Zip-top can aluminum foil rolling process parameter optimization method

Abstract

The invention relates to the technical field of rolling optimization, in particular to a ring-pull can aluminum foil rolling process parameter optimization method which comprises the following steps: ring-pull can aluminum foil rolling optimization: setting rolling speed and pressure to ensure uniform thickness and smooth surface; parameters are adjusted through multiple rounds of rolling, and a joint optimization cycle is established; microwave and electromagnetic heating are introduced, rolling and heating are iteratively optimized cooperatively, an optimal parameter combination is obtained, and the problems that due to the fact that the control precision of key parameters such as temperature and pressure is limited, non-uniform distribution of the thickness of aluminum foil and reduction of the surface quality in the ring-pull can production process are directly caused are solved. According to the technical scheme, the technical problems that in the prior art, the rolling process parameters of the aluminum foil of the zip-top can are controlled in a refined mode due to collaborative iteration optimization rolling and multi-stage heating, the thickness uniformity and the surface smoothness of the aluminum foil of the zip-top can are remarkably improved, and support is provided for comprehensively improving the quality of the zip-top can product are solved.

Description

Technical Field

[0001] The present invention relates to the technical field related to rolling optimization, and in particular to a method for optimizing rolling process parameters of aluminum foil for cans. Background Art

[0002] The rolling process of aluminum foil for cans is an important part of aluminum processing, which directly affects the quality of can products, such as thickness uniformity, surface finish and the forming performance of the final product. Conventional aluminum foil rolling processes often rely on empirical function adjustment. First, slight differences in aluminum foil thickness may cause a series of problems in the can manufacturing process, such as uneven can strength, reduced sealing performance, etc., which in turn affect the overall quality of can products; secondly, due to the inaccuracy of parameter adjustment and the difficulty of process control, it is difficult to ensure the absolute smoothness of the aluminum foil surface, and scratches and other defects are prone to occur. At the same time, with the continuous improvement of the quality requirements for can products and the increase in the demand for production efficiency, the limitations of conventional aluminum foil rolling processes are gradually exposed.

[0003] In summary, the prior art has technical problems in that the control accuracy of key parameters such as temperature and pressure is limited, which directly leads to the uneven distribution of aluminum foil thickness and the decline of surface quality during the production process of cans, thereby affecting the overall quality of the can products. Summary of the invention

[0004] The present application provides a method for optimizing the rolling process parameters of aluminum foil for cans, aiming to solve the technical problem in the prior art that the limited control accuracy of key parameters such as temperature and pressure directly leads to the uneven distribution of aluminum foil thickness and the decline in surface quality during the production process of cans, thereby affecting the overall quality of the can products.

[0005] In view of the above problems, the technical solution to implement this application is: The present application provides a method for optimizing the rolling process parameters of aluminum foil for cans, wherein the method comprises: determining the rolling index of aluminum foil for cans based on the rolling process of aluminum foil for cans, wherein the rolling index of aluminum foil for cans comprises rolling speed and rolling pressure; reading the quality specification of aluminum foil for cans, and setting the quality index of cans, wherein the quality index of cans comprises thickness uniformity and surface finish; based on the rolling index of aluminum foil for cans, after the surface oxide layer of the rolling raw material of the cans is removed, connecting the rolling mill for primary rolling configuration, and obtaining a first rolling parameter set, wherein the first rolling parameter set comprises the single-row roller speed corresponding to N rows of rolling rollers, Single-row roller pressure, single-row roller gap; through the rolling mill, repeatedly M rounds of rolling and successively clamping the rolling rollers, collecting the second rolling parameter set, the third rolling parameter set, ..., the Mth rolling parameter set; according to the first rolling parameter set, the second rolling parameter set, the third rolling parameter set, ..., the Mth rolling parameter set, establishing a rolling parameter joint optimization loop corresponding to the N rows of rolling rollers; based on the rolling parameter joint optimization loop, introducing an internal microwave heating process and an external electromagnetic induction heating process, combined with the can quality index, conducting collaborative iterative optimization of multi-stage heating and M rounds of rolling, and obtaining the optimal parameter combination.

[0006] In summary, one or more technical solutions provided in this application solve the technical problem that the limited control accuracy of key parameters such as temperature and pressure directly leads to the uneven distribution of aluminum foil thickness and the decline in surface quality during the production of cans, which in turn affects the overall quality of the can products. It realizes the coordinated iterative optimization of rolling and multi-stage heating, and finely controls the rolling process parameters of aluminum foil for cans, which significantly improves the thickness uniformity and surface smoothness of aluminum foil for cans, providing technical effects to support the comprehensive improvement of the quality of can products. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A flow chart of a method for optimizing rolling process parameters of aluminum foil for cans is provided for this application.

[0008] Figure 2 A schematic flow chart of loop jumps in nested loops in a method for optimizing rolling process parameters of aluminum foil for cans is provided for this application. DETAILED DESCRIPTION

[0009] The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present application provides a method for optimizing rolling process parameters of aluminum foil for cans, wherein the method comprises: S1: Based on the rolling process of aluminum foil for cans, determine rolling indicators of aluminum foil for cans, wherein the rolling indicators of aluminum foil for cans include rolling speed and rolling pressure.

[0010] In the rolling process of aluminum foil for cans, rolling speed and rolling pressure are two crucial rolling indicators, which directly affect the finished product quality and production efficiency of aluminum foil. Among them, rolling speed is an important parameter in the rolling process of aluminum foil, which directly affects the production efficiency and surface quality of aluminum foil. Furthermore, the higher the rolling speed, the greater the amount of aluminum foil produced per unit time, thereby improving production efficiency. However, the choice of rolling speed is not the higher the better, and the comprehensive influence of other factors needs to be considered; rolling speed that is too fast may cause fine bubbles and lines on the surface of aluminum foil, affecting the surface finish, Therefore, when selecting the rolling speed, it is necessary to weigh the relationship between production efficiency and surface quality. Generally speaking, the maximum speed of the roughing mill can reach 1800m / min~2000m / min, the intermediate rolling mill can reach 1500m / min, and the finishing mill should not exceed 1200m / min; there is a speed effect in the rolling process of aluminum foil, that is, the thickness of the foil becomes thinner with the increase of rolling speed, which is caused by the change of the friction state between the working roll and the rolled material and the increase of temperature in the rolling deformation zone. Therefore, when rolling at high speed, corresponding measures need to be taken to control the thickness of the aluminum foil.

[0011] Rolling pressure is another key parameter in the aluminum foil rolling process, which directly affects the thickness uniformity and surface quality of the aluminum foil. Furthermore, too low a rolling pressure will lead to uneven thickness of the aluminum foil and defects such as ripples; while too high a pressure will cause a certain probability of making the surface of the aluminum foil rough and brittle. Therefore, it is necessary to select a suitable rolling pressure to ensure the thickness uniformity of the aluminum foil; the rolling pressure also has a significant impact on the surface quality of the aluminum foil; a reasonable rolling pressure will make the surface of the aluminum foil smoother and brighter; in the aluminum foil rolling process, the rolling pressure needs to be adjusted in real time according to factors such as the thickness, width and rolling speed of the aluminum foil. Modern aluminum foil rolling mills are usually equipped with advanced automatic control systems to achieve precise control of the rolling pressure.

[0012] In summary, the rolling speed and rolling pressure in the rolling process of aluminum foil for cans are two interrelated and crucial parameters. In actual production, it is necessary to select appropriate rolling speed and rolling pressure according to the specific situation to ensure the finished product quality and production efficiency of the aluminum foil. At the same time, it is also necessary to pay attention to other factors in the rolling process, such as temperature, lubrication, etc., to ensure the smooth progress of the rolling process.

[0013] S2: Read the quality specification of aluminum foil of cans and set the quality index of cans, wherein the quality index of cans includes thickness uniformity and surface finish.

[0014] The quality specifications of aluminum foil for cans usually include multiple aspects, among which thickness uniformity and surface finish are two important quality indicators. Furthermore, the thickness of aluminum foil for cans needs to be precisely controlled to ensure its stability and durability during use. Generally speaking, the thickness tolerance of aluminum foil for cans varies according to specific application requirements and manufacturing processes. For example, some occasions with higher requirements will control the thickness tolerance within ±0.005mm; the specific thickness range will also be determined according to the purpose of the aluminum foil and the design requirements of the cans. For example, the thickness of 5052 aluminum sheet and 3104 aluminum sheet commonly used in can covers is usually between 0.208mm and 0.360mm; during the production process, various detection methods are used to monitor the thickness uniformity of the aluminum foil. For example, the thickness of the aluminum foil is measured at multiple points using high-precision measuring tools to ensure that it can maintain a consistent thickness throughout the width and length.

[0015] Specifically, the surface finish of aluminum foil for cans is usually measured by surface roughness. Surface roughness refers to the number and degree of tiny protrusions and depressions on the surface of aluminum foil, which is usually expressed by Ra value (arithmetic mean deviation of profile). Generally speaking, the surface roughness of aluminum foil for cans needs to be controlled within a certain range to ensure that its surface is flat and smooth; the specific Ra value range will vary depending on the purpose and manufacturing process of the aluminum foil. For example, the surface roughness Ra value of some aluminum foil for cans is required to be between 0.38μm and 0.64μm; the surface of aluminum foil for cans is not allowed to have obvious defects, such as cracks, scratches, bubbles, coating leakage, corrosion, oil spots, peeling, stripes, color difference, spots, roller marks, periodic ripples, etc. Surface defects will affect the appearance quality and performance of aluminum foil; commonly, in order to improve the surface finish and subsequent processing performance of aluminum foil for cans, the surface of aluminum foil is usually pretreated, for example, a uniform layer of pre-coating oil is applied to protect the surface of aluminum foil from corrosion and contamination, and to improve its lubricity and processability.

[0016] In summary, the quality indicators of aluminum foil for cans include thickness uniformity and surface finish. Thickness uniformity and surface finish jointly determine the finished product quality and performance of aluminum foil for cans. In actual production, it is necessary to formulate corresponding quality specifications and testing standards based on specific application requirements and manufacturing processes to ensure that the quality of aluminum foil for cans meets the requirements.

[0017] S3: Based on the rolling index of the aluminum foil of the cans, after removing the surface oxide layer of the rolling raw materials of the cans, connect the rolling mill to perform primary rolling configuration to obtain a first rolling parameter set, wherein the first rolling parameter set includes a single-row roller speed, a single-row roller pressure, and a single-row roller gap corresponding to N rows of rolling rollers.

[0018] In the production process of aluminum foil for cans, the rolling process of aluminum foil for cans is a key link, which directly affects the quality of the finished aluminum foil. The rolling process is further refined, and after removing the oxide layer on the surface of the raw material, the primary rolling configuration and parameter acquisition are carried out. Specifically, first, select the aluminum foil rolling raw materials that meet the requirements; removing the oxide layer on the surface of the raw material is very important to ensure the subsequent processing performance and finished product quality of the aluminum foil. Commonly used methods for removing the oxide layer include chemical cleaning, mechanical grinding, electrochemical polishing, etc.

[0019] The processed raw materials are connected to the rolling mill for rolling. The rolling mill is the core equipment for aluminum foil rolling. Its performance stability and precision directly affect the rolling quality of the aluminum foil. Furthermore, before rolling, the rolling mill needs to be configured for primary rolling according to the rolling indicators and raw material characteristics, including adjusting the rolling force, rolling speed, roll gap and other parameters of the rolling mill to ensure the smooth progress of the rolling process and the quality of the finished aluminum foil; during the primary rolling process, the rolling parameters are monitored and recorded in real time to obtain the first rolling parameter set, providing strong support for subsequent rolling process optimization and quality control.

[0020] The first rolling parameter set includes the single-row roller speed, single-row roller pressure, and single-row roller gap corresponding to N rows of rolling rollers. Specifically, the single-row roller speed refers to the rotation speed of each row of rollers on the rolling mill during the rolling process. The speeds of different rows of rollers need to be adjusted according to the rolling process and the characteristics of the aluminum foil to ensure the uniformity and stability of rolling; the single-row roller pressure refers to the pressure applied to the aluminum foil by each row of rollers on the rolling mill. The single-row roller pressure needs to be adjusted according to parameters such as the thickness, width and rolling speed of the aluminum foil to ensure that the aluminum foil can be uniformly deformed during the rolling process and achieve the required thickness and surface quality; the single-row roller gap refers to the gap between two adjacent rows of rollers on the rolling mill. The size of the single-row roller gap will affect the deformation degree and surface quality of the aluminum foil during the rolling process. Therefore, the roller gap needs to be fine-tuned according to actual conditions during the rolling process.

[0021] S4: by using the rolling mill, repeatedly rolling for M rounds and clamping the rolling rollers one by one, a second rolling parameter set, a third rolling parameter set, ..., an Mth rolling parameter set are collected.

[0022] Multiple rounds of rolling are performed to further process the aluminum foil. At the same time, the rolling parameters are gradually adjusted to achieve the desired thickness and surface quality. Specifically, the aluminum foil is repeatedly rolled by a rolling mill, and the rounds are recorded as M. Each round of rolling is a further processing of the aluminum foil, so that its thickness gradually decreases and its surface quality gradually improves. Before each round of rolling, it is necessary to make necessary adjustments to the rolling parameters based on the results of the previous round of rolling and the current state of the aluminum foil. The corresponding rolling parameters include but are not limited to rolling speed, rolling pressure, and roller gap.

[0023] Then, the rolling rollers are clamped one by one. As the number of rolling rounds increases, the thickness of the aluminum foil gradually decreases. Therefore, the rolling rollers need to be clamped one by one to reduce the gap between the rollers to ensure that the aluminum foil can pass through the rollers evenly and stably and reach the required thickness. At the same time, the process of clamping the rolling rollers needs to be precisely controlled to avoid excessive pressure on the aluminum foil or uneven rolling. Furthermore, in each rolling process, the corresponding rolling parameters are collected and recorded to form the second rolling parameter set, the third rolling parameter set, ..., the Mth rolling parameter set, which include the speed, pressure, gap and other key parameters of the rolling rollers during each rolling round. By comparing and analyzing the rolling parameters of different rounds, the key factors affecting the quality of the aluminum foil are found, and the rolling process is improved accordingly.

[0024] Specifically, the specific value of M depends on the initial thickness, target thickness and rolling process requirements of the aluminum foil. In actual production, the value of M needs to be determined according to specific circumstances; before and during each round of rolling, the rolling parameters need to be finely adjusted. The corresponding adjustment is based on the results of the previous round of rolling and the current state of the aluminum foil, aiming to ensure that the aluminum foil can pass through the rollers evenly and stably and achieve the required thickness and surface quality; as the number of rolling rounds increases and the thickness of the aluminum foil decreases, the rolling rollers need to be clamped one by one to reduce the roller gap, and the process of clamping the rolling rollers one by one needs to be precisely controlled to ensure the stability of rolling and the quality of the aluminum foil.

[0025] By repeatedly rolling M rounds and gradually clamping the rolling rollers, the aluminum foil is gradually processed to the required thickness and surface quality. At the same time, the parameter set of each round of rolling is collected and recorded to provide support for subsequent analysis and optimization of the rolling process. In actual production, it is necessary to finely control the rolling rounds, rolling parameters and the clamping degree of the rolling rollers according to the specific circumstances to ensure the quality of the finished product of the aluminum foil for cans.

[0026] S5: Establishing a rolling parameter joint optimization loop corresponding to the N rows of rolling rollers based on the first rolling parameter set, the second rolling parameter set, the third rolling parameter set, ..., the Mth rolling parameter set.

[0027] A detailed data analysis and comparison is performed on each set of rolling parameter collected (the first to Mth rolling parameter sets); the analysis includes the changing trends of key parameters such as rolling speed, rolling pressure, and roll gap, as well as the impact on quality indicators such as aluminum foil thickness and surface finish; through data analysis, the key rolling parameters that have the greatest impact on aluminum foil quality are identified, as well as the changing trends of the parameters in different rolling rounds. For example, it is found that with the increase in rolling rounds, the roll gap needs to be gradually reduced to maintain uniform rolling of the aluminum foil.

[0028] Based on the results of data analysis, a rolling parameter joint optimization model corresponding to N rows of rolling rollers is established. The rolling parameter joint optimization model needs to be able to comprehensively consider all key rolling parameters, as well as the interactions and influences between the parameters, so as to obtain the optimal rolling parameter combination; in the rolling parameter joint optimization model, optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) are introduced to search for the optimal solution, which can find the rolling parameter combination that optimizes the aluminum foil quality indicators (such as thickness uniformity and surface finish) through iterative calculation within a given parameter range.

[0029] When establishing a joint optimization model for rolling parameters, it is necessary to clearly define the optimization objectives (such as maximizing thickness uniformity, minimizing surface roughness, etc.) and constraints (such as the range of rolling speed, the limitation of rolling pressure, etc.); the objectives and constraints will guide the search process of the optimization algorithm to ensure that the solution found meets both quality requirements and actual production conditions.

[0030] The established joint optimization model is embedded in the rolling process to form a rolling parameter joint optimization loop. Before each round of rolling, the optimization model is used to calculate the optimal rolling parameter combination for the current round, and the settings of the rolling mill are adjusted to apply the optimal rolling parameter combination for the current round. During the rolling process, the quality of the aluminum foil and the changes in the rolling parameters are monitored in real time and adjusted as needed. At the same time, new rolling parameter sets are collected as input data for the next round of optimization. The rolling parameter joint optimization loop is an ongoing process. With the accumulation of production experience and data, the optimization model is continuously optimized and improved, the accuracy and efficiency of the optimization algorithm are improved, and the quality of the finished aluminum foil is further improved.

[0031] By establishing a joint optimization cycle of rolling parameters corresponding to N rows of rolling rollers based on multiple rounds of rolling parameter sets, the rolling process of aluminum foil for cans can be finely controlled and optimized. While ensuring the quality of aluminum foil for cans and improving production efficiency, in actual production, it is necessary to pay close attention to the changes in rolling parameters and make timely adjustments and optimizations as needed.

[0032] S6: Based on the rolling parameter joint optimization cycle, an internal microwave heating process and an external electromagnetic induction heating process are introduced, and combined with the can quality index, a collaborative iterative optimization of multi-stage heating and M-round rolling is performed to obtain the optimal parameter combination.

[0033] On the basis of the joint optimization cycle of rolling parameters, internal microwave heating process and external electromagnetic induction heating process are introduced. Among them, internal microwave heating is to heat the inside of the aluminum foil by microwave radiation during the rolling process. The internal microwave heating method can quickly and evenly increase the temperature of the aluminum foil, which helps to improve its plasticity and machinability, and reduce stress concentration and crack generation during the rolling process. External electromagnetic induction heating is to heat the surface of the aluminum foil using the principle of electromagnetic induction. The external electromagnetic induction heating method can accurately control the heating area and temperature, which helps to improve the surface finish of the aluminum foil and reduce the formation of an oxide layer.

[0034] The internal microwave heating and external electromagnetic induction heating processes are integrated into the rolling parameter joint optimization cycle. Before or during each round of rolling, the heating parameters (such as heating time, heating temperature, heating area, etc.) are adjusted according to the current state of the aluminum foil and the rolling parameters to achieve the best synergistic effect of heating and rolling; by real-time monitoring of the quality of the aluminum foil and the changes in rolling parameters, multi-stage coordinated iterations of heating and rolling are performed, and each round of iteration adjusts and optimizes the parameters according to the results of the previous round to gradually approach the optimal parameter combination.

[0035] In the collaborative iteration process, the quality indicators of cans (such as thickness uniformity, surface finish, etc.) are always used as feedback signals. By measuring and analyzing the changes in the quality indicators of cans, the effect of the current heating and rolling parameter combination is evaluated, and the next round of iterative optimization is guided accordingly. After multiple rounds of collaborative iterative optimization, a set of optimal heating and rolling parameter combinations is gradually converged. The optimal parameter combination can maximize the processing efficiency while ensuring the quality of the aluminum foil of the cans. Furthermore, the optimal parameter combination is verified in the actual production environment to ensure its effectiveness and stability in actual applications. If any deviations or problems are found, the parameters are adjusted and optimized in a timely manner to ensure the smooth progress of the production process and the stable improvement of the quality of the aluminum foil.

[0036] By introducing internal microwave heating and external electromagnetic induction heating processes and combining them with the joint optimization cycle of rolling parameters, coordinated iterative optimization of multi-stage heating and M-round rolling is carried out to effectively improve the processing efficiency and finished product quality of aluminum foil for cans.

[0037] Furthermore, based on the rolling parameter joint optimization cycle, an internal microwave heating process and an external electromagnetic induction heating process are introduced, and the can quality index is combined to perform collaborative iterative optimization of multi-stage heating and M-round rolling to obtain the optimal parameter combination. The method of this application includes: A temperature real-time feedback control unit is connected, and the temperature real-time feedback control unit uses a PID controller to interact with an infrared thermometer using an Internet of Things protocol; based on the internal microwave heating process and the external electromagnetic induction heating process, a fuzzy logic algorithm is used to perform smooth control of multi-stage heating; in the process of smooth control of multi-stage heating, a machine learning model is used to predict the temperature change trend, and in combination with the can quality index, a power adjustment instruction is issued, and the power adjustment instruction is used to activate the temperature real-time feedback control unit.

[0038] The temperature real-time feedback control unit is connected and equipped with a PID controller. The data is exchanged with the infrared thermometer through the Internet of Things protocol. The PID controller can automatically adjust the heating power based on the current temperature deviation to ensure that the temperature is stable within the set range. The internal microwave heating process and the external electromagnetic induction heating process are introduced. During the rolling process of the aluminum foil of the can, the internal microwave heating process and the external electromagnetic induction heating process are used to control the temperature of the material to ensure that the material remains in an ideal state at different processing stages. The fuzzy logic algorithm is used to deal with the uncertainty in the heating process to make the heating control more flexible and smooth, ensuring that the temperature changes are in line with expectations.

[0039] A machine learning model is used to predict the temperature change trend over time based on historical data, and the corresponding temperature fluctuations are calculated in advance so that the heating power can be adjusted in time. The quality indicators of the aluminum foil of the can, such as thickness uniformity and surface finish, are comprehensively considered to ensure that product quality is guaranteed while optimizing parameters. According to the prediction results of the machine learning model and the quality indicators of the can, a power adjustment instruction will be automatically issued. The power adjustment instruction is used to activate the real-time temperature feedback control unit, namely the PID controller, to adjust the heating power and maintain precise control of the heating process.

[0040] Through continuous iteration and simultaneous adjustment of heating parameters and rolling parameters, the coordinated optimization of multi-stage heating and M-round rolling is achieved. The coordinated optimization process will continue until the optimal parameter combination that meets all quality indicators of the aluminum foil for cans is found. After multiple iterative optimizations, a set of parameters will be determined that can ensure the efficient operation of the heating and rolling processes, and ensure that the aluminum foil for cans has the best physical properties and product quality.

[0041] By introducing a real-time temperature feedback control unit, PID controller, Internet of Things protocol, fuzzy logic algorithm and machine learning model, it is possible to predict temperature change trends based on real-time data, and dynamically adjust based on quality indicators to achieve precise control and optimization of the heating and rolling process of aluminum foil for cans.

[0042] Furthermore, the application method includes: A thickness detection sensor is introduced to measure the thickness of the aluminum foil rolling mold in real time. Based on the thickness of the aluminum foil rolling mold, an adaptive control algorithm is used to dynamically adjust the single-row roller gap of the N-row rolling rollers controlled by the servo motor, synchronously capture the thickness change of the aluminum foil rolling mold, and perform thickness uniformity control of M-round rolling.

[0043] Furthermore, the application method includes: The servo motor performs synchronous correction of timing information based on the response time corresponding to the thickness detection sensor and the dynamic characteristics of the servo system.

[0044] Thickness detection sensors are introduced and installed at key positions of the aluminum foil rolling production line. The thickness detection sensors can monitor the thickness of the aluminum foil during the rolling process in real time and provide instant feedback on the actual thickness data of the aluminum foil. At the beginning, the aluminum foil rolling mold is a cube, and then M rounds of rolling are performed to obtain the aluminum foil for cans. The thickness detection sensors continuously collect the thickness data of the aluminum foil, and the thickness data of the aluminum foil will be used in subsequent control algorithms to adjust the rolling parameters, thereby achieving precise control of the thickness.

[0045] Based on the collected thickness data, an adaptive control algorithm is used to dynamically adjust the single-row roller gap of the N-row rolling rollers controlled by the servo motor. As the thickness of the aluminum foil changes, the distance between each roller is automatically adjusted to compensate for the thickness difference and maintain the consistency of the overall thickness. Furthermore, the timing information of the servo motor is synchronously corrected. In order to ensure that the data of the thickness detection sensor can be reflected in the control of the servo motor in a timely manner, the servo motor needs to perform synchronous correction of the timing information based on the response time of the sensor and its own dynamic characteristics (including the acceleration, deceleration of the motor and the inertia of the mechanical transmission components, etc.), ensuring the timeliness and accuracy of the control signal and avoiding control errors caused by signal delays or system response time differences.

[0046] During the entire M-round rolling process, the thickness of the aluminum foil will be continuously collected to ensure the uniformity of the thickness of the aluminum foil. Each thickness detection result will trigger the adjustment of the servo motor. Through continuous feedback and control, the thickness consistency of the aluminum foil after multiple rounds of rolling can be achieved. The data feedback from the thickness detection sensor is compared with the preset thickness standard to optimize the roller gap until the thickness of the aluminum foil meets the requirements.

[0047] The introduction of thickness detection sensors and adaptive control algorithms, combined with the precise control of servo motors, significantly improves the thickness uniformity of the aluminum foil rolling mold for cans, captures changes in aluminum foil thickness in real time, and dynamically adjusts rolling parameters to ensure that the quality of the finished aluminum foil meets production requirements.

[0048] Furthermore, the thickness change of the aluminum foil rolling mold base is captured synchronously to control the thickness uniformity of the M-round rolling. The method of the present application also includes: Based on the thickness change of the aluminum foil rolling mold base, a first collaborative loop corresponding to the internal microwave heating process is generated; based on the thickness change of the aluminum foil rolling mold base, a second collaborative loop corresponding to the external electromagnetic induction heating process is generated; the rolling parameter joint optimization loop is used as the outer loop, and the first collaborative loop corresponding to the internal microwave heating process and the second collaborative loop corresponding to the external electromagnetic induction heating process are used as inner loops to establish a nested loop, and the nested loop is used to perform collaborative iterative optimization of multi-stage heating and M-round rolling to obtain the optimal parameter combination.

[0049] According to the thickness change of the aluminum foil rolling mold, the first collaborative cycle associated with the internal microwave heating process is generated, which involves analyzing the impact of internal microwave heating on the internal temperature of the material, forming a temperature distribution map, and establishing a first parameter space based on this. In the first parameter space, by searching through the maximized quality grade of the cans and energy utilization rate, the optimal coordination of internal microwave heating (such as microwave power) and rolling is determined.

[0050] Similarly, by monitoring the thickness change of the aluminum foil rolling mold, the control algorithm is used to dynamically adjust the parameters of the electromagnetic induction heating (such as current intensity, heating frequency, etc.), and a second synergistic loop related to the external electromagnetic induction heating process is generated to achieve precise control of the aluminum foil heating process. The second synergistic loop tends to the synergistic effect of heating and rolling, but focuses on the influence of the external heating source on the material surface; the rolling parameter joint optimization loop is regarded as the outer loop, while the first synergistic loop (internal microwave heating) and the second synergistic loop (external electromagnetic induction heating) are regarded as inner loops, nested in the outer loop, and a nested loop is established. The nested loop structure allows the optimization of heating and rolling parameters at different levels.

[0051] Using nested loops, we started to conduct collaborative iterative optimization of multi-stage heating and M-round rolling. Furthermore, before or during each round of rolling, we dynamically adjusted the heating and rolling parameters according to the real-time data of the aluminum foil thickness and the heating effect. Through continuous collaborative iterative optimization, we fine-tuned the heating and rolling parameters until we found the optimal parameter combination that can simultaneously meet the requirements of thickness uniformity, surface finish and production efficiency. This step involves a lot of data collection, simulation operation and actual testing to ensure the validity and reliability of the parameters. After finding a set of feasible parameter combinations, regular feedback and adjustments are required to cope with the influence of factors such as changes in material properties and equipment aging to ensure stability and consistency in the long-term production process. A collaborative loop corresponding to the internal microwave heating process and the external electromagnetic induction heating process is established to achieve precise control of multi-stage heating and M-round rolling during the rolling process of aluminum foil for cans.

[0052] Further, based on the thickness change of the aluminum foil rolling mold base, a first coordinated cycle corresponding to the internal microwave heating process is generated, and the method of the present application includes: Based on the internal microwave heating process, an internal temperature distribution map of the aluminum foil rolling mold is generated; based on the internal temperature distribution map of the aluminum foil rolling mold, the first rolling parameter set, the second rolling parameter set, the third rolling parameter set, ..., the Mth rolling parameter set corresponding to M rounds of rolling are used to establish a first parameter space; based on the first parameter space, a search is performed with the maximized quality grade of cans and energy utilization rate, and the first collaborative cycle is established.

[0053] The aluminum foil rolling mold is heated through an internal microwave heating process. At the same time, an infrared thermometer is used to monitor the temperature of the mold in real time and collect temperature data. The collected temperature data will be used to generate an internal temperature distribution map of the aluminum foil rolling mold. The temperature distribution map reflects the temperature changes at different positions and time points.

[0054] Based on the internal temperature distribution map and the first rolling parameter set, the second rolling parameter set, and so on to the Mth rolling parameter set collected during the M rounds of rolling, a parameter space is constructed. The first parameter space includes all parameters related to the rolling process, such as roller speed, pressure, gap, etc. during each round of rolling, and parameters related to the internal microwave heating process, such as heating power, time, and frequency.

[0055] In the first parameter space, maximizing the quality grade of cans and the energy utilization rate is taken as the optimization goal. Based on the set optimization goal, the best parameter combination that can achieve the goal is searched in the first parameter space. Usually, with the help of mathematical optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms or simulated annealing algorithms, the parameter space is searched to find the parameter set that maximizes the objective function (quality grade of cans and energy utilization rate); it is iterated repeatedly until a satisfactory solution is found or a predetermined number of iterations is reached.

[0056] Based on the search results corresponding to the first parameter space, a first collaborative loop is established. The first collaborative loop closely links the internal microwave heating process, the internal temperature distribution map of the aluminum foil rolling mold base, and the rolling parameter set of multiple rounds of rolling to form a closed-loop control system; in the cycle, according to the real-time monitoring of the internal temperature distribution of the aluminum foil rolling mold base and the data feedback during the rolling process, the internal microwave heating parameters and rolling parameters are dynamically adjusted to maintain the uniformity of the internal temperature distribution of the aluminum foil rolling mold base, while optimizing the rolling effect, so as to achieve the goal of maximizing the quality grade of cans and energy utilization rate; through continuous iteration and optimization, the first collaborative loop will gradually approach the optimal solution and realize an efficient and stable aluminum foil rolling process for cans in actual production.

[0057] Even if the expected effect is achieved, it is still necessary to regularly monitor factors such as the equipment condition and raw material properties on the production line, because changes in factors such as the equipment condition and raw material properties on the production line will affect the effectiveness of the parameters. Therefore, it is necessary to establish a set of continuous improvement mechanisms to regularly re-evaluate and adjust parameters to maintain the high efficiency and product quality of the aluminum foil rolling production line, thereby achieving deep synergy between the internal microwave heating process and the aluminum foil rolling process.

[0058] Furthermore, if Figure 2 As shown, the rolling parameter joint optimization loop is used as an outer loop, and the first cooperative loop corresponding to the internal microwave heating process and the second cooperative loop corresponding to the external electromagnetic induction heating process are used as inner loops to establish a nested loop. The method of the present application also includes: By using fluid dynamics simulation technology, a simulation model of an aluminum foil rolling mold is constructed by comparing the single-row roller speed and single-row roller pressure corresponding to N rows of rolling rollers; based on the simulation model of the aluminum foil rolling mold, thickness defect characteristics and surface defect characteristics are identified, and the defect types corresponding to the surface defect characteristics include cracks and ripples; based on the thickness defect characteristics and surface defect characteristics, the thickness uniformity and surface smoothness of the can quality indicators are used as constraints, and the nested loop is subjected to loop jumps with multi-layer branch cutting with the goal of accelerating the production rate.

[0059] Set up the simulation environment, and further, select software suitable for fluid dynamics simulation, such as ANSYS Fluent, COMSOL Multiphysics, etc.; according to the geometric dimensions of the N-row rolling rollers and the single-row roller, establish a three-dimensional model in the CAD software and import it into the simulation software; define the physical properties of materials such as aluminum foil and rollers, such as density, elastic modulus, Poisson's ratio, thermal conductivity, etc.; set boundary conditions, and further, according to the speed of the single-row roller, set the linear velocity of the roller surface; according to the pressure of the single-row roller, set the contact pressure of the roller on the aluminum foil; define the fluid domain between the aluminum foil and the roller, and consider the plastic deformation and fluid dynamics effect of the aluminum foil; perform fine meshing of the roller, aluminum foil and fluid domain to ensure the accuracy of the simulation results; run the simulation model of the aluminum foil rolling mold base to observe the deformation, temperature distribution, stress distribution, etc. during the rolling process of the aluminum foil.

[0060] The thickness distribution, surface morphology and other data of the aluminum foil are extracted from the simulation results; the thickness defect characteristics and surface defect characteristics are identified. Furthermore, by comparing the thickness of each area of ​​the aluminum foil, the area with uneven thickness is identified; the defect types corresponding to the surface defect characteristics include cracks and ripples, among which the surface stress concentration area or the area of ​​sudden change is detected. Theoretical analysis shows that the surface stress concentration area or the area of ​​sudden change is the starting point of the crack; the surface morphology data is analyzed to identify periodic or irregular fluctuations.

[0061] The thickness uniformity (the thickness of the aluminum foil fluctuates within the allowable range) and surface finish (reducing or eliminating surface defects such as cracks and ripples) of the can quality indicators are used as constraints. The goal is to accelerate the production rate while meeting these quality standards. Furthermore, each link in the rolling process is analyzed, and the steps for parallel processing or optimization are identified, and loop jumps with multi-layer branch cutting are performed on the nested loops.

[0062] In the actual application process, the optimization strategy is constantly adjusted, including updating the simulation model parameters, adjusting the structure and parameters of the nested loop, to adapt to the changes in the production environment and continuously improve product quality and production efficiency. Through the above steps, the rolling process of aluminum foil for cans is systematically optimized to ensure that the aluminum foil for cans meets strict quality standards while ensuring production efficiency.

[0063] Furthermore, taking the thickness uniformity and surface finish of the can quality index as constraints and accelerating the production rate as the goal, the nested loop is subjected to multi-layer branch pruning loop jumps, and the method of the present application includes: Based on the first parameter space, a multi-layer branch architecture is established; in the multi-layer branch architecture, in each layer of branches, the thickness uniformity and surface smoothness of the can quality indicators are used as constraints, and single-layer branch pruning is performed with the goal of accelerating the production rate; after completing the single-layer branch pruning, the multi-layer branches are traversed and depth-first search and breadth-first exploration are performed in turn, and multi-layer branch pruning is performed on the nested loops; the multi-layer branch pruning is associated one-to-one with the nested loop jump logic.

[0064] According to the key parameters in the can production process (such as rolling pressure, speed, temperature, etc.) and the corresponding operating variables, a multi-layer branch architecture is constructed. Each layer represents a decision point or parameter adjustment point, which contains multiple branches, and each branch represents a different parameter setting or operation choice; in each layer of the multi-layer branch architecture, the following steps are performed to perform single-layer branch pruning: the thickness uniformity and surface finish of the cans are explicitly set as constraints, which means that any branches that do not meet these quality standards will be pruned, and only the combinations that can meet the thickness uniformity and surface finish requirements will be retained; with the acceleration of production rate as the main goal, among the branches that meet the quality constraints, the impact of each branch on the production rate is evaluated, and the branch with the highest probability of increasing the production rate is selected for retention; based on the above analysis and evaluation, the branches that do not meet the quality constraints or are not conducive to improving the production rate are pruned.

[0065] After completing the single-layer branch pruning, it is necessary to traverse the entire multi-layer branch architecture and perform further multi-layer branch pruning on the remaining branches, and perform depth-first search and breadth-first exploration in sequence. Furthermore, the depth-first search refers to starting from the root node and searching as deeply as possible along a branch until a leaf node is reached or a certain stopping condition is met (such as reaching the maximum depth, finding a solution that meets all conditions, etc.); in the depth-first search process, the retained branches of each layer are further evaluated, and branches that do not meet the final goal are pruned as needed; the breadth-first exploration is different from the depth-first search. The breadth-first search will first explore all branches of the same layer, and then go deeper into the next layer. In the breadth-first search process, it is easier to see the impact of different parameter combinations on the production rate and can quality, so as to make more comprehensive decisions. However, since the breadth-first search involves a lot of calculations, it is also necessary to weigh the specific situation in actual applications.

[0066] The multi-layer branch pruning is associated with the nested loop jump logic one by one. Furthermore, the logic association is that the process of multi-layer branch pruning is actually an optimization of the nested loop; in the nested loop, each layer of loop will be regarded as a layer in the multi-layer branch architecture; by pruning the branches that do not meet the conditions, it is actually skipping those invalid loop iterations, thereby speeding up the entire production process; in the process of multi-layer branch pruning, when encountering a branch that does not meet the quality constraint or is not conducive to improving the production rate, the branch and all subsequent sub-branches are directly skipped by setting the jump logic, which is equivalent to exiting the inner loop in advance in the nested loop and adjusting the iteration of the outer loop according to the conditions. By gradually refining and screening the parameter space, combined with the depth and width of the search strategy, the production quality and efficiency of the aluminum foil of the can are effectively balanced.

[0067] After multiple rounds of cutting and exploration, the optimized multi-layer branch architecture and jump logic were applied to actual production. Based on the actual production data feedback, the multi-layer branch architecture and jump logic were continuously adjusted and optimized to ensure the stability and efficiency of the production process.

[0068] In summary, the beneficial effects of the embodiments of the present application are: 1. By introducing an automated parameter optimization cycle and combining internal microwave heating with external electromagnetic induction heating, precise control of parameters such as temperature, pressure, and speed is achieved, thereby improving the thickness uniformity and surface finish of the aluminum foil.

[0069] 2. The use of modern sensing technologies and adaptive control algorithms such as temperature real-time feedback control unit and thickness detection sensor has realized the intelligentization of the production process and greatly improved the production speed and stability.

[0070] 3. The coordinated iterative optimization of multi-stage heating and M-wheel rolling, as well as the defect identification and optimization based on the simulation model, can effectively reduce energy consumption and reduce the waste of resources caused by substandard quality.

[0071] 4. Based on the first parameter space, a multi-layer branch architecture is established; in the multi-layer branch architecture, in each layer of branches, the thickness uniformity and surface finish of the can quality indicators are constrained, and the single-layer branch cutting is carried out with the goal of accelerating the production rate; after completing the single-layer branch cutting, the multi-layer branches are traversed and depth-first search and breadth-first exploration are carried out in turn, and multi-layer branch cutting is carried out on the nested loops; multi-layer branch cutting is associated with the nested loop jump logic one by one. By gradually refining and screening the parameter space, combined with the depth and width search strategy, the production quality and efficiency of the aluminum foil of the cans are effectively balanced.

[0072] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor, and no unnecessary restrictions are made here.

[0073] Furthermore, the above-mentioned technical scheme only reflects the preferred technical scheme of the technical scheme of the embodiment of the present application. Some changes that may be made to some parts thereof by technicians in this technical field all reflect the principles of the embodiment of the present application. Obviously, technicians in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A method for optimizing rolling process parameters of aluminum foil for cans, characterized in that: The method comprises: Based on the rolling process of aluminum foil for cans, determining rolling indicators of aluminum foil for cans, wherein the rolling indicators of aluminum foil for cans include rolling speed and rolling pressure; Read the quality specification of aluminum foil for cans and set the quality index of cans, wherein the quality index of cans includes thickness uniformity and surface finish; Based on the rolling index of the aluminum foil of the can, after the surface oxide layer of the rolling raw material of the can is removed, the rolling mill is connected to perform primary rolling configuration to obtain a first rolling parameter set, wherein the first rolling parameter set includes a single-row roller speed, a single-row roller pressure, and a single-row roller gap corresponding to N rows of rolling rollers; By means of the rolling mill, M rounds of rolling are repeatedly performed and rolling rollers are clamped successively, and a second rolling parameter set, a third rolling parameter set, ..., an Mth rolling parameter set are collected; According to the first rolling parameter set, the second rolling parameter set, the third rolling parameter set, ..., the Mth rolling parameter set, a rolling parameter joint optimization cycle corresponding to the N rows of rolling rollers is established; Based on the rolling parameter joint optimization cycle, an internal microwave heating process and an external electromagnetic induction heating process are introduced, and combined with the can quality index, a collaborative iterative optimization of multi-stage heating and M-round rolling is carried out to obtain the optimal parameter combination.

2. The method for optimizing rolling process parameters of aluminum foil for cans according to claim 1, characterized in that: Based on the rolling parameter joint optimization cycle, an internal microwave heating process and an external electromagnetic induction heating process are introduced, and combined with the can quality index, a collaborative iterative optimization of multi-stage heating and M-round rolling is performed to obtain an optimal parameter combination. The method includes: Connecting a temperature real-time feedback control unit, wherein the temperature real-time feedback control unit uses a PID controller to interact with the infrared thermometer using an Internet of Things protocol; Based on the internal microwave heating process and the external electromagnetic induction heating process, a fuzzy logic algorithm is used to smoothly control the multi-stage heating; In the process of smooth control of multi-stage heating, a machine learning model is used to predict the temperature change trend, and combined with the can quality index, a power adjustment instruction is issued, and the power adjustment instruction is used to activate the temperature real-time feedback control unit.

3. The method for optimizing rolling process parameters of aluminum foil for cans according to claim 2, characterized in that: The method comprises: A thickness detection sensor is introduced, and the thickness detection sensor is used to measure the thickness of the aluminum foil rolling mold base in real time; Based on the thickness of the aluminum foil rolling mold, an adaptive control algorithm is used to dynamically adjust the gap between the single-row rollers of the N-row rolling rollers controlled by the servo motor, synchronously capture the thickness change of the aluminum foil rolling mold, and perform thickness uniformity control of the M-round rolling.

4. The method for optimizing rolling process parameters of aluminum foil for cans according to claim 3, characterized in that: The servo motor performs synchronous correction of timing information based on the response time corresponding to the thickness detection sensor and the dynamic characteristics of the servo system.

5. The method for optimizing rolling process parameters of aluminum foil for cans according to claim 3, characterized in that: Synchronously capturing the thickness change of the aluminum foil rolling mold base and performing thickness uniformity control of M-round rolling, the method further includes: generating a first coordinated cycle corresponding to the internal microwave heating process based on a thickness change of the aluminum foil rolling mold base; Based on the thickness change of the aluminum foil rolling mold base, generating a second coordinated cycle corresponding to the external electromagnetic induction heating process; The rolling parameter joint optimization loop is used as the outer loop, and the first collaborative loop corresponding to the internal microwave heating process and the second collaborative loop corresponding to the external electromagnetic induction heating process are used as the inner loop to establish a nested loop. The nested loop is used to perform collaborative iterative optimization of multi-stage heating and M-round rolling to obtain the optimal parameter combination.

6. A method for optimizing rolling process parameters of aluminum foil for cans as claimed in claim 5, characterized in that: Based on the thickness change of the aluminum foil rolling mold base, generating a first coordinated cycle corresponding to the internal microwave heating process, the method comprising: Based on the internal microwave heating process, generating an internal temperature distribution map of the aluminum foil rolling mold base; Based on the internal temperature distribution map of the aluminum foil rolling mold base, the first rolling parameter set, the second rolling parameter set, the third rolling parameter set, ..., the Mth rolling parameter set corresponding to M rounds of rolling, a first parameter space is established; Based on the first parameter space, a search is performed with the maximized can quality grade and energy utilization rate, and the first collaborative cycle is established.

7. A method for optimizing rolling process parameters of aluminum foil for cans as claimed in claim 6, characterized in that: The rolling parameter joint optimization loop is used as an outer loop, and the first cooperative loop corresponding to the internal microwave heating process and the second cooperative loop corresponding to the external electromagnetic induction heating process are used as inner loops to establish a nested loop, and the method further includes: Using fluid dynamics simulation technology, the simulation model of aluminum foil rolling die was constructed by comparing the single-row roller speed and single-row roller pressure corresponding to N rows of rolling rollers; Based on the aluminum foil rolling die simulation model, thickness defect characteristics and surface defect characteristics are identified, and the defect types corresponding to the surface defect characteristics include cracks and ripples; Based on the thickness defect characteristics and surface defect characteristics, the thickness uniformity and surface finish of the can quality indicators are used as constraints, and the nested loops are subjected to loop jumps with multi-layer branch pruning with the goal of accelerating the production rate.

8. The method for optimizing rolling process parameters of aluminum foil for cans according to claim 7, characterized in that: Taking thickness uniformity and surface finish of the can quality index as constraints and accelerating the production rate as the goal, the nested loop is subjected to loop jump of multi-layer branch pruning, and the method comprises: Based on the first parameter space, establishing a multi-layer branch architecture; In the multi-layer branch architecture, in each layer of branches, the thickness uniformity and surface finish of the can quality indicators are used as constraints, and single-layer branch cutting is performed with the goal of accelerating the production rate; After completing the single-layer branch pruning, traverse the multi-layer branches and perform depth-first search and breadth-first exploration in sequence to perform multi-layer branch pruning on the nested loop; The multi-layer branch pruning is associated one-to-one with the nested loop jump logic.

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