A method for optimizing the curing of adhesive tape in an oven

Abstract

The application relates to an adhesive tape conveying and curing optimization method for an oven in the field of intelligent manufacturing, which comprises the following steps: acquiring real-time position data, material parameter and adhesive tape surface temperature distribution data in the adhesive tape conveying process to obtain initial temperature field data; detecting temperature abnormal area and classifying abnormal types according to the initial temperature field data to obtain abnormal category labels and real-time position prediction sequences of the abnormal area; determining regional hot air control instructions according to the initial temperature field data to obtain heat distribution states by adopting a partitioned hot air control algorithm; dynamically adjusting oven heat source power according to the updated heat distribution states to obtain optimized temperature gradients by adopting a fuzzy control algorithm; real-time updating flow velocity and flow direction parameters of a hot air circulation system according to the optimized temperature gradients to obtain a stable adhesive tape curing temperature field; and adjusting the running state of the hot air circulation system according to the final curing parameters and real-time monitoring adhesive tape surface bonding strength to obtain consistent curing quality data.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, and more particularly to the field of tape curing, specifically to an optimized method for tape conveying and curing in ovens. Background Technology

[0002] Curing and baking in tape production is a crucial step in ensuring product quality, directly affecting the tape's adhesive strength, weather resistance, and lifespan. During production, the oven heats the tape to induce a chemical reaction in the adhesive layer, forming a stable bond. However, existing curing methods have revealed significant shortcomings in practical applications. Traditional ovens typically rely on fixed heat sources and single air duct designs, resulting in uneven heat distribution and significant differences in heating levels across different areas of the tape. This can easily lead to localized overheating causing bubbles or under-curing, resulting in insufficient adhesion. Furthermore, hot air flow control often relies on manual adjustments, resulting in slow response times and an inability to adapt to changes in material or thickness during tape transport. This lag leads to unstable curing quality, high energy consumption, and low efficiency. A deeper challenge lies in simultaneously achieving both dynamic and precise heat transfer. During curing, the tape needs to be heated gradually according to its physical properties to avoid residual stress accumulation caused by rapid heating. However, existing equipment lacks the ability to dynamically control hot air flow and temperature gradients. For example, in the production of wide-width adhesive tape, the edge areas may be cooler due to heat loss, while the central area may overheat due to concentrated heat, leading to decreased tape toughness or even cracking. The uniformity of hot air circulation becomes a core factor affecting curing results, and this factor is directly related to the real-time nature of temperature control. Existing systems cannot quickly adjust the hot air velocity and direction based on real-time temperature feedback, making it difficult to achieve precise temperature control for different areas of the tape. Therefore, how to achieve uniform distribution of hot air circulation during tape curing and dynamically adjust the temperature gradient through real-time feedback to match the tape's thermal sensitivity requirements has become a key issue in improving curing quality and production efficiency. Summary of the Invention

[0003] This invention provides an optimized method for conveying and curing adhesive tape in an oven, mainly comprising:

[0004] Real-time position data, material parameters, and surface temperature distribution data of the tape during the conveying process are acquired to obtain initial temperature field data. Temperature anomaly areas are detected and anomaly types are classified based on the initial temperature field data, resulting in anomaly category labels and a real-time position prediction sequence for the anomaly areas. A zoned hot air control algorithm is used to determine hot air control commands for each area based on the initial temperature field data, resulting in a heat distribution state. The temperature of the edge areas in the heat distribution state is compared with a preset threshold; if it is lower than the preset threshold, the air duct guide device is adjusted to obtain an updated heat distribution state. A fuzzy control algorithm is used to dynamically adjust the oven based on the updated heat distribution state. The heat source power is used to obtain an optimized temperature gradient. Based on this optimized temperature gradient, the flow rate and direction parameters of the hot air circulation system are updated in real time to obtain a stable tape curing temperature field. The temperature of the central region within this stable curing temperature field is compared with a preset threshold. If the temperature exceeds the preset threshold, the hot air flow rate in the central region is reduced to suppress overheating and ensure consistent temperature distribution. Based on the suppressed overheating temperature distribution and the tape material and thickness parameters, an adaptive control algorithm is used to optimize the hot air circulation frequency and determine the final curing parameters. The operating status of the hot air circulation system is adjusted based on these final curing parameters, and the adhesive strength of the tape surface is monitored in real time to obtain consistent curing quality data. Furthermore, the step of acquiring real-time position data, material parameters, and tape surface temperature distribution data during the tape conveying process to obtain initial temperature field data includes: continuously collecting real-time position data and material parameters during the tape conveying process through a sensor array, simultaneously collecting the tape surface temperature distribution, extracting temperature values ​​of corresponding position points from the real-time position data and temperature distribution to form a position-related temperature sequence, processing the position-related temperature sequence using a convolutional neural network to obtain temperature field spatial features, judging the temperature distribution uniformity based on the temperature field spatial features and material parameters, marking abnormal areas if the temperature difference exceeds a preset threshold, determining the duration of the abnormal area by tracking the movement trajectory of the abnormal area during the conveying process using the real-time position data, classifying the abnormal type using a support vector machine based on the duration of the abnormal area and the temperature field spatial features to obtain an abnormal category label, and generating a real-time position prediction sequence for the abnormal area based on the abnormal category label and the real-time position data.Furthermore, the step of determining the hot air control command for each region based on the initial temperature field data using a zoned hot air control algorithm to obtain the heat distribution status includes: dividing the initial temperature field data into multiple independent control regions; extracting local temperature distribution features for each control region to obtain the regional temperature deviation; calculating the hot air velocity setpoint corresponding to the regional temperature deviation using the zoned hot air control algorithm to obtain the hot air velocity for each region; calculating the temperature compensation amount under the hot air velocity for each region using the heat balance equation to obtain the temperature setpoint for each region; if the difference between the temperature setpoints of adjacent regions exceeds a preset threshold, marking the existence of a hot air flow direction interference region to obtain an interference region set; adjusting the hot air flow direction parameters according to the interference region set to obtain optimized hot air flow direction adjustment parameters; and obtaining the final hot air control command for each region by combining the hot air velocity and temperature setpoint with the optimized hot air flow direction adjustment parameters. Furthermore, the step of comparing the temperature of the edge region in the heat distribution state with a preset threshold, and adjusting the air duct guide device to obtain an updated heat distribution state if it is lower than the preset threshold, includes: acquiring real-time temperature sensor data to determine the temperature value of the edge region; comparing the temperature value of the edge region with the preset threshold to determine whether it is lower than the preset threshold; if it is lower than the preset threshold, calculating the current heat distribution deviation to obtain the temperature difference between the edge region and the center region; determining the adjustment angle of the air duct guide device based on the temperature difference using a proportional control algorithm; increasing the hot air flow rate of the edge region by adjusting the air duct guide device to obtain an updated heat distribution state; obtaining a new temperature value from the updated heat distribution state to determine whether the temperature of the edge region has recovered to above the preset threshold; if it is still lower than the preset threshold, further increasing the adjustment angle of the air duct guide device and repeatedly increasing the hot air flow rate until the heat distribution is balanced. Furthermore, the step of dynamically adjusting the oven heat source power using a fuzzy control algorithm based on the updated heat distribution state to obtain an optimized temperature gradient includes: acquiring real-time temperature data from the updated heat distribution state; processing the real-time temperature data using a fuzzy control algorithm to obtain a power adjustment command; dynamically adjusting the oven heat source power according to the power adjustment command and adding hot air circulation assistance to obtain an updated temperature distribution; extracting temperature gradient values ​​from the updated temperature distribution to determine whether the gradient values ​​are uniform; if they are not uniform, calculating the degree of deviation to determine the zoning control parameters; and adjusting the heat source distribution in the internal areas of the oven according to the zoning control parameters to obtain a balanced distribution state and thus an optimized temperature gradient.Furthermore, the step of updating the flow rate and flow direction parameters of the hot air circulation system in real time according to the optimized temperature gradient to obtain a stable tape curing temperature field includes: collecting temperature data of the tape area through multi-point sensors to obtain the current temperature gradient distribution; adjusting the hot air flow rate parameters according to the current temperature gradient distribution using a PID control algorithm to determine the updated flow rate value; wherein the PID control algorithm uses the current temperature gradient distribution as input, controls the deviation through proportional parameters, eliminates steady-state errors through integral parameters, and predicts the trend through differential parameters to output the updated flow rate value; if the updated flow rate value exceeds a preset threshold, the hot air flow direction parameters are directionally corrected to obtain a new flow direction configuration; and the new flow direction configuration is applied to the hot air circulation system to obtain a stable tape curing temperature field. Furthermore, the step of comparing the temperature of the central region in the stable tape curing temperature field with a preset threshold, and reducing the hot air flow rate in the central region to suppress overheating and temperature distribution if the temperature exceeds the preset threshold, includes: acquiring temperature data of the central region in real time through sensors to obtain the comparison result between the current temperature value and the preset threshold to determine whether the preset threshold is exceeded; if the preset threshold is exceeded, triggering the hot air flow rate control module to obtain a command signal to reduce the flow rate and determine the specific parameters for flow rate adjustment; adjusting the operating state of the hot air equipment in the central region according to the command signal to reduce the flow rate, obtaining the adjusted hot air flow rate data to determine whether the heat distribution tends to be balanced; acquiring the heat distribution state of the central region after adjustment through the heat distribution monitoring system to obtain the trend of the distribution state change and determine whether the effect of suppressing overheating has been achieved; if balance has not yet been achieved, obtaining new flow rate control parameters and updating the operating configuration of the hot air equipment by iteratively calculating the adjustment range of the hot air flow rate; if there is still a local overheating phenomenon in the temperature distribution, classifying and analyzing the heat distribution data through the support vector machine algorithm to obtain the specific location of the abnormal area. Furthermore, the step of optimizing the hot air circulation frequency using an adaptive control algorithm based on the temperature distribution after overheat suppression, combined with the tape material and thickness parameters, to determine the final curing parameters includes: acquiring the temperature distribution data after overheat suppression; extracting temperature gradient change regions based on the temperature distribution data; determining the thermal conductivity difference for the temperature gradient change regions in conjunction with the tape material; adjusting the thermal conductivity difference using the tape thickness parameter to obtain a corrected temperature distribution; processing the corrected temperature distribution using an adaptive control algorithm to obtain a hot air circulation frequency adjustment amount; if the hot air circulation frequency adjustment amount exceeds a preset threshold, reducing the circulation frequency to obtain an overheat suppression frequency; updating the hot air circulation frequency based on the overheat suppression frequency to obtain an optimized circulation frequency; and determining the final curing process parameters using the optimized circulation frequency in conjunction with the corrected temperature distribution.Furthermore, the step of adjusting the operating status of the hot air circulation system according to the final curing parameters and monitoring the adhesive strength of the tape surface in real time to obtain consistent curing quality data includes: adjusting the hot air circulation system according to the final curing parameters to obtain the operating status; monitoring the adhesive strength of the tape surface according to the operating status to determine the strength data; using the strength data to verify the quality consistency and obtain the quality index; and calibrating the temperature distribution for the quality index to obtain consistent curing quality data.

[0005] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0006] This invention discloses an optimized method for curing adhesive tape in ovens. Addressing the problem of uneven temperature distribution and inconsistent curing quality due to low temperatures at the edges and overheating at the center during tape curing, the method collects real-time temperature field data of the tape surface and uses a zoned hot air control algorithm to dynamically calculate the hot air velocity and temperature requirements of each zone. Combining fuzzy control and adaptive control algorithms, it optimizes the flow direction, velocity, and heat source power of the hot air circulation system, precisely adjusting the temperature gradient to achieve balanced heat distribution and a stable curing temperature field. Simultaneously, it optimizes curing parameters based on the tape material and thickness to ensure consistent adhesive strength. This invention, through multi-algorithm collaboration and real-time monitoring, significantly improves the stability and consistency of tape curing quality, reduces energy consumption, and increases production efficiency. Attached Figure Description

[0007] Figure 1 This is a flowchart of an optimized method for conveying and curing adhesive tape in an oven according to the present invention. Detailed Embodiments

[0008] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. 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 only for explaining the relevant invention and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0009] like Figure 1 This embodiment of an optimized method for conveying and curing adhesive tape in an oven may specifically include:

[0010] S101. Obtain real-time position data and material parameters of the tape during the conveying process, and collect the surface temperature distribution of the tape through a sensor array to obtain initial temperature field data.

[0011] A sensor array continuously collects real-time position data and material parameters during the conveyor belt transport process, while simultaneously acquiring the surface temperature distribution of the belt. Temperature values ​​at corresponding locations are extracted from the real-time position and temperature data to form a position-correlated temperature sequence. A convolutional neural network is used to process this sequence to obtain the spatial characteristics of the temperature field. Based on these characteristics and material parameters, the uniformity of the temperature distribution is assessed; if the temperature difference exceeds a preset threshold, an abnormal area is marked. The movement trajectory of the abnormal area during transport is tracked using real-time position data to determine its duration. Based on the duration and spatial characteristics of the temperature field, a support vector machine is used to classify the anomaly type, obtaining an anomaly category label. Finally, a real-time position prediction sequence for the abnormal area is generated using the anomaly category label and the real-time position data.

[0012] First, an RFID reader and tagging system is installed every 1 meter along the conveyor belt. Multiple passive RFID tags are embedded on the conveyor belt. When the conveyor belt moves at a speed of 0.5 m / s, the reader captures the tag ID in real time and calculates the position by combining it with the pulse of the conveyor belt encoder. For example, if the current pulse count is 12000 and each pulse corresponds to 0.1 mm, then the real-time position of the front end of the conveyor belt is 1200 meters. At the same time, the material parameters of the conveyor belt batch are queried from the material management system database, such as thickness 2.5 mm, width 1200 mm, material is polyester fiber reinforced polyvinyl chloride, thermal conductivity 0.18 W / (m·K), and specific heat capacity 1300 J / (kg·K). Next, an array of infrared thermal imagers, positioned 50 mm above the conveyor belt and 50 mm above the tape surface, was used. Each imager had a resolution of 640×480 pixels, a frame rate of 30 Hz, and a temperature resolution of 0.05℃. The imagers scanned the temperature distribution on the tape surface in real time. After blackbody correction and non-uniformity correction, the radiation intensity was converted into temperature values ​​using the Stefan-Boltzmann law, forming an initial temperature field matrix of 3200×1000 pixels. The typical temperature range in the matrix was 25.3℃ to 38.7℃, with an average temperature of 32.1℃ in the central area and slightly lower at the edges due to heat dissipation, at 28.4℃. The temperature field data was then smoothed using a Gaussian filter with a kernel size of 5×5 and a standard deviation of 1.2 to eliminate sensor noise. A heat conduction model was then established using the finite difference method to calculate the temperature gradient field, where the maximum gradient value in the horizontal direction reached 2.8℃ / m and in the vertical direction reached 1.5℃ / m. Principal component analysis was used to extract the main feature vectors of the temperature distribution. The first three principal components explained 96.7% of the variance, thus providing an accurate initial temperature field data foundation for subsequent anomaly detection and process optimization.

[0013] S102. Based on the initial temperature field data, a zoned hot air control algorithm is used to calculate the hot air velocity and temperature requirements of each zone and determine the hot air flow direction adjustment parameters.

[0014] Based on the initial temperature field data, multiple independent control zones are obtained. Local temperature distribution characteristics are extracted for each control zone to obtain the zone temperature deviation. A zoned hot air control algorithm is used to calculate the hot air velocity setpoint corresponding to the zone temperature deviation, thus obtaining the hot air velocity for each zone. The temperature compensation amount under the hot air velocity in each zone is calculated using the heat balance equation, resulting in the temperature setpoint for each zone. If the difference between the temperature setpoints of adjacent zones exceeds a preset threshold, a hot air flow direction interference zone is identified, resulting in a set of interference zones. The hot air flow direction parameters are adjusted based on the interference zone set to obtain optimized hot air flow direction adjustment parameters. Finally, the hot air control command for each zone is obtained by combining the hot air velocity and temperature setpoint with the optimized hot air flow direction adjustment parameters.

[0015] First, initial temperature field data is acquired. For example, temperature distribution in a drying chamber divided into four zones is collected using a sensor grid: zone 1 has an average temperature of 65.2℃, zone 2 72.8℃, zone 3 58.5℃, and zone 4 68.1℃. The target uniform temperature is set at 70.0℃. A zoned hot air control algorithm is used to divide the space into independent control zones. The current temperature deviation is calculated using CFD simulation or a simplified zonal model. Zone 1, with a deviation of -4.8℃, requires additional heating, while zone 3, with a deviation of +11.5℃, requires enhanced cooling. Based on the energy conservation equation Q = m·cp·ΔT and the convective heat transfer formula h·A·(Ts - Ta), the hot air demand for each region is calculated using a PID control algorithm, where the proportional coefficient Kp = 1.5, the integral Ki = 0.3, and the derivative Kd = 0.1. By iteratively solving for the deviation e(t) = target temperature - current temperature, the hot air temperature demand is output as follows: hot air temperature of 75.0℃ for region 1, 72.0℃ for region 2, 60.0℃ for region 3, and 70.5℃ for region 4. Simultaneously, the flow velocity requirements are calculated using the Reynolds number Re=ρ·v·d / μ and the Nusselt number Nu=h·d / k correlation. Assuming air density ρ=1.2 kg / m³, specific heat cp=1005 J / kg·K, and thermal conductivity k=0.026 W / m·K, and a convection coefficient h target of 30 W / m²·K, the calculated flow velocity requirements are 8.5 m / s for region 1 to enhance heat transfer, 12.0 m / s for region 3 to accelerate heat dissipation, and 9.2 m / s and 10.1 m / s for other regions. By analyzing the thermal imbalance that exists when the temperature gradient ΔT > 5℃ between adjacent areas, a genetic algorithm is used to optimize the hot air flow direction. The air outlet angle parameters are adjusted to tilt inward by 15° in area 1 and outward by 20° in area 3 to minimize the overall temperature variance to within 1.2℃, ensuring that the hot air is guided from the high-temperature area to the low-temperature area and achieving a uniform temperature field distribution. The entire process is carried out by solving the momentum and energy equations through numerical iteration. After convergence, the total thermal balance error is verified to be less than 0.5%, thereby achieving precise zoned hot air control.

[0016] S103. If the temperature in the edge area is lower than the preset threshold, the hot air flow in the edge area is increased by adjusting the air duct guide device to obtain a balanced heat distribution.

[0017] Real-time temperature sensor data is acquired to determine the temperature value of the edge region. The edge region temperature value is compared with a preset threshold to determine if it is below the threshold. If it is below the threshold, the current heat distribution deviation is calculated to obtain the temperature difference between the edge and center regions. Based on this temperature difference, a proportional control algorithm is used to determine the adjustment angle of the air duct guide device. By adjusting the air duct guide device, the hot air flow rate in the edge region is increased, resulting in an updated heat distribution state. A new temperature value is obtained from the updated heat distribution state to determine if the edge region temperature has recovered to above the preset threshold. If it is still below the preset threshold, the adjustment angle of the air duct guide device is further increased, and the hot air flow rate is repeatedly increased until the heat distribution is balanced.

[0018] First, the system monitors the temperature of edge areas in real time using multiple temperature sensors integrated inside the oven, such as the upper left and lower right corners. Data is collected every 5 seconds. If the average temperature of the edge area is detected to be lower than a preset threshold of 150°C (e.g., set to 90% of the central area temperature), adjustment logic is triggered. Next, the algorithm uses a PID control model to calculate the adjustment amount. The proportional term P is based on the current deviation (the difference between the actual temperature and the threshold, such as a deviation of 20°C), the integral term I accumulates the deviations of the past 10 times to eliminate steady-state error, and the derivative term D predicts the temperature change trend. The calculation formula is adjustment coefficient K = Kp * deviation + Ki * integral deviation + Kd * deviation change rate, where Kp = 0.5, Ki = 0.1, and Kd = 0.2. Through iterative optimization, the value of K is found to be approximately 1.2, indicating that the hot air flow needs to be increased by 20%. Then, the control module drives the servo motor to rotate the air duct guide device according to the K value, for example, adjusting the guide plate angle from the initial 30° to 45°, thereby tilting the hot air from the central area to the edge area, increasing the edge hot air flow by about 25%. Simulation analysis shows that this adjustment can reduce the temperature gradient from the initial 15°C to within 5°C. Finally, the system iteratively verifies the adjustment effect. By comparing the temperature data before and after, if an equilibrium state is achieved (the edge temperature rises to 148°C, close to the central 150°C), the current guide angle is locked; otherwise, it iterates and adjusts until the deviation is less than 2°C, forming a closed-loop feedback to ensure uniform heat distribution. This entire process is executed automatically by the embedded processor without manual intervention.

[0019] S104. First, obtain the adjusted heat distribution state, then use the fuzzy control algorithm to dynamically adjust the oven heat source power, and finally determine the temperature gradient optimization scheme.

[0020] Real-time temperature data is obtained from the adjusted heat distribution state, and the data is processed using a fuzzy control algorithm to obtain a power adjustment command. Based on the power adjustment command, the oven's heat source power is dynamically adjusted, and hot air circulation is added to assist, resulting in an updated temperature distribution. Temperature gradient values ​​are extracted from the updated temperature distribution, and their uniformity is determined. If non-uniform, the degree of deviation is calculated to determine the zoning control parameters. Based on the zoning control parameters, the heat source distribution within the oven's internal areas is adjusted to obtain a balanced distribution state, resulting in an optimized temperature gradient. For the optimized temperature gradient, a temperature gradient optimization scheme is generated.

[0021] In one implementation, the system first acquires the adjusted heat distribution state, a step achieved through a network of temperature sensors deployed within the oven.

[0022] Specifically, these sensors are distributed in different areas of the oven, such as the top, bottom, and side walls, collecting temperature data at fixed time intervals to form an overall heat distribution map. By comparing the temperature differences between the central and edge areas, the system calculates the current heat distribution status.

[0023] For example, if the edge temperature is 10 degrees Celsius lower than the center, it is marked as an uneven state. This acquisition process ensures an accurate basis for subsequent adjustments.

[0024] It should be noted that the acquisition of heat distribution status relies on real-time data fusion, which avoids the influence of errors from a single sensor.

[0025] For example, a fuzzy control algorithm is used to process this temperature data. A fuzzy control algorithm is a control method based on fuzzy logic that transforms precise temperature input into a fuzzy set and describes the fuzzy states of temperature such as "high," "medium," and "low" by defining membership functions.

[0026] For example, if the temperature deviation is within 5 degrees Celsius, it can be classified into a "low deviation" fuzzy set, with the membership degree gradually changing from 0 to 1. The advantage of this algorithm lies in handling the uncertainty of heat distribution within the oven, such as the randomness of hot air flow, rather than relying on a precise mathematical model. Furthermore, the algorithm constructs a rule base, such as "if the temperature gradient is high, increase the heat source power." These rules are derived from empirical knowledge, ensuring that the control decisions are closer to human intuition.

[0027] In one possible implementation, dynamically adjusting the oven's heat source power is based on the output of a fuzzy control algorithm. The specific process includes fuzzy inference and defuzzification steps: First, the fuzzy value of the current heat distribution state is input into a rule base for inference, generating a fuzzy output such as "power increase moderately"; then, through defuzzification methods, such as the center-of-gravity method, the fuzzy output is converted into a precise power adjustment value, for example, increasing the heat source power from an initial 800 watts to 950 watts. This adjustment is dynamic; the system reassesses the distribution state every minute, forming a closed-loop control.

[0028] Preferably, in a multi-layer oven scenario, this adjustment can be applied independently to different layers to ensure balanced heat distribution across each layer. Further, determining the temperature gradient optimization scheme involves analyzing the effect of the adjustment. The system compares the temperature gradient before and after adjustment; for example, if the initial gradient is 15 degrees Celsius and it decreases to 3 degrees Celsius after adjustment, if a preset threshold is met, the scheme is determined to be a combination of the current power configuration and fuzzy rules. This scheme determination process is achieved through iterative verification.

[0029] Understandably, it supports multiple oven types, such as household convection ovens or industrial baking equipment, demonstrating the versatility of the technology.

[0030] In one embodiment, in a commercial oven environment, a fuzzy control algorithm can be integrated into an embedded controller. The process begins by acquiring the heat distribution status, and the algorithm defines input variables such as temperature deviation and rate of change, with the output being a power adjustment. Through rules such as "if the deviation is large and the rate of change is positive, then the power increases significantly," the system simulates various scenarios to ensure gradient optimization.

[0031] For example, in bread baking, this method can ensure even temperature distribution and reduce localized overheating. This approach improves heat utilization efficiency and is suitable for continuously operating oven production lines.

[0032] Specifically, the design of the rule base for the fuzzy control algorithm is a crucial step. It involves incorporating expert knowledge, such as categorizing temperature gradients into "slight," "moderate," and "severe" levels, each corresponding to a different power response curve. Through multiple simulations, the rule base can be optimized into a set of 20 rules, ensuring coverage of the oven's heat source adjustment range from low to high power. This design process clearly explains the transition from fuzzy input to precise output, avoiding the oscillation problems of traditional control. In another implementation, for small household ovens, dynamic adjustment can be combined with the user-set temperature. After obtaining the distribution state, the fuzzy algorithm assesses whether power fine-tuning is needed, such as increasing power by 10% during the preheating stage for rapid equalization. The optimized scheme is recorded in a parameter table for easy reuse later.

[0033] It should be noted that the final determination of the temperature gradient optimization scheme includes a verification phase. After the system runs the adjustment cycle, temperature data from multiple points is collected, and the average gradient is calculated. If the average gradient value is below the 2-degree Celsius threshold, the scheme is locked. This verification enhances the reliability of the scheme and makes it applicable to different heating modes, such as grilling or fermentation, within the same oven field.

[0034] For example, in industrial oven production lines, this technology can be extended to multi-cavity structures, with each cavity having its power adjusted independently. Based on fuzzy control, overall optimization is achieved to ensure product consistency.

[0035] S105. Based on the temperature gradient optimization scheme, update the flow rate and flow direction parameters of the hot air circulation system in real time to obtain a stable tape curing temperature field.

[0036] Temperature data of the tape area is collected by multiple sensors to obtain the current temperature gradient distribution. Based on this temperature gradient distribution, a PID control algorithm is used to adjust the hot air flow rate parameters to determine an updated flow rate value. The PID control algorithm uses the temperature gradient distribution as input, controls deviation through proportional parameters, eliminates steady-state error through integral parameters, and predicts trends through derivative parameters, outputting the updated flow rate value. If the updated flow rate value exceeds a preset threshold, the hot air flow direction parameters are directionally corrected to obtain a new flow direction configuration. This new flow direction configuration is applied to the circulation system to obtain a stable tape curing temperature field.

[0037] In one implementation, based on a temperature gradient optimization scheme, the current temperature distribution data within the oven is first acquired, and temperature changes on the tape surface are monitored by sensors installed in the curing area. These sensors are distributed at the top, middle, and bottom, forming a temperature monitoring network to detect uneven heat distribution. The optimization scheme, based on a previously calculated temperature matrix, identifies areas with large temperature gradients, such as when the upper temperature is higher than the lower temperature, triggering a parameter adjustment mechanism. This approach ensures the responsiveness of the hot air circulation system, enabling precise control of the tape curing process. By analyzing the temperature gradient, the system calculates the flow rate and direction parameters that need optimization, providing foundational data for subsequent updates.

[0038] Specifically, the temperature gradient optimization scheme refers to a control strategy determined through deviation analysis, which compares the target temperature field with the actual measured value and generates adjustment instructions.

[0039] For example, in tape curing scenarios, if the lower temperature is detected, the solution will prioritize increasing the proportion of downward hot airflow. The principle behind this solution is to utilize heat conduction and convection to balance heat distribution and avoid uneven curing caused by localized overheating or undercooling.

[0040] It's important to note that this optimization scheme doesn't rely on a fixed threshold but dynamically adapts to changes in production batches. For example, it adjusts the calculation weights for different tape thicknesses to maintain temperature field stability. Through this strategy, the system can handle various curing requirements, ensuring uniform heating of the tape during baking. Furthermore, when updating the flow rate parameters of the hot air circulation system in real time, the system outputs instructions based on the optimization scheme to adjust the fan speed to control airflow velocity. For example...

[0041] In one possible implementation, if the temperature gradient indicates heat buildup in the central region, the system increases the flow rate by 15% from its initial value, achieving precise control via a frequency converter. This process involves a flow rate calculation model that considers air density and oven volume to generate flow rate values ​​that match the heat requirements. Updating the flow rate parameters ensures that hot air is evenly distributed across the tape surface, reducing temperature fluctuations and creating a stable curing environment. This update mechanism's operational process includes three stages: data acquisition, solution application, and parameter execution. Each stage is linked to sensor feedback, forming a closed-loop control system.

[0042] For example, adjusting the flow direction parameter is a crucial step in obtaining the temperature field for tape curing. The flow direction parameter refers to the directional distribution of hot air within the oven, such as upward, downward, or horizontal circulation, achieved through adjustable dampers. Optimization schemes calculate the flow direction ratio based on the temperature gradient, for example, directing 60% of the hot air to the lower region to compensate for heat loss. This adjustment is based on fluid mechanics; the hot air flow direction affects convection efficiency, ensuring that the temperature of different parts of the tape tends to be uniform. In practical applications, this parameter update can handle continuous production scenarios, such as dynamically changing the flow direction during the curing of long tapes to adapt to changes in the roll position, thereby obtaining a uniform temperature field.

[0043] Preferably, after obtaining a stable tape curing temperature field, the system verifies the uniformity of the temperature distribution and determines whether it is lower than the preset value by calculating the standard deviation.

[0044] For example, if the standard deviation is less than 2°C, the temperature field is considered stable. This validation process integrates the combined effects of flow rate and flow direction parameters, demonstrating the practicality of the optimization scheme.

[0045] In one embodiment, for curing thin tapes, setting the flow rate to a medium level combined with a horizontal flow direction effectively reduces the gradient and improves the curing quality. This method is common in industrial baking and is applicable to similar heat treatment processes.

[0046] Understandably, a hot air circulation system is a device integrating a fan, duct, and control unit to generate controlled airflow within an oven. Its flow rate parameter is defined as the air volumetric flow rate per unit time, while the flow direction parameter is controlled by the damper angle. The system's real-time updates rely on the output of an optimization scheme to ensure that the parameters match the temperature requirements. The optimization scheme output includes flow rate values ​​and flow direction angles calculated through temperature field simulation. During operation, such as when the tape enters the curing stage, the system first evaluates the initial temperature field and then iteratively updates the parameters until a stable state is reached.

[0047] In another implementation, for thick tape curing scenarios, the optimization scheme emphasizes vertical adjustment of the flow direction, such as increasing upward flow to enhance heat penetration. The flow rate is dynamically scaled according to the tape thickness; for example, the flow rate increases accordingly as the thickness increases to maintain heat transfer efficiency. This embodiment demonstrates the flexibility of the solution, covering various tape types through different parameter combinations, ensuring a stable temperature field unaffected by material variations. Furthermore, the business objective of stabilizing the tape curing temperature field is to improve product consistency, such as reducing curing defects like bubbles or unevenness. Through real-time parameter updates, the system can operate continuously on the production line, adapting to changes in ambient temperature. This technical effect stems from the tight integration of the optimization scheme with the circulation system, providing reliable thermal management.

[0048] For example, in a continuous curing production line, the system monitors the conveyor belt speed and adjusts the flow rate and direction parameters accordingly. If the speed increases, the flow rate increases accordingly to match the heating requirements. This example illustrates the application of the solution in a dynamic business environment, ensuring a consistently stable temperature field.

[0049] S106. If the temperature in the central area exceeds the preset threshold, the heat concentration is adjusted by reducing the hot air flow rate in the central area to obtain a temperature distribution that suppresses overheating.

[0050] Temperature data in the central area is collected in real time by sensors. The current temperature value is compared with a preset threshold to determine if it exceeds the standard range. If the current temperature exceeds the preset threshold, the hot air flow rate control module is triggered to obtain a command signal to reduce the flow rate and determine the specific parameters for flow rate adjustment. Based on the command signal to reduce the flow rate, the operating status of the hot air equipment in the central area is adjusted, and the adjusted hot air flow rate data is obtained to determine if the heat distribution is approaching equilibrium. The heat distribution monitoring system collects the heat distribution status in the central area after adjustment, obtains the trend of the distribution status, and determines whether the overheating suppression effect has been achieved. If the heat distribution status has not yet reached equilibrium, the adjustment range of the hot air flow rate is iteratively calculated to obtain new flow rate control parameters and determine whether further adjustment is needed. Based on the new flow rate control parameters, the operating configuration of the hot air equipment is updated, and the updated heat distribution data is obtained to determine whether the temperature distribution meets the expected state. If local overheating still exists in the temperature distribution, the heat distribution data is classified and analyzed using a support vector machine algorithm to obtain the specific location of the abnormal area and determine the key areas for subsequent adjustment.

[0051] When the temperature sensor in the central area detects that the temperature has reached the preset threshold of 180℃, the system immediately triggers the PID algorithm to calculate the deviation value e = 180 - target temperature 160 = 20℃. The proportional term Kp is set to 0.8, the integral term Ki to 0.02, and the derivative term Kd to 0.1. Using the formula u = Kp × e + Ki × ∫e dt + Kd × de / dt, the required reduction in hot air velocity Δv = -12 m³ / min is quickly obtained. The controller adds this Δv to the original central hot air velocity setpoint of 45 m³ / min, generating a new velocity command of 33 m³ / min, which is then sent to the variable frequency fan. The fan speed synchronously decreases from 1500 rpm to 1100 rpm to achieve precise velocity adjustment. Simultaneously, the system calls the heat concentration calculation model. Based on the wind field simulation data under the new velocity, the heat distribution coefficient decreases from the original 0.72 to 0.58, achieving the desired overheat suppression effect. Next, the temperature field finite element analysis module refreshes the mesh data every 2 seconds. It was found that the temperature at the center point dropped to 168℃ in the 8th second after adjustment and stabilized at 162℃ in the 15th second. The deviation was less than 2℃, confirming that the goal of suppressing overheating was achieved. The entire process was completed automatically in a closed loop by PLC and edge computing unit without manual intervention. It forms a complete logical chain from temperature exceeding the threshold → deviation quantification → precise reduction of flow rate → heat redistribution → temperature field verification, ensuring that the temperature uniformity in the oven is improved to within ±1.5℃.

[0052] S107. Obtain the temperature distribution after overheating suppression, and combine the tape material and thickness parameters with an adaptive control algorithm to optimize the hot air circulation frequency and determine the final curing parameters.

[0053] Acquire temperature distribution data after overheating suppression. Extract temperature gradient change regions from the temperature distribution data. Determine the thermal conductivity differences for these regions based on the tape material. Adjust the thermal conductivity differences using the tape thickness parameter to obtain a corrected temperature distribution. Use an adaptive control algorithm to process the corrected temperature distribution and obtain the hot air circulation frequency adjustment. If the hot air circulation frequency adjustment exceeds a preset threshold, reduce the circulation frequency to obtain the overheat suppression frequency. Update the hot air circulation frequency based on the overheat suppression frequency to obtain the optimized circulation frequency. Determine the final curing process parameters by combining the optimized circulation frequency with the corrected temperature distribution.

[0054] When obtaining the temperature distribution after overheating suppression, infrared thermal imaging technology combined with data processing software can be used to collect temperature data of the cured area in real time. Assuming the collected temperature distribution range is 50°C to 120°C, the distribution ratio of the high-temperature area (above 100°C) is analyzed in detail, and it is found that its proportion is 15%. The data is visualized using thermal mapping software for subsequent optimization. Next, combined with the tape material and thickness parameters, for example, the tape material is polyimide, the thickness is 0.05mm, its heat resistance limit is 180°C, and the thermal conductivity is 0.12W / m·K. The heat transfer process is simulated using finite element analysis software to calculate the thermal stress value of the tape under the current temperature distribution. The maximum stress is found to be 2.5MPa, which does not exceed the material limit of 3.0MPa, confirming safety. Based on this, an adaptive control algorithm was used to optimize the hot air circulation frequency. Assuming an initial frequency of 5Hz, it was dynamically adjusted using a PID control algorithm. The target temperature was set at 90°C, with an error range of ±2°C. The algorithm calculated the deviation based on real-time temperature feedback (e.g., current temperature 95°C) and adjusted the frequency to 4.5Hz. After iterative calculation, it stabilized at 4.2Hz, reducing the risk of overheating. Simultaneously, energy consumption data for each adjustment was recorded, and the energy-saving effect was analyzed, revealing an energy consumption reduction of approximately 10%. Finally, the curing parameters were determined. A machine learning model was used to predict the optimal curing time and temperature. Inputting the tape parameters and the optimized frequency, the model output a curing time of 30 minutes and a temperature of 88°C. Verification using historical data showed an error rate of only 1.2%, ensuring process stability. To form a logical chain, the curing parameters were linked to subsequent quality inspection. An automated inspection system collected the tack data of the cured tape (standard value 5 N / cm, actual value 5.1 N / cm), confirming that parameter optimization improved product quality, thus completing a closed-loop control process from temperature distribution to final parameters.

[0055] S108. Adjust the operating status of the hot air circulation system according to the final curing parameters, monitor the bonding strength of the tape surface in real time, and obtain consistent curing quality data.

[0056] By adjusting the curing parameters and the hot air circulation system, the operating status is obtained. Based on the operating status, the adhesion strength of the tape surface is monitored to determine the strength data. Using the strength data, quality consistency is verified to obtain quality indicators. For these quality indicators, the temperature distribution is calibrated to obtain consistent curing quality data.

[0057] In one implementation, the final curing parameters include hot air temperature, air velocity, and circulation time. These parameters are determined through preliminary testing or process optimization to guide the complete cross-linking and curing of the tape coating. Based on these parameters, after the hot air circulation system is started, the control unit adjusts the power output of the heater and the rotation speed of the circulating fan according to the set values.

[0058] Specifically, the system collects the actual temperature inside the cavity using a temperature sensor, compares it with the target temperature, and calculates the heating power adjustment amount using a PID algorithm. Simultaneously, it adjusts the damper opening to maintain a uniform airflow distribution. Furthermore, to achieve closed-loop control, real-time monitoring of the adhesive strength on the tape surface is required. Adhesive strength reflects the bonding force between the adhesive layer and the substrate, gradually increasing with temperature and time during curing until a stable value is reached. In one embodiment, an infrared spectroscopy detection device is installed near the curing cavity outlet. This device emits infrared light of a specific wavelength onto the tape surface, receives the reflected or transmitted spectra, and analyzes the intensity and position changes of characteristic peaks. These changes correspond to the degree of molecular cross-linking, thereby calculating the current adhesive strength value.

[0059] Preferably, the infrared detection process includes the following steps: First, the tape surface is continuously scanned to collect multi-point spectral data. Then, a pre-established calibration model is used to map the spectral characteristic parameters to adhesive strength values. This model is obtained based on regression analysis of a large number of offline peel strength tests and corresponding spectral data. The detection device collects data multiple times per second to ensure full coverage of the tape width. In another embodiment, an ultrasonic non-destructive testing method can be used. An ultrasonic probe is fixed at the curing line exit, emits high-frequency ultrasonic waves to the tape surface, receives the reflected echoes, and judges the tightness of the interface bonding by the difference in echo amplitude and propagation time, thereby calculating the adhesive strength. This method is sensitive to changes in tape thickness and requires compensation correction using a thickness sensor. Based on the adhesive strength data obtained from real-time monitoring, the control unit compares it with the target strength range. If the detected value is lower than expected, the hot air temperature is increased or the cycle time is extended; if it is higher than expected, the temperature or air speed is appropriately reduced to avoid over-curing and increased brittleness.

[0060] For example, in the production of double-sided tape, when monitoring detects low strength in the edge area, the system automatically increases the airflow on that side to achieve local compensation, thereby ensuring a uniform strength distribution across the entire roll of tape. Through these adjustments and monitoring methods, the entire curing process forms a closed-loop feedback loop, ultimately achieving highly consistent curing quality data between batches and at different locations within the same roll of tape, facilitating subsequent quality traceability and stable process control.

[0061] It should be noted that the above implementation methods are all carried out on the tape hot air curing production line to ensure that the curing quality meets the requirements of downstream bonding applications.

[0062] It should be noted that the above examples are merely some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should be considered within the scope of protection of this invention.

Claims

1. An optimized method for curing adhesive tape in an oven, characterized in that, include: The initial temperature field data is obtained by acquiring real-time position data, material parameters, and surface temperature distribution data of the tape during the tape conveying process. Based on the initial temperature field data, abnormal temperature regions are detected and classified into abnormal types, resulting in abnormal category labels and real-time location prediction sequences for abnormal regions. A zoned hot air control algorithm is used to determine hot air control commands for each region based on the initial temperature field data, yielding a heat distribution state. The temperature of the edge regions in the heat distribution state is compared with a preset threshold; if it is lower than the preset threshold, the air duct guide device is adjusted to obtain an updated heat distribution state. A fuzzy control algorithm is used to dynamically adjust the oven heat source power based on the updated heat distribution state, resulting in an optimized temperature gradient. The flow rate and direction parameters of the hot air circulation system are updated in real-time based on the optimized temperature gradient, resulting in a stable tape curing temperature field. The temperature of the central region in the stable tape curing temperature field is compared with a preset threshold; if it exceeds the preset threshold, the hot air flow rate in the central region is reduced to suppress overheating. Based on the suppressed overheating temperature distribution, combined with tape material and thickness parameters, an adaptive control algorithm is used to optimize the hot air circulation frequency, determining the final curing parameters. The operating state of the hot air circulation system is adjusted based on the final curing parameters, and the adhesive strength of the tape surface is monitored in real-time to obtain consistent curing quality data.

2. The oven-safe tape-based curing optimization method according to claim 1, characterized in that, The process of acquiring real-time position data, material parameters, and tape surface temperature distribution data during tape conveying to obtain initial temperature field data further includes: continuously collecting real-time position data and material parameters during tape conveying via a sensor array, and simultaneously collecting tape surface temperature distribution; extracting temperature values ​​at corresponding positions from the real-time position data and temperature distribution to form a position-related temperature sequence; processing the position-related temperature sequence using a convolutional neural network to obtain temperature field spatial features; judging the temperature distribution uniformity based on the temperature field spatial features and material parameters; marking abnormal areas if the temperature difference exceeds a preset threshold; determining the duration of abnormal areas by tracking the movement trajectory of the abnormal areas during conveying using the real-time position data; classifying abnormal types using a support vector machine based on the duration of abnormal areas and temperature field spatial features to obtain abnormal category labels; and generating a real-time position prediction sequence for abnormal areas based on the abnormal category labels and real-time position data.

3. The optimized method for curing and conveying adhesive tape in an oven according to claim 1, characterized in that, The step of determining the hot air control command for each region based on the initial temperature field data using a zoned hot air control algorithm to obtain the heat distribution state further includes: dividing the initial temperature field data into multiple independent control regions; extracting local temperature distribution characteristics for each control region to obtain the regional temperature deviation; calculating the hot air velocity setpoint corresponding to the regional temperature deviation using the zoned hot air control algorithm to obtain the hot air velocity for each region; calculating the temperature compensation amount under the hot air velocity for each region using the heat balance equation to obtain the temperature setpoint for each region; if the difference between the temperature setpoints of adjacent regions exceeds a preset threshold, marking the existence of a hot air flow direction interference region to obtain an interference region set; adjusting the hot air flow direction parameters according to the interference region set to obtain optimized hot air flow direction adjustment parameters; and obtaining the final hot air control command for each region by combining the hot air velocity and temperature setpoint with the optimized hot air flow direction adjustment parameters.

4. The oven-safe tape-based curing optimization method according to claim 1, characterized in that, The step of comparing the temperature of the edge region in the heat distribution state with a preset threshold, and adjusting the air duct guide device to obtain an updated heat distribution state if the temperature is lower than the preset threshold, further includes: acquiring real-time temperature sensor data to determine the temperature value of the edge region; comparing the temperature value of the edge region with the preset threshold to determine if it is lower than the preset threshold; if it is lower than the preset threshold, calculating the current heat distribution deviation to obtain the temperature difference between the edge region and the center region; determining the adjustment angle of the air duct guide device based on the temperature difference using a proportional control algorithm; increasing the hot air flow rate of the edge region by adjusting the air duct guide device to obtain an updated heat distribution state; obtaining a new temperature value from the updated heat distribution state to determine if the temperature of the edge region has recovered to above the preset threshold; if it is still lower than the preset threshold, further increasing the adjustment angle of the air duct guide device and repeatedly increasing the hot air flow rate until the heat distribution is balanced.

5. The oven-safe tape-based curing optimization method according to claim 1, characterized in that, The step of dynamically adjusting the oven heat source power using a fuzzy control algorithm based on the updated heat distribution state to obtain an optimized temperature gradient further includes: obtaining real-time temperature data from the updated heat distribution state; processing the real-time temperature data using a fuzzy control algorithm to obtain a power adjustment command; dynamically adjusting the oven heat source power according to the power adjustment command and adding hot air circulation assistance to obtain an updated temperature distribution; extracting temperature gradient values ​​from the updated temperature distribution to determine whether the gradient values ​​are uniform; if they are not uniform, calculating the degree of deviation to determine the zoning control parameters; and adjusting the heat source distribution in the internal areas of the oven according to the zoning control parameters to obtain a balanced distribution state and thus an optimized temperature gradient.

6. The oven-safe tape-conveying curing optimization method according to claim 1, characterized in that, The step of updating the flow rate and flow direction parameters of the hot air circulation system in real time according to the optimized temperature gradient to obtain a stable tape curing temperature field further includes: collecting temperature data of the tape area through multi-point sensors to obtain the current temperature gradient distribution; adjusting the hot air flow rate parameters according to the current temperature gradient distribution using a PID control algorithm to determine the updated flow rate value; wherein the PID control algorithm uses the current temperature gradient distribution as input, controls the deviation through proportional parameters, eliminates steady-state errors through integral parameters, and predicts the trend through differential parameters to output the updated flow rate value; if the updated flow rate value exceeds a preset threshold, the hot air flow direction parameters are directionally corrected to obtain a new flow direction configuration; and the new flow direction configuration is applied to the hot air circulation system to obtain a stable tape curing temperature field.

7. The oven-safe tape-based curing optimization method according to claim 1, characterized in that, The process of comparing the temperature of the central region in the stable tape curing temperature field with a preset threshold, and reducing the hot air flow rate in the central region to suppress overheating and temperature distribution if the temperature exceeds the preset threshold, further includes: acquiring temperature data of the central region in real time through sensors to obtain the comparison result between the current temperature value and the preset threshold to determine whether the temperature exceeds the preset threshold; if the temperature exceeds the preset threshold, triggering the hot air flow rate control module to obtain a command signal to reduce the flow rate and determine the specific parameters for flow rate adjustment; adjusting the operating state of the hot air equipment in the central region according to the command signal to reduce the flow rate, obtaining the adjusted hot air flow rate data to determine whether the heat distribution tends to be balanced; acquiring the heat distribution state of the central region after adjustment through the heat distribution monitoring system to obtain the trend of the distribution state change and determine whether the effect of suppressing overheating has been achieved; if balance has not yet been achieved, obtaining new flow rate control parameters by iteratively calculating the adjustment range of the hot air flow rate and updating the operating configuration of the hot air equipment; if there is still a local overheating phenomenon in the temperature distribution, classifying and analyzing the heat distribution data through the support vector machine algorithm to obtain the specific location of the abnormal area.

8. The oven-safe tape-conveying curing optimization method according to claim 1, characterized in that, The step of optimizing the hot air circulation frequency using an adaptive control algorithm based on the temperature distribution after overheat suppression, combined with the tape material and thickness parameters, to determine the final curing parameters further includes: acquiring the temperature distribution data after overheat suppression; extracting temperature gradient change regions based on the temperature distribution data; determining the thermal conductivity difference for the temperature gradient change regions in conjunction with the tape material; adjusting the thermal conductivity difference using the tape thickness parameter to obtain a corrected temperature distribution; processing the corrected temperature distribution using an adaptive control algorithm to obtain a hot air circulation frequency adjustment amount; if the hot air circulation frequency adjustment amount exceeds a preset threshold, reducing the circulation frequency to obtain an overheat suppression frequency; updating the hot air circulation frequency based on the overheat suppression frequency to obtain an optimized circulation frequency; and determining the final curing process parameters using the optimized circulation frequency in conjunction with the corrected temperature distribution.

9. The oven-safe tape-conveying curing optimization method according to claim 1, characterized in that, The step of adjusting the operating status of the hot air circulation system according to the final curing parameters and monitoring the adhesive strength of the tape surface in real time to obtain consistent curing quality data further includes: adjusting the hot air circulation system according to the final curing parameters to obtain the operating status; monitoring the adhesive strength of the tape surface according to the operating status to determine the strength data; using the strength data to verify the quality consistency to obtain the quality index; and calibrating the temperature distribution for the quality index to obtain consistent curing quality data.

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