deviation of multiple factors, providing a basis for subsequent analysis of the influence of each factor on the polishing process.

[0143] Step S4: The polishing deviation factor is weighted by the entropy weight method to obtain the polishing deviation factor weight. The polishing deviation factor vector and the polishing deviation factor weight are combined to calculate the polishing alarm correction index. When the polishing alarm correction coefficient exceeds the preset threshold, an alarm is immediately triggered and the real-time polishing process parameters are dynamically adjusted to obtain the corrected geometric feature data.

[0144] Organize data and build a data matrix .

[0145] thermal stress deviation rate Polishing pressure deviation rate Surface roughness deviation rate Historical data is integrated into a matrix form to provide basic data for subsequent calculations.

[0146] Standardized processing

[0147]

[0148] in, This represents the value of the i-th sample after standardization on the j-th indicator. For the first Thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate for each sample. and These are the minimum and maximum values ​​of the indicator, respectively. For each indicator... Standardize the (heat, pressure, coarse) data to eliminate the influence of dimensions and map the data to Interval.

[0149] Calculate the entropy value:

[0150] ,

[0151] in, Indicates the calculation of the first Each sample in the indicator The proportion in the data reflects the data distribution; Information entropy formula, a metric Uncertainty The larger the indicator The more evenly the data is distributed, the lower its importance to decision-making, where n is the number of indicators.

[0152] Calculate the weights:

[0153]

[0154] in, It is the polishing deviation factor weight. This represents the entropy value related to the j-th indicator. This represents the entropy value related to the k-th index, where k is the index used to traverse the three deviation factors: thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate. Calculation indicators The difference coefficient (the smaller the entropy value, the larger the difference coefficient, and the higher the weight) is then normalized to obtain the weight. This weighting is based on the inherent variability of the data and objectively reflects the degree of influence of each deviation factor (heat, pressure, roughness) on the polishing process, avoiding the bias of subjectively setting weights.

[0155] Introducing a dynamic weighting mechanism, weights Over time Changes reflect the importance of recent data. A time decay factor is set. ( ), historical weight These are the initial weights.

[0156]

[0157] in, At time t, the dynamic weights of thermal stress, polishing pressure, and surface roughness deviation rate are given, where j is the deviation factor (thermal stress, polishing pressure, surface roughness), and t is time. is the initial weight of the polishing deviation factor, m is the time step, and T is the current time step.

[0158] Polishing alarm correction index:

[0159] =

[0160] in, It is the polishing alarm correction index. The dynamic weights of thermal stress, polishing pressure, and surface roughness deviation rate at time t. The deviation rate of deviation factor j (e.g.) This is the thermal stress deviation rate. This refers to the polishing pressure deviation rate. (Surface roughness deviation rate).

[0161] Correction strategy:

[0162] Red Alert (Alarm Coefficient ≥ 60%): Immediately suspend the polishing equipment and trigger an audible and visual alarm. Simultaneously, send a work order containing detailed deviation data (δheat, δpressure, δphase) to the engineer's terminal, along with historical data curves (alarm coefficients and deviation changes for the past 10 batches) to facilitate rapid root cause identification.

[0163] Yellow alert (30% ≤ alarm coefficient < 60%): Employs a multi-parameter coupling adjustment strategy.

[0164] Temperature control:

[0165] -

[0166] in, This indicates the value after correcting for the polishing fluid temperature. This indicates the current measured temperature of the polishing fluid, which is the basic value for temperature adjustment. Thermal stress temperature adjustment coefficient (e.g.) This is used to quantify the effect of thermal deviation rate on temperature regulation. It is the thermal stress deviation rate. The larger, The larger the value, the more the amount is subtracted from the measured temperature, reflecting the dominant role of thermal deviation in temperature regulation. The pressure-temperature cross-influence coefficient (e.g.) This is used to measure the indirect effect of pressure deviation rate on temperature; It is the polishing pressure deviation rate, pressure deviation rate. The larger, The larger the value, the more it is subtracted from the measured temperature to ensure that the pressure deviation can also participate in temperature regulation; The surface roughness adjustment coefficient is used to quantify the impact of the surface roughness deviation rate on surface roughness adjustment. It is the surface roughness deviation rate.

[0167] Pressure regulation:

[0168]

[0169] in, It is the corrected polishing pressure. This is the actual measured polishing pressure value. It is the thermal stress deviation rate. It is the polishing pressure deviation rate. This refers to the surface roughness deviation rate. By considering the thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate, the actual measured polishing pressure is corrected, thereby optimizing the polishing process and improving polishing quality.

[0170] After each adjustment, the corrected data is collected in real time. , It is the corrected measured edge deformation value (the edge deformation value is measured in real time using a ruler after the material has been treated by the corrected polishing process).

[0171] Step S5: Collect coating stage data, extract features from the coating stage data using a two-dimensional feature extraction method to obtain coating material response data and coating processing environment data, calculate the material response deviation index based on the coating material response data and raw material standard data, and calculate the processing environment disturbance index based on the coating processing environment data and the corrected geometric feature data.

[0172] After polishing, the lenses enter the coating stage. Data is collected during the coating stage:

[0173] Evaporation rate collection:

[0174] Measurement Principle: A quartz crystal microbalance (QCM) is used. The crystal vibration frequency is inversely proportional to the mass of the film deposited on the surface. The evaporation rate is calculated in real time by measuring the frequency change. The QCM probe is placed near the coating source to ensure accurate sensing of film deposition. Before each batch of coating, the QCM is calibrated using a standard film deposition rate to ensure measurement accuracy. The data acquisition system reads the QCM output signal once per second, converts it into an evaporation rate (unit: mg / s), and stores it in the process database.

[0175] Vacuum chamber pressure ( collection:

[0176] Measurement principle: The cavity pressure is determined by measuring the relationship between the thermal conductivity of the gas and the pressure using a Pirani vacuum gauge. Multiple Pirani vacuum gauges are installed on the vacuum cavity (such as on the top and sides of the cavity) to avoid local pressure differences affecting the measurement. The data acquisition system collects data from each vacuum gauge every 2 seconds, takes the average value as the current cavity pressure (unit: Pa), and displays and stores it in real time.

[0177] Base temperature rise ( )collection:

[0178] Measurement Principle: An infrared thermal imager is used to measure temperature by detecting changes in the intensity of infrared radiation on the substrate surface. The imager is aligned with the substrate surface, ensuring that the measurement area covers key areas (such as the center and edges). Before each batch of coating, the thermal imager is calibrated with thermocouples to ensure measurement accuracy. The imager generates one temperature image per second, extracts the average substrate temperature, and calculates the difference between the average temperature and the initial temperature. (Unit: °C), stored in the database.

[0179] Actual measured coefficient of thermal expansion The coefficient of thermal expansion is obtained through measurement using a laser interferometer, which monitors changes in the material's dimensions (such as length) during temperature variations and then calculates the coefficient of thermal expansion. The actual elastic modulus is typically measured using a stress meter, which applies tensile or compressive stress to the material, records the stress (σ) and strain (ε) data, and then uses a formula... The elastic modulus was calculated. .

[0180] Error handling: If a parameter exceeds a threshold (e.g.) If the deviation from the target value is ±5%, the system will immediately trigger an audible and visual alarm, and simultaneously highlight the abnormal parameter on the operation interface to prompt the operator to check.

[0181] All collected data is synchronized in real time to the workshop monitoring screen and the engineer's mobile device, facilitating remote monitoring and decision-making.

[0182] Feature extraction was performed on the coating stage data using a two-dimensional feature extraction method to obtain coating material response data and coating processing environment data.

[0183] The response data for the coating material includes the actual measured coefficient of thermal expansion. The actual measured elastic modulus The actual measured refractive index The environmental data for the coating process includes evaporation rate F and substrate temperature rise. Vacuum chamber pressure .

[0184] Record (The corrected measured edge deformation value) and the collected data (Base temperature rise) is used to calculate the actual coefficient of thermal expansion:

[0185]

[0186] in, It is the actual measured coefficient of thermal expansion. This is the corrected measured edge deformation value. It is the original length of the material. The temperature rise of the substrate is used to assess the matching degree of material properties.

[0187] Material response deviation index formula:

[0188]

[0189] in, It is the material response deviation index, which comprehensively measures the degree of deviation between the substrate characteristics and the raw material benchmark value, and evaluates the matching degree of material characteristics with the coating process; , , These are the weights for the coefficient of thermal expansion, the elastic modulus, and the refractive index, respectively. Through orthogonal experiments, the weights for each factor (i.e.,...) can be determined. The degree of influence of E and n on the comprehensive performance index is then determined. , , The weight.

[0190] ; It is the actual measured coefficient of thermal expansion; Baseline value for coefficient of thermal expansion (raw materials); It is the actual measured elastic modulus; The baseline value for the modulus of elasticity; Actual measured refractive index It is the reference value for the refractive index;

[0191] Formula for processing environment disturbance index:

[0192]

[0193] in, It is the processing environment disturbance index, used to measure the stability of environmental parameters during the coating stage. The higher the index, the more unstable the environment. , , The weights of the evaporation rate, vacuum chamber pressure, and substrate temperature rise on the coating process are respectively represented. It is the actual measured evaporation rate (unit: e.g., mg / s), reflecting the film deposition rate; This is the set target value for the evaporation rate, which is the ideal value for the process; It is the allowable fluctuation threshold of the evaporation rate, used to define the range of deviation; The actual measured vacuum chamber pressure (unit: e.g., Pa) affects the purity of the film and the deposition quality. The goal is to set a target value for the vacuum chamber pressure, which is the ideal pressure for the process. It is the allowable fluctuation threshold of the vacuum chamber pressure; Base temperature rise value, It is the set base temperature rise value. It is the allowable fluctuation threshold of the base temperature rise value.

[0194] Step S6: Combine the material response deviation index with the processing environment disturbance index to calculate the coating process comprehensive deviation index. Based on the coating process comprehensive deviation index, perform real-time monitoring and intelligent control of the security camera lens production process.

[0195] By combining the material response deviation index with the processing environment disturbance index, the comprehensive deviation index of the coating process is calculated:

[0196]

[0197] in: It is the comprehensive deviation index of the coating process. For time Dynamically changing weighting factors are used to adjust the weight of the material response deviation index in the overall deviation index. For example, in the initial stage of coating... hour, (Focusing on material properties); Mid-term hour, Adjustments will be made dynamically based on material stability in the later stages. As an environmental correction factor, it is calculated in real time using sensor data such as equipment status, workshop temperature and humidity (e.g., when equipment is severely aging). (Increase to enhance the influence of material properties on the comprehensive index); The material response deviation index reflects the degree of matching between the substrate properties (coefficient of thermal expansion, elastic modulus, refractive index, etc.) and the standard data of the raw materials. The processing environment disturbance index measures the stability of environmental parameters such as evaporation rate, vacuum chamber pressure, and substrate temperature rise.

[0198] Set a threshold for judgment

[0199] like If the condition is determined to be normal, it indicates that the fluctuations in material properties and environmental parameters during the current coating process are within a controllable range and have a minimal impact on the quality of the film. Continue to operate with the current coating parameters while continuously monitoring the data to prevent subsequent fluctuations.

[0200] like To trigger root cause analysis, it is necessary to delve into the core factors that cause the comprehensive index to exceed the standard in order to make precise adjustments.

[0201] like and :

[0202] The root cause was determined to be "fluctuations in raw material properties." This indicates that key characteristics of the substrate, such as its coefficient of thermal expansion, elastic modulus, or refractive index, deviate significantly from the initial model, which is the main factor affecting coating quality, while environmental parameters remain relatively stable.

[0203] Control: Trace back the established raw material standard data, and remeasure the substrate properties using high-precision testing equipment (such as laser interferometer to measure thermal expansion, dynamic mechanical analyzer to measure elastic modulus, and ellipsometer to measure refractive index) to update the coefficient of thermal expansion. Elastic modulus Refractive index This ensures that the model matches the actual materials.

[0204] If the test reveals that the characteristics of the raw materials exceed the reasonable tolerance range, the raw material batch should be replaced in a timely manner, and the incoming inspection and model update should be carried out again to ensure the stability of the material characteristics from the source.

[0205] like and :

[0206] Root cause: The issue was identified as "abnormal data during the coating stage." This indicates that environmental parameters such as evaporation rate, vacuum chamber pressure, and substrate temperature rise fluctuated significantly, threatening the stability of the coating process, while the material properties remained relatively stable.

[0207] Adjustment: Calibrate the evaporation rate control device, check the sensor accuracy and signal transmission link of the quartz crystal microbalance, and adjust the control algorithm parameters to ensure that the evaporation rate is stable at the target value. Within the range.

[0208] Perform multi-point calibration on the Pirani vacuum gauge, check the sealing of the vacuum chamber and the performance of the pumping system, repair leaks or replace worn parts, and restore the vacuum chamber pressure. .

[0209] Optimize the substrate temperature control logic based on infrared thermal imager data, and adjust the power output curves of the heating or cooling devices to ensure substrate temperature rise. It meets the process requirements.

[0210] like and :

[0211] The root cause was determined to be "synergistic failure of materials and environment." This means that fluctuations in material properties and abnormal environmental parameters coexisted, and their interaction exacerbated the negative impact on coating quality.

[0212] Adjustments: Replace with less sensitive materials, such as using a substrate with a low coefficient of thermal expansion, to reduce the impact of material property fluctuations on thermal stress; select materials resistant to environmental changes to reduce the interference of environmental parameter fluctuations on its performance.

[0213] Strengthen environmental control and upgrade the vacuum chamber pressure control system (such as by using more precise pressure sensors and closed-loop control algorithms) to improve stability; optimize the evaporation rate control module (such as by introducing a real-time feedback compensation mechanism) to achieve more precise control of film deposition rate, thereby improving coating process conditions through a two-pronged approach.

[0214] This method links the raw material response model with the dynamic behavior modeling of the polishing process and the film deposition disturbance sensitivity identification mechanism. Instead of monitoring single process parameters in isolation, it realizes information connection and trend correlation judgment between multiple process segments, significantly improving the mass production quality and economic benefits of security camera lenses, and providing an innovative solution for the manufacturing of high-precision optical components.

[0215] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, the phrase "comprising an element defined as..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0216] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

Claims

1. A method for dynamic multi-parameter process monitoring in the production process of security camera lenses, characterized in that, Includes the following steps: Step S1: Collect optical property data and physical response data of raw material samples for security camera lenses; Step S2: After standardizing the optical property data and physical response data, classify them according to material type and performance to generate raw material standard data; Step S3: Collect real-time polishing data, which includes real-time geometric feature data and real-time polishing process parameters. Based on the raw material standard data and real-time polishing data, calculate the surface roughness deviation rate, polishing pressure deviation rate and thermal stress deviation rate. Combine the surface roughness deviation rate, polishing pressure deviation rate and thermal stress deviation rate to obtain the polishing deviation factor vector. Step S4: The polishing deviation factor is weighted by the entropy weight method to obtain the polishing deviation factor weight. The polishing deviation factor vector and the polishing deviation factor weight are combined to calculate the polishing alarm correction index. When the polishing alarm correction coefficient exceeds the preset threshold, an alarm is immediately triggered and the real-time polishing process parameters are dynamically adjusted to obtain the corrected geometric feature data. Step S5: Collect coating stage data, extract features from the coating stage data using a two-dimensional feature extraction method to obtain coating material response data and coating processing environment data, calculate the material response deviation index based on the coating material response data and raw material standard data, and calculate the processing environment disturbance index based on the coating processing environment data and the corrected geometric feature data. Step S6: Combine the material response deviation index with the processing environment disturbance index to calculate the coating process comprehensive deviation index, and monitor the security camera lens production process in real time based on the coating process comprehensive deviation index.

2. The method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 1, characterized in that, The collection of optical property data and physical response data from raw material samples of security camera lenses includes: Optical property testing: Raw material samples were placed in a spectral transmission device, and refractive index data were recorded every 1°C or 5%RH under different humidity conditions. Five batches of each material were tested and the average value was taken to eliminate batch differences. Physical response test: Thermal expansion trend: The coefficient of thermal expansion is calculated by monitoring the length change of the sample during the heating process using a laser interferometer. : ； Where ΔL is the change in sample length. ΔT is the original length of the sample, and ΔT is the temperature change. Mechanical stability: An extended stress was applied to the sample using a stress tester until fracture, the stress-strain curve was recorded, and the elastic modulus was calculated. , It is stress. It is strain; yield strength .

3. The method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 2, characterized in that, The generated raw material standard data includes: Standardization process: Using 25℃, 50%RH, and no stress as the baseline conditions, all parameters are normalized to the [0,1] range to eliminate dimensional differences; Classification labels and database: Labels are assigned based on material type and performance indicators; Standardized data, labels, and test conditions are stored in a structured database to create a "digital profile" for each material, generating standard data for raw materials: material ID, label, and refractive index. Temperature variation curve, reference value of coefficient of thermal expansion The benchmark value of elastic modulus Benchmark value of yield strength .

4. The method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 3, characterized in that, The surface roughness deviation rate, polishing pressure deviation rate, and thermal stress deviation rate are calculated based on the raw material standard data and real-time polishing data, including: Calculation of thermal stress deviation rate: Measured edge deformation was obtained using a laser displacement meter. , compared with theoretical value By comparison, the thermal stress deviation rate is obtained: ； in, It is the thermal stress deviation rate, which reflects the degree of deviation in thermal stress release. It is the measured edge deformation. This is the theoretical value of edge deformation; Calculation of polishing pressure deviation rate: Based on the yield strength of the raw materials Determine the pressure safety threshold Calculate the measured pressure Deviation rate: ； in, This represents the polishing pressure deviation rate, used to quantify the degree of deviation between the actual pressure and the safety threshold. It is the actual measured polishing pressure value, acquired in real time through a pressure sensor. This is the safe threshold for polishing pressure; exceeding this value may result in permanent deformation or damage to the material. Surface roughness deviation rate calculation: Obtain the measured surface roughness , and target value contrast: ； in, This represents the surface roughness deviation rate, used to quantify the degree of deviation between the actual roughness and the target value. It is the surface roughness value actually measured by a surface profilometer. This is a pre-set target value for surface roughness based on process requirements. This parameter is used to evaluate whether the surface roughness after polishing meets the process requirements. A large value indicates that the actual roughness deviates significantly from the target value, which may affect the adhesion and uniformity of subsequent coatings. In this case, the polishing parameters need to be adjusted to ensure that the surface quality meets the requirements and to provide stable substrate conditions for subsequent processes.

5. The method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 4, characterized in that, The step of combining the surface roughness deviation rate, polishing pressure deviation rate, and thermal stress deviation rate to obtain a polishing deviation factor vector includes: Based on thermal stress, polishing pressure, and surface roughness deviation rate, a polishing deviation factor vector is defined and generated: ), indicating the polishing deviation factor vector It is a vector that includes thermal stress, polishing pressure, and surface roughness deviation rate. This form comprehensively expresses the deviation of multiple factors, providing a basis for subsequent analysis of the influence of each factor on the polishing process.

6. The method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 5, characterized in that, The step of calculating the weights of the polishing deviation factor using the entropy weight method to obtain the polishing deviation factor weights includes: Organize data and build a data matrix ; thermal stress deviation rate Polishing pressure deviation rate Surface roughness deviation rate Historical data is integrated into a matrix format to provide foundational data for subsequent calculations; Standardized processing ； in, This represents the value of the i-th sample after standardization on the j-th indicator. For the first Thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate for each sample. and These are the minimum and maximum values ​​of the indicator, respectively. For each indicator... The data is standardized to eliminate the influence of units and mapped to... interval; Calculate the entropy value: , ； in, Indicates the calculation of the first Each sample in the indicator The proportion in the data reflects the data distribution; Information entropy formula, a metric Uncertainty The larger the indicator The more uniform the data distribution, the lower its importance to decision-making; where n is the number of indicators. Calculate the weights: ； in, It is the polishing deviation factor weight. This represents the entropy value related to the k-th index, where k is the index used to traverse the three deviation factors: thermal stress deviation rate, polishing pressure deviation rate, and surface roughness deviation rate. Calculation indicators The difference coefficient is then normalized to obtain the weights. This weight is based on the variability of the data itself, objectively reflecting the degree of influence of each deviation factor on the polishing process, and avoiding the bias of subjectively setting weights.

7. The method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 6, characterized in that, The polishing alarm correction index is calculated by combining the polishing deviation factor vector and the polishing deviation factor weight. When the polishing alarm correction coefficient exceeds a preset threshold, an alarm is immediately triggered and the real-time polishing process parameters are dynamically adjusted to obtain the corrected geometric feature data, including: Polishing alarm correction index: = ； in, It is the polishing alarm correction index. The dynamic weights of thermal stress, polishing pressure, and surface roughness deviation rate at time t. Let be the deviation rate of the deviation factor j; Correction strategy: Red alert: Immediately stop the polishing equipment, trigger the audible and visual alarm, and send a work order containing detailed deviation data to the engineer's terminal, along with historical data curves, to facilitate quick identification of the root cause; Yellow alert: Employing a multi-parameter coupling adjustment strategy: Temperature control: - ； in, This indicates the value after correcting for the polishing fluid temperature. This indicates the current measured temperature of the polishing fluid, which is the baseline value for temperature adjustment. This is the thermal stress temperature regulation coefficient, used to quantify the impact of thermal deviation rate on temperature regulation. It is the thermal stress deviation rate. The larger, The larger the value, the more the amount is subtracted from the measured temperature, reflecting the dominant role of thermal deviation in temperature regulation. This is the pressure-temperature cross-influence coefficient, used to measure the indirect impact of pressure deviation rate on temperature. It is the polishing pressure deviation rate, pressure deviation rate. The larger, The larger the value, the more it is subtracted from the measured temperature to ensure that the pressure deviation can also participate in temperature regulation; The surface roughness adjustment coefficient is used to quantify the effect of the surface roughness deviation rate on surface roughness adjustment. It is the surface roughness deviation rate; Pressure regulation: ； in, It is the corrected polishing pressure. This is the actual measured polishing pressure value. It is the thermal stress deviation rate. It is the polishing pressure deviation rate. It is the surface roughness deviation rate. By taking into account the thermal stress deviation rate, polishing pressure deviation rate and surface roughness deviation rate, the actual measured polishing pressure is corrected, thereby optimizing the polishing process and improving the polishing quality. After each adjustment step, the corrected geometric feature data is collected in real time: , It is the corrected measured edge deformation value, which is measured in real time using a ruler after the material has been treated by the corrected polishing process.

8. The method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 7, characterized in that, The calculation of the material response deviation index based on the coating material response data and raw material standard data includes: Record With the collection Used to calculate the actual coefficient of thermal expansion: ； in, It is the actual measured coefficient of thermal expansion. This is the corrected measured edge deformation value. It is the original length of the material. The temperature rise of the substrate is used to assess the matching degree of material properties; Material response deviation index formula: ； in, It is the material response deviation index, which comprehensively measures the degree of deviation between the substrate characteristics and the raw material benchmark value, and evaluates the matching degree of material characteristics with the coating process; , , These are the weights of the coefficient of thermal expansion, the elastic modulus, and the refractive index, respectively. Through orthogonal experiments, the degree of influence of each factor on the comprehensive performance index is determined, thereby determining... , , The weights; It is the actual measured coefficient of thermal expansion; The baseline value for the coefficient of thermal expansion; It is the actual measured elastic modulus; The baseline value for the modulus of elasticity; It is the actual measured refractive index. It is the reference value for refractive index.

9. A method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 8, characterized in that, The calculation of the processing environment disturbance index based on the coating processing environment data and the corrected geometric feature data includes: Formula for processing environment disturbance index: ； in, It is the processing environment disturbance index, used to measure the stability of environmental parameters during the coating stage. The higher the index, the more unstable the environment. , , The weights of the evaporation rate, vacuum chamber pressure, and substrate temperature rise on the coating process are respectively represented. It is the actual measured evaporation rate, reflecting the film deposition rate; This is the set target value for the evaporation rate, which is the ideal value for the process; It is the allowable fluctuation threshold of the evaporation rate, used to define the range of deviation; The actual measured vacuum chamber pressure affects the purity of the film and the deposition quality. This is the set target value for the vacuum chamber pressure, which is the ideal pressure for the process; It is the allowable fluctuation threshold of the vacuum chamber pressure; Measured base temperature rise It is the set base temperature rise value. It is the allowable fluctuation threshold of the base temperature rise value.

10. A method for dynamic multi-parameter process monitoring in the production process of security camera lenses according to claim 9, characterized in that, The step of combining the material response deviation index with the processing environment disturbance index to calculate the comprehensive deviation index of the coating process includes: By combining the material response deviation index with the processing environment disturbance index, the comprehensive deviation index of the coating process is calculated: ； in: It is the comprehensive deviation index of the coating process. For time Dynamically changing weighting factors are used to adjust the weight of the material response deviation index in the overall deviation index; medium term. hour, Adjustments will be made dynamically based on material stability in the later stages. The environmental correction factor is calculated in real time using data from equipment status and workshop temperature and humidity sensors. The material response deviation index reflects the degree of matching between the substrate properties and the standard data of the raw materials; The processing environment disturbance index measures the stability of environmental parameters such as evaporation rate, vacuum chamber pressure, and substrate temperature rise.

Images