Automated Molding Control System and Method for Expanded Polystyrene

Abstract

This invention relates to the field of expanded polystyrene (EPS) production technology, and discloses an automated molding control system and method for EPS. The system includes a detection module, a data analysis module, and a central control module. The detection module collects real-time data on mold temperature distribution, local density of the molded preform, and steam pressure. The data analysis module calculates a temperature fluctuation index based on the temperature data, obtains a dynamic density compensation coefficient using a density model, and determines the molding quality deviation level. The central control module adjusts the steam heating power according to the deviation level, determines and corrects the mold opening and closing rate by combining pressure and density change rate, and generates motion trajectory control parameters. The method includes data acquisition, calculation and analysis, parameter adjustment, and command output. This invention achieves automated and precise molding through multi-parameter collaborative control, improving quality stability and production efficiency, and is applicable to the production of related products.

Description

Technical Field

[0001] This invention relates to the field of expanded polystyrene production technology, specifically to an automated molding control system and method for expanded polystyrene. Background Technology

[0002] Expanded polystyrene (EPS), a widely used lightweight polymer material, is extensively used in packaging, building insulation, and sound insulation due to its excellent thermal insulation and cushioning properties. However, with the increasing demands for quality in EPS products across various industries, traditional manual or semi-automatic molding processes are no longer sufficient to meet the precision, efficiency, and stability requirements of modern production.

[0003] Traditional molding processes have many drawbacks. Regarding temperature control, the lack of real-time and comprehensive temperature monitoring methods makes it impossible to accurately grasp the temperature distribution within the foaming area of ​​the mold. Uneven temperature leads to uneven heating during polystyrene foaming. Some areas may over-foam due to excessively high temperatures, resulting in bubble bursts or insufficient density; other areas may under-foam due to insufficient temperatures, causing uneven product density and affecting overall mechanical properties and performance.

[0004] In density control, traditional methods typically rely on manual experience to roughly judge the density of the molded blank, failing to achieve precise measurement and real-time feedback of local density values. This makes it difficult to detect density anomalies in a timely manner during production, and by the time problems are discovered, a large number of defective products have often been produced, resulting in raw material waste and increased production costs. Furthermore, the lack of a scientific density model makes it impossible to predict and adjust density distribution based on different process parameters (such as steam pressure and cooling time), leading to poor product quality stability.

[0005] Steam pressure control is also a weak point in traditional processes. During the foaming stage, fluctuations in steam pressure directly affect the foaming rate and uniformity. Traditional control systems are slow to monitor and regulate steam pressure, failing to adjust heating power and mold opening / closing speed in a timely manner based on real-time pressure data. This can easily lead to excessively high or low pressure, thus affecting the molding quality of the product. For example, excessive pressure may cause mold deformation or cracks in the product; excessively low pressure will prolong the foaming time and reduce production efficiency.

[0006] Furthermore, in terms of mold motion control, the opening and closing rate of the mold in traditional processes mainly relies on manual setting, lacking a linkage adjustment mechanism with process parameters such as temperature, pressure, and density. An unreasonable mold opening and closing rate can lead to poor material flow during foaming, affecting the molding accuracy and surface quality of the product, and may even cause equipment failure due to excessively fast or slow rates, increasing production safety hazards.

[0007] With the development of industrial automation technology, although some companies have attempted to introduce automation into the EPS molding process, most existing systems only achieve automatic control of a single parameter, lacking the ability to coordinate and dynamically adjust multiple parameters. For example, some systems can only adjust the heating power based on temperature data, ignoring the comprehensive impact of density and pressure data on molding quality; or although they can monitor multiple parameters, they cannot establish a nonlinear relationship model between the parameters, making it difficult to accurately predict and control the molding quality.

[0008] Therefore, there is an urgent need to develop an automated molding control system and method that can monitor multiple process parameters in real time, establish a dynamic correlation model between parameters, and realize multi-dimensional coordinated control of heating power, mold movement, etc., in order to solve the problems of unstable quality, low production efficiency, and waste of raw materials in traditional processes, and meet the needs of modern industry for high-quality and high-efficiency production of expanded polystyrene products. Summary of the Invention

[0009] The purpose of this invention is to provide an automated molding control system and method for expanded polystyrene to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: an automated molding control system for expanded polystyrene, the system comprising:

[0011] The detection module includes a data analysis unit, a density detection unit, and a pressure detection unit. The data analysis unit is used to collect temperature distribution data of the foaming area inside the mold in real time; the density detection unit is used to measure the local density value of the molded preform; and the pressure detection unit is used to monitor the real-time steam pressure value during the foaming stage.

[0012] The data analysis module is used to obtain a temperature fluctuation index based on the degree of deviation between the temperature distribution data and the preset temperature range, substitute the local density value into the density model pre-established by the data analysis module to obtain a dynamic density compensation coefficient, and determine the molding quality deviation level based on the nonlinear relationship between the temperature fluctuation index and the dynamic density compensation coefficient.

[0013] The central control module is used to adjust the heating power reference value of the steam generator according to the molding quality deviation level; determine the initial parameters of the mold opening and closing rate based on the difference between the real-time steam pressure value and the preset pressure threshold; perform secondary correction on the opening and closing rate according to the temperature fluctuation index; and generate mold motion trajectory control parameters based on the corrected opening and closing rate.

[0014] Preferably, the density model pre-established by the data analysis module includes:

[0015] Obtain the density distribution dataset from historical molding data and filter out the outliers in the dataset. The dataset includes peak steam pressure, cooling time, and corresponding density gradient.

[0016] Based on the coupling effect of the peak steam pressure and cooling time, a partial differential equation for heat conduction is established.

[0017] The coefficient matrix of the partial differential equation is optimized using the gradient descent method, and the convergence error is calculated.

[0018] Based on the relationship between the convergence error value and the preset accuracy range, the coefficient matrix is ​​dynamically updated until the convergence error value stabilizes within the accuracy range.

[0019] Preferably, the adjustment of the heating power reference value of the steam generator includes:

[0020] Obtain the deviation range corresponding to the molding quality deviation level, and determine the power correction amount based on the relationship between the deviation range and the first and second power adjustment factors configured in the central control module:

[0021] When the deviation range falls within the first deviation interval, the central control module uses the product of the preset reference power value and the first power adjustment factor as the heating power reference value.

[0022] When the deviation range falls within the second deviation interval, the central control module calculates the power compensation amount based on the ratio of the dynamic density compensation coefficient to the standard density, and uses the algebraic sum of the reference power value and the power compensation amount as the heating power reference value.

[0023] Preferably, when the central control module calculates the power compensation amount, it includes:

[0024] The relationship between the ratio and the preset first and second boundary values ​​is obtained, and a corresponding compensation strategy is selected: when the ratio is less than or equal to the first boundary value, a trigonometric function is used to periodically correct the ratio to generate a compensation factor; when the ratio is greater than the first boundary value and less than the second boundary value, a polynomial fitting algorithm is used to generate a compensation factor; when the ratio is greater than or equal to the second boundary value, a moving average method is used to smooth the ratio to generate a compensation factor.

[0025] Preferably, when the central control module determines the initial parameters of the mold opening and closing rate, it includes:

[0026] Based on the absolute value of the pressure difference between the real-time steam pressure value and the preset pressure standard value, combined with the maximum speed limit of the mold mechanical structure, and based on the ratio of the absolute value of the pressure difference to the pressure tolerance threshold, the product of the maximum speed limit and the ratio is used as the initial opening and closing speed, and the speed is limited to not exceeding the mechanical structure bearing threshold.

[0027] Preferably, when the central control module performs a secondary correction on the opening and closing rate, it includes:

[0028] Obtain the density change rate between the current molding density and the density of the previous detection cycle, and select a rate correction mode based on the comparison result of the density change rate and a preset density change threshold.

[0029] When the rate of change of density exceeds the positive threshold, the linear deceleration mode is activated, reducing the current rate by a fixed proportion;

[0030] When the density change rate is below the negative threshold, the step acceleration mode is activated, and the current rate is adjusted according to the discrete incremental algorithm.

[0031] Preferably, the discrete incremental algorithm specifically includes:

[0032] Based on the density change direction of consecutive detection cycles, the change trend weight value is calculated, and the product of the weight value and the preset time decay factor is used as the increment base. The correction rate is iteratively calculated according to the cumulative result of the baseline increment step size and the increment base. At the same time, an abnormal fluctuation counter is introduced to dynamically compensate and suppress consecutive abnormal cycles.

[0033] Preferably, the generation of the mold motion trajectory control parameters includes:

[0034] The corrected opening and closing rate is decomposed into axial components in a three-dimensional coordinate system, and a spatial motion equation is established based on the mold geometry parameters. A continuous and differentiable sequence of mold motion control points is generated by B-spline curve interpolation.

[0035] Preferably, the implementation of the B-spline curve interpolation method includes:

[0036] Piecewise basis functions are constructed for each control point node interval, and continuity constraints are applied to ensure the continuity of the first derivative of the overall trajectory. The coordinates of the control points of each interval basis function are obtained by matrix inversion.

[0037] Preferably, the present invention further includes an automated molding control method for expanded polystyrene, applicable to the automated molding control system for expanded polystyrene described in any of the above claims, comprising:

[0038] Real-time acquisition of mold temperature distribution data, molding density gradient, and steam pressure data;

[0039] Calculate the temperature fluctuation index and obtain the dynamic density compensation coefficient based on the density model;

[0040] The initial parameters for heating power reference value and opening / closing rate are determined based on the molding quality deviation level.

[0041] The opening and closing rate is initially adjusted based on steam pressure data, and then a second correction is made based on the density change rate.

[0042] Optimized mold motion control commands are generated and output to the drive mechanism.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] The detection module, through the coordinated operation of the data analysis unit, density detection unit, and pressure detection unit, achieves comprehensive and real-time acquisition of the internal temperature distribution of the mold, the local density value of the molded blank, and the real-time steam pressure value during the foaming stage. This multi-dimensional data acquisition capability enables the system to monitor the changes of key parameters during the molding process in real time, providing a rich and accurate data foundation for subsequent precise control. Compared with the single-parameter monitoring method in traditional processes, this system can more comprehensively reflect the molding status and promptly detect potential quality problems.

[0045] The data analysis module achieves accurate assessment of molding quality deviation levels by establishing a density model and analyzing the nonlinear relationship between the temperature fluctuation index and the dynamic density compensation coefficient. The density model, through in-depth mining and processing of historical molding data, establishes a scientific correlation between peak steam pressure, cooling time, and density gradient, enabling the prediction and compensation of density deviations based on real-time collected density data. The introduction of the temperature fluctuation index further quantifies the impact of temperature distribution on molding quality, allowing the system to comprehensively assess molding quality from two key dimensions: temperature and density, thus improving the scientific rigor and accuracy of quality assessment. This data model-based analysis method overcomes the limitations of traditional processes that rely on manual experience, achieving automation and precision in quality assessment.

[0046] The central control module adjusts the heating power reference value of the steam generator according to the molding quality deviation level, dynamically compensating for the impact of temperature fluctuations and density deviations on molding quality. By setting different deviation ranges and corresponding power adjustment factors, the system can flexibly adjust the heating power according to the actual deviation, ensuring that the internal temperature of the mold remains within a preset reasonable range and guaranteeing the stability of the polystyrene foaming process. For example, when the deviation range falls within the first deviation range, the heating power is directly adjusted through the preset power adjustment factor to quickly respond to temperature fluctuations; when the deviation range falls within the second deviation range, the power compensation amount is calculated in conjunction with the dynamic density compensation coefficient, achieving coordinated adjustment of temperature and density parameters, further improving the targeting and effectiveness of heating power adjustment.

[0047] In terms of mold opening and closing rate control, the central control module first determines the initial parameters based on the difference between the real-time steam pressure value and the preset pressure threshold. Then, it performs secondary corrections based on the temperature fluctuation index and density change rate, achieving multi-parameter linkage adjustment of the mold movement. This adjustment mechanism allows the mold opening and closing rate to be adjusted in real time according to changes in steam pressure, temperature, and density, ensuring smooth material flow during foaming and improving the molding accuracy and surface quality of the products. For example, when the density change rate exceeds the positive threshold, a linear deceleration mode is activated to avoid defects caused by excessive material flow due to excessive speed; when the density change rate is below the negative threshold, a stepped acceleration mode is activated to speed up the production process and improve production efficiency. Simultaneously, the application of advanced algorithms such as discrete incremental algorithms and B-spline curve interpolation generates continuously differentiable mold motion trajectory control parameters, making the mold movement smoother and more precise, reducing equipment wear and failure risks, and extending the equipment's service life.

[0048] This invention achieves full automation and intelligent control of the expanded polystyrene molding process through the organic integration of multiple modules and dynamic collaborative control of multiple parameters. The automation of the entire process, from data acquisition and analysis to evaluation and control adjustment, significantly reduces manual intervention, lowers labor costs, and improves production efficiency. Simultaneously, precise parameter control and quality assessment mechanisms significantly enhance product quality stability, reduce the incidence of defective products, and minimize raw material waste, resulting in significant economic and social benefits. Furthermore, the system possesses strong scalability and adaptability, allowing for flexible adjustments based on different production needs and process parameters. It is suitable for the production of various specifications and types of expanded polystyrene products, demonstrating broad application prospects. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the working principle of the expanded polystyrene automated molding control system described in this invention;

[0050] Figure 2 Flowchart for the density model;

[0051] Figure 3 A flowchart for adjusting the heating power of a steam generator;

[0052] Figure 4 Flowchart for determining the initial parameters of the mold opening and closing rate;

[0053] Figure 5 This is a flowchart for the secondary correction of the mold opening and closing rate. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figures 1-5 The present invention relates to an automated molding control system for expanded polystyrene, comprising a detection module, a data analysis module, and a central control module. The specific implementation steps are as follows:

[0056] The detection module includes a data analysis unit, a density detection unit, and a pressure detection unit. The data analysis unit collects real-time temperature distribution data of the foaming area inside the mold; the density detection unit measures the local density value of the molded blank; and the pressure detection unit monitors the real-time steam pressure value during the foaming stage. The data analysis module obtains a temperature fluctuation index based on the deviation of the temperature distribution data from a preset temperature range. It then substitutes the local density value into a pre-established density model to obtain a dynamic density compensation coefficient. Based on the nonlinear relationship between the temperature fluctuation index and the dynamic density compensation coefficient, it determines the molding quality deviation level. The central control module adjusts the heating power reference value of the steam generator according to the molding quality deviation level; determines the initial parameters of the mold opening and closing rate based on the difference between the real-time steam pressure value and the preset pressure threshold; performs a secondary correction on the opening and closing rate based on the temperature fluctuation index; and generates mold motion trajectory control parameters based on the corrected opening and closing rate.

[0057] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0058] Example 1: In this example, the density model construction process pre-established by the data analysis module is as follows: First, a density distribution dataset from historical molding data is acquired. This dataset includes peak steam pressure, cooling time, and the corresponding density gradient. Then, outliers in the dataset are filtered to eliminate interference from abnormal data on the model. Next, based on the coupling effect of peak steam pressure and cooling time, a partial differential equation for heat conduction is established. This equation describes the influence of steam pressure and cooling time on the heat conduction process, which is then reflected in the density distribution. Afterward, the coefficient matrix of the partial differential equation is optimized using the gradient descent method. During the optimization process, the convergence error value is calculated to evaluate the accuracy of the model. Finally, based on the relationship between the convergence error value and the preset accuracy range, the coefficient matrix is ​​dynamically updated, and the model parameters are continuously adjusted until the convergence error value stabilizes within the accuracy range. This ensures that the established density model accurately reflects the density variation patterns in actual production, providing a reliable basis for obtaining dynamic density compensation coefficients. In practical applications, by continuously accumulating new historical molding data, the density model can be further optimized to improve its adaptability and accuracy. The detection module continuously collects new density data, providing real-time data support for model updates and ensuring that the model accurately reflects the density distribution under current production conditions. The central control module, based on the dynamic density compensation coefficient output by the density model and combined with the temperature fluctuation index, more accurately determines the molding quality deviation level, thus providing a more reliable basis for subsequent adjustments to the heating power benchmark value and mold opening and closing rate. Throughout the establishment and application of the density model, the modules collaborate closely, forming a closed-loop feedback system that continuously optimizes the molding control process of expanded polystyrene, improving product quality and production efficiency.

[0059] When acquiring density distribution datasets from historical molding data, it is first necessary to collect relevant data from multiple sources. These sources include, but are not limited to, sensor records from the production process, quality inspection reports, and process parameter logs. Peak vapor pressure refers to the maximum vapor pressure reached during foaming, which significantly impacts the degree of expansion and final density of polystyrene. Cooling time refers to the time required from the end of foaming to the product reaching a demolding temperature; it directly relates to the formation of the product's internal structure and density distribution. Density gradient reflects the density variations at different locations within the product and is an important indicator for assessing product quality uniformity.

[0060] After collecting the raw data, it needs to be preprocessed to improve its quality. This includes steps such as data cleaning, missing value handling, and data standardization. Data cleaning aims to remove noise and erroneous records from the data, such as outlier data points caused by sensor malfunctions. Missing value handling involves filling in or deleting missing values ​​to ensure data integrity. Data standardization transforms data from different sources and with different dimensions into a unified scale for subsequent analysis and modeling.

[0061] Filtering outliers in a dataset is a crucial step in ensuring model accuracy. Outliers are data points that deviate significantly from other data points, possibly due to measurement errors, equipment malfunctions, or special process conditions. Without processing, these outliers can mislead the model, causing it to fail to accurately reflect the true density variations. There are various methods for filtering outliers, such as those based on statistical analysis and machine learning.

[0062] Among statistical analysis-based outlier filtering methods, the Z-score method and the IQR method are commonly used. The Z-score method calculates the deviation of each data point from the dataset mean and converts it to a standard normal distribution Z-score. Generally, a data point is considered an outlier when the absolute value of the Z-score exceeds 3. The IQR method calculates the interquartile range (IQR) of the dataset and compares the distance of a data point to a quartile with the IQR. A data point is considered an outlier when the distance to a quartile exceeds a certain multiple of the IQR.

[0063] In machine learning-based outlier filtering methods, commonly used algorithms include Isolation Forest and Local Anomaly Factor (LOF). Isolation Forest is a tree-based anomaly detection method that constructs a decision tree by randomly selecting features and split points to isolate outlier data points. The Local Anomaly Factor algorithm, on the other hand, calculates the local density of a data point and compares it with the density of surrounding data points to determine whether the data point is an outlier.

[0064] When establishing the partial differential equation for heat conduction based on the coupling effect of peak vapor pressure and cooling time, multiple physical factors need to be considered. Heat conduction is a complex physical phenomenon involving heat transfer, diffusion, and absorption. The peak vapor pressure affects the internal heat transfer rate of polystyrene; the higher the pressure, the faster the heat transfer rate and the greater the expansion of the material. Cooling time affects the internal temperature distribution and crystallization process of the material; both excessively long and short cooling times can lead to uneven density distribution.

[0065] When establishing the partial differential equations for heat conduction, it is necessary to base them on Fourier's law of heat conduction and the law of conservation of energy. Fourier's law of heat conduction describes the relationship between the rate of heat transfer within a substance and the temperature gradient. The law of conservation of energy ensures that energy is conserved during heat conduction. By combining these two laws and considering the effects of peak vapor pressure and cooling time, a partial differential equation describing the heat conduction process can be established.

[0066] When establishing the partial differential equation for heat conduction, it is necessary to consider the material's thermophysical parameters, such as thermal conductivity, specific heat capacity, and density. These parameters change with temperature and pressure, therefore they need to be expressed as functions of temperature and pressure in the equation. Furthermore, the geometry of the mold and boundary conditions also need to be considered, as these factors significantly influence the heat conduction process.

[0067] Optimizing the coefficient matrix of a partial differential equation using gradient descent is a crucial step in model training. Gradient descent is a commonly used optimization algorithm that iteratively updates model parameters, gradually decreasing the value of the objective function. In the optimization process, a suitable objective function must first be defined; typically, the mean squared error (MSE) is chosen as the objective function, which is the average of the squared differences between the model's predicted and actual values.

[0068] In each iteration, gradient descent calculates the gradient of the objective function with respect to the coefficient matrix and updates the coefficient matrix in the opposite direction of the gradient. The gradient represents the rate of change of the objective function at the current parameter values; updating the parameters in the opposite direction of the gradient gradually decreases the value of the objective function. The update step size is controlled by the learning rate; an excessively large learning rate will prevent the algorithm from converging, while an excessively small learning rate will result in a slow convergence speed.

[0069] During optimization, calculating the convergence error is a crucial metric for evaluating model training performance. The convergence error represents the degree of difference between the model's predicted and actual values, typically measured using mean squared error (MSE) or root mean square error (RMSE). As the number of iterations increases, the convergence error gradually decreases. When the convergence error decreases to a certain level or no longer changes significantly, the model is considered to have converged.

[0070] Dynamically updating the coefficient matrix is ​​a crucial method to ensure model accuracy, based on the relationship between the convergence error and the preset accuracy range. The preset accuracy range is an error threshold set according to practical application requirements. When the convergence error exceeds the preset accuracy range, it indicates that the model's prediction performance is not ideal, and further adjustments to the coefficient matrix are needed. There are various methods for dynamically updating the coefficient matrix, such as adjusting the learning rate, increasing the number of iterations, or changing the optimization algorithm.

[0071] In practical applications, the density model can be further optimized by continuously accumulating new historical molding data. As the production process progresses, new molding data is constantly generated, containing more information about production conditions and process parameters. Adding this new data to the historical dataset and retraining the model allows it to better adapt to the constantly changing production environment, improving the model's predictive accuracy and adaptability.

[0072] The detection module continuously collects new density data, providing real-time data support for model updates. The density detection unit within the module measures the local density values ​​of the formed blank in real time, and this data is promptly fed back to the data analysis module. The data analysis module integrates the newly collected data with historical data and updates and adjusts the density model based on the new data, ensuring that the model always accurately reflects the density distribution under current production conditions.

[0073] The central control module uses the dynamic density compensation coefficient output from the density model, combined with the temperature fluctuation index, to more accurately determine the molding quality deviation level. The dynamic density compensation coefficient is a correction factor calculated based on current production conditions and density distribution, reflecting the degree of difference between the actual and ideal density. The temperature fluctuation index reflects the stability of the temperature distribution in the foaming area inside the mold. By comprehensively considering these two factors, the central control module can more accurately assess molding quality and determine the molding quality deviation level.

[0074] Based on the molding quality deviation level, the central control module provides a more reliable basis for subsequent adjustments to the heating power reference value and mold opening and closing rate. A high molding quality deviation level indicates significant problems in the molding process, requiring substantial adjustments to the heating power reference value and mold opening and closing rate. Conversely, a low molding quality deviation level indicates a relatively stable molding process, allowing for fine-tuning or maintaining the heating power reference value and mold opening and closing rate. This method enables precise control of the expanded polystyrene molding process, improving product quality and production efficiency.

[0075] Throughout the establishment and application of the density model, the various modules collaborate closely, forming a closed-loop feedback system. The detection module is responsible for collecting real-time data, the data analysis module is responsible for processing the data and building the model, and the central control module is responsible for making decisions and controlling based on the model output. This closed-loop feedback system can promptly identify problems in the production process and take corresponding measures to adjust them, thereby continuously optimizing the molding control process of expanded polystyrene and improving product quality and production efficiency.

[0076] Example 2: In this example, the process of the central control module adjusting the heating power reference value of the steam generator is as follows: First, the deviation range corresponding to the molding quality deviation level is obtained. Then, based on the relationship between this deviation range and the first and second power adjustment factors configured by the central control module, the power correction amount is determined. When the deviation range falls within the first deviation interval, it indicates that the molding quality deviation is small. The central control module uses the product of the preset reference power value and the first power adjustment factor as the heating power reference value, and adjusts the heating power slightly in this way to quickly restore the molding quality to the normal range. When the deviation range falls within the second deviation interval, it indicates that the molding quality deviation is large. At this time, the central control module needs to calculate the power compensation amount based on the ratio of the dynamic density compensation coefficient to the standard density, and use the algebraic sum of the reference power value and the power compensation amount as the heating power reference value. When calculating the power compensation amount, it is necessary to obtain the relationship between this ratio and the preset first and second boundary values, and select the corresponding compensation strategy. When the ratio is less than or equal to the first boundary value, it indicates a significant difference between the dynamic density compensation coefficient and the standard density, exhibiting a certain periodicity. A trigonometric function is used to periodically correct the comparison value and generate a compensation factor to adapt to this periodic change, making the adjustment of heating power more precise. When the ratio is greater than the first boundary value but less than the second boundary value, the change in the ratio is more complex. A polynomial fitting algorithm is used to generate a compensation factor, which can better fit the nonlinear relationship between the ratio and the compensation factor, improving the calculation accuracy of the power compensation. When the ratio is greater than or equal to the second boundary value, it indicates a small difference between the dynamic density compensation coefficient and the standard density, tending to be stable. A moving average method is used to smooth the comparison value and generate a compensation factor to avoid frequent adjustments to the heating power due to small fluctuations, ensuring the stability of the production process. By adjusting the heating power benchmark value in this interval-based, strategy-based manner, the heating power can be precisely controlled according to different molding quality deviations, thereby optimizing the molding process of expanded polystyrene and improving the density uniformity and quality stability of the product. In actual production, parameters such as the preset reference power value, first power adjustment factor, second power adjustment factor, first boundary value, and second boundary value can be flexibly adjusted according to different product specifications and production process requirements to adapt to diverse production needs. Simultaneously, the central control module monitors the adjustment effect of the heating power in real time, and adjusts the heating power reference value promptly based on new molding quality deviation levels and dynamic density compensation coefficients, forming a dynamic closed-loop control process. This ensures that the heating power is always in an optimal state, providing strong support for the high-quality molding of expanded polystyrene.

[0077] When obtaining the deviation range corresponding to the molding quality deviation level, the central control module first needs to accurately classify the molding quality deviation level. The molding quality deviation level is typically determined based on a combination of factors, including the product's density distribution, dimensional accuracy, and surface quality. Each level corresponds to a specific deviation range, which reflects the degree of difference between the product quality and the ideal standard. Determining the deviation range requires referencing a large amount of historical production data and process standards to ensure its accuracy and reliability.

[0078] When determining the power correction amount based on the relationship between the deviation range and the first and second power adjustment factors configured in the central control module, the sensitivity of power adjustment within different deviation ranges needs to be considered. The first and second power adjustment factors are pre-configured parameters used for power adjustment in different deviation intervals. The first power adjustment factor is typically suitable for smaller deviation ranges, where molding quality deviations are small, requiring fine-tuned power adjustments. The second power adjustment factor is suitable for larger deviation ranges, where larger power adjustments are needed to quickly correct quality deviations.

[0079] When the deviation falls within the first deviation range, the central control module uses the product of the preset reference power value and the first power adjustment factor as the heating power reference value. This method enables small-scale, precise adjustments to the heating power. The preset reference power value is a basic power value pre-set based on the product characteristics and process requirements; it represents the heating power required under ideal conditions. By multiplying it by the first power adjustment factor, fine-tuning can be performed based on the reference power value, making the heating power more suitable for current production needs.

[0080] When the deviation falls within the second deviation range, the central control module needs to calculate the power compensation amount based on the ratio of the dynamic density compensation coefficient to the standard density. The dynamic density compensation coefficient is a correction coefficient calculated by the data analysis module based on current production conditions and density distribution; it reflects the degree of difference between the actual density and the ideal density. The standard density is the target density value required by the product design. By calculating the ratio of these two values, the degree of deviation between the current density and the standard density can be assessed, thereby determining the required power compensation amount.

[0081] When calculating the power compensation amount, it is necessary to obtain the relationship between this ratio and preset first and second boundary values, and then select the corresponding compensation strategy. The first and second boundary values ​​are two pre-set thresholds used to divide the applicable range of different compensation strategies. The setting of these two boundary values ​​needs to take into account factors such as the stability of the production process, product quality requirements, and equipment performance limitations.

[0082] When the ratio is less than or equal to the first boundary value, it indicates a significant difference between the dynamic density compensation coefficient and the standard density, exhibiting a certain periodicity. In this case, a trigonometric function is used to periodically correct the comparison value and generate a compensation factor. Trigonometric functions, with their periodic variation characteristics, can effectively fit this periodic density change pattern. By adjusting the parameters of the trigonometric function, the compensation factor can more accurately reflect the periodic characteristics of density changes, thereby achieving precise adjustment of the heating power.

[0083] When the ratio is greater than the first boundary value but less than the second boundary value, the change in the ratio becomes more complex and may exhibit a non-linear trend. In this case, a polynomial fitting algorithm is used to generate the compensation factor. The polynomial fitting algorithm can construct a polynomial function by fitting multiple data points, thus better describing the non-linear relationship between the ratio and the compensation factor. By selecting appropriate polynomial order and coefficients, the fitted function can more accurately reflect the actual changing pattern, improving the calculation accuracy of the power compensation.

[0084] When the ratio is greater than or equal to the second boundary value, it indicates that the difference between the dynamic density compensation coefficient and the standard density is small and tends to be stable. In this case, the moving average method is used to smooth the comparison value and generate the compensation factor. The moving average method is a commonly used data smoothing method that reduces data fluctuations by calculating the average value of data within a certain time window. Smoothing by comparing the value can avoid frequent adjustments to heating power due to small density fluctuations, thus ensuring the stability of the production process.

[0085] By adjusting the heating power reference value in this segmented and strategy-based manner, the heating power can be precisely controlled according to different molding quality deviations. In actual production, parameters such as the preset reference power value, the first power adjustment factor, the second power adjustment factor, the first boundary value, and the second boundary value can be flexibly adjusted according to different product specifications and production process requirements. For example, for products with high density requirements, the first and second boundary values ​​can be appropriately reduced to improve the sensitivity of power adjustment; for products with relatively stable production processes, the first and second power adjustment factors can be appropriately increased to accelerate the power adjustment speed.

[0086] The central control module monitors the adjustment effect of heating power in real time and adjusts the heating power reference value in a timely manner based on the new molding quality deviation level and dynamic density compensation coefficient. This dynamic closed-loop control process ensures that the heating power is always at its optimal state. Through continuous monitoring and adjustment, abnormalities in the production process can be detected in a timely manner, and corresponding corrective measures can be taken to ensure high-quality molding of expanded polystyrene.

[0087] Example 3: In this example, when the central control module determines the initial parameters of the mold opening and closing rate, it first calculates the absolute value of the pressure difference between the real-time steam pressure value and the preset pressure standard value. Then, combining this with the maximum speed limit of the mold's mechanical structure, and based on the ratio of the absolute pressure difference to the pressure tolerance threshold, the product of the maximum speed limit and the ratio is used as the initial opening and closing rate. For example, if the absolute pressure difference is large, it indicates a large deviation between the current steam pressure and the preset pressure standard value. To ensure molding quality, the mold opening and closing rate needs to be adjusted accordingly. In this case, the ratio is larger, and the initial opening and closing rate will also be higher. However, the rate will be limited to not exceeding the mechanical structure's bearing threshold to ensure the safety and stability of the mold's mechanical structure. In practical applications, the pressure tolerance threshold can be set according to different mold types and production processes to adapt to different production conditions. When the real-time steam pressure value changes, the central control module will calculate the new absolute pressure difference and ratio in real time, thereby dynamically adjusting the initial opening and closing rate so that the mold opening and closing rate can respond promptly to changes in steam pressure, ensuring the stability and reliability of the molding process. Furthermore, the maximum speed limit of the mold's mechanical structure is determined based on the mold's design parameters and mechanical strength. Under no circumstances will the initial opening and closing speed exceed this limit to avoid damage to the mold. By determining the initial parameters of the mold's opening and closing speed based on the absolute value and proportional relationship of the pressure difference, changes in steam pressure can be organically combined with the mold's opening and closing speed, achieving precise control of the molding process and laying the foundation for subsequent secondary adjustments to the opening and closing speed. Simultaneously, this control method has strong real-time performance and adaptability, enabling rapid response to parameter changes during the production process, improving production efficiency and product quality.

[0088] When determining the initial parameters for the mold opening and closing rate, the central control module first needs to acquire the real-time steam pressure value. This data is collected in real time by the pressure detection unit in the detection module and transmitted to the central control module. The real-time steam pressure value reflects the current pressure state inside the mold and is an important basis for determining the mold opening and closing rate. The preset pressure standard value is the ideal pressure value determined according to the product design requirements and production process. It represents the optimal pressure conditions that can ensure product quality during the mold opening and closing process.

[0089] Calculating the absolute value of the pressure difference between the real-time steam pressure and the preset pressure standard value is a crucial step in subsequent processing. This calculation directly reflects the degree of deviation between the current steam pressure and the ideal state. The larger the absolute value of the pressure difference, the more the current pressure state deviates from the ideal conditions, requiring a greater adjustment to the mold opening and closing rate; conversely, the smaller the absolute value of the pressure difference, the closer the current pressure state is to the ideal conditions, and the less adjustment is needed to the mold opening and closing rate.

[0090] Determining the maximum speed limit of the mold's mechanical structure is crucial for ensuring the safe operation of the system. This maximum speed limit is determined by factors such as the mold's design parameters, material properties, and manufacturing process. During the mold design phase, engineers calculate the maximum speed the mold can safely withstand, based on its intended use and operating environment. Exceeding this limit can lead to structural damage, accelerated wear of moving parts, and even safety accidents. Therefore, the maximum speed limit must be considered as a constraint when determining the initial opening and closing speed.

[0091] The core method of this embodiment is to calculate the initial opening and closing rate based on the ratio of the absolute value of the pressure difference to the pressure tolerance threshold. The pressure tolerance threshold is a pre-set parameter that represents the range of steam pressure fluctuations that the system can tolerate. When the absolute value of the pressure difference is within the pressure tolerance threshold range, it indicates that the steam pressure fluctuation is within an acceptable range, and the mold opening and closing rate can be adjusted slightly. When the absolute value of the pressure difference exceeds the pressure tolerance threshold, it indicates that the steam pressure fluctuation is large, and a larger adjustment to the mold opening and closing rate is required.

[0092] The initial opening and closing rate is calculated by multiplying the maximum speed limit by a proportional value. This method achieves a linear mapping between steam pressure changes and the mold opening and closing rate. A larger proportional value indicates a greater absolute pressure difference relative to the pressure tolerance threshold, and the initial opening and closing rate will correspondingly increase to adapt to steam pressure changes. In this way, the system can dynamically adjust the mold opening and closing rate based on real-time steam pressure changes, matching the mold movement with the steam pressure state, thereby ensuring the stability of the molding process and the consistency of product quality.

[0093] In practical applications, the pressure tolerance threshold can be set according to different mold types and production processes. Different mold types have different structural characteristics and operating requirements, and their sensitivity to steam pressure also varies. For example, some precision molds are more sensitive to pressure changes and require a smaller pressure tolerance threshold to more accurately control the mold opening and closing rate; while some molds with simpler structures have a higher tolerance for pressure changes and can have a larger pressure tolerance threshold to improve production efficiency. Different production processes also affect the setting of the pressure tolerance threshold. For example, some production processes have high requirements for steam pressure stability and require a smaller pressure tolerance threshold; while for some production processes with lower requirements for pressure stability, a larger pressure tolerance threshold can be set.

[0094] When the real-time steam pressure changes, the central control module calculates the new absolute and proportional values ​​of the pressure difference in real time, thereby dynamically adjusting the initial opening and closing rate. This real-time adjustment mechanism enables the system to respond quickly to changes in steam pressure, ensuring the stability and reliability of the molding process. During production, steam pressure may fluctuate due to various factors, such as the stability of the steam supply system and the thermal conductivity of the mold. By monitoring the steam pressure in real time and dynamically adjusting the mold opening and closing rate, the system can promptly compensate for the impact of these fluctuations on the molding process, ensuring consistent product quality.

[0095] The maximum speed limit of a mold's mechanical structure is a fixed value. However, in practical applications, to further improve the safety and reliability of the system, the maximum speed limit can be dynamically adjusted according to the actual operating state of the mold. For example, in the initial stage of mold operation, since the break-in between the moving parts is not yet complete, the maximum speed limit can be appropriately reduced to decrease wear and malfunctions. After the mold has been running for a period of time, when the break-in between the moving parts is good, the maximum speed limit can be appropriately increased to improve production efficiency.

[0096] By determining the initial parameters of the mold opening and closing rate based on the absolute value and proportional relationship of the pressure difference, the changes in steam pressure can be organically combined with the mold opening and closing rate. This combination not only enables precise control of the molding process but also lays the foundation for subsequent secondary correction of the opening and closing rate. In the subsequent secondary correction process, the system can further adjust the initial opening and closing rate based on other factors such as temperature fluctuation index and density change rate to obtain a more accurate mold opening and closing rate, thereby further improving product quality and production efficiency.

[0097] This control method boasts strong real-time performance and adaptability, enabling rapid response to parameter changes during the production process. In modern industrial production, the production environment is complex and ever-changing, and various factors can affect the stability of the production process and product quality. By monitoring steam pressure in real time and dynamically adjusting the mold opening and closing rate, the system can adapt to these changes promptly, ensuring the smooth operation of the production process. Furthermore, this control method offers high flexibility, allowing adjustments based on different production needs and process requirements to meet diverse production demands.

[0098] During the determination of the initial parameters for the mold opening and closing rate, the central control module needs to work closely with the detection module. The detection module is responsible for collecting steam pressure data in real time and transmitting it to the central control module; the central control module then performs calculations and makes decisions based on this data, and sends control commands to the actuators. This collaborative mechanism ensures that the system can acquire key parameters in the production process in a timely and accurate manner, and make corresponding adjustments based on these parameters, thereby achieving precise control of the molding process.

[0099] Furthermore, to ensure system stability and reliability, the calculation process and decision-making logic of the central control module need to be optimized. For example, techniques such as redundancy design, fault diagnosis, and fault-tolerance mechanisms can be employed to improve the system's anti-interference and fault handling capabilities. Simultaneously, regular maintenance and upgrades of the system's software and hardware can be performed to ensure the system is always in optimal operating condition.

[0100] Determining the initial parameters for mold opening and closing rates based on the absolute value and proportional relationship of pressure difference can effectively improve the molding quality and production efficiency of expanded polystyrene. This control method has been widely applied in actual production and has achieved good results. With the continuous development of industrial automation technology, it is believed that this control method will continue to be improved and optimized, providing even stronger support for the development of the expanded polystyrene industry.

[0101] Example 4: In this example, when the central control module performs a secondary correction on the opening and closing rate, it first obtains the density change rate between the current molding density and the density of the previous detection cycle. Then, based on the comparison result between this density change rate and the preset density change threshold, it selects the rate correction mode. When the density change rate exceeds the positive threshold, it indicates that the molding density has increased too quickly in a short period of time, which may lead to product quality problems. At this time, the linear deceleration mode is activated, reducing the current rate by a fixed proportion to slow down the increase in density and make the molding process more stable. For example, the fixed proportion can be set according to production experience and product requirements, usually a small value, such as 5% or 10%. By gradually reducing the mold opening and closing rate, the density change tends to be smooth, ensuring product quality. When the density change rate is lower than the negative threshold, it indicates that the molding density has decreased too quickly, which may affect the performance and quality of the product. At this time, the stepped acceleration mode is activated, adjusting the current rate according to the discrete incremental algorithm. The discrete incremental algorithm specifically includes: calculating the change trend weight value based on the density change direction of consecutive detection cycles. This weight value reflects the trend and intensity of density change. The product of the weight value and the preset time decay factor is then used as the increment base. The preset time decay factor is used to account for the influence of time on the density change trend. As time goes on, the impact of early density changes on the current rate adjustment will gradually weaken. Next, the correction rate is iteratively calculated based on the cumulative result of the baseline increment step and the increment base. By gradually increasing the mold opening and closing rate, the trend of density decrease is adapted to restore the molding density to the normal range. Simultaneously, an abnormal fluctuation counter is introduced to dynamically compensate and suppress continuous abnormal cycles. When abnormal density changes are detected in multiple consecutive cycles, the abnormal fluctuation counter records these abnormalities and, based on the number and severity of the records, appropriately compensates or suppresses the correction rate to avoid over- or under-adjustment of the rate due to abnormal fluctuations, thus improving the accuracy and stability of the rate correction. By selecting different rate correction modes based on the density change rate, the mold opening and closing rate can be more accurately corrected in the second stage, adapting to the dynamic changes in density during the molding process, further improving molding quality and production efficiency. In actual production, parameters such as the preset density change threshold, fixed ratio, baseline increment step, and preset time decay factor can be adjusted according to different product types and production processes to achieve the best rate correction effect. The central control module monitors the density change rate and abnormal fluctuations in real time, and adjusts the correction rate in a timely manner to form a dynamic feedback control process, ensuring that the mold opening and closing rate can always adapt to the needs of the molding process.

[0102] When obtaining the density change rate between the current molding density and the density of the previous testing cycle, it is first necessary to clarify the setting of the testing cycle. The testing cycle is determined based on the requirements of the production process and the motion characteristics of the mold, and is usually a short time interval, such as a few seconds or tens of seconds, to ensure that the density change trend can be captured in a timely manner. The central control module obtains the molding density data of each testing cycle in real time through the density detection unit in the detection module, and stores the density value of the previous testing cycle for difference calculation. The formula for calculating the density change rate is: (current molding density - density of the previous testing cycle) / density of the previous testing cycle × 100%. This calculation result is expressed as a percentage, indicating the magnitude and direction of the density change; a positive sign indicates an increase in density, and a negative sign indicates a decrease in density.

[0103] The preset density change thresholds include positive and negative thresholds, which correspond to the maximum allowable rates of density increase and decrease, respectively. The positive threshold setting must consider the natural expansion characteristics of polystyrene during the foaming process and the cooling capacity of the mold. If the density change rate exceeds this threshold, it may indicate insufficient steam supply or excessively rapid cooling, leading to premature solidification and accumulation of the material, forming localized high-density areas. The negative threshold must be considered in conjunction with the material's foaming ratio limit and the mechanical response speed of the mold opening and closing. If the density change rate is below this threshold, it may indicate excessively high steam pressure or excessively slow mold opening and closing, causing excessive material expansion or even rupture. The specific values ​​of these two thresholds need to be determined through process debugging. For example, for packaging products requiring high density uniformity, the positive and negative thresholds can be set to ±3% / cycle, while for thick-walled industrial products, this can be appropriately relaxed to ±5% / cycle.

[0104] When the density change rate exceeds the positive threshold, a linear deceleration mode is activated. The core of this mode is to reduce the current rate by a fixed percentage, determined based on the mold's inertial characteristics and the sensitivity to density changes. For example, if the current mold opening / closing rate is 10 mm / s and the fixed percentage is set to 5%, the reduced rate will be 9.5 mm / s. This linear reduction provides a stable rate adjustment gradient, preventing mechanical shock to the mold due to abrupt deceleration. Over multiple consecutive detection cycles, if the density change rate continues to exceed the positive threshold, the central control module will continuously reduce the rate by the same percentage until it falls back to the threshold range or reaches the minimum rate limit of the mold's mechanical structure. The minimum rate limit must be set to ensure the mold can complete normal opening and closing actions, for example, not less than 2 mm / s, to avoid a significant drop in production efficiency due to excessively low rates.

[0105] When the density change rate is below the negative threshold, a stepped acceleration mode is activated. This mode adjusts the rate using a discrete incremental algorithm, with the following steps: First, the trend weight value is calculated based on the direction of density change (increasing or decreasing) over N consecutive detection periods (e.g., N=3). If the density decreases for three consecutive periods, the weight value is +1.5; if it decreases twice and increases once, the weight value is +1.0; if it decreases once and increases twice, the weight value is +0.5; if it increases twice, the weight value is 0. This weighting method quantifies the trend strength of density change. The more periods of continuous change in the same direction, the larger the weight value, indicating a more pronounced trend. The preset time decay factor is a coefficient between 0 and 1, such as 0.9, used to decay the weight value of earlier periods, reflecting the greater impact of recent data on the current adjustment. For example, if the current period is period t, the weight value of the previous period (t-1) is directly calculated, the weight value of the previous two periods (t-2) is multiplied by 0.9, the weight value of the previous three periods (t-3) is multiplied by 0.9², and so on.

[0106] The increment base is obtained by summing the products of the weight values ​​of each period and the time decay factor. The baseline increment step size is a fixed rate adjustment unit, such as 0.5 mm / s. The correction rate = current rate + baseline increment step size × increment base. For example, if the current rate is 8 mm / s and the increment base is 1.2, then the correction rate is 8 + 0.5 × 1.2 = 8.6 mm / s. Through this iterative calculation method, the rate adjustment magnitude will dynamically change according to the trend strength and time distance, which avoids overshoot caused by aggressive adjustment and can respond promptly to the continuous density decline trend.

[0107] The function of the abnormal fluctuation counter is to identify and suppress occasional abnormal density changes that interfere with rate adjustment. When the absolute value of the density change rate in a certain cycle exceeds a preset abnormal threshold (e.g., ±10% / cycle), the counter increments by 1; if the density change rate returns to normal in subsequent cycles, the counter decrements by 1. When the counter value reaches a preset upper limit (e.g., 3), it indicates the existence of continuous abnormal fluctuations. At this time, the central control module will forcibly multiply the incremental base by a suppression coefficient (e.g., 0.5) to reduce the rate adjustment amplitude and avoid erroneous adjustments caused by sensor noise or brief process fluctuations. For example, under normal circumstances, the incremental base is 1.5, which becomes 0.75 after suppression, and the corresponding correction rate adjustment amplitude is halved. When the counter value drops to zero due to continuous normal cycles, the system resumes normal adjustment logic.

[0108] In actual production, parameters such as preset density change threshold, fixed ratio, baseline increment step size, preset time decay factor, anomaly threshold, and suppression coefficient need to be optimized through process experiments and long-term production data accumulation. For example, for polystyrene grades with poor thermal stability, the fixed ratio can be reduced to 3% to avoid drastic temperature field changes caused by rapid rate reduction; for the production process of high-density products, the baseline increment step size can be increased to 1 mm / s to accelerate density recovery. The central control module stores historical adjustment records of each parameter and corresponding density change curves in a real-time database for process engineers to analyze and optimize, forming a parameter self-learning mechanism.

[0109] The entire secondary correction process forms a closed-loop feedback system: the detection module collects density data in real time → the central control module calculates the rate of change and determines the mode → corresponding rate adjustments are executed → the density detection unit feeds back new data → adjustments are recalculated until the density change rate stabilizes within the threshold range. This dynamic adjustment mechanism can adapt to various disturbances during the foaming process, such as changes in material properties, mold temperature fluctuations, and steam pressure drift, ensuring dynamic matching between the mold opening and closing rate and density changes, thereby effectively controlling the density uniformity and molding accuracy of the product. Simultaneously, the combination of discrete incremental algorithms and abnormal fluctuation suppression ensures both a fast response to trend changes and enhances the system's robustness to random disturbances, improving the stability and reliability of the entire molding control system.

[0110] Example 5: In this example, when the central control module performs a secondary correction on the mold opening and closing rate, it first obtains the density change rate between the current molding density and the density of the previous detection cycle. Then, based on the comparison result of this density change rate with a preset density change threshold, it selects the corresponding rate correction mode. When the density change rate exceeds the positive threshold, a linear deceleration mode is activated, reducing the current rate by a fixed proportion; when the density change rate is below the negative threshold, a stepped acceleration mode is activated, adjusting the current rate according to a discrete incremental algorithm. The discrete incremental algorithm specifically includes: calculating the trend weight value based on the density change direction of N consecutive detection cycles, using the product of the weight value and a preset time decay factor as the increment base, and iteratively calculating the correction rate based on the cumulative result of the baseline increment step size and the increment base. Simultaneously, an abnormal fluctuation counter is introduced to dynamically compensate and suppress continuous abnormal cycles. In this way, the mold opening and closing rate can be more accurately corrected secondaryly, adapting to the dynamic changes in density during the molding process, further improving molding quality and production efficiency.

[0111] When obtaining the density change rate between the current molding density and the density of the previous detection cycle, it is first necessary to clarify the setting of the detection cycle. The detection cycle is determined based on the requirements of the production process and the motion characteristics of the mold, and is usually a short time interval, such as a few seconds or tens of seconds, to ensure that the density change trend can be captured in a timely manner. The central control module acquires the molding density data of each detection cycle in real time through the density detection unit in the detection module, and stores the density value of the previous detection cycle for difference calculation. The formula for calculating the density change rate is: in, This represents the rate of change in density, expressed as a percentage. This indicates the molding density for the current testing cycle; This indicates the forming density of the previous testing cycle. This formula is used to calculate the magnitude and direction of density change between two adjacent testing cycles; a positive sign indicates an increase in density, and a negative sign indicates a decrease in density.

[0112] The preset density change thresholds include positive and negative thresholds, which correspond to the maximum allowable rates of density increase and decrease, respectively. The positive threshold setting must consider the natural expansion characteristics of polystyrene during the foaming process and the cooling capacity of the mold. If the density change rate exceeds this threshold, it may indicate insufficient steam supply or excessively rapid cooling, leading to premature solidification and accumulation of the material, forming localized high-density areas. The negative threshold must be considered in conjunction with the material's foaming ratio limit and the mechanical response speed of the mold opening and closing. If the density change rate is below this threshold, it may indicate excessively high steam pressure or excessively slow mold opening and closing, causing excessive material expansion or even rupture. The specific values ​​of these two thresholds need to be determined through process debugging. For example, for packaging products requiring high density uniformity, the positive and negative thresholds can be set to ±3% / cycle, while for thick-walled industrial products, this can be appropriately relaxed to ±5% / cycle.

[0113] When the density change rate exceeds the positive threshold, a linear deceleration mode is activated. The core of this mode is to reduce the current rate by a fixed percentage, determined based on the mold's inertial characteristics and the sensitivity to density changes. For example, if the current mold opening / closing rate is 10 mm / s and the fixed percentage is set to 5%, the reduced rate will be 9.5 mm / s. This linear reduction provides a stable rate adjustment gradient, preventing mechanical shock to the mold due to abrupt deceleration. Over multiple consecutive detection cycles, if the density change rate continues to exceed the positive threshold, the central control module will continuously reduce the rate by the same percentage until it falls back to the threshold range or reaches the minimum rate limit of the mold's mechanical structure. The minimum rate limit must be set to ensure the mold can complete normal opening and closing actions, for example, not less than 2 mm / s, to avoid a significant drop in production efficiency due to excessively low rates.

[0114] When the density change rate is below the negative threshold, a stepped acceleration mode is activated. This mode adjusts the rate using a discrete incremental algorithm, with the following steps: First, the trend weight value is calculated based on the direction of density change (increasing or decreasing) over N consecutive detection periods (e.g., N=3). If the density decreases for three consecutive periods, the weight value is +1.5; if it decreases twice and increases once, the weight value is +1.0; if it decreases once and increases twice, the weight value is +0.5; if it increases twice, the weight value is 0. This weighting method quantifies the trend strength of density change. The more periods of continuous change in the same direction, the larger the weight value, indicating a more pronounced trend. The preset time decay factor is a coefficient between 0 and 1, such as 0.9, used to decay the weight value of earlier periods, reflecting the greater impact of recent data on the current adjustment. For example, if the current period is period t, the weight value of the previous period (t-1) is directly calculated, the weight value of the previous two periods (t-2) is multiplied by 0.9, the weight value of the previous three periods (t-3) is multiplied by 0.9², and so on.

[0115] The increment base is obtained by summing the products of the weight values ​​of each period and the time decay factor. The baseline increment step size is a fixed rate adjustment unit, such as 0.5 mm / s. The correction rate = current rate + baseline increment step size × increment base. For example, if the current rate is 8 mm / s and the increment base is 1.2, then the correction rate is 8 + 0.5 × 1.2 = 8.6 mm / s. Through this iterative calculation method, the rate adjustment magnitude will dynamically change according to the trend strength and time distance, which avoids overshoot caused by aggressive adjustment and can respond promptly to the continuous density decline trend.

[0116] The function of the abnormal fluctuation counter is to identify and suppress occasional abnormal density changes that interfere with rate adjustment. When the absolute value of the density change rate in a certain cycle exceeds a preset abnormal threshold (e.g., ±10% / cycle), the counter increments by 1; if the density change rate returns to normal in subsequent cycles, the counter decrements by 1. When the counter value reaches a preset upper limit (e.g., 3), it indicates the existence of continuous abnormal fluctuations. At this time, the central control module will forcibly multiply the incremental base by a suppression coefficient (e.g., 0.5) to reduce the rate adjustment amplitude and avoid erroneous adjustments caused by sensor noise or brief process fluctuations. For example, under normal circumstances, the incremental base is 1.5, which becomes 0.75 after suppression, and the corresponding correction rate adjustment amplitude is halved. When the counter value drops to zero due to continuous normal cycles, the system resumes normal adjustment logic.

[0117] In actual production, parameters such as preset density change threshold, fixed ratio, baseline increment step size, preset time decay factor, anomaly threshold, and suppression coefficient need to be optimized through process experiments and long-term production data accumulation. For example, for polystyrene grades with poor thermal stability, the fixed ratio can be reduced to 3% to avoid drastic temperature field changes caused by rapid rate reduction; for the production process of high-density products, the baseline increment step size can be increased to 1 mm / s to accelerate density recovery. The central control module stores historical adjustment records of each parameter and corresponding density change curves in a real-time database for process engineers to analyze and optimize, forming a parameter self-learning mechanism.

[0118] The entire secondary correction process forms a closed-loop feedback system: the detection module collects density data in real time → the central control module calculates the rate of change and determines the mode → corresponding rate adjustments are executed → the density detection unit feeds back new data → adjustments are recalculated until the density change rate stabilizes within the threshold range. This dynamic adjustment mechanism can adapt to various disturbances during the foaming process, such as changes in material properties, mold temperature fluctuations, and steam pressure drift, ensuring dynamic matching between the mold opening and closing rate and density changes, thereby effectively controlling the density uniformity and molding accuracy of the product. Simultaneously, the combination of discrete incremental algorithms and abnormal fluctuation suppression ensures both a fast response to trend changes and enhances the system's robustness to random disturbances, improving the stability and reliability of the entire molding control system.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0120] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A foamed polystyrene automated forming control system, characterized by, The method comprises the following steps: A detection module comprises a data analysis unit, a density detection unit, and a pressure detection unit. The data analysis unit is used to collect temperature distribution data of a foaming area inside a mold in real time. The density detection unit is used to measure local density values of a formed blank. The pressure detection unit is used to monitor real-time steam pressure values in a foaming stage. A data analysis module is used to obtain a temperature fluctuation index according to the deviation of the temperature distribution data from a preset temperature interval, to obtain a dynamic density compensation coefficient by substituting the local density values into a density model previously established by the data analysis module, and to determine a forming quality deviation level according to a nonlinear relationship between the temperature fluctuation index and the dynamic density compensation coefficient. A central control module is used to adjust a heating power reference value of a steam generating device according to the forming quality deviation level, to determine initial parameters of a mold opening and closing rate based on the difference between the real-time steam pressure value and a preset pressure threshold value, to perform secondary correction on the opening and closing rate according to the temperature fluctuation index, and to generate mold motion trajectory control parameters based on the corrected opening and closing rate. The density model previously established by the data analysis module comprises the following steps: Obtain density distribution data sets in historical forming data, and filter outliers in the data sets, wherein the data sets include steam pressure peak values, cooling times, and corresponding density gradients. Establish a heat conduction partial differential equation according to the coupling effect of the steam pressure peak values and the cooling times. Optimize the coefficient matrix of the partial differential equation by the gradient descent method, and calculate a convergence error value. Based on the relationship between the convergence error value and a preset accuracy range, dynamically update the coefficient matrix until the convergence error value is stable within the accuracy range.

2. The foamed polystyrene automated forming control system of claim 1, wherein, The adjustment of the heating power reference value of the steam generating device comprises the following steps: Obtain a deviation range corresponding to the forming quality deviation level, and determine a power correction amount according to the relationship between the deviation range and first and second power adjustment factors configured by the central control module: When the deviation range belongs to a first deviation interval, the central control module takes the product of a preset reference power value and the first power adjustment factor as the heating power reference value. When the deviation range belongs to a second deviation interval, the central control module calculates a power compensation amount according to the ratio of the dynamic density compensation coefficient to a standard density, and takes the algebraic sum of the reference power value and the power compensation amount as the heating power reference value.

3. The foamed polystyrene automated forming control system of claim 2, wherein, When the central control module calculates the power compensation amount, the following steps are included: Obtain the relationship between the ratio and preset first and second boundary values, and select a corresponding compensation strategy: when the ratio is less than or equal to the first boundary value, a trigonometric function is used to periodically correct the ratio to generate a compensation factor; when the ratio is greater than the first boundary value and less than the second boundary value, a polynomial fitting algorithm is used to generate a compensation factor; and when the ratio is greater than or equal to the second boundary value, a moving average method is used to smooth the ratio to generate a compensation factor.

4. The foamed polystyrene automated forming control system of claim 3, wherein, When the central control module determines the initial parameters of the mold opening and closing rate, the following steps are included: According to the absolute value of the pressure difference between the real-time steam pressure value and the preset pressure standard value, in combination with the maximum rate upper limit of the mold mechanical structure, based on the proportional relationship of the absolute value of the pressure difference to the pressure tolerance threshold, the product of the maximum rate upper limit and the proportional value is taken as the initial opening and closing rate, and the rate is limited not to exceed the mechanical structure bearing threshold.

5. The foamed polystyrene automated forming control system of claim 4, wherein, When the central control module performs secondary correction on the opening and closing rate, it includes: Obtain the density change rate of the current forming density and the previous detection cycle density, and select the rate correction mode according to the comparison result of the density change rate and the preset density change threshold: When the density change rate exceeds the positive threshold, the linear speed reduction mode is enabled, and the current speed is reduced by a fixed proportion; When the density change rate is lower than the negative threshold, the step acceleration mode is enabled, and the current speed is adjusted by a discrete increment algorithm.

6. The foamed polystyrene automated forming control system of claim 5, wherein, The discrete increment algorithm specifically includes: According to the density change direction of the continuous detection cycle, calculate the change trend weight value, and take the product of the weight value and the preset time attenuation factor as the increment base, and calculate the correction rate by the cumulative result of the reference increment step and the increment base, while introducing an abnormal fluctuation counter to dynamically compensate and suppress continuous abnormal cycles.

7. The foamed polystyrene automated forming control system of claim 6, wherein, The generation of the mold motion trajectory control parameters includes: Decompose the corrected opening and closing rate into axial components in a three-dimensional coordinate system, and establish a space motion equation according to the mold geometric parameters, and generate a continuous derivable mold motion control point sequence through B-spline curve interpolation method.

8. The foamed polystyrene automated forming control system of claim 7, wherein, The implementation of the B-spline curve interpolation method includes: Construct a segmented base function in each control point node interval, and apply continuity constraint conditions to make the first derivative of the overall trajectory continuous, and obtain the control point coordinates of each interval base function through matrix inverse operation.

9. A foamed polystyrene automated forming control method, suitable for use in a foamed polystyrene automated forming control system as claimed in any one of claims 1 to 8, characterized by, It includes: Real-time acquisition of mold temperature distribution data, forming density gradient and steam pressure data; Calculate the temperature fluctuation index and obtain the dynamic density compensation coefficient based on the density model; Determine the heating power reference value and the initial parameters of the opening and closing rate according to the forming quality deviation level; Combine the steam pressure data to initially adjust the opening and closing rate, and perform secondary correction according to the density change rate; Generate optimized mold motion control instructions and output to the driving mechanism.

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