A weather resistance detection device for new ink material production

Abstract

The application is suitable for the field of weather resistance detection of ink materials, and provides a weather resistance detection device for production of new ink materials.The device comprises a detection box, a light source and a heater are arranged on the top of the detection box, a sprayer is connected to the side wall of the detection box, a motor B is arranged at the bottom of the detection box, an output shaft of the motor B is fixedly connected with a placing groove, a substrate smeared with new ink material is placed in the placing groove, a circulating air pump is connected to the detection box, a flow guide assembly for uniform distribution of airflow is connected to the air outlet of the circulating air pump, and an air outlet plate is arranged at the air inlet of the circulating air pump; and a detection precision optimization system is further included.The application effectively improves the uniformity of environmental parameters in the detection box, reduces the influence of light source attenuation and sample difference on test results, shortens the stabilization time of temperature and humidity parameters through dynamic adjustment of airflow distribution, and improves the test efficiency.

Description

Technical Field

[0001] This invention belongs to the field of weather resistance testing of ink materials, and particularly relates to a weather resistance testing device for the production of new ink materials. Background Technology

[0002] Ink materials, especially new high-performance inks, are increasingly widely used in outdoor advertising, packaging printing, and industrial signage. Their weather resistance—the ability to resist aging caused by environmental factors such as light, temperature, humidity, and rain—is a key indicator determining product lifespan and performance stability. Therefore, accelerated weather resistance testing of ink materials is crucial during the research and development and production stages.

[0003] Currently, common weather resistance testing devices typically include a testing chamber containing components that simulate natural environments, such as a light source (simulating sunlight), a heater (simulating high temperatures), and a sprayer (simulating rain). These devices can perform accelerated aging tests on ink-coated substrate samples within a sealed space.

[0004] However, existing detection devices have several significant problems:

[0005] First, environmental parameters within the testing chamber, such as temperature, humidity, and irradiance, are often unevenly distributed, leading to inconsistent sample testing conditions and reduced comparability and accuracy of test results. Second, traditional control methods are relatively crude, typically only controlling a single parameter at a setpoint, lacking an overall assessment of the testing process and failing to reflect in real-time the combined impact of various factors such as light source attenuation, differences in initial sample conditions, and temperature and humidity fluctuations on the final test results. Furthermore, airflow organization is crucial for the uniformity of the chamber environment, but existing equipment uses relatively simple airflow control methods, either being non-adjustable or offering only one adjustment dimension (such as adjusting only fan speed), making it difficult to achieve rapid and precise environmental equilibration, thus affecting the reliability of the test data.

[0006] More critically, existing technologies lack the ability to coordinate and intelligently control multiple key parameters during the detection process. For example, they cannot quantify the impact of the state of detection variables, the state of the detection process, and the state of the sample on the final detection results, nor can they dynamically optimize airflow control strategies based on these assessment results. This limitation makes it difficult to guarantee the reliability and repeatability of the detection results, which is detrimental to the research and development and quality control of ink materials.

[0007] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0008] The purpose of this invention is to provide a weather resistance testing device for the production of new ink materials, aiming to solve the problem that the existing technology lacks the ability to coordinate and intelligently control multiple key parameters in the testing process.

[0009] This invention is implemented as follows: a weather resistance testing device for the production of a new type of ink material includes a testing chamber. A light source and a heater are installed on the top of the testing chamber. A sprayer is connected to the side wall of the testing chamber. A motor B is installed at the bottom of the testing chamber. The output shaft of the motor B is fixedly connected to a placement groove, in which a substrate coated with the new ink material is placed. The testing chamber is connected to a circulating air pump. The outlet of the circulating air pump is connected to a flow guide assembly for evenly distributing the airflow. An air outlet plate is installed at the inlet of the circulating air pump. The device also includes a testing accuracy optimization system, which comprises:

[0010] The detection variable state assessment module constructs a detection variable state assessment model based on the irradiance attenuation rate, temperature uniformity, and humidity uniformity inside the detection chamber, and outputs the detection variable state assessment coefficients.

[0011] The testing process evaluation module constructs a testing process evaluation model based on the cumulative testing time of ink materials, the deviation time of temperature and humidity setpoints, and the evaluation coefficients of the testing variables, and outputs the testing process evaluation coefficients.

[0012] The sample condition assessment module constructs a sample condition assessment model based on the substrate surface roughness deviation, ink coating thickness deviation, and ink curing degree deviation, and outputs the sample condition assessment coefficients.

[0013] The test result deviation assessment module constructs a test result deviation assessment model based on the test process assessment coefficient and the sample state assessment coefficient under the influence of total irradiance, and outputs the test result deviation assessment coefficient.

[0014] The airflow target decision module constructs an airflow target decision model based on the detection result deviation evaluation coefficient, temperature uniformity, and humidity uniformity, and outputs the target airflow velocity index.

[0015] The airflow coordination control module constructs an airflow coordination control model based on the current guide vane aperture index, real-time airflow velocity index, and target airflow velocity index in the detection chamber, and outputs fan speed control signals and guide vane aperture control signals.

[0016] In a further technical solution, the flow guiding assembly includes an air guide plate, an adjustment plate, and a motor A;

[0017] The air guide plate is connected to the outlet of the circulating air pump. An adjusting plate is slidably connected to the air guide plate. A motor A is fixedly connected to the air guide plate. The output shaft of the motor A is threadedly connected to the adjusting plate. All the guide holes on the air guide plate partially overlap with the guide holes on the adjusting plate. An airflow passage is formed between the air guide plate and the guide holes on the adjusting plate.

[0018] A further technical solution involves subtracting the current irradiance from the initial irradiance, dividing the result by the difference between the initial irradiance and the minimum allowable irradiance, to obtain the irradiance index; subtracting the minimum temperature from the maximum temperature at each location within the current testing chamber, and dividing this difference by the maximum allowable temperature difference, to obtain the temperature deviation index, and subtracting the temperature deviation index from 1 to obtain the temperature uniformity index; subtracting the minimum humidity from the maximum humidity at each location within the current testing chamber, and dividing this difference by the maximum allowable humidity difference, to obtain the humidity deviation index, and subtracting the humidity deviation index from the humidity uniformity index, to obtain the humidity uniformity index; the state evaluation model for the detected variables is as follows:

[0019] ;

[0020] in This is the irradiance weighting coefficient. Temperature weighting coefficient, Humidity weighting coefficient ,and , as well as All greater than , The irradiance index, The temperature uniformity index, The humidity uniformity index To evaluate the state of the detection variable.

[0021] A further technical solution involves dividing the current tested time by the total tested time to obtain the cumulative tested time index; dividing the cumulative time of the current temperature and humidity deviation from the set value by the tested time to obtain the temperature and humidity set value deviation duration index; and using the detection process evaluation model as follows:

[0022] ;

[0023] in To test the cumulative time weighting coefficient, The weighting factor is the deviation of temperature and humidity. To detect the state weight coefficients of variables, For adjustment coefficients, ,and , , as well as All greater than , To test the cumulative time index, The temperature and humidity setpoints deviate from the duration index. To detect the state evaluation coefficient of the variable, This is the evaluation coefficient for the detection process.

[0024] A further technical solution involves substituting the actual surface roughness of the substrate, the actual thickness of the ink coating, and the actual curing degree of the ink into a normalization formula for processing. This generates, sequentially, an index for the deviation of the substrate surface roughness, an index for the deviation of the ink coating thickness, and an index for the deviation of the ink curing degree. The normalization formula is: |Actual value - Target value| / Maximum allowable deviation. The sample state evaluation model is as follows:

[0025] ;

[0026] in This is the substrate roughness weighting coefficient. This is the weighting coefficient for ink coating thickness. This is the ink curability weighting coefficient. ,and , as well as All greater than , This is the surface roughness deviation index of the substrate. This refers to the ink coating thickness deviation index. This refers to the ink curing degree deviation index. The coefficient is used to evaluate the state of the sample.

[0027] A further technical solution involves dividing the cumulative irradiance by the maximum permissible irradiance to obtain the total irradiance index. The deviation evaluation model for the detection results is as follows:

[0028] ;

[0029] in To adjust the parameters, The value is between 0 and 1. The total radiation index, The coefficient for evaluating the state of the sample. The evaluation coefficient for the detection process. This is the evaluation coefficient for the deviation of the test results.

[0030] A further technical solution is proposed: the airflow target decision model is as follows:

[0031] ;

[0032] in The deviation influence coefficient of the test results. This is the temperature influence coefficient. Humidity influence coefficient ,and , as well as All greater than , The deviation evaluation coefficient for the test results. The temperature uniformity index, The humidity uniformity index The target airflow velocity index, The value of is between 0 and 1.

[0033] A further technical solution involves substituting the real-time airflow velocity and the real-time orifice diameter into the maximum-minimum normalization formula for processing, and sequentially generating the real-time airflow velocity index and the real-time orifice diameter index. The airflow cooperative control model is as follows:

[0034] ;

[0035] ;

[0036] in For fan control gain parameters, The gain parameter is for controlling the flow aperture. and The value of is between 0 and 1. The target airflow velocity index, This is the real-time airflow velocity index. For real-time flow orifice index, This is the fan speed control signal. This is the orifice diameter control signal.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This application effectively improves the uniformity of environmental parameters within the testing chamber, reduces the impact of light source attenuation and sample differences on test results, and shortens the stabilization time of temperature and humidity parameters by dynamically adjusting the airflow distribution, thereby improving testing efficiency. The introduction of a quantitative evaluation model makes the testing process adaptive, enhancing the comparability of test data from different batches. The closed-loop control system achieves real-time matching of environmental parameters and sample conditions, improving the reliability of weather resistance test results.

[0039] This application can dynamically adjust the airflow target based on real-time detection deviation, effectively suppressing the accumulation of detection errors caused by light source attenuation and sample state differences. At the same time, through the synergistic optimization of temperature and humidity uniformity, it solves the problem of local environmental parameter deviation caused by unreasonable airflow organization in existing devices, thereby improving the reliability and repeatability of weather resistance test data.

[0040] This application addresses the problem of insufficient uniformity of the chamber environment caused by the single dimension of airflow control in existing detection devices. By coordinating the control of fan speed and guide orifice diameter in real time, it can quickly match the target airflow state and improve the uniformity of temperature and humidity distribution. Through normalization processing and gain parameter constraints, it reduces the adjustment deviation caused by dimensional differences and response speed mismatch during the control process, thereby ensuring the reliability and consistency of detection data. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of the present invention;

[0042] Figure 2 This is a schematic diagram of the internal structure of the detection box in this invention;

[0043] Figure 3 This is a schematic diagram of the flow guiding component;

[0044] Figure 4 A schematic diagram illustrating the principle of the system optimized for detection accuracy.

[0045] In the attached diagram: 1. Detection box; 2. Light source; 3. Heater; 4. Sprayer; 5. Circulating air pump; 6. Air outlet plate; 7. Flow guide assembly; 71. Air guide plate; 72. Adjustment plate; 73. Motor A; 8. Motor B; 9. Placement slot. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0047] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0048] like Figures 1-4 As shown, an embodiment of the present invention provides a weather resistance testing device for the production of a new type of ink material, comprising a testing chamber 1, a light source 2 and a heater 3 disposed on the top of the testing chamber 1, a sprayer 4 connected to the side wall of the testing chamber 1, a motor B8 disposed at the bottom of the testing chamber 1, and a placement groove 9 fixedly connected to the output shaft of the motor B8, in which a substrate for applying the new ink material is placed; a circulating air pump 5 is connected to the testing chamber 1, and a flow guiding component 7 for evenly distributing the airflow is connected to the air outlet of the circulating air pump 5; an air outlet plate 6 is disposed at the air inlet of the circulating air pump 5; the device also includes a testing accuracy optimization system, comprising:

[0049] The detection variable state assessment module constructs a detection variable state assessment model based on the irradiance attenuation rate, temperature uniformity, and humidity uniformity within detection chamber 1, and outputs the detection variable state assessment coefficients.

[0050] The testing process evaluation module constructs a testing process evaluation model based on the cumulative testing time of ink materials, the deviation time of temperature and humidity setpoints, and the evaluation coefficients of the testing variables, and outputs the testing process evaluation coefficients.

[0051] The sample condition assessment module constructs a sample condition assessment model based on the substrate surface roughness deviation, ink coating thickness deviation, and ink curing degree deviation, and outputs the sample condition assessment coefficients.

[0052] The test result deviation assessment module constructs a test result deviation assessment model based on the test process assessment coefficient and the sample state assessment coefficient under the influence of total irradiance, and outputs the test result deviation assessment coefficient.

[0053] The airflow target decision module constructs an airflow target decision model based on the detection result deviation evaluation coefficient, temperature uniformity, and humidity uniformity, and outputs the target airflow velocity index.

[0054] The airflow coordination control module constructs an airflow coordination control model based on the current guide plate aperture index, real-time airflow velocity index, and target airflow velocity index in the detection box 1, and outputs fan speed control signal and guide plate aperture control signal.

[0055] In this embodiment, the detection variable state evaluation module is a functional unit that constructs a mathematical model based on irradiance attenuation rate, temperature uniformity, and humidity uniformity to quantify the stability of environmental parameters. Specifically, it can be implemented using embedded sensors and data processing chips to reflect the impact of light source attenuation and environmental fluctuations on the test. The detection process evaluation module is a functional unit that constructs an evaluation model based on cumulative test time, temperature and humidity deviation duration, and detection variable state evaluation coefficients. Specifically, it can be implemented using time series analysis algorithms to dynamically evaluate the degree to which the detection process deviates from preset conditions. The sample state evaluation module is a functional unit that constructs an evaluation model based on substrate surface roughness, ink coating thickness, and curing degree deviation. Specifically, it can be implemented using a laser rangefinder and optical sensors to eliminate the influence of initial sample differences on the results. The detection result deviation evaluation module is a functional unit that constructs an evaluation model by integrating process and sample state deviations under total irradiance constraints. Specifically, it can be implemented using a multivariate regression algorithm to reflect the cumulative effect of combined factors on the test results. The airflow target decision module is a functional unit that generates a target flow velocity index based on the deviation coefficient and environmental uniformity. Specifically, it can be implemented using a fuzzy logic controller to achieve dynamic matching between detection requirements and real-time status. The airflow coordination control module is a functional unit that adjusts airflow by linking the diameter of the guide plate holes with the fan speed. Specifically, it can be implemented using a PID control algorithm and a stepper motor to ensure precise control of airflow distribution.

[0056] Specifically, the light source 2 and heater 3 inside the testing chamber 1 generate combined environmental stress, and motor B8 drives the placement slot 9 to rotate, ensuring uniform testing of the substrate. The circulating air pump 5 adjusts the airflow distribution through the guide assembly 7, and the air outlet plate 6 maintains airflow circulation. The detection variable state assessment module collects irradiance, temperature, and humidity data in real time and calculates the environmental parameter uniformity index. The detection process assessment module evaluates the stability of the detection process by combining the test time and parameter deviation duration. The sample state assessment module measures the substrate roughness, coating thickness, and curing degree to quantify sample preparation deviation. The detection result deviation assessment module integrates the above data and calculates the result deviation coefficient under the constraint of total irradiance. The airflow target decision module generates a target flow rate based on the deviation coefficient and environmental uniformity, and the airflow coordination control module adjusts the guide plate aperture and fan speed in conjunction to form a dynamic airflow adjustment mechanism. All modules form a closed-loop control through data interaction and feedback to optimize environmental parameters.

[0057] Compared to existing technologies, traditional devices employ independent parameter control and a single airflow adjustment method, which cannot address test deviations caused by the coupling of multiple factors. This solution achieves comprehensive optimization of environmental parameters, process states, and sample deviations by establishing a hierarchical evaluation model and a collaborative control mechanism. Existing technologies lack quantitative assessment of light source attenuation and sample preparation differences; this solution constructs a dynamic compensation system for detection result deviations through multi-dimensional data fusion. Traditional airflow adjustment only changes the flow rate; this solution improves the control accuracy of airflow distribution through dual adjustment of the guide plate aperture and fan speed.

[0058] like Figure 3 As shown, in a preferred embodiment of the present invention, the flow guiding assembly 7 includes an air guide plate 71, an adjustment plate 72, and a motor A73;

[0059] The air guide plate 71 is connected to the air outlet of the circulating air pump 5. An adjusting plate 72 is slidably connected to the air guide plate 71. A motor A73 is fixedly connected to the air guide plate 71. The output shaft of the motor A73 is threadedly connected to the adjusting plate 72. All the guide holes on the air guide plate 71 partially overlap with the guide holes on the adjusting plate 72. An airflow passage is formed between the guide holes on the air guide plate 71 and the adjusting plate 72.

[0060] In this embodiment, specifically, the air guide plate 71 is fixed to the outlet of the circulating air pump 5, and its guide holes provide an initial distribution path for the airflow. The adjusting plate 72 covers the surface of the air guide plate 71 via a sliding connection, and the motor A73 drives the adjusting plate 72 to move along the plane of the air guide plate 71 via a threaded drive. When the motor A73 drives the adjusting plate 72 to slide, the overlapping area of ​​the two sets of guide holes changes, forming an adjustable flow passage. The opening of the flow passage is linearly related to the displacement of the adjusting plate 72, and the flow area of ​​the flow passage can be precisely adjusted by controlling the rotation angle of the motor A73. When the airflow passes through the flow passage, different velocity distributions are generated due to the change in the flow cross-section, thereby achieving dynamic adjustment of the airflow velocity within the detection chamber 1. The partially overlapping design of the guide holes avoids abrupt changes in the flow cross-section, maintains the laminar flow state of the airflow, and reduces local temperature and humidity fluctuations caused by turbulence.

[0061] like Figure 4As shown, in a preferred embodiment of the present invention, the difference between the initial irradiance and the current irradiance is divided by the difference between the initial irradiance and the minimum allowable irradiance to obtain the irradiance index; the difference between the maximum temperature value and the minimum temperature value at each location in the current detection chamber 1 is divided by the maximum allowable temperature difference to obtain the temperature deviation index, and the temperature uniformity index is obtained by subtracting the temperature deviation index from the detection chamber 1; the difference between the maximum humidity value and the minimum humidity value at each location in the current detection chamber 1 is divided by the maximum allowable humidity difference to obtain the humidity deviation index, and the humidity uniformity index is obtained by subtracting the humidity deviation index from the detection chamber 1; the state evaluation model for the detected variables is as follows:

[0062] ;

[0063] in This is the irradiance weighting coefficient. Temperature weighting coefficient, Humidity weighting coefficient ,and , as well as All greater than , The irradiance index, The temperature uniformity index, The humidity uniformity index To detect the state evaluation coefficient of the variable, The value ranges from 0 to 1.

[0064] In this embodiment, the irradiance index is a standardized parameter obtained by dividing the difference between the initial irradiance and the current irradiance by the difference between the initial irradiance and the minimum allowable irradiance. Specifically, it can be calculated using real-time data acquisition from a light intensity sensor, substituted into the formula, to quantify the attenuation degree of light source 2. The temperature uniformity index is obtained by dividing the maximum and minimum temperature difference within the detection chamber 1 by the maximum allowable temperature difference, and then subtracting this ratio from a value of 1. Specifically, it can be measured and calculated using a multi-point temperature sensor array, to characterize the uniformity of temperature distribution. The humidity uniformity index uses the same calculation logic as the temperature uniformity index, and can be obtained through a humidity sensor array to eliminate the influence of local humidity fluctuations on the test. Weighting coefficients. , as well as Specifically, priority can be dynamically adjusted through preset empirical values ​​or adaptive algorithms to achieve priority configuration under different detection scenarios.

[0065] Specifically, during the operation of testing chamber 1, a light intensity sensor monitors irradiance data in real time and calculates an irradiance index reflecting the attenuation degree of light source 2. Temperature and humidity sensor groups collect data from different locations within the chamber, calculating temperature and humidity deviation indices based on the difference between maximum and minimum values, which are then converted into uniformity indices. The state evaluation model for the detected variables nonlinearly combines these three indices according to preset weighting coefficients. The irradiance index uses a squared term to enhance its sensitivity to change; the temperature uniformity index uses an exponential function to highlight the critical threshold effect; and the humidity uniformity index uses a logarithmic function to balance nonlinear effects. This model outputs a single evaluation coefficient, providing a quantitative basis for optimizing the subsequent testing process.

[0066] Through the above technical solution, this application effectively solves the problem of irradiance calculation deviation caused by the attenuation of light source 2, eliminates the influence of local fluctuations in temperature and humidity fields on test results, and provides an accurate data basis for the control of the detection process by quantitatively evaluating the dynamic changes of environmental parameters, thereby improving the reliability and comparability of weather resistance test results.

[0067] like Figure 4 As shown, in a preferred embodiment of the present invention, the cumulative test time index is obtained by dividing the current test time by the total test time; the cumulative time of the current temperature and humidity deviation from the set value is divided by the test time to obtain the temperature and humidity set value deviation duration index; the detection process evaluation model is as follows:

[0068] ;

[0069] in To test the cumulative time weighting coefficient, The weighting factor is the deviation of temperature and humidity. To detect the state weight coefficients of variables, For adjustment coefficients, ,and , , as well as All greater than , To test the cumulative time index, The temperature and humidity setpoints deviate from the duration index. To detect the state evaluation coefficient of the variable, The evaluation coefficient for the detection process. The value ranges from 0 to 1.

[0070] In this embodiment, the cumulative test time index refers to the proportion of the current test time to the preset total test time. This is achieved by recording the tested time with a timer and calculating the ratio with the preset total test time, reflecting the impact of test progress on the test results. The temperature and humidity setpoint deviation time index refers to the proportion of the cumulative time that the temperature and humidity parameters deviate from the setpoint to the total tested time. This is achieved by real-time monitoring of temperature and humidity deviations by sensors and statistical analysis of the cumulative deviation time, quantifying the negative impact of temperature and humidity control stability on the testing process. The detection variable state evaluation coefficient characterizes the dynamic fluctuation state of the current testing environment parameters. Weighting coefficients. , , These are used to adjust the contribution ratio of the cumulative effect of test time, the degree of temperature and humidity deviation, and the fluctuation of environmental parameters in the evaluation model. Specifically, they can be dynamically adjusted through preset empirical values ​​or adaptive algorithms to achieve priority configuration under different testing scenarios.

[0071] Specifically, the model uses an exponential function. A non-linear decay process is applied to the cumulative test time exponent, ensuring that its impact on the evaluation results gradually weakens as the test time increases, thus avoiding error amplification caused by linear accumulation. This is achieved through the squared term. The sensitivity of the temperature and humidity deviation duration index is enhanced; as the duration of temperature and humidity deviation increases, this term significantly decreases, thus highlighting the negative impact of temperature and humidity control failure in the model. This is achieved by introducing the natural logarithm of the state evaluation coefficient of the detection variable. Dynamic parameters such as irradiance attenuation, temperature, and humidity distribution uniformity are incorporated into the evaluation process in a non-linear manner to ensure that the impact of environmental parameter fluctuations on the detection process is accurately reflected. Weighting coefficients. , , The normalization constraint allows the contribution ratios of the three factors to be adjustable while maintaining a constant sum. For example, when the detection environment fluctuates significantly, the normalization constraint can be increased. This increases the weight of environmental parameter status assessment, thereby achieving a multi-dimensional dynamic balance assessment of the detection process.

[0072] Compared to existing technologies, traditional methods typically control only a single parameter independently. For example, they may judge the detection progress solely by accumulating test time or trigger alarms by counting the number of temperature and humidity deviations. These methods lack comprehensive correlation analysis of the cumulative effect of test time, the stability of temperature and humidity control, and fluctuations in environmental parameters. This solution constructs a composite model that dynamically couples these three factors, including exponential decay, squared sensitivity, and logarithmic correlation terms. This solves the problem of misjudging the detection process status caused by isolated evaluation in existing technologies, achieving accurate quantification of the overall detection process status.

[0073] Through the above technical solution, this application can dynamically balance the impact of accumulated test time, temperature and humidity control deviations, and environmental parameter fluctuations on the test results, avoiding bias caused by single-factor evaluation. For example, in long-term testing, the model automatically reduces the weight of accumulated test time while enhancing the sensitivity to temperature and humidity deviations and environmental parameter fluctuations, thereby accurately identifying the risk of detection errors caused by light source attenuation or temperature and humidity runaway. This model provides a reliable evaluation basis for subsequent airflow control, ensuring the environmental uniformity and comparability of results during the testing process.

[0074] like Figure 4 As shown, in a preferred embodiment of the present invention, the actual surface roughness of the substrate, the actual thickness of the ink coating, and the actual curing degree of the ink are respectively substituted into the normalization formula for processing, and the substrate surface roughness deviation index, the ink coating thickness deviation index, and the ink curing degree deviation index are generated sequentially. The normalization formula is: |actual value - set target value| / maximum allowable deviation value. The sample state evaluation model is:

[0075] ;

[0076] in This is the substrate roughness weighting coefficient. This is the weighting coefficient for ink coating thickness. This is the ink curability weighting coefficient. ,and , as well as All greater than , This is the surface roughness deviation index of the substrate. This refers to the ink coating thickness deviation index. This refers to the ink curing degree deviation index. The coefficient for evaluating the state of the sample. The value range is between 0 and 1.

[0077] In this embodiment, the normalization formula refers to converting the absolute deviations of the actual measured values ​​of substrate surface roughness, ink coating thickness, and curing degree from the target values ​​into a standardized index. Specifically, this can be achieved by combining absolute value calculations with a maximum permissible deviation threshold, eliminating comparability barriers between parameters of different dimensions. The substrate surface roughness deviation index refers to the degree to which the substrate surface processing accuracy deviates from the target value. It can be calculated by measuring the actual values ​​using a contact roughness meter or optical profilometer, and is used to quantify the impact of substrate surface condition on ink adhesion. The ink coating thickness deviation index refers to the degree of deviation between the actual coating thickness and the process requirements. It can be calculated by acquiring data using a laser thickness gauge or ultrasonic thickness gauge, and is used to reflect the interference of coating uniformity on weather resistance testing. The ink curing degree deviation index refers to the difference between the actual curing degree of the ink and the preset standard. It can be calculated by acquiring data using infrared spectroscopy analysis or hardness testing, and is used to assess the risk of material performance degradation due to incomplete curing. Weighting coefficients. , as well as This refers to the dynamic allocation factor of the influence of each parameter on the sample state. Specifically, it can be determined by regression analysis based on historical test data and used to adjust the sensitivity of the evaluation model according to the ink type or substrate characteristics.

[0078] Specifically, before the testing begins, the actual measured values ​​of substrate surface roughness, ink coating thickness, and curing degree are collected and input into the evaluation system. The actual deviations of each parameter are converted into an index within the range of 0-1 using a normalization formula. For example, if the maximum allowable deviation for substrate roughness is ±5μm, and the actual deviation is 3μm, then the deviation index is 0.6. Subsequently, weighting coefficients are assigned to each parameter based on the ink material characteristics. For example, for high-gloss ink, the weighting coefficient for substrate roughness can be set to 0.5, the weighting coefficient for coating thickness to 0.3, and the weighting coefficient for curing degree to 0.2. During the weighted summation process, each deviation index is converted into a corresponding state contribution value. For example, when the coating thickness deviation index is 0.8, its corresponding (1-Td) term is 0.2, and multiplied by the weighting coefficient 0.3, the contribution value is 0.06. The final generated sample state evaluation coefficient comprehensively reflects the potential influence of substrate processing, coating process, and curing conditions on the test results, providing a quantitative basis for subsequent test data correction.

[0079] Compared with existing technologies, traditional detection methods only focus on environmental parameter control while ignoring the differences in physical properties during sample preparation, resulting in poor data comparability between different samples under the same test conditions. This solution, however, establishes a multi-parameter normalization model and, for the first time, incorporates substrate roughness, coating thickness, and curing degree deviation into the evaluation system of the detection process, thus solving the problem of systematic errors introduced by differences in the initial state of the samples.

[0080] Through the above technical solution, this application achieves a quantitative assessment of sample preparation quality, and can automatically identify sample state abnormalities caused by substrate processing errors, coating process fluctuations, or improper curing conditions. During the testing process, this assessment coefficient is used to dynamically correct environmental control parameters. For example, when the sample surface roughness deviation is large, airflow disturbance is automatically enhanced to compensate for the temperature and humidity distribution differences caused by surface unevenness, thereby ensuring that samples in different states obtain comparable and accurate weather resistance data under the same testing conditions.

[0081] like Figure 4 As shown, in a preferred embodiment of the present invention, the total irradiance index is obtained by dividing the cumulative irradiance by the maximum permissible irradiance, and the detection result deviation evaluation model is as follows:

[0082] ;

[0083] in To adjust the parameters, The value is between 0 and 1. The total radiation index, The coefficient for evaluating the state of the sample. The evaluation coefficient for the detection process. This is the evaluation coefficient for the deviation of the test results.

[0084] In this embodiment, the total irradiance index quantifies the cumulative photoaging effect experienced by the material during testing by using the ratio of cumulative irradiance to the maximum permissible irradiance. Specifically, it can be implemented by real-time data acquisition and integration using an irradiance sensor, reflecting the direct impact of cumulative irradiance on material performance degradation. The detection process evaluation coefficient characterizes the stability of environmental control during the detection process. The sample state evaluation coefficient reflects the inherent influence of the sample's own state on the test results. The adjustment parameter η is a weighting factor used to balance the contribution of the detection process and sample state to the deviation assessment. Specifically, it can be dynamically adjusted using a preset empirical value or an adaptive algorithm to prevent a single parameter from excessively dominating the evaluation results.

[0085] Specifically, this technical solution dynamically couples the cumulative irradiation effect, fluctuations in detection process parameters, and differences in sample state by establishing a mathematical relationship. The total irradiance index, as the numerator, directly reflects the cumulative intensity of photoaging. The product of the detection process evaluation coefficient and the sample state evaluation coefficient serves as a composite correction factor, used to characterize the interaction between the dynamic process and the inherent state. The denominator term adjusts parameters... By applying a nonlinear weighting to both parameters, the evaluation coefficient automatically decreases to reflect its negative impact on the final result when any parameter in the detection process or sample state deviates significantly. For example, if the temperature and humidity deviate from the set values ​​for an extended period, leading to a decrease in the evaluation coefficient of the detection process, or if the surface roughness of the substrate exceeds the allowable range, causing a decrease in the evaluation coefficient of the sample state, the composite correction factor in the numerator will decrease, thereby improving the sensitivity of the evaluation coefficient for the deviation of the detection result. This model achieves real-time quantitative evaluation of the degree of deviation in test results by dynamically balancing the interactions among multiple factors.

[0086] Through the above technical solution, this application can effectively quantify the combined impact of cumulative irradiation and environmental parameter fluctuations on test results, solving the problem of poor comparability of test data due to differences in sample conditions. By dynamically adjusting the weight relationship between the detection process and sample conditions, excessive interference of single parameter anomalies on evaluation results is avoided, improving the accuracy and reliability of deviation assessment. This model provides a precise decision-making basis for subsequent airflow control, enabling environmental control to adaptively adjust to the current degree of deviation, thereby improving the overall accuracy of weather resistance testing.

[0087] like Figure 4 As shown, in a preferred embodiment of the present invention, the airflow target decision model is as follows:

[0088] ;

[0089] in The deviation influence coefficient of the test results. This is the temperature influence coefficient. Humidity influence coefficient ,and , as well as All greater than , The deviation evaluation coefficient for the test results. The temperature uniformity index, The humidity uniformity index As an adjustment constant, The target airflow velocity index, The value of is between 0 and 1.

[0090] In this embodiment, the detection result deviation evaluation coefficient correlates the cumulative deviation during the detection process with the deviation of the sample's own state, providing a dynamic basis for airflow adjustment. The temperature uniformity index reflects the impact of the spatial consistency of the temperature field on the detection results. The humidity uniformity index reflects the spatial distribution of the humidity field. Weighting coefficients. , as well as To balance the bias in detection results and the influence of temperature and humidity, a dynamic weight allocation algorithm can be used to adjust the weights according to real-time detection needs. Its function is to dynamically allocate the priority of airflow regulation requirements for different environmental parameters. (Adjustment constant) It refers to a very small constant used to prevent the denominator from being zero, and its function is to ensure the numerical stability of the model operation.

[0091] Specifically, when uneven temperature distribution occurs within test chamber 1, the temperature uniformity index... Reduced, leading to As the term increases, the temperature influence coefficient... The corresponding weights will drive the target airflow velocity index Enhance airflow circulation. When the test result deviation evaluation coefficient... When the square term increases due to abnormal sample condition or process control deviation, Rapid growth will significantly increase the numerator value, prompting airflow regulation to prioritize compensation for systematic biases in the detection results. The linear term in the denominator and the regulation constant... The combination of these factors maintains the weight ratio of each influencing factor while avoiding computational overflow under extreme conditions. By constraining the normalized weight coefficients, the dynamic balance between temperature, humidity, and detection result deviations is ensured during the decision-making process. The final output target airflow velocity index passes through a 0-1 normalized range, providing a unified benchmark for the subsequent coordinated control of fan speed and guide orifice diameter.

[0092] Compared with existing technologies, traditional methods typically use fixed thresholds or single-parameter linear control of airflow, which cannot cope with the dynamic changes caused by the coupling of multiple factors during the detection process. This solution, however, constructs a nonlinear decision model combining quadratic and linear terms. This enhances the airflow regulation response speed when temperature uniformity deteriorates, prioritizes compensation for systematic errors when detection result deviations increase, and achieves differentiated control of different environmental parameters through dynamic weight allocation, significantly improving the accuracy of environmental parameter control under complex operating conditions.

[0093] like Figure 4 As shown, in a preferred embodiment of the present invention, the real-time airflow velocity and the real-time orifice diameter are respectively substituted into the maximum-minimum normalization formula for processing, and the real-time airflow velocity index and the real-time orifice diameter index are generated sequentially. The airflow cooperative control model is as follows:

[0094] ;

[0095] ;

[0096] in For fan control gain parameters, The gain parameter is for controlling the flow aperture. and The value of is between 0 and 1. The target airflow velocity index, This is the real-time airflow velocity index. For real-time flow orifice index, This is the fan speed control signal. For the flow orifice control signal, , The value range is between 0 and 1.

[0097] In this embodiment, the real-time airflow velocity index refers to a value mapped from the actual flow velocity to the range of 0-1 through maximum-minimum normalization. Specifically, this can be achieved by linearly transforming the velocity data collected by sensors, thus eliminating the influence of different dimensions on the control model. The real-time orifice diameter index refers to a value mapped from the actual opening size of the guide vane to the range of 0-1 through maximum-minimum normalization. Specifically, this can be calculated using image recognition or displacement sensor detection of the orifice diameter, thus unifying the dimensions of mechanical adjustment parameters and control signals. The fan control gain parameter is a coefficient used to adjust the fan speed response speed. Specifically, it can be determined using empirical values ​​or adaptive algorithms, and its value is between 0-1 to suppress overshoot caused by sudden speed changes. The orifice diameter control gain parameter is a coefficient used to adjust the rate of change of the guide vane opening. Specifically, it can be calculated by matching the mechanical transmission ratio with the motor stepping accuracy, and its value is between 0-1 to balance adjustment speed and positioning accuracy.

[0098] Specifically, by collecting real-time airflow velocity and guide vane orifice diameter data within the detection chamber 1, these data are normalized and converted into dimensionless exponents, allowing fan speed control and guide vane diameter control to operate collaboratively within the same mathematical framework. In the airflow collaborative control model, the difference between the target airflow velocity index and the real-time airflow velocity index serves as the control benchmark. This benchmark is multiplied by the fan control gain parameter and the guide vane diameter control gain parameter, respectively, and then superimposed onto the current state to generate the fan speed control signal and the guide vane diameter control signal. For example, when the detected real-time velocity is lower than the target value, the model dynamically adjusts the increase in fan speed through the gain parameter, while simultaneously proportionally expanding the guide vane diameter to reduce airflow resistance, forming a dual regulation mechanism of velocity enhancement and distribution optimization. Thus, the rapid response characteristics of fan speed are used to achieve coarse adjustment of airflow intensity, while the mechanical adjustment of the guide vane diameter is used to achieve fine adjustment of airflow distribution. The complementary nature of these two methods shortens the convergence time of environmental parameters.

[0099] Compared to existing technologies, current detection devices typically adjust airflow intensity solely by the speed of a single fan, failing to simultaneously optimize airflow distribution patterns. Furthermore, a dimensional mismatch exists between the mechanical adjustment components and the control signal. This proposed solution, however, unifies the dimensions of the control variables through normalization and establishes a dual-channel collaborative control model, enabling synchronous dynamic adjustment of airflow intensity and distribution patterns. In addition, the introduction of a gain parameter avoids system oscillations caused by excessively rapid response in traditional proportional control, while also suppressing wear caused by frequent start-stop cycles of mechanical components.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A weather resistance testing device for the production of a novel ink material, comprising a testing chamber, a light source and a heater being provided on the top of the testing chamber, a sprayer being connected to the side wall of the testing chamber, a motor B being provided at the bottom of the testing chamber, the output shaft of the motor B being fixedly connected to a placement groove, a substrate for applying the novel ink material being placed in the placement groove, a circulating air pump being connected to the testing chamber, a flow guiding component for evenly distributing the airflow being connected to the outlet of the circulating air pump, and an air outlet plate being provided at the inlet of the circulating air pump; characterized in that, It also includes a detection accuracy optimization system, which includes: The detection variable state assessment module constructs a detection variable state assessment model based on the irradiance attenuation rate, temperature uniformity, and humidity uniformity inside the detection chamber, and outputs the detection variable state assessment coefficients. The testing process evaluation module constructs a testing process evaluation model based on the cumulative testing time of ink materials, the deviation time of temperature and humidity setpoints, and the evaluation coefficients of the testing variables, and outputs the testing process evaluation coefficients. The sample condition assessment module constructs a sample condition assessment model based on the substrate surface roughness deviation, ink coating thickness deviation, and ink curing degree deviation, and outputs the sample condition assessment coefficients. The test result deviation assessment module constructs a test result deviation assessment model based on the test process assessment coefficient and the sample state assessment coefficient under the influence of total irradiance, and outputs the test result deviation assessment coefficient. The airflow target decision module constructs an airflow target decision model based on the detection result deviation evaluation coefficient, temperature uniformity, and humidity uniformity, and outputs the target airflow velocity index. The airflow coordination control module constructs an airflow coordination control model based on the current guide vane aperture index, real-time airflow velocity index, and target airflow velocity index in the detection chamber, and outputs fan speed control signals and guide vane aperture control signals. The irradiance index is obtained by subtracting the current irradiance from the initial irradiance and dividing the result by subtracting the minimum allowable irradiance from the initial irradiance. The temperature deviation index is obtained by subtracting the minimum temperature from the maximum temperature at each location within the current testing chamber and dividing the result by the maximum allowable temperature difference. The temperature uniformity index is obtained by subtracting the temperature deviation index from 1. The humidity deviation index is obtained by subtracting the humidity deviation index from the humidity at each location within the current testing chamber and dividing the result by the maximum allowable humidity difference. The humidity uniformity index is obtained by subtracting the humidity deviation index from the humidity uniformity index. The state assessment model for the detected variables is as follows: ； in This is the irradiance weighting coefficient. Temperature weighting coefficient, Humidity weighting coefficient ,and , as well as All greater than , The irradiance index, The temperature uniformity index, The humidity uniformity index To evaluate the state of the detection variable; The cumulative test time index is obtained by dividing the current test time by the total test time; the deviation time of the current temperature and humidity from the set value is obtained by dividing the current cumulative time by the test time; the deviation time of the temperature and humidity set value is obtained; the evaluation model for the detection process is as follows: ； in To test the cumulative time weighting coefficient, The weighting factor is the deviation of temperature and humidity. To detect the state weight coefficients of variables, For adjustment coefficients, ,and , , as well as All greater than , To test the cumulative time index, The temperature and humidity setpoints deviate from the duration index. To detect the state evaluation coefficient of the variable, This is the evaluation coefficient for the testing process; The actual surface roughness of the substrate, the actual thickness of the ink coating, and the actual degree of ink curing are substituted into the normalization formula for processing, and the substrate surface roughness deviation index, ink coating thickness deviation index, and ink curing degree deviation index are generated sequentially. The normalization formula is: |actual value - target value| / maximum allowable deviation value. The sample state evaluation model is: ； in This is the substrate roughness weighting coefficient. This is the weighting coefficient for ink coating thickness. This is the ink curability weighting coefficient. ,and , as well as All greater than , This is the surface roughness deviation index of the substrate. This refers to the ink coating thickness deviation index. This refers to the ink curing degree deviation index. The coefficient for evaluating the state of the sample; The total irradiance index is obtained by dividing the cumulative irradiance by the maximum permissible irradiance. The deviation evaluation model for the detection results is as follows: ； in To adjust the parameters, The value is between 0 and 1. The total radiation index, The coefficient for evaluating the state of the sample. The evaluation coefficient for the detection process. The deviation evaluation coefficient for the test results; The airflow target decision model is as follows: ； in The deviation influence coefficient of the test results. This is the temperature influence coefficient. Humidity influence coefficient ,and , as well as All greater than , The deviation evaluation coefficient for the test results. The temperature uniformity index, The humidity uniformity index The target airflow velocity index, The value of is between 0 and 1; The real-time airflow velocity and real-time orifice diameter are substituted into the maximum-minimum normalization formula for processing, and the real-time airflow velocity index and real-time orifice diameter index are generated sequentially. The airflow cooperative control model is as follows: ； ； in For fan control gain parameters, The gain parameter is for controlling the flow aperture. and The value of is between 0 and 1. The target airflow velocity index, This is the real-time airflow velocity index. For real-time flow orifice index, This is the fan speed control signal. This is the orifice diameter control signal.

2. The weather resistance testing device for the production of novel ink materials according to claim 1, characterized in that, The flow guiding assembly includes an air guide plate, an adjustment plate, and a motor A; The air guide plate is connected to the outlet of the circulating air pump. An adjusting plate is slidably connected to the air guide plate. A motor A is fixedly connected to the air guide plate. The output shaft of the motor A is threadedly connected to the adjusting plate. All the guide holes on the air guide plate partially overlap with the guide holes on the adjusting plate. An airflow passage is formed between the air guide plate and the guide holes on the adjusting plate.

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