A plastic sheet manufacturing system and its manufacturing method

Abstract

The present invention discloses a plastic sheet manufacturing system and its manufacturing method, which relates to the technical field of plastic sheet manufacturing. After optimizing a number of initial cooling rates through a genetic algorithm, the final cooling rate is output for the current plastic sheet processing. During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the manufacturing system monitors the sheet data and device data in real time. After analyzing the sheet data and device data through an intelligent optimization model, it determines whether it is necessary to dynamically adjust the final cooling rate, and generates a corresponding adjustment strategy based on the judgment result. Before processing the plastic sheet, global optimization is automatically performed through an anomaly algorithm to obtain the optimal cooling rate, and during the processing, the cooling rate is adjusted in real time according to the state of the sheet and the air cooling device, effectively ensuring the production efficiency and quality of the plastic sheet.

Description

Technical Field

[0001] The present invention relates to the technical field of plastic sheet manufacturing, and particularly relates to a plastic sheet manufacturing system and a manufacturing method thereof. Background Art

[0002] Plastic sheet manufacturing systems play an important role in modern industry. With the widespread application of plastic materials, their manufacturing technologies have been continuously developed to meet the needs of various industries for lightweight, high-strength, and corrosion-resistant materials. Plastic sheets are widely used in packaging, construction, automotive, electronics and other fields due to their excellent processing performance, cost-effectiveness, and diverse physical properties.

[0003] The existing technologies have the following deficiencies:

[0004] In the existing plastic sheet processing, the cooling speed of the air cooling device is usually set manually according to experience, which cannot ensure that the cooling speed is the optimal solution, thus unable to guarantee the cooling quality and production efficiency of plastic sheets. And during the real-time processing, adopting a fixed cooling speed may, on the one hand, lead to slow cooling and reduce the production efficiency of plastic sheets, and on the other hand, may lead to too fast cooling, resulting in uneven cooling of plastic sheets and quality problems such as cracking.

[0005] Based on this, the present invention proposes a plastic sheet manufacturing system and a manufacturing method thereof. Before processing the plastic sheet, through an abnormal algorithm, global optimization is automatically performed to obtain the optimal cooling speed, and during the processing, the cooling speed is adjusted in real time according to the state of the sheet and the air cooling device, effectively guaranteeing the production efficiency and quality of plastic sheets. Summary of the Invention

[0006] The purpose of the present invention is to provide a plastic sheet manufacturing system and a manufacturing method thereof to solve the deficiencies in the background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A plastic sheet manufacturing method, the manufacturing method includes the following steps:

[0008] The manufacturing system obtains the historical manufacturing data of plastic sheets based on the workshop management platform, generates several initial cooling speeds for the plastic sheets based on the historical manufacturing data, and after performing optimization processing on the several initial cooling speeds based on the genetic algorithm, outputs the final cooling speed for the current plastic sheet processing;

[0009] The plastic sheet raw material is transported to the heating device and heated to a molten state, and then integrally formed by the forming device. The forming device shapes the molten plastic into a plastic sheet with the required thickness and width, and transports the shaped plastic sheet to the air cooling device;

[0010] During the process of cooling the plastic sheet by the air cooling device according to the final cooling rate, the manufacturing system monitors the sheet data and device data in real time. After analyzing the sheet data and device data through the intelligent optimization model, it determines whether it is necessary to dynamically adjust the final cooling rate, and generates a corresponding adjustment strategy based on the judgment result.

[0011] In a preferred embodiment, the manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform, and generates several initial cooling rates for the plastic sheet based on the historical manufacturing data, including the following steps:

[0012] The manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform. The historical manufacturing data includes divisor conditions and the same plastic sheet thickness. After matching the same plastic sheet thickness in the database, it obtains several cooling rates corresponding to the same plastic sheet thickness, and after screening out the cooling rates that do not meet the divisor conditions, the remaining cooling rates are used as the initial cooling rates.

[0013] In a preferred embodiment, after optimizing several initial cooling rates based on the genetic algorithm, the final cooling rate is output for the current plastic sheet processing, including the following steps:

[0014] After obtaining all the initial cooling rates, calculate the fitness value of each initial cooling rate based on the fitness function, and calculate the selection probability of each initial cooling rate based on the fitness value. According to the selection probability, use the roulette wheel selection method to select some initial cooling rates for randomization operations. Establish a set of all the initial cooling rates after the randomization operation is completed, recalculate the fitness value of each initial cooling rate in the set, and perform iterative loops to generate multiple sets. When the convergence condition is met, output multiple sets, and select the initial cooling rate with the largest fitness value in all sets as the final cooling rate.

[0015] In a preferred embodiment, calculate the selection probability of each initial cooling rate based on the fitness value, and use the roulette wheel selection method to select some initial cooling rates for randomization operations according to the selection probability, including the following steps:

[0016] Sum up the fitness values of all the initial cooling rates to obtain the total fitness value. Divide the fitness value by the total fitness value to obtain the selection probability of each initial cooling rate. Map the selection probability of each initial cooling rate to a virtual roulette wheel. Each selection probability corresponds to a sector area on the virtual roulette wheel. The larger the selection probability of the initial cooling rate, the larger the corresponding sector area. Start the rotation of the virtual roulette wheel. When the virtual roulette wheel stops rotating, select the initial cooling rate corresponding to the sector area pointed to by the virtual pointer at the top of the virtual roulette wheel. When the number of selected initial cooling rates is equal to the number threshold, perform mutation operations on the selected initial cooling rates.

[0017] In a preferred embodiment, after analyzing the sheet data and the device data through the intelligent optimization model, it is determined whether dynamic adjustment of the final cooling rate is required, including the following steps:

[0018] Input the defect index and the anomaly index into the intelligent optimization model. After comprehensively analyzing the defect index and the anomaly index, the intelligent optimization model outputs an adjustment coefficient;

[0019] Compare the obtained adjustment coefficient with a preset first coefficient threshold and a second coefficient threshold. The first coefficient threshold is used to determine whether an increase adjustment of the final cooling rate is required, and the second coefficient threshold is used to determine whether a decrease adjustment of the final cooling rate is required;

[0020] If the adjustment coefficient is less than the first coefficient threshold, it is determined that an increase adjustment of the final cooling rate is required. If the adjustment coefficient is greater than the second coefficient threshold, it is determined that a decrease adjustment of the final cooling rate is required. If the first coefficient threshold is less than or equal to the adjustment coefficient and the adjustment coefficient is less than or equal to the second coefficient threshold, it is determined that no adjustment of the final cooling rate is required.

[0021] In a preferred embodiment, a corresponding adjustment strategy is generated based on the determination result, including the following steps:

[0022] When it is determined that an increase adjustment of the final cooling rate is required, the final cooling rate is dynamically adjusted based on the increase adjustment comparison table. When it is determined that a decrease adjustment of the final cooling rate is required, the final cooling rate is dynamically adjusted based on the decrease adjustment comparison table.

[0023] In a preferred embodiment, during the cooling process of the plastic sheet, multiple monitoring points are set for the plastic sheet. At each monitoring point, the thickness is monitored in real time by an ultrasonic thickness gauge, and a thickness set is established for the thickness of each monitoring point. The defect index is calculated, and the expression is:

[0024] In the formula, qxz is the defect index, k is the number of monitoring points, q i is the thickness of the i-th monitoring point, q avg is the average thickness;

[0025] The calculation logic of the anomaly index is as follows: Obtain the fan speed deviation, the device vibration amplitude, and the noise decibel of the air cooling device; perform normalization processing on the fan speed deviation, the device vibration amplitude, and the noise decibel so that the value ranges of the fan speed deviation, the device vibration amplitude, and the noise decibel are mapped to between [0, 1]. Obtain the normalized value of the fan speed deviation, the normalized value of the device vibration amplitude, and the normalized value of the noise decibel. After summing the normalized value of the fan speed deviation, the normalized value of the device vibration amplitude, and the normalized value of the noise decibel, the anomaly index is obtained.

[0026] A plastic sheet manufacturing system includes a cooling rate optimization module, a sheet processing module, and a dynamic optimization module;

[0027] Cooling rate optimization module: Based on the workshop management platform, obtain the historical manufacturing data of plastic sheets. Based on the historical manufacturing data, generate several initial cooling rates for plastic sheets. After optimizing several initial cooling rates using the genetic algorithm, output the final cooling rate for current plastic sheet processing;

[0028] Sheet processing module: After the plastic sheet raw material is transported to the heating device and heated to a molten state, it is integrally formed by the forming equipment. The forming equipment shapes the molten plastic into a plastic sheet with the required thickness and width, and transports the shaped plastic sheet to the air cooling device. During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the sheet data and device data are monitored in real time;

[0029] Dynamic optimization module: After analyzing the sheet data and device data through the intelligent optimization model, judge whether it is necessary to dynamically adjust the final cooling rate, and generate corresponding adjustment strategies based on the judgment results.

[0030] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0031] After the present invention optimizes several initial cooling rates using the genetic algorithm and outputs the final cooling rate for current plastic sheet processing, during the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the manufacturing system monitors the sheet data and device data in real time. After analyzing the sheet data and device data through the intelligent optimization model, judge whether it is necessary to dynamically adjust the final cooling rate, and generate corresponding adjustment strategies based on the judgment results. Before processing the plastic sheet, automatically perform global optimization through the anomaly algorithm to obtain the optimal cooling rate, and during the processing, adjust the cooling rate in real time according to the state of the sheet and the air cooling device, effectively ensuring the production efficiency and quality of plastic sheets. Brief Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is the method flow chart of the present invention. Detailed Embodiments

[0034] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Embodiment 1: Please refer to Figure 1 As shown, a method for manufacturing a plastic sheet in this embodiment, the manufacturing method includes the following steps:

[0036] The manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform, generates several initial cooling rates for the plastic sheet based on the historical manufacturing data, performs an optimization process on the several initial cooling rates based on the genetic algorithm, and outputs the final cooling rate for the current plastic sheet processing. After the plastic sheet raw material (usually plastic particles or resin sheets) is transferred to the heating device and heated to a molten state, it is integrally formed by a forming device. The forming device shapes the molten plastic into a plastic sheet with the required thickness and width. The shaped plastic sheet is transferred to the air cooling device. During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the manufacturing system monitors the sheet data and device data in real time. After analyzing the sheet data and device data through the intelligent optimization model, it determines whether it is necessary to dynamically adjust the final cooling rate, and generates a corresponding adjustment strategy based on the determination result.

[0037] In this application, after performing an optimization process on several initial cooling rates based on the genetic algorithm, the final cooling rate is output for the current plastic sheet processing. During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the manufacturing system monitors the sheet data and device data in real time. After analyzing the sheet data and device data through the intelligent optimization model, it determines whether it is necessary to dynamically adjust the final cooling rate, and generates a corresponding adjustment strategy based on the determination result. Before processing the plastic sheet, global optimization is automatically performed through an anomaly algorithm to obtain the optimal cooling rate, and during the processing, the cooling rate is adjusted in real time according to the state of the sheet and the air cooling device, effectively ensuring the production efficiency and quality of the plastic sheet.

[0038] Embodiment 2: The manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform, and generates several initial cooling rates for the plastic sheet based on the historical manufacturing data, including the following steps:

[0039] The manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform, and the historical manufacturing data includes divisor conditions and the thickness of the same plastic sheet;

[0040] After matching the same plastic sheet thickness in the database, obtain several cooling rates corresponding to the same plastic sheet thickness. After screening out the cooling rates that do not meet the divisor condition, the remaining cooling rates are used as the initial cooling rates;

[0041] For example:

[0042] Data extraction: Extract the thickness and corresponding cooling rate data of all plastic sheets from the database. For example, assume that the sheets with a thickness of 3 mm have the following cooling rate records: 5 m / s, 8 m / s, 10 m / s, 12 m / s.

[0043] Match the thickness: Through query, match the cooling rates of all plastic sheets with a thickness of 3 mm.

[0044] The result is: 5 m / s, 8 m / s, 10 m / s, 12 m / s.

[0045] Set the constraint conditions: Determine the constraint conditions for the cooling rate, such as: minimum cooling rate: 6 m / s, maximum cooling rate: 10 m / s;

[0046] Screen the cooling rates: Compare the extracted cooling rates with the constraint conditions and screen out the rates that do not meet the conditions. From the example, screen out 5 m / s and 12 m / s, and retain 8 m / s and 10 m / s.

[0047] Output the initial cooling rates: The remaining cooling rates are used as the initial cooling rates, and the final result is: initial cooling rates: 8 m / s, 10 m / s.

[0048] After optimizing several initial cooling rates based on the genetic algorithm, output the final cooling rate for the current plastic sheet processing, including the following steps:

[0049] After obtaining all the initial cooling rates, calculate the fitness value of each initial cooling rate based on the fitness function, and calculate the selection probability of each initial cooling rate based on the fitness value. Select some initial cooling rates for randomization operations according to the selection probability. Establish a set of all the initial cooling rates after the randomization operation is completed, recalculate the fitness value of each initial cooling rate in the set, and perform iterative loops to generate multiple sets. When the convergence condition is met, output multiple sets, and select the initial cooling rate with the largest fitness value in all the sets as the final cooling rate.

[0050] Calculate the fitness value of each initial cooling rate based on the fitness function. The expression of the fitness function is: In the formula, sd zLet \(f\) be the fitness value, \(\delta\) be the sheet quality index, \(\tau\) be the power consumption, \(\varepsilon\) be the productivity, and \(\alpha\), \(\beta\), \(\gamma\) be the proportionality coefficients of the sheet quality index, power consumption, and productivity respectively. And \(\alpha\), \(\beta\), \(\gamma\) are all greater than 0. The larger the fitness value, the better the performance of the initial cooling rate.

[0051] The calculation logic of the sheet quality index is as follows: Obtain the tensile strength, surface roughness, and deformation rate of the plastic sheet, perform normalization processing on the tensile strength, surface roughness, and deformation rate to map the value ranges of the tensile strength, surface roughness, and deformation rate to between [0, 1], obtain the normalized value of the tensile strength, the normalized value of the surface roughness, and the normalized value of the deformation rate, and subtract the normalized value of the surface roughness and the normalized value of the deformation rate from the normalized value of the tensile strength to obtain the sheet quality index. The larger the sheet quality index, the better the quality of the plastic sheet at the current initial cooling rate.

[0052] The acquisition logic of the power consumption is as follows: Install an ammeter in the power cord of the air cooling device to monitor the power consumption of the plastic sheet from the start of cooling to the end of cooling. The larger the power consumption, the more electrical energy is consumed for cooling the plastic sheet at the current initial cooling rate, increasing the production cost.

[0053] The acquisition logic of the productivity is as follows: Obtain the number of plastic sheets cooled per unit time, and divide the number of plastic sheets cooled per unit time by the monitoring duration to obtain the productivity. The larger the productivity, the more plastic sheets are cooled at the current initial cooling rate.

[0054] Calculate the selection probability of each initial cooling rate based on the fitness value, and use the roulette wheel selection method to select some initial cooling rates for randomization operations according to the selection probability, including the following steps:

[0055] Sum the fitness values of all initial cooling rates to obtain the total fitness value, divide the fitness value by the total fitness value to obtain the selection probability of each initial cooling rate, map the selection probability of each initial cooling rate to a virtual roulette wheel, and each selection probability corresponds to a sector area on the virtual roulette wheel. The larger the selection probability of the initial cooling rate, the larger the corresponding sector area. Start the rotation of the virtual roulette wheel. When the virtual roulette wheel stops rotating, select the initial cooling rate corresponding to the sector area pointed by the virtual pointer at the top of the virtual roulette wheel. When the number of selected initial cooling rates is equal to the number threshold, perform a mutation operation on the selected initial cooling rates, that is, make a small modification to the current initial cooling rate. For example, if the initial cooling rate is 8 m / s, the mutated initial cooling rate is 8.12 m / s.

[0056] Generate multiple sets through iterative loops. When the convergence condition is met, output multiple sets, including the following steps:

[0057] When the number of iteration cycles is equal to the cycle threshold or there exists an initial cooling rate in the set whose fitness value is greater than or equal to the fitness threshold, it is determined that the convergence condition is satisfied.

[0058] The raw material of the plastic sheet (usually plastic particles or resin sheets) is transported to a heating device and heated to a molten state, and then integrally formed by a forming device. The forming device shapes the molten plastic into a plastic sheet with the required thickness and width, and transports the shaped plastic sheet to an air cooling device, including the following steps:

[0059] Raw material preparation: Pretreat the raw material, such as drying to remove moisture to ensure the quality of subsequent processing. Raw material transportation: Transport the pretreated plastic particles or resin sheets to the heating device through a conveying system.

[0060] Heating device: The raw material is heated to a molten state in the heating device. This process requires monitoring the temperature to ensure that the plastic reaches the required melting temperature. Use a temperature sensor to monitor in real time, and the heating equipment adjusts the temperature according to the feedback.

[0061] Forming device: Feed the molten plastic into a forming device (such as an extruder or an injection molding machine). The forming device shapes the molten plastic into a plastic sheet with the required thickness and width. This step includes: extruding or injecting the material into the mold. Maintain appropriate pressure and time in the mold to ensure that the plastic is fully formed.

[0062] Transport to the cooling device: The shaped plastic sheet is sent to the air cooling device through a transport system (such as a belt conveyor).

[0063] During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the manufacturing system monitors the sheet data and the device data in real time, including the following steps:

[0064] The manufacturing system monitors the sheet data in real time. The sheet data includes a defect index, and the device data includes an anomaly index;

[0065] The calculation logic of the defect index is as follows: During the cooling process of the plastic sheet, set multiple monitoring points for the plastic sheet. At each monitoring point, the thickness is monitored in real time by an ultrasonic thickness gauge. Establish a thickness set for the thickness of each monitoring point, and calculate the defect index. The expression is:

[0066] In the formula, qxz is the defect index, k is the number of monitoring points, q i is the thickness of the i-th monitoring point, q avg is the average thickness. The larger the defect index, the worse the thickness uniformity of the plastic sheet, and the more necessary it is to reduce the cooling rate. The specific reasons are as follows:

[0067] During the forming process of plastic sheets, uneven thickness can lead to differences in cooling rates in different regions. For example, thicker parts require more time to cool, while thinner parts cool faster. This cooling difference further exacerbates the uneven thickness because the uneven shrinkage during cooling causes the original dimensional deviation to expand. Reducing the cooling rate allows the uneven thick and thin parts more time to balance out, making the temperature field more uniform and avoiding additional thickness changes caused by local overcooling.

[0068] Rapid cooling can cause a large temperature gradient to form inside the plastic sheet. Due to the uneven thickness, the temperature gradient leads to different cooling rates in each region, thereby generating internal stress. The increase in internal stress not only affects the mechanical properties of the material but also causes problems such as warping and deformation during subsequent use. Reducing the cooling rate allows more time for temperature and stress equalization inside the plastic sheet, reducing the residual stress caused by uneven thickness.

[0069] The fluidity of plastic in the molten state has a great impact on the thickness uniformity of the finished product. Rapid cooling quickly reduces the fluidity of the melt, leading to an increased flow difference between the thick and thin regions. Especially in the thick parts, the melt freezes prematurely and cannot fully fill the mold. Reducing the cooling rate can increase the flow time of the melt, especially for thick regions. The enhanced fluidity makes the material distribution more uniform, thus reducing the problem of uneven thickness.

[0070] When a plastic sheet with uneven thickness is rapidly cooled, cooling marks or uneven surface quality may form on the surface. A slower cooling rate can make the sheet surface smoother and reduce defects during the cooling process.

[0071] Summary: The worse the thickness uniformity of the plastic sheet, the more reducing the cooling rate can reduce the additional dimensional deviation, internal stress, and surface defects caused by uneven cooling, helping to improve the overall performance and quality of the material. Therefore, in the case of uneven thickness, appropriately reducing the cooling rate is to allow the material more time to balance temperature and flow and reduce unnecessary problems.

[0072] The calculation logic of the anomaly index is as follows: Obtain the fan speed deviation, device vibration amplitude, and noise decibels of the air cooling device;

[0073] Normalize the fan speed deviation, the device vibration amplitude, and the noise decibels, so that the value ranges of the fan speed deviation, the device vibration amplitude, and the noise decibels are mapped to between [0, 1]. Obtain the normalized value of the fan speed deviation, the normalized value of the device vibration amplitude, and the normalized value of the noise decibels. After summing up the normalized value of the fan speed deviation, the normalized value of the device vibration amplitude, and the normalized value of the noise decibels, obtain the anomaly index. The larger the anomaly index, the worse the state of the air cooling device, and the more necessary it is to reduce the cooling speed. The specific reasons are as follows:

[0074] When the anomaly index is relatively large, it indicates that the operation of the cooling system may deviate from the normal state and cannot effectively or stably provide the expected cooling effect. If the relatively high cooling speed is continued to be maintained, the equipment load will further increase, leading to further deterioration or failure of the equipment.

[0075] Reducing the cooling speed can reduce the pressure on the cooling system, allow the equipment to operate at a lower load, and thus reduce the risk of further damage.

[0076] When the anomaly index becomes larger, the cooling efficiency of the air cooling device may be affected. Cooling non-uniformity will cause local temperature fluctuations of the object to be cooled. Especially during rapid cooling, the temperature gradient will be larger, resulting in a more serious phenomenon of cooling non-uniformity. This may have an adverse impact on the quality of the cooling object (such as plastic sheets or other materials).

[0077] By reducing the cooling speed, the equipment and the cooling object can have more time for heat exchange, balance the temperature of each part, and reduce the quality problems caused by cooling non-uniformity.

[0078] When the equipment state is poor, an overly fast cooling process may increase the load on the equipment, making the device in a more tense working state. Especially for air cooling equipment, components such as fans, compressors, and heat exchangers may overheat or wear due to long-term high-intensity work, and even accelerate the occurrence of failures.

[0079] Reducing the cooling speed can reduce the load on the equipment, extend the stability and lifespan of the equipment operation, and reduce the possibility of failures.

[0080] In the case of a relatively high anomaly index, the air cooling device may already be close to its working limit. If the high-speed cooling is continued to be maintained, the components of the equipment may not be able to cooperate effectively, resulting in system instability, increased vibration, or reduced energy efficiency. Reducing the cooling speed helps to relieve the complex load of the system and improve the overall operation stability.

[0081] When the anomaly index is large, it may mean that there is a problem with the control system of the cooling device. For example, the feedback of the cooling air volume or wind speed is inaccurate, resulting in out-of-control cooling effect. If cooling continues at a relatively fast speed, phenomena such as overcooling or insufficient cooling may occur. Especially rapid cooling can cause quality defects on the surface or inside of the product. By reducing the cooling speed, the cooling process can be controlled more smoothly to ensure that the cooling effect meets expectations.

[0082] Summary: The larger the anomaly index, the worse the state of the air cooling device. At this time, reducing the cooling speed can reduce the equipment load, avoid further damage, improve cooling uniformity, maintain system stability, and reduce quality problems or control errors. Therefore, reducing the cooling speed is a preventive strategy that helps maintain the stability and quality of the equipment and the cooling object when the cooling equipment is in poor condition.

[0083] After analyzing the sheet data and device data through the intelligent optimization model, it is judged whether dynamic adjustment of the final cooling speed is required, including the following steps:

[0084] Input the defect index and the anomaly index into the intelligent optimization model. After comprehensively analyzing the defect index and the anomaly index, the intelligent optimization model outputs an adjustment coefficient. The larger the adjustment coefficient, the more it indicates that the final cooling speed needs to be reduced. The smaller the adjustment coefficient, the more it indicates that the final cooling speed needs to be increased;

[0085] Compare the obtained adjustment coefficient with the preset first coefficient threshold and second coefficient threshold. The first coefficient threshold is used to judge whether an increase adjustment of the final cooling speed is required, and the second coefficient threshold is used to judge whether a decrease adjustment of the final cooling speed is required;

[0086] If the adjustment coefficient is less than the first coefficient threshold, it is judged that an increase adjustment of the final cooling speed is required. If the adjustment coefficient is greater than the second coefficient threshold, it is judged that a decrease adjustment of the final cooling speed is required. If the first coefficient threshold is less than or equal to the adjustment coefficient and less than or equal to the second coefficient threshold, it is judged that no adjustment of the final cooling speed is required;

[0087] Input the defect index and the anomaly index into the intelligent optimization model. After comprehensively analyzing the defect index and the anomaly index, the intelligent optimization model outputs an adjustment coefficient. The model expression is:

[0088] TJ = ε1qxz - ε2xtj, where TJ is the adjustment coefficient, qxz is the defect index, xtj is the anomaly index, ε1 and ε2 are the weights of the defect index and the anomaly index respectively, and ε1 + ε2 = 1.

[0089] Generate corresponding adjustment strategies based on the judgment results, including the following steps:

[0090] When it is determined that an increase adjustment of the final cooling rate is required, the final cooling rate is dynamically adjusted based on the increase adjustment comparison table. When it is determined that a decrease adjustment of the final cooling rate is required, the final cooling rate is dynamically adjusted based on the decrease adjustment comparison table;

[0091] The increase adjustment comparison table is shown in Table 1:

[0092]

[0093] Table 1

[0094] In Table 1, TJ is the adjustment coefficient, SZ1 is the first coefficient threshold, SZX1 is the first increase gradient threshold, SZX2 is the second increase gradient threshold, SZX n-1 is the (n - 1)th increase gradient threshold, SZX n is the nth increase gradient threshold. The first increase gradient threshold, the second increase gradient threshold, the (n - 1)th increase gradient threshold, and the nth increase gradient threshold are used to distinguish the increase gradients of the final cooling rate, and the first increase gradient threshold is greater than the second increase gradient threshold, which is greater than the (n - 1)th increase gradient threshold, which is greater than the nth increase gradient threshold.

[0095] The decrease adjustment comparison table is shown in Table 2:

[0096]

[0097] Table 2

[0098] In Table 2, TJ is the adjustment coefficient, SZ2 is the second coefficient threshold, SJX1 is the first decrease gradient threshold, SJX2 is the second decrease gradient threshold, SJX m-1 is the (m - 1)th decrease gradient threshold, SJX m is the mth decrease gradient threshold. The first decrease gradient threshold, the second decrease gradient threshold, the (m - 1)th decrease gradient threshold, and the mth decrease gradient threshold are used to distinguish the decrease gradients of the final cooling rate, and the first decrease gradient threshold is greater than the second decrease gradient threshold, which is greater than the (m - 1)th decrease gradient threshold, which is greater than the mth decrease gradient threshold;

[0099] It should be noted that the adjusted final cooling rate needs to meet the constraint conditions. When the adjusted final cooling rate exceeds the maximum cooling rate limit, the maximum cooling rate is used as the adjusted final cooling rate. When the adjusted final cooling rate is lower than the minimum cooling rate limit, the manufacturing system controls the air cooling device to stop running and issues an alarm prompt, indicating that there may be a fault in the cooling device or the quality of the plastic sheet is too poor.

[0100] After the manufacturing system obtains the adjusted final cooling rate, it controls the air cooling device to cool the plastic sheet at the adjusted final cooling rate.

[0101] Example 3: Please refer to Figure 1 As shown in the figure, a plastic sheet manufacturing system described in this embodiment includes a cooling rate optimization module, a sheet processing module, and a dynamic optimization module;

[0102] Cooling rate optimization module: Based on the workshop management platform, obtain the historical manufacturing data of plastic sheets. Based on the historical manufacturing data, generate several initial cooling rates for plastic sheets. After optimizing several initial cooling rates using the genetic algorithm, output the final cooling rate for the current plastic sheet processing, and send the final cooling rate to the dynamic optimization module;

[0103] Sheet processing module: The plastic sheet raw material (usually plastic particles or resin sheets) is transported to a heating device and heated to a molten state. Then, it is integrally formed through a forming device. The forming device shapes the molten plastic into a plastic sheet with the required thickness and width, and transports the shaped plastic sheet to an air cooling device. During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the sheet data and device data are monitored in real time, and the sheet data and device data are sent to the dynamic optimization module;

[0104] Dynamic optimization module: After analyzing the sheet data and device data through an intelligent optimization model, determine whether it is necessary to dynamically adjust the final cooling rate, and generate a corresponding adjustment strategy based on the judgment result.

[0105] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0106] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. Specifically, it can be understood by referring to the context before and after.

[0107] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0108] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0109] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for manufacturing a plastic sheet, characterized in that: The manufacturing method includes the following steps: The manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform, generates several initial cooling rates for the plastic sheet based on the historical manufacturing data, optimizes the several initial cooling rates through the genetic algorithm, and outputs the final cooling rate for the current plastic sheet processing; After the plastic sheet raw material is transported to the heating device and heated to the molten state, it is integrally formed by the forming device. The forming device shapes the molten plastic into a plastic sheet with the required thickness and width, and transports the shaped plastic sheet to the air cooling device; During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, the manufacturing system monitors the sheet data and the device data in real time. After analyzing the sheet data and the device data through the intelligent optimization model, it judges whether it is necessary to dynamically adjust the final cooling rate, and generates a corresponding adjustment strategy based on the judgment result; After obtaining all the initial cooling rates, calculate the fitness value of each initial cooling rate based on the fitness function. The expression of the fitness function is , where is the fitness value,[[]] is the sheet quality index,[[]] is the power consumption,[[]] is the production rate,[[]] are the proportionality coefficients of the sheet quality index, power consumption, and production rate respectively, and are all greater than 0. Calculate the selection probability of each initial cooling rate based on the fitness value. Use the roulette wheel selection method to select some initial cooling rates for randomization operations according to the selection probability. Establish a set of all the initial cooling rates after the randomization operations are completed. Recalculate the fitness value of each initial cooling rate in the set, and perform iterative loops to generate multiple sets. When the convergence condition is met, output multiple sets, and select the initial cooling rate with the largest fitness value in all the sets as the final cooling rate; After analyzing the sheet data and the device data through the intelligent optimization model, judging whether it is necessary to dynamically adjust the final cooling rate includes the following steps: Input the defect index and the anomaly index into the intelligent optimization model. After comprehensively analyzing the defect index and the anomaly index, the intelligent optimization model outputs an adjustment coefficient; Compare the obtained adjustment coefficient with a preset first coefficient threshold and a second coefficient threshold. The first coefficient threshold is used to judge whether it is necessary to increase the adjustment of the final cooling rate, and the second coefficient threshold is used to judge whether it is necessary to decrease the adjustment of the final cooling rate; If the adjustment coefficient is less than the first coefficient threshold, it is judged that it is necessary to increase the adjustment of the final cooling rate. If the adjustment coefficient is greater than the second coefficient threshold, it is judged that it is necessary to decrease the adjustment of the final cooling rate. If the first coefficient threshold is less than or equal to the adjustment coefficient and less than or equal to the second coefficient threshold, it is judged that there is no need to adjust the final cooling rate; The calculation logic of the defect index is as follows: During the cooling process of the plastic sheet, multiple monitoring points are set for the plastic sheet, and the thickness is monitored in real time by an ultrasonic thickness gauge at each monitoring point. A thickness set is established for the thickness of each monitoring point, and the defect index is calculated. The expression is: , where is the defect index, is the number of monitoring points, is the th thickness of the monitoring point, is the average thickness; The calculation logic of the anomaly index is as follows: Obtain the fan speed deviation, the device vibration amplitude, and the noise decibel of the air cooling device; normalize the fan speed deviation, the device vibration amplitude, and the noise decibel so that the value ranges of the fan speed deviation, the device vibration amplitude, and the noise decibel are mapped to between [0, 1]. Obtain the normalized value of the fan speed deviation, the normalized value of the device vibration amplitude, and the normalized value of the noise decibel. After summing the normalized value of the fan speed deviation, the normalized value of the device vibration amplitude, and the normalized value of the noise decibel, obtain the anomaly index.

2. The method for manufacturing a plastic sheet according to claim 1, characterized in that: The manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform, and generates several initial cooling rates for the plastic sheet based on the historical manufacturing data, including the following steps: The manufacturing system obtains the historical manufacturing data of the plastic sheet based on the workshop management platform. The historical manufacturing data includes divisor conditions and the thickness of the same plastic sheet; after matching the thickness of the same plastic sheet in the database, obtain several cooling rates corresponding to the thickness of the same plastic sheet, and screen out the cooling rates that do not meet the divisor conditions. The remaining cooling rates are used as the initial cooling rates.

3. A method for manufacturing a plastic sheet according to claim 2, characterized in that: Calculate the selection probability of each initial cooling rate based on the fitness value. According to the selection probability, use the roulette wheel selection method to select some initial cooling rates for randomization operations, including the following steps: Sum up the fitness values of all initial cooling rates to obtain the total fitness value. Divide the fitness value by the total fitness value to obtain the selection probability of each initial cooling rate. Map the selection probability of each initial cooling rate to a virtual roulette wheel. Each selection probability corresponds to a sector area on the virtual roulette wheel. The larger the selection probability of the initial cooling rate, the larger the corresponding sector area. Start the rotation of the virtual roulette wheel. When the virtual roulette wheel stops rotating, select the initial cooling rate corresponding to the sector area pointed by the virtual pointer at the top of the virtual roulette wheel. When the number of selected initial cooling rates is equal to the quantity threshold, perform mutation operations on the selected initial cooling rates.

4. A method for manufacturing a plastic sheet according to claim 3, characterized in that: Generate corresponding adjustment strategies based on the judgment results, including the following steps: When it is judged that the final cooling rate needs to be increased, dynamically adjust the final cooling rate based on the increase adjustment comparison table. When it is judged that the final cooling rate needs to be decreased, dynamically adjust the final cooling rate based on the decrease adjustment comparison table.

5. A plastic sheet manufacturing system for implementing the manufacturing method according to any one of claims 1-4, characterized in that: Include a cooling rate optimization module, a sheet processing module, and a dynamic optimization module; Cooling rate optimization module: Obtain the historical manufacturing data of plastic sheets based on the workshop management platform. Generate several initial cooling rates for the plastic sheets based on the historical manufacturing data. After optimizing several initial cooling rates based on the genetic algorithm, output the final cooling rate for the current plastic sheet processing; Sheet processing module: Transfer the plastic sheet raw material to a heating device to heat it to a molten state, and then integrally form it through a forming device. The forming device shapes the molten plastic into a plastic sheet with the required thickness and width. Transfer the shaped plastic sheet to an air cooling device. During the process of the air cooling device cooling the plastic sheet according to the final cooling rate, monitor the sheet data and device data in real time; Dynamic optimization module: After analyzing the sheet data and device data through an intelligent optimization model, judge whether it is necessary to dynamically adjust the final cooling rate, and generate corresponding adjustment strategies based on the judgment results.

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