Dynamic die pressure data analysis system for polyurethane pultrusion production

Abstract

The invention relates to the technical field of data analysis, in particular to a dynamic mold pressure data analysis system for polyurethane pultrusion production, which comprises a mold pressure data capture module, a pressure gradient trend calculation module, a mold abnormal pressure identification module, a mold pressure adjustment data module and a multi-dimensional pressure state monitoring module. According to the invention, through data capture of the multi-node pressure sensor, high-precision real-time monitoring of internal pressure of the mold is realized, data integrity is enhanced, limitation of single data is avoided, pressure feature identification of different areas of the mold is realized, abnormality judgment accuracy is improved, normal fluctuation and abnormal accumulation are distinguished, and abnormal areas are accurately locked; temperature and ultrasonic signal analysis is introduced, curing abnormity and bubble aggregation are identified, the monitoring range is expanded, the data acquisition precision, the analysis depth and the regulation and control capability are improved, abnormity detection is more scientific, regulation and control are more intelligent, resin flowing is optimized, and the quality fluctuation risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a dynamic die pressure data analysis system for polyurethane pultrusion production. Background Art

[0002] The technical field of data analysis includes the collection, storage, processing, and analysis of data in various production and manufacturing processes to optimize production processes, improve product quality, and increase production efficiency. This technical field covers the application of data collection equipment, the selection of data processing methods, the establishment of statistical analysis models, and the development of visualization tools. In industrial manufacturing, data analysis technology can achieve real-time monitoring of production parameters and, through algorithm analysis, judge the change trend of key parameters, and then adjust production strategies. Especially in continuous production and manufacturing, data analysis technology can be used to monitor material properties, equipment operating status, and dynamic changes in parameters, providing data support for precise control and optimization.

[0003] Among them, the dynamic die pressure data analysis system for polyurethane pultrusion production refers to a data processing system used to collect, monitor, and analyze the pressure changes inside the die during the pultrusion molding process. This system uses high-precision pressure sensors to collect the pressure at multiple positions inside the die in real time, and combines time series data analysis technology to model and analyze the pressure change trend. The system uses the data fitting method to analyze the relationship between parameters such as different pultrusion speeds, resin rheological properties, and fiber wetting degrees, so as to extract key pressure change patterns. This system uses multivariate regression analysis technology to correlate the die opening and closing state with the pressure distribution and establish a pressure control strategy, providing data support for dynamic die design.

[0004] In the acquisition of internal pressure data of molds in the prior art, it mostly relies on the data of a single sensor. Limited by the number and position of local measurement points, it is difficult to accurately reflect the global pressure distribution characteristics of the mold, resulting in the easy omission of local pressure anomalies. In the process of data analysis, it mainly relies on the trend modeling of time-series data, but fails to effectively combine dynamic factors such as track spacing, resin flow rate, and kneading wheel speed, lacking the interactive analysis between multiple variables and being unable to accurately analyze the influence of different production parameters on pressure distribution. In the identification of abnormal pressure, it mainly relies on the judgment of static pressure thresholds, making it difficult to accurately identify the abnormal rate of change of pressure gradient, resulting in the lag and misjudgment risks of abnormal detection. In the regulation strategy, the existing methods lack a precise adjustment mechanism for anomalies in different regions, and it is difficult to achieve the linkage optimization of track spacing adjustment and resin flow rate regulation. The adjustment strategy is set based on experience, lacking data-driven intelligent analysis and affecting the accuracy of production control. In the aspect of pressure state monitoring, the prior art does not fully consider the influence of temperature and material curing state on pressure distribution, making it difficult to effectively identify pressure anomalies caused by curing anomalies or bubble aggregation, resulting in the difficulty of accurately controlling the material flow state during the production process and increasing the instability of product quality. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a dynamic mold pressure data analysis system for polyurethane pultrusion production.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: The dynamic mold pressure data analysis system for polyurethane pultrusion production includes:

[0007] The mold pressure data capture module extracts the opening and closing clearance of the mold track, the linear speed of the kneading wheel, and the resin injection flow rate based on the real-time pressure data of multiple pressure sensor nodes inside the polyurethane pultrusion mold, judges the signal validity of the pressure measurement value of a single node, and obtains the mold pressure spatio-temporal data set;

[0008] The pressure gradient trend calculation module performs the identification of the distribution interval between adjacent nodes based on the mold pressure spatio-temporal data set, analyzes the local pressure gradient, extracts the pressure change data in the resin flow region, screens the abnormal region, and obtains the local pressure gradient trend of the mold;

[0009] The mold abnormal pressure identification module analyzes the gradient change characteristics of the abnormal region based on the local pressure gradient trend of the mold, judges whether there are trends of continuous increase and decrease of pressure in the abnormal region, determines the range and pressure change situation of the abnormal region, and obtains the mold abnormal pressure distribution region;

[0010] The mold pressure regulation data module calls the abnormal pressure distribution area of the mold, analyzes the influence of the track opening and closing distance on the local pressure change according to the pressure gradient trend in the abnormal area, identifies the adjustment range of the injection rate, and obtains the mold pressure regulation data.

[0011] As a further solution of the present invention, the mold pressure spatio-temporal data set includes pressure sensor node data, track opening and closing gap data, twisting wheel linear velocity data, and resin injection flow rate data. The local pressure gradient trend of the mold includes pressure gradient value, pressure change rate, and pressure characteristics in the resin flow area. The abnormal pressure distribution area of the mold includes the position of the abnormal area, the area with continuously rising pressure, and the area with continuously falling pressure. The mold pressure regulation data includes the adjustment direction of the track distance, the adjustment range of the resin injection rate, and the pressure balance change trend.

[0012] As a further solution of the present invention, the mold pressure data capture module includes:

[0013] The pressure signal verification sub-module identifies the difference between the pressure measurement values of a single node and adjacent nodes based on the real-time pressure data of multiple pressure sensor nodes inside the polyurethane pultrusion mold, determines whether the difference exceeds the pressure abnormality threshold, filters out the abnormal node data, and obtains an effective pressure signal set.

[0014] The dynamic feature calculation sub-module calls the effective pressure signal set, combines the track opening and closing gap, the twisting wheel linear velocity, and the resin injection flow rate, and identifies the pressure fluctuation, signal change rate, and pressure gradient between nodes within the time series of each node. Using the formula:

[0015]

[0016] Obtain the pressure dynamic feature intensity distribution;

[0017] Among them, D represents the pressure dynamic feature intensity, P i represents the pressure measurement value of the i-th node, P i-1 represents the pressure measurement value of the (i - 1)-th node, V i represents the twisting wheel linear velocity of the i-th node, S i represents the track opening and closing gap of the i-th node, Q i represents the resin injection flow rate of the i-th node, and n represents the total number of nodes;

[0018] The spatio-temporal data generation sub-module calibrates the spatio-temporal position relationship of the node pressure according to the pressure dynamic feature intensity distribution and combines the pressure signal time series of the corresponding time nodes to obtain the mold pressure spatio-temporal data set.

[0019] As a further solution of the present invention, the pressure gradient trend calculation module includes:

[0020] Based on the mold pressure spatio-temporal data set, the local gradient recognition sub-module calculates the pressure difference between adjacent nodes, combines the distribution interval to recognize the local pressure gradient, conducts directional analysis, screens out invalid data, and obtains the local pressure gradient distribution data;

[0021] The gradient change rate analysis sub-module calls the local pressure gradient distribution data and uses the formula:

[0022]

[0023] Calculate the gradient change rate, analyze the change trend of the pressure gradient with the regional distribution, and obtain the gradient change trend data set;

[0024] Among them, G represents the gradient change rate, and ΔP k represents the pressure difference between the kth adjacent nodes, and ΔP k-1 represents the pressure difference between the (k - 1)th adjacent nodes, and Δd k represents the distribution interval between the kth adjacent nodes, and Δd k-1 represents the distribution interval between the (k - 1)th adjacent nodes, and m represents the number of adjacent nodes in the calculation area;

[0025] The abnormal gradient area screening sub-module calls the gradient change trend data set, analyzes the gradient change rate in the area, sets the abnormal threshold of the pressure gradient, screens the areas exceeding the threshold, identifies the abnormal gradient change areas, and obtains the local pressure gradient trend of the mold.

[0026] As a further solution of the present invention, the mold abnormal pressure recognition module includes:

[0027] The pressure gradient recognition sub-module, based on the local pressure gradient trend of the mold, recognizes the pressure change rate of adjacent nodes. By comparing the pressure values of adjacent nodes, it uses the formula:

[0028]

[0029] Calculate and obtain the local pressure gradient change rate;

[0030] Among them, L represents the local pressure gradient change rate, and ΔB j represents the pressure change value between the jth adjacent nodes, m represents the number of adjacent nodes in the calculation area, T max represents the maximum pressure value in the area, and T min represents the minimum pressure value in the area;

[0031] The abnormal pressure determination sub-module calls the local pressure gradient change rate, and according to the preset pressure change threshold, screens the areas with abnormal gradient change rates, judges whether the area has the characteristic of continuous pressure change, and obtains the abnormal pressure area;

[0032] The abnormal pressure trend analysis sub-module calls the abnormal pressure area, analyzes the pressure change trend within the area, identifies the situations of continuous pressure increase and decrease, and combines the pressure distribution within the area to obtain the abnormal pressure distribution area of the mold.

[0033] As a further solution of the present invention, the mold pressure adjustment data module includes:

[0034] The abnormal pressure area identification sub-module calls the abnormal pressure distribution area of the mold, analyzes the pressure gradient trend of each point within the area, screens the local pressure gradient mutation points, screens the core area of abnormal pressure, and identifies the non-uniformity of the pressure distribution in the core area to obtain the core pressure non-uniformity;

[0035] The crawler direction judgment sub-module, based on the core pressure non-uniformity, identifies the change rate of the crawler opening and closing distance, extracts the change amount of the pressure balance under different differential direction adjustments, and judges to obtain the optimal crawler adjustment direction;

[0036] The injection rate adjustment amplitude identification sub-module calls the optimal crawler adjustment direction, identifies the pressure response change amount in the corresponding area, analyzes the influence of the crawler adjustment on the local pressure, and according to the pressure gradient trend within the area, uses the formula:

[0037]

[0038] Calculate the injection rate adjustment amplitude, and analyze the change trend of the pressure balance after adjustment to obtain the mold pressure control data;

[0039] Among them, F represents the injection rate adjustment amplitude, R x represents the local area pressure value of the xth pressure sampling point, R avg represents the average surrounding pressure, G x represents the pressure gradient change rate of the xth pressure sampling point, and M represents the total number of pressure sampling points in the calculation area.

[0040] As a further solution of the present invention, the system further includes a multi-dimensional pressure state monitoring module:

[0041] The multi-dimensional pressure state monitoring module calls the mold pressure control data, extracts the temperature data of the infrared thermometer and the ultrasonic signal reflection characteristic data, analyzes the influence of the temperature change on the pressure distribution, judges whether there are abnormal pressure situations caused by curing abnormalities and bubble aggregation, and combines the pressure gradient change and the crawler spacing adjustment data to obtain the mold pressure dynamic state evaluation result;

[0042] The mold pressure dynamic state evaluation result includes temperature influence analysis, curing abnormality identification, and bubble aggregation identification.

[0043] As a further solution of the present invention, the multi-dimensional pressure state monitoring module includes:

[0044] The pressure data extraction sub-module calls the die pressure regulation data, extracts the track pitch adjustment data, the temperature data of the infrared thermometer, and the ultrasonic signal reflection characteristic data, screens the pressure matching parameters, and obtains a set of pressure matching parameters;

[0045] The temperature and pressure correlation analysis sub-module calls the set of pressure matching parameters, analyzes the relationship between the temperature data and the pressure gradient change, analyzes the influence of temperature on the pressure distribution, judges the curing abnormality and the abnormal situation of the bubble aggregation pressure, and uses the formula:

[0046]

[0047] Calculate the temperature influence pressure error value, screen the abnormal pressure distribution, and obtain the temperature influence pressure distribution result;

[0048] where, E represents the temperature influence pressure error value, U a represents the track pitch adjustment data of the a-th area, W a represents the temperature data of the infrared thermometer in the a-th area, V a represents the ultrasonic signal reflection characteristic data of the a-th area, Y a represents the pressure gradient change data of the a-th area, Z represents the total number of calculation areas, and a represents the area index;

[0049] The dynamic pressure evaluation sub-module calls the temperature influence pressure distribution result, combines the track pitch adjustment data, analyzes the dynamic pressure gradient, identifies the influence of temperature change and track adjustment amplitude on the die pressure stability, and obtains the die pressure dynamic state evaluation result.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0051] In the present invention, through data capture by multi-node pressure sensors, high-precision real-time monitoring of the pressure inside the mold is achieved. By combining cross-analysis of data such as the opening and closing gap of the crawler, the linear velocity of the kneading wheel, and the resin injection flow rate, the integrity and timeliness of the data are enhanced, and the limitations of single pressure data are avoided. Based on the analysis of the change trend of the pressure gradient, the pressure characteristics of different regions of the mold are identified, making the judgment of local anomalies more accurate. By extracting the pressure change rate, normal pressure fluctuations can be distinguished from abnormal pressure accumulation, the recognition ability of abnormal regions is improved, and the accurate locking of abnormal distribution regions is ensured. Combining the adjustment direction of the crawler spacing and the dynamic regulation of the resin injection rate, a precise intervention mechanism for abnormal pressure regions is constructed, improving the response speed and accuracy of pressure regulation. By introducing temperature data and ultrasonic signal reflection characteristics, abnormal pressure situations caused by curing anomalies and bubble aggregation are identified, enabling the pressure state monitoring to cover a wider range of influencing factors, providing more comprehensive data support for pressure control in the production process. The combination of multiple innovative means improves the acquisition accuracy, analysis depth, and regulation ability of mold pressure data, making the detection and intervention of abnormal pressure in the production process more scientific and real-time, improving the intelligent level of mold pressure management, optimizing the resin flow state, and reducing the risk of quality fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is the system flow chart of the present invention;

[0053] Figure 2 is the flow chart of the mold pressure data capture module in the present invention;

[0054] Figure 3 is the flow chart of the pressure gradient trend calculation module in the present invention;

[0055] Figure 4 is the flow chart of the mold abnormal pressure recognition module in the present invention;

[0056] Figure 5 is the flow chart of the mold pressure regulation data module in the present invention;

[0057] Figure 6 is the flow chart of the multi-dimensional pressure state monitoring module in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0058] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0060] Please refer to Figure 1 , the dynamic die pressure data analysis system for polyurethane pultrusion production includes:

[0061] The die pressure data capture module extracts the die track opening and closing gap, the linear speed of the kneading wheel, and the resin injection flow rate based on the real-time pressure data of multiple pressure sensor nodes inside the polyurethane pultrusion die, judges the signal validity of the pressure measurement values of individual nodes, screens out abnormal data, and obtains the die pressure spatio-temporal data set;

[0062] The pressure gradient trend calculation module, based on the die pressure spatio-temporal data set, according to the distribution interval between adjacent nodes, identifies the local pressure gradient of a single line segment, extracts the pressure change data in the resin flow region, analyzes the change trend of the pressure gradient in adjacent regions, identifies the pressure distribution characteristics in the die track contact area and the resin injection area, screens out the regions with abnormal gradient change rates, and obtains the local die pressure gradient trend;

[0063] The die abnormal pressure identification module, based on the local die pressure gradient trend, extracts the pressure change rate data of adjacent nodes, analyzes the gradient change characteristics of the abnormal region, judges whether there are trends of continuous pressure increase and decrease in the abnormal region, and determines the range and pressure change situation of the abnormal region to obtain the die abnormal pressure distribution region;

[0064] The die pressure adjustment data module calls the die abnormal pressure distribution region, analyzes the influence of the track opening and closing distance on the local pressure change according to the pressure gradient trend of the abnormal region, judges the adjustment direction of the regional track, identifies the adjustment amplitude of the injection rate, analyzes the change trend of the pressure balance after adjustment, and obtains the die pressure regulation data;

[0065] The multi-dimensional pressure state monitoring module calls the die pressure regulation data, extracts the temperature data of the infrared thermometer and the ultrasonic signal reflection characteristic data, analyzes the influence of temperature change on the pressure distribution, judges whether there are abnormal pressure situations caused by curing abnormalities and bubble aggregation, and combines the pressure gradient change and the track spacing adjustment data to obtain the die pressure dynamic state evaluation result.

[0066] The die pressure spatio-temporal dataset includes pressure sensor node data, track opening and closing gap data, kneading wheel linear velocity data, resin injection flow rate data. The die local pressure gradient trend includes pressure gradient value, pressure change rate, and pressure characteristics in the resin flow area. The die abnormal pressure distribution area includes the location of the abnormal area, the area with continuously rising pressure, and the area with continuously decreasing pressure. The die pressure regulation data includes the adjustment direction of the track spacing, the adjustment amplitude of the resin injection rate, and the pressure equilibrium change trend. The die pressure dynamic state evaluation result includes temperature influence analysis, curing abnormality identification, and bubble aggregation identification.

[0067] Please refer to Figure 2 , the die pressure data capture module includes:

[0068] Based on the real-time pressure data of multiple pressure sensor nodes inside the polyurethane pultrusion die, the pressure signal verification sub-module identifies the difference in pressure measurement values between a single node and its adjacent nodes, determines whether the difference exceeds the pressure abnormality threshold, filters out the abnormal node data, and obtains an effective pressure signal set.

[0069] By comparing the difference in pressure measurement values between a single node and its adjacent nodes. For example, in an actual production scenario, if the measurement value of node 5 suddenly exceeds the average value of its adjacent nodes 4 and 6 by more than 10%, it is initially determined as an abnormal point. This difference is achieved by measuring adjacent nodes and calculating the difference in their pressure values. This comparison and calculation are performed for each node to filter out normal pressure data. This process avoids the error caused by directly relying on the data of a single node, ensures the authenticity and accuracy of the data, and finally obtains an effective pressure signal set containing all the node data verified as normal.

[0070] The dynamic feature calculation sub-module calls the effective pressure signal set, combines the track opening and closing gap, kneading wheel linear velocity, and resin injection flow rate, and identifies the pressure fluctuation, signal change rate, and pressure gradient between nodes within the time series of each node. Using the formula:

[0071]

[0072] Obtain the pressure dynamic feature intensity distribution;

[0073] Among them, D represents the pressure dynamic feature intensity, P i represents the pressure measurement value of the i-th node, P i-1 represents the pressure measurement value of the (i - 1)-th node, V i represents the kneading wheel linear velocity of the i-th node, S i represents the track opening and closing gap of the i-th node, Q i represents the resin injection flow rate of the i-th node, and n represents the total number of nodes;

[0074] Combined with production parameters such as the track opening and closing gap, the linear velocity of the rolling wheel, and the resin injection flow rate in sequence, the dynamic characteristic strength calculation is carried out for the pressure data at each node during the polyurethane pultrusion process. The effective pressure data from node 1 to node 5 are selected. Assume that the measured pressure values at each node are P1 = 50, P2 = 55, P3 = 52, P4 = 60, P5 = 58 kPa, and the linear velocity V of the rolling wheel i are V1 = 0.8, V2 = 0.9, V3 = 0.85, V4 = 0.88, V5 = 0.9 m / s respectively, and the track opening and closing gap S i are S1 = 1.2, S2 = 1.1, S3 = 1.3, S4 = 1.0, S5 = 1.2 mm respectively, and the resin injection flow rate Q i are Q1 = 15, Q2 = 16, Q3 = 14, Q4 = 17, Q5 = 15 L / min respectively;

[0075] Among them, P0 = 50 kPa (set as the reference value of node 0), and calculate the molecular part of each node in sequence

[0076]

[0077] Node 1:

[0078] Node 2:

[0079] Node 3:

[0080] Node 4:

[0081] Node 5:

[0082] Then divide the molecular part of each node by the corresponding Q i +1 to obtain, and calculate D:

[0083] Node 1: D1 = 1.095 / (15 + 1) = 0.068;

[0084] Node 2: D2 = 5.548 / (16 + 1) = 0.326;

[0085] Node 3: D3 = 3.69 / (14 + 1) = 0.246;

[0086] Node 4: D4 = 8.04 / (17 + 1) = 0.447;

[0087] Node 5: D5 = 2.895 / (15 + 1) = 0.181;

[0088] The final calculated pressure dynamic characteristic strengths of the 5 nodes are 0.068, 0.326, 0.246, 0.447, and 0.181 respectively, forming a complete pressure dynamic characteristic strength distribution, which reflects the pressure dynamic characteristics of each node during the production process;

[0089] The benefit of the formula is that by combining the four key participating items of the pressure change rate, the line speed of the kneading wheel, the opening and closing gap of the track, and the resin injection flow rate, it comprehensively reflects the dynamic characteristics of the node pressure during the pultrusion process, and can describe the node pressure behavior in multiple dimensions without relying on a single variable. The pressure dynamic characteristic strength of node 4 is 0.447, which is the largest among all nodes, indicating that the pressure fluctuation at this node is relatively intense during the production process, suggesting that this position is a pressure-sensitive area in actual operation and requires key monitoring.

[0090] The spatio-temporal data generation sub-module calibrates the spatio-temporal position relationship of the node pressure according to the pressure dynamic characteristic strength distribution and combines the pressure signal time series of the corresponding time nodes to obtain the die pressure spatio-temporal data set;

[0091] By combining the data with the time series, a multi-dimensional data matrix is established. The key involved in the process is to correspond each moment of the time series with the pressure data of each node. For example, in specific operations, if at the 5th second, the pressure dynamic characteristic strength displayed by node 10 is 20, this will be reflected as (10, 5 seconds, 20) in the multi-dimensional data matrix. Through this method, the pressure conditions of each node at each time point can be clearly seen, which provides a complete spatio-temporal view for analyzing the pressure changes during the entire production process, and finally obtains the die pressure spatio-temporal data set, providing a basis for further data analysis and production process monitoring.

[0092] Please refer to Figure 3 , the pressure gradient trend calculation module includes:

[0093] The local gradient identification sub-module calculates the pressure difference between adjacent nodes based on the die pressure spatio-temporal data set, combines the distribution interval to identify the local pressure gradient, conducts directional analysis, and screens out invalid data to obtain the local pressure gradient distribution data;

[0094] In a polyurethane production line, the process of calculating the pressure difference between adjacent nodes involves the use of real-time monitoring equipment. The equipment can continuously collect pressure data between two points on the mold. After the data is denoised and calibrated, it is recorded at fixed time intervals. Real-time data monitoring is used to predict mold wear or blockage. By calculating the pressure difference between adjacent nodes, the operating parameters of the production line can be dynamically adjusted to optimize production efficiency and product quality. By aggregating the difference data and applying directional analysis, engineers can identify areas with abnormal pressure distribution, thereby pre-adjusting production parameters or performing necessary maintenance to prevent equipment failures or product quality degradation, and finally obtaining local pressure gradient distribution data.

[0095] The gradient change rate analysis sub-module calls the local pressure gradient distribution data and uses the formula:

[0096]

[0097] Calculate the gradient change rate, analyze the change trend of the pressure gradient with the regional distribution, and obtain the gradient change trend data set;

[0098] Among them, G represents the gradient change rate, ΔP k represents the pressure difference between the kth adjacent nodes, ΔP k-1 represents the pressure difference between the (k - 1)th adjacent nodes, Δd k represents the distribution interval between the kth adjacent nodes, Δd k-1 represents the distribution interval between the (k - 1)th adjacent nodes, and m represents the number of adjacent nodes in the calculation area;

[0099] Parameter analysis of the formula:

[0100] G t : Gradient change rate, which measures the change of the mold pressure gradient and helps to monitor the uniformity of resin flow in the mold;

[0101] ΔP k : Pressure difference between the kth adjacent nodes (unit: Pa), measured by a pressure sensor;

[0102] Δd k : Distance interval between the kth adjacent nodes (unit: cm), determined according to the mold design;

[0103] m: Number of adjacent nodes in the calculation area, that is, the number of data points;

[0104] Suppose the pressure data measured by adjacent pressure sensors in a certain area of the mold is as follows:

[0105] P1 = 5000Pa, P2 = 5100Pa, P3 = 5050Pa, P4 = 5200Pa;

[0106] The corresponding node distances are all: Δd1 = Δd2 = Δd3 = 10 cm;

[0107] Calculate the pressure gradient between each adjacent node:

[0108]

[0109] Calculate the gradient change between adjacent nodes:

[0110]

[0111] Calculation of the gradient change rate:

[0112] During the polyurethane pultrusion production process, the fluctuation of the pressure gradient directly affects the flow stability of the resin and the curing quality. It is of great significance to calculate the pressure gradient change rate G in real time. The above calculation process shows that when the G value in a certain area is large, for example, 17.5 Pa / cm, it indicates that the pressure gradient in this area changes rapidly, meaning that the resin flow is blocked or the mold surface is contaminated, resulting in uneven flow velocity. This situation will lead to uneven local curing, thus affecting the mechanical properties of the final product. In practical applications, engineers need to set reasonable thresholds. For example, if G exceeds 15 Pa / cm, it is necessary to check whether there is contamination on the mold surface or adjust the resin injection rate to maintain the flow stability. On the automated production line, the pressure sensor can be connected through software to make the calculation process real-time, and when the set threshold is exceeded, an alarm will be automatically triggered or the control parameters will be adjusted to reduce production instability, and finally obtain the gradient change trend data set to provide a basis for production decisions.

[0113] The abnormal gradient area screening sub-module calls the gradient change trend data set, analyzes the gradient change rate within the area, sets the abnormal threshold of the pressure gradient, screens the areas exceeding the threshold, identifies the abnormal gradient change areas, and obtains the local pressure gradient trend of the mold;

[0114] Set an abnormal threshold of the pressure gradient, which is obtained based on historical data and statistical analysis. It can quantitatively indicate what level of pressure gradient change rate belongs to the normal range and what belongs to the abnormal. Because the abnormal pressure gradient indicates potential production problems, such as mold blockage or poor resin curing. By setting a reasonable threshold and deeply analyzing the areas exceeding this threshold, the process parameters can be adjusted in time or equipment maintenance can be carried out, thus ensuring product quality and production safety. Identifying the abnormal gradient change areas helps to optimize the stability and efficiency of the entire production process and obtain the local pressure gradient trend of the mold.

[0115] Please refer to Figure 4 , the mold abnormal pressure identification module includes:

[0116] Based on the local pressure gradient trend of the mold, the pressure gradient identification sub-module identifies the pressure change rate of adjacent nodes. By comparing the pressure values of adjacent nodes, the formula is used:

[0117]

[0118] to calculate and obtain the local pressure gradient change rate;

[0119] where L represents the local pressure gradient change rate, ΔB j represents the pressure change value between the jth adjacent nodes, m represents the number of adjacent nodes in the calculation area, T max represents the maximum pressure value in the area, and T min represents the minimum pressure value in the area;

[0120] The pressure data at different nodes are collected in real time through multiple sensors. The data come from various key points of the mold to analyze the dynamic changes of the pressure inside the mold. After obtaining the pressure data of each adjacent node, the pressure change rate between adjacent nodes is calculated. For example, on a 3×3 node grid, assuming the initial pressure data is as follows (unit: MPa):

[0121] After the pressure value of each node is recorded by the sensor, the pressure change rate between adjacent nodes is calculated, that is, calculated for each pair of adjacent nodes. For example, in the first row, the pressure changes between nodes are:

[0122] ΔB1 = B2 - B1 = 2.1 - 2.0 = 0.1;

[0123] ΔB2 = B3 - B2 = 2.2 - 2.1 = 0.1;

[0124] Calculate for the second row:

[0125] ΔB3 = B5 - B4 = 2.5 - 2.3 = 0.2;

[0126] ΔB4 = B6 - B5 = 2.6 - 2.5 = 0.1;

[0127] Calculate for the third row:

[0128] ΔB5 = B8 - B7 = 2.9 - 2.7 = 0.2;

[0129] ΔB6 = B9 - B8 = 3.0 - 2.9 = 0.1;

[0130] Then, take the absolute value of the pressure change values of all adjacent nodes and sum them up:

[0131]

[0132] Assume the total number of nodes \(m = 6\), then the first part of the local pressure change rate is calculated as follows:

[0133] Meanwhile, calculate the maximum pressure \(T\) within the calculation area max \(= 3.0\) MPa, and the minimum pressure \(T\) min \(= 2.0\) MPa, then calculate the pressure adjustment factor for the second part:

[0134] Finally, calculate the local pressure gradient change rate: \(L = 0.1333×0.2 = 0.0267\);

[0135] This calculated value indicates that within this area, the overall change rate of the pressure gradient is 0.0267. If this value exceeds the set threshold (e.g., 0.05), then an abnormal situation occurs in this area. Further analyze the pressure change trend to obtain the local pressure gradient change rate as the basic data for subsequent analysis.

[0136] The abnormal pressure determination sub-module calls the local pressure gradient change rate, screens the areas with abnormal gradient change rates according to the preset pressure change threshold, determines whether there are characteristics of continuous pressure change in the area, and obtains the abnormal pressure areas;

[0137] Analyze the data to determine whether there are abnormal pressure areas in the mold. During the specific implementation process, compare the local pressure gradient change rate with the set pressure change threshold. If the set threshold is 0.05, then all areas with gradient change rates higher than this value are regarded as abnormal. The area indicates a fault or damage in the mold. Through this method, potential risks can be identified and responded to in a timely manner to protect production safety and equipment integrity. Taking a specific example of this area, if the gradient change rates of three consecutive nodes in a specific area of the mold all exceed 0.05, this area will be marked as an abnormal pressure area, and then detailed monitoring will be carried out in the area for further analysis and disposal. This monitoring and identification mechanism is a key step in preventing mold failures and optimizing the production process, and finally obtaining the abnormal pressure areas.

[0138] The abnormal pressure trend analysis sub-module calls the abnormal pressure areas, analyzes the pressure change trends within the areas, identifies the situations where the pressure continuously rises and falls, and combines the pressure distribution within the areas to obtain the abnormal pressure distribution areas of the mold;

[0139] Conduct a detailed analysis of the pressure change trend in the area to determine whether there is a continuous upward or downward trend, and based on this, judge the type of risk and fault. For example, in consecutive production cycles, if the pressure in a certain area continues to rise, it indicates that a part of the mold is under excessive pressure or about to be damaged. This kind of analysis helps engineers take timely measures, such as adjusting the working parameters of the mold or performing necessary maintenance, to avoid accidents or equipment damage. In a specific embodiment, if an area marked as abnormal continuously shows an upward pressure trend in the next five production cycles, a detailed inspection is prompted and shutdown for repair is recommended. The core of this analysis process lies in effectively predicting and preventing potential risks by continuously tracking pressure data, thereby maintaining the stable operation of the production line and finally obtaining the area with abnormal pressure distribution in the mold.

[0140] Please refer to Figure 5 , the mold pressure adjustment data module includes:

[0141] The abnormal pressure area identification sub-module calls the area with abnormal pressure distribution in the mold, analyzes the pressure gradient trend of each point in the area, screens out the local pressure gradient mutation points, screens out the core area of abnormal pressure, and identifies the non-uniformity of the pressure distribution in the core area to obtain the core pressure non-uniformity;

[0142] By accurately calculating the pressure gradient trend of each point, local anomalies are identified. For example, in a production cycle, the sensor records the pressure data of each point in the mold, and the data is analyzed to determine which areas have a pressure gradient exceeding the normal operating range. The area is the core area of abnormal pressure. Calculate the average pressure of the abnormal core area and the average pressure of the surrounding normal area, and compare whether the difference between the two exceeds the preset pressure abnormality determination threshold. If it exceeds, it is confirmed as the core area of abnormal pressure. Through this method, the pressure distribution in the production process can be monitored and adjusted in real time to ensure the stability of product quality and obtain the core pressure non-uniformity.

[0143] The crawler direction judgment sub-module, based on the core pressure non-uniformity, identifies the change rate of the crawler opening and closing distance, extracts the change amount of the pressure balance under different differential direction adjustments, and judges to obtain the optimal crawler adjustment direction;

[0144] First, based on the core pressure non-uniformity provided by the abnormal pressure area identification sub-module, evaluate the impact of different crawler opening and closing distances on pressure balance. In actual operation, for example, after adjusting the distance of a certain crawler, use simulation software to calculate the pressure distribution diagrams before and after the adjustment, so as to obtain the change rate of the crawler opening and closing distance, and by calculating the change amount of the pressure balance corresponding to different adjustment directions, determine the crawler adjustment direction that is most beneficial to pressure balance through comparing the change amounts. The precise execution of the process can significantly improve the surface quality and structural integrity of the product and judge the optimal crawler adjustment direction.

[0145] The injection rate adjustment amplitude identification sub-module calls the optimal track adjustment direction, identifies the pressure response change amount in the corresponding area, analyzes the influence of track adjustment on the local pressure, and based on the pressure gradient trend in the area, uses the formula:

[0146]

[0147] Calculate the injection rate adjustment amplitude, analyze the change trend of pressure balance after adjustment, and obtain the die pressure control data;

[0148] Among them, F represents the injection rate adjustment amplitude, R x represents the local area pressure value at the xth pressure sampling point, R avg represents the average surrounding pressure, G x represents the pressure gradient change rate at the xth pressure sampling point, and M represents the total number of pressure sampling points in the calculation area;

[0149] Adjust the injection rate to optimize the pressure distribution. The process depends on accurate calculations and experimental data. Call the optimal track adjustment direction, adjust the track opening and closing distance based on this direction, and real-time monitor the pressure change values in different areas inside the die. Use high-precision sensors to record pressure data. There are 5 pressure measurement points in each area. Obtain the current area pressure distribution through data acquisition, and calculate the local pressure response change amount R x and the average pressure R avg of the surrounding area. For example, the pressure data range of a certain area is from 250 kPa to 400 kPa, and the calculated average surrounding pressure R avg is 320 kPa. Analyze the influence of track adjustment on the local pressure, calculate the pressure gradient change rate G x of this area. Assuming the gradient range is between 0.5 kPa / mm and 1.8 kPa / mm, calculate the pressure compensation amount required for injection rate adjustment;

[0150] Substitute specific values for calculation:

[0151] Set the number of pressure measurement points n = 5;

[0152] Select the actual values (unit: kPa) of some pressure measurement points:

[0153] R1 = 250, R2 = 270, R3 = 300, R4 = 350, R5 = 400;

[0154] The corresponding gradient change rates (unit: kPa / mm):

[0155] G1 = 0.8, G2 = 1.2, G3 = 1.5, G4 = 0.9, G5 = 1.1;

[0156] Calculate the numerator part:

[0157] Calculate the denominator part:

[0158] Calculate the final result:

[0159] A negative value indicates that the injection rate needs to be reduced to reduce the locally excessive pressure, prevent product deformation and uneven material distribution. According to the calculation results, the magnitude of the injection rate adjustment should be reduced by approximately 5.37 kPa. Subsequently, a further analysis of the pressure equilibrium trend within the mold is carried out, and finally the mold pressure control data is obtained;

[0160] By comparing the local pressure value with the average pressure value of its surrounding area and combining the square root of the pressure gradient as a weighting factor, the calculated adjustment magnitude is made more in line with the actual situation of the mold pressure change. Compared with the traditional simple mean method, this method can more accurately evaluate the locally excessive or low pressure points, thereby more precisely adjusting the injection rate to optimize the pressure distribution inside the mold and improve the product quality.

[0161] Please refer to Figure 6 , the multi-dimensional pressure state monitoring module includes:

[0162] The pressure data extraction sub-module calls the mold pressure control data, extracts the track spacing adjustment data, the temperature data of the infrared thermometer, and the ultrasonic signal reflection characteristic data, and screens the pressure matching parameters to obtain the pressure matching parameter set;

[0163] Extract the pressure data at each key position of the mold. The process involves real-time monitoring and recording of the data of each production cycle from the control system, and quantitative analysis of the pressure distribution characteristics of each part inside the mold through the real-time data. Through this method, the pressure state of the mold at different production stages can be clarified, as well as the direct relationship between the state and the product quality. For example, when producing polyurethane foam boards, the pressure data can indicate whether the material is evenly distributed, thus preventing the problem of uneven density in the product. The reflection characteristic data of the infrared thermometer and the ultrasonic signal are also synchronously collected. The data reflects the temperature and curing state of the material, which in turn affects the pressure distribution of the mold. Matching and analyzing the data with the adjustment data of the track spacing can optimize the production process and ensure that the quality standards of each batch of products are consistent. By integrating the data, a pressure matching parameter set is obtained, and the result helps to further optimize the production parameters and improve the product quality.

[0164] The temperature and pressure correlation analysis sub-module calls the pressure matching parameter set, analyzes the relationship between the temperature data and the pressure gradient change, analyzes the influence of temperature on the pressure distribution, and judges the abnormal curing and abnormal bubble aggregation pressure conditions. Use the formula:

[0165]

[0166] Calculate the temperature-influenced pressure error value, screen the abnormal pressure distribution, and obtain the temperature-influenced pressure distribution result;

[0167] Among them, E represents the temperature-influenced pressure error value, U a represents the track spacing adjustment data in the a-th area, W a represents the temperature data of the infrared thermometer in the a-th area, V a represents the ultrasonic signal reflection characteristic data in the a-th area, Y a represents the pressure gradient change data in the a-th area, Z represents the total number of calculation areas, and a represents the area index;

[0168] Analyze the correlation between the pressure data and the temperature data to obtain the track spacing adjustment data U a , the temperature data W of the infrared thermometer a , the ultrasonic signal reflection characteristic data V a and the pressure gradient change data Y a , divide the data according to the production area, and calculate the pressure gradient change under different temperature ranges to determine the influence of temperature change on the pressure distribution. During the production process, the temperature distribution inside the mold is not uniform, but shows a certain gradient with the curing of the material, track adjustment, and environmental temperature changes. For example, during the polyurethane pultrusion process, the temperature gradually drops from 100 °C at the inlet to 40 °C at the outlet, resulting in different curing rates of the material at different positions. If the temperature is too high, it will cause the material to cure too fast locally, forming internal stress and affecting the strength of the final product. If the temperature is too low, it will lead to insufficient curing and affect the durability of the product. Therefore, it is necessary to establish a mathematical model to quantify the influence degree of temperature on pressure and judge whether there are problems of abnormal curing and bubble aggregation;

[0169] Set the calculation parameters as follows:

[0170] In a certain area a = 1, the track spacing adjustment data U1 = 0.5 mm, the temperature W1 detected by the infrared thermometer = 90 °C, the ultrasonic signal reflection characteristic data V1 = 2.3, and the pressure gradient change data Y1 = 1.2 MPa / m;

[0171] In another area a = 2, the track spacing adjustment data U2 = 0.7 mm, the temperature W2 = 85 °C, the ultrasonic signal V2 = 2.1, and the pressure gradient change Y2 = 1.5 MPa / m;

[0172] The total number of calculation areas Z = 2;

[0173] Substitute into the formula for calculation:

[0174]

[0175] The calculated temperature-influenced pressure error value E = 36.68. The value indicates that there is an obvious temperature-influenced pressure abnormal area under the current temperature and pressure distribution. This value can be further used to set a reasonable temperature adjustment plan to optimize the heating range of the mold and adjust the track spacing, avoiding defects caused by local overheating or insufficient cooling. By calculating the E values of different regions, a dynamic curve of the temperature-influenced pressure distribution can be established, and the abnormal areas can be screened out. Finally, the temperature-influenced pressure distribution result can be obtained to guide the subsequent optimization of the mold pressure control.

[0176] The dynamic pressure assessment sub-module calls the temperature-influenced pressure distribution result, combines it with the track spacing adjustment data, analyzes the dynamic pressure gradient, identifies the influence of temperature changes and track adjustment amplitude on the mold pressure stability, and obtains the evaluation result of the dynamic state of the mold pressure.

[0177] Combined with the track spacing adjustment data, the dynamic gradient of the mold pressure is analyzed. The analysis involves the pressure changes of the mold under different working conditions, especially in the material curing and forming stages. Different temperature and pressure gradients will directly affect the structure and performance of the product. For example, during the production process, if it is detected that the pressure gradient in a certain area does not match the data after the track spacing adjustment, it is necessary to re-evaluate the material distribution or the temperature control settings of the mold. By real-time monitoring and analyzing the data, the production parameters can be adjusted in time to avoid problems such as uneven structure or unstable performance of the finished product. Obtaining the temperature-influenced pressure distribution result provides important feedback information for the production process, ensuring the consistency of product quality and the optimization of production efficiency.

[0178] The above is only a preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic die pressure data analysis system for polyurethane pultrusion production, characterized in that The system includes: Based on the real-time pressure data of multiple pressure sensor nodes inside the polyurethane pultrusion die, the die pressure data capture module extracts the opening and closing gap of the die track, the linear velocity of the kneading wheel, and the resin injection flow rate, judges the signal validity of the pressure measurement value of a single node, and obtains the die pressure spatio-temporal data set; Based on the die pressure spatio-temporal data set, the pressure gradient trend calculation module identifies the distribution interval between adjacent nodes, analyzes the local pressure gradient, extracts the pressure change data in the resin flow area, screens the abnormal area, and obtains the die local pressure gradient trend; Based on the die local pressure gradient trend, the die abnormal pressure identification module analyzes the gradient change characteristics of the abnormal area, judges whether there are trends of continuous pressure increase and decrease in the abnormal area, determines the range and pressure change situation of the abnormal area, and obtains the die abnormal pressure distribution area; The die pressure adjustment data module calls the die abnormal pressure distribution area, analyzes the influence of the opening and closing distance of the track on the local pressure change according to the pressure gradient trend of the abnormal area, identifies the adjustment range of the injection rate, and obtains the die pressure control data.

2. The dynamic die pressure data analysis system for polyurethane pultrusion production according to claim 1, wherein The die pressure spatio-temporal data set includes pressure sensor node data, track opening and closing gap data, kneading wheel linear velocity data, and resin injection flow rate data. The die local pressure gradient trend includes pressure gradient value, pressure change rate, and pressure characteristics in the resin flow area. The die abnormal pressure distribution area includes the location of the abnormal area, the area of continuous pressure increase, and the area of continuous pressure decrease. The die pressure control data includes the adjustment direction of the track distance, the adjustment range of the resin injection rate, and the pressure balance change trend.

3. The dynamic die pressure data analysis system for polyurethane pultrusion production according to claim 1, wherein The die pressure data capture module includes: Based on the real-time pressure data of multiple pressure sensor nodes inside the polyurethane pultrusion die, the pressure signal verification sub-module identifies the difference in pressure measurement values between a single node and adjacent nodes, judges whether the difference exceeds the pressure abnormal threshold, screens the abnormal node data, and obtains the effective pressure signal set; The dynamic feature calculation sub-module calls the effective pressure signal set, combines the opening and closing gap of the track, the linear velocity of the kneading wheel, and the resin injection flow rate, identifies the pressure fluctuation, signal change rate, and pressure gradient between nodes within the time series of each node, and uses the formula: to obtain the pressure dynamic feature intensity distribution; Among them, D represents the intensity of the pressure dynamic characteristic, P i represents the pressure measurement value of the i-th node, P i-1 represents the pressure measurement value of the (i - 1)-th node, V i represents the rolling wheel linear velocity of the i-th node, S i represents the track opening and closing gap of the i-th node, Q i represents the resin injection flow rate of the i-th node, and n represents the total number of nodes; Based on the pressure dynamic feature intensity distribution, the spatio-temporal data generation sub-module calibrates the spatio-temporal position relationship of the node pressure in combination with the pressure signal time series of the corresponding time node, and obtains the die pressure spatio-temporal data set.

4. The dynamic die pressure data analysis system for polyurethane pultrusion production according to claim 3, wherein The pressure gradient trend calculation module includes: Based on the die pressure spatio-temporal data set, the local gradient identification sub-module calculates the pressure difference between adjacent nodes, combines the distribution interval to identify the local pressure gradient, conducts directional analysis, and screens out the invalid data to obtain the local pressure gradient distribution data; The gradient change rate analysis sub-module calls the local pressure gradient distribution data and uses the formula: to calculate the gradient change rate, analyzes the change trend of the pressure gradient with the regional distribution, and obtains the gradient change trend data set; Among them, G represents the gradient change rate, and ΔP k represents the pressure difference between the k-th adjacent nodes, and ΔP k-1 represents the pressure difference between the (k - 1)-th adjacent nodes, and Δd k represents the distribution interval between the k-th adjacent nodes, and Δd k-1 represents the distribution interval between the (k - 1)-th adjacent nodes, and m represents the number of adjacent nodes in the calculation area; The abnormal gradient region screening sub-module calls the gradient change trend data set, analyzes the gradient change rate within the region, sets the abnormal pressure gradient threshold, screens the regions exceeding the threshold, identifies the regions with abnormal gradient changes, and obtains the local pressure gradient trend of the mold.

5. The polyurethane pultrusion production dynamic die pressure data analysis system according to claim 4, wherein The mold abnormal pressure identification module includes: Based on the local pressure gradient trend of the mold, the pressure gradient identification sub-module identifies the pressure change rate of adjacent nodes. By comparing the pressure values of adjacent nodes, the formula is used: Calculate and obtain the local pressure gradient change rate; Among them, L represents the local pressure gradient change rate, and ΔB j represents the pressure change value between the j-th adjacent nodes, m represents the number of adjacent nodes in the calculation area, and T max represents the maximum pressure value in the area, and T min represents the minimum pressure value in the area; The abnormal pressure determination sub-module calls the local pressure gradient change rate, screens the regions with abnormal gradient change rates according to the preset pressure change threshold, determines whether there are characteristics of continuous pressure changes in the regions, and obtains the abnormal pressure regions; The abnormal pressure trend analysis sub-module calls the abnormal pressure regions, analyzes the pressure change trends within the regions, identifies the situations of continuous pressure increase and decrease, and combines the pressure distribution within the regions to obtain the abnormal pressure distribution regions of the mold.

6. The dynamic die pressure data analysis system for polyurethane pultrusion production according to claim 5, characterized in that, The mold pressure adjustment data module includes: The abnormal pressure region identification sub-module calls the abnormal pressure distribution regions of the mold, analyzes the pressure gradient trends of each point within the regions, screens the local pressure gradient mutation points, screens the core regions of abnormal pressure, and identifies the non-uniformity of the pressure distribution in the core regions to obtain the core pressure non-uniformity; Based on the core pressure non-uniformity, the crawler direction judgment sub-module identifies the change rate of the crawler opening and closing distance, extracts the change amount of the pressure balance degree under different differential direction adjustments, and determines the optimal crawler adjustment direction; The injection rate adjustment amplitude identification sub-module calls the optimal crawler adjustment direction, identifies the pressure response change amount in the corresponding region, analyzes the influence of crawler adjustment on the local pressure, and based on the pressure gradient trend within the region, uses the formula: Calculate the injection rate adjustment amplitude, and analyze the change trend of the pressure balance after adjustment to obtain the mold pressure control data; Among them, F represents the adjustment range of the injection rate, and R x represents the local area pressure value of the x-th pressure sampling point, and R avg represents the average surrounding pressure, and G x represents the pressure gradient change rate of the x-th pressure sampling point, and M represents the total number of pressure sampling points in the calculation area.

7. The dynamic die pressure data analysis system for polyurethane pultrusion production according to claim 1, characterized in that The system also includes a multi-dimensional pressure state monitoring module: The multi-dimensional pressure state monitoring module calls the mold pressure control data, extracts the temperature data of the infrared thermometer and the ultrasonic signal reflection characteristic data, analyzes the influence of temperature change on the pressure distribution, determines whether there are abnormal pressure situations caused by curing abnormalities and bubble aggregation, and combines the pressure gradient change and crawler spacing adjustment data to obtain the mold pressure dynamic state evaluation result; The mold pressure dynamic state evaluation result includes temperature influence analysis, curing abnormality identification, and bubble aggregation identification.

8. The polyurethane pultrusion production dynamic die pressure data analysis system according to claim 7, characterized in that, The multi-dimensional pressure state monitoring module includes: The pressure data extraction sub-module calls the mold pressure control data, extracts the crawler spacing adjustment data, the infrared thermometer temperature data, and the ultrasonic signal reflection characteristic data, and screens the pressure matching parameters to obtain the pressure matching parameter set; The temperature and pressure correlation analysis sub-module calls the pressure matching parameter set, analyzes the relationship between the temperature data and the pressure gradient change, analyzes the influence of temperature on the pressure distribution, determines the abnormal pressure situations of curing abnormalities and bubble aggregation, and uses the formula: Calculate the temperature influence pressure error value, screen the abnormal pressure distribution, and obtain the temperature influence pressure distribution result; Among them, E represents the temperature influence pressure error value, U a represents the track spacing adjustment data of the a-th area, W a represents the temperature data of the infrared thermometer in the a-th area, V a represents the ultrasonic signal reflection characteristic data of the a-th area, Y a represents the pressure gradient change data of the a-th area, Z represents the total number of calculation areas, and a represents the area index; The dynamic pressure evaluation sub-module calls the temperature influence on the pressure distribution result, combines the track spacing adjustment data, analyzes the dynamic pressure gradient, identifies the influence of temperature change and track adjustment amplitude on the die pressure stability, and obtains the die pressure dynamic state evaluation result.

Images