Data analysis and process simulation method and system for negative pressure forming of glass fiber reinforced plastics

Abstract

The invention discloses a data analysis and process simulation method and system for glass fiber reinforced plastic negative pressure forming, and relates to the technical field of data analysis, the method comprises the following steps: S100, collecting technological parameters in the glass fiber reinforced plastic negative pressure forming process, and preprocessing the collected technological parameters; s200, on the basis of the preprocessed data, characteristic parameters influencing the glass fiber reinforced plastic negative pressure forming process are extracted, the bending modulus and the resin impregnation speed are analyzed, and a process parameter optimization model is established; s300, analyzing the parameter stability of the process parameter optimization model, and constructing a process performance objective function to comprehensively evaluate the process performance according to the bending modulus, the resin impregnation speed and the stability index; and S400, an initial process parameter template is set, the process parameter template is dynamically adjusted according to product quality and equipment operation state feedback, and data analysis of glass fiber reinforced plastic negative pressure forming is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of data analysis, and in particular to a data analysis and process simulation method and system for glass fiber reinforced plastic negative pressure forming. Background Art

[0002] As modern manufacturing industry develops vigorously towards high precision and high efficiency, FRP negative pressure forming technology is increasingly used in many fields such as construction, shipbuilding, new energy, etc. In these fields, the quality and production efficiency of FRP negative pressure forming products play a decisive role in the competitiveness of enterprises, and the complex process control, equipment operation and maintenance, and process optimization involved are key factors.

[0003] However, in the field of FRP negative pressure forming, traditional process data analysis and process control methods have exposed many drawbacks: First, data collection and processing are inefficient. The FRP negative pressure forming process will generate a large amount of data covering mold temperature, resin properties, vacuum pressure and other aspects. The traditional method relies on manual recording and preliminary sorting. In large-scale and continuous production scenarios, workers need to spend a lot of energy to deal with the data flood, which is not only prone to errors, but also makes it difficult to extract key process information in time, resulting in the inability to grasp the process status in real time, delaying the adjustment opportunity, and seriously affecting the stability of product quality; second, there is a lack of intelligent analysis depth. In the past, the analysis of process parameter fluctuations was mostly based on experience judgment or simple single factor comparison. It is difficult to dig out hidden rules and potential problems from complex and interrelated multi-dimensional data. Especially in the face of complex product requirements and changing material properties, traditional methods cannot accurately understand the optimal combination of process parameters, which greatly limits the advancement of process optimization. Summary of the invention

[0004] The object of the present invention is to provide a data analysis and process simulation method and system for glass fiber reinforced plastic negative pressure forming, so as to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming, the method comprising the following steps:

[0006] S100, collecting process parameters during the negative pressure forming process of the glass fiber reinforced plastics, and preprocessing the collected process parameters;

[0007] S200, based on the pre-processed data, extract the characteristic parameters that affect the FRP negative pressure forming process, analyze the bending modulus and the resin impregnation speed, and establish a process parameter optimization model;

[0008] S300, analyzing the parameter stability of the process parameter optimization model, and constructing a process performance objective function to comprehensively evaluate the process performance based on the bending modulus, resin impregnation speed and stability index;

[0009] S400, set the initial process parameter template, and dynamically adjust the process parameter template according to product quality and equipment operation status feedback.

[0010] In step S100, the specific steps include:

[0011] S101, collecting process parameters during the negative pressure molding of FRP, including mold temperature, resin flow, vacuum, pressure and heating rate, wherein the mold temperature is monitored by temperature sensors installed on the mold surface and inside, the resin flow is collected by a flow sensor at the resin injection port, the vacuum and pressure are measured by pressure sensors in the mold cavity and vacuum pipeline, and the heating rate is monitored by a power sensor in the heating system;

[0012] S102, preprocessing the collected data, including data cleaning, denoising, format unification and outlier detection, using a smoothing filter algorithm to remove high-frequency noise from the mold temperature, resin flow, vacuum, pressure and heating rate data, correcting the heating rate temperature data to eliminate abnormal temperature fluctuations caused by external environment temperature fluctuations, converting the collected process parameters of the glass fiber reinforced plastic negative pressure molding process into the same time series format, using a Min-Max normalization algorithm to normalize all collected data, cleaning the collected data based on rules, and removing outliers and noise.

[0013] In step S200, the specific steps include:

[0014] S201, based on the pre-processed data, extract characteristic parameters that affect the glass fiber reinforced plastic negative pressure forming process, including temperature data, resin flow data and pressure data generated by vacuum degree, synchronize the extracted characteristic data, store the processed data in a database, and establish a characteristic data table;

[0015] S202. Analyze the bending modulus, pay attention to the influence of the temperature distribution of the mold and the fluidity of the resin on the bending performance, and the calculation formula is as follows: E b =d+e*T1+f*T2+g*Q r ; Among them, E b represents the bending modulus, d represents the reference value related to the inherent bending modulus characteristics of the material itself under ideal conditions without considering the influence of mold temperature and resin flow rate changes, T1 and T2 represent the temperatures at different positions of the mold, e and f represent the sensitivity of the temperature changes at different positions of the mold to the bending modulus, Q r represents the resin flow rate, g represents the correlation coefficient of the influence of resin flow rate on bending modulus, and the calculation formula for analyzing the resin impregnation effect is as follows: imp = k*ΔP / μ; where vimp represents the resin impregnation speed, k represents the permeability of the fiber preform, ΔP represents the pressure difference caused by the vacuum degree, and μ represents the viscosity of the resin, thereby establishing a process parameter optimization model, which is defined as follows: F = w1*E b +w2*v imp ; Where F represents the objective function of the process parameter optimization model, E b represents the bending modulus, v imp represents the resin impregnation speed, w1 and w2 represent the weight coefficients of the bending modulus and the resin impregnation speed in the objective function, respectively.

[0016] In step S300, the specific steps include:

[0017] S301. Measure the stability of process parameters and calculate the quality performance indicators of different process cycles. The quality performance indicators are defined as follows:

[0018]

[0019] Among them, Q t represents the mass deviation of the tth cycle, P i represents the performance index value of the i-th product, P target represents the target performance value, and N represents the number of products in a single cycle;

[0020] S302, according to the bending modulus, resin impregnation speed and stability index of process parameters, the process performance objective function is obtained, which is defined as follows: J = E b / E max +v i / v max +Q t ; Among them, J represents the index for comprehensive evaluation of process performance, E max Indicates the target maximum bending modulus within the process allowable range, v max It represents the maximum impregnation speed that can be achieved based on material properties and mold structure, Q t It represents the stability index of process parameters.

[0021] In step S400, the specific steps include:

[0022] S401, setting an initial process parameter template based on historical FRP negative pressure molding result data, material properties and product requirements, wherein the initial process parameter template includes mold temperature range, resin flow rate, vacuum degree, pressure range and heating rate;

[0023] S402. After the production process is completed, the initial process parameter template is adjusted by evaluating the product quality monitoring data and the equipment operation status. The product quality monitoring data includes surface quality and dimensional accuracy. Real-time detection and quality evaluation are used to determine whether the current process parameters meet the product requirements. The equipment operation status includes pressure stability, heating rate consistency and equipment failure rate. The process is evaluated by monitoring the equipment performance to determine whether it is in the best state. The initial process parameter template is dynamically adjusted based on the feedback data of product quality and equipment operation status. When the product quality monitoring data shows that the surface quality has bubbles and cracks, and the dimensional accuracy exceeds the allowable tolerance range, the mold temperature and resin flow rate parameters are adjusted in a targeted manner based on the process parameter adjustment experience corresponding to similar problems in the historical data. When the equipment operation status monitoring finds that the pressure stability is poor and the fluctuation exceeds the normal range, the vacuum system and pressure control system are checked and the vacuum setting value is adjusted. When the heating rate consistency is poor, resulting in large differences in the degree of curing of different parts of the product, the control parameters of the heating equipment are adjusted and the optimized template is updated to the database to improve the subsequent production process.

[0024] A data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming, the system comprising a data acquisition module, an analysis module, a process performance evaluation module and a process parameter management module, the data acquisition module being used to collect process parameters in the glass fiber reinforced plastic negative pressure forming process and preprocessing the collected process parameters, the analysis module being used to extract characteristic parameters affecting the glass fiber reinforced plastic negative pressure forming process based on the preprocessed data, analyzing the bending modulus and the resin impregnation speed, and establishing a process parameter optimization model, the process performance evaluation module being used to analyze the parameter stability of the process parameter optimization model, constructing a process performance objective function to comprehensively evaluate the process performance based on the bending modulus, the resin impregnation speed and the stability index, and the process parameter management module being used to set an initial process parameter template, and dynamically adjusting the process parameter template based on product quality and equipment operation status feedback.

[0025] The data acquisition module includes a process parameter acquisition unit and a data preprocessing unit. The process parameter acquisition unit is responsible for real-time acquisition of process parameters, including mold temperature, resin flow, vacuum degree, pressure and heating rate, through sensors during the negative pressure forming process of FRP. The data preprocessing unit is used to clean the process parameter data acquired by the process parameter acquisition unit, remove abnormal values ​​caused by external environment temperature fluctuations, uniformly convert the collected process parameters during the negative pressure forming process of FRP into the same time series format, use the Min-Max normalization algorithm to normalize all collected data, clean the collected data based on rules, and eliminate abnormal values ​​and noise. The output end of the process parameter acquisition unit is connected to the input end of the data preprocessing unit, and the output end of the data preprocessing unit is connected to the input end of the analysis module.

[0026] The analysis module includes a feature extraction unit, a bending modulus analysis unit, a resin infusion speed calculation unit and a process parameter optimization model construction unit. The feature extraction unit is used to extract feature parameters affecting the glass fiber reinforced plastic negative pressure forming process based on the preprocessed data, including temperature data, resin flow data and pressure data generated by vacuum degree; the bending modulus analysis unit is used to analyze the influence of the temperature distribution of the mold and the fluidity of the resin on the bending performance; the resin infusion speed calculation unit is used to analyze the influence of the pressure generated by the vacuum degree on the resin infusion speed; the process parameter optimization model construction unit is used to construct a process parameter optimization model according to the bending modulus obtained by the bending modulus analysis unit and the resin infusion speed calculation unit, the output end of the feature extraction unit is connected to the input end of the bending modulus analysis unit and the resin infusion speed calculation unit, and the output end of the bending modulus analysis unit and the resin infusion speed calculation unit is connected to the input end of the process parameter optimization model construction unit.

[0027] The process performance evaluation module includes a quality performance index calculation unit and a process performance target function construction unit. The quality performance index calculation unit is used to measure the stability of process parameters and calculate the quality performance indexes of different process cycles; the process performance target function construction unit is used to obtain the process performance target function according to the bending modulus, resin infusion speed and stability index of process parameters. The output ends of the quality performance index calculation unit, the bending modulus analysis unit and the resin infusion speed calculation unit are connected to the input end of the process performance target function construction unit, and the output end of the process performance target function construction unit is connected to the output end of the process parameter management module.

[0028] The process parameter management module includes an initial parameter template setting unit and an adjustment unit. The initial parameter template setting unit is used to set the initial process parameter template based on historical FRP negative pressure forming result data, material properties and product requirements. The adjustment unit is used to dynamically adjust the process parameter template based on product quality and equipment operation status feedback. The output end of the initial parameter template setting unit is connected to the input end of the adjustment unit.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. By establishing a bending modulus analysis model and a resin impregnation effect evaluation model, the influence of mold temperature distribution and resin fluidity on product performance is analyzed, and an optimization model is constructed based on the relationship between fiber impregnation speed and process parameters, which is helpful to find the optimal process parameter combination;

[0031] 2. By constructing the process parameter optimization objective function and quality stability evaluation index, the comprehensive performance optimization of the molding process is realized, the parameters affecting the molding quality are clarified, and quantitative analysis methods are provided to help users predict process deviations at an early stage and make timely adjustments;

[0032] 3. By dynamically optimizing the initial process parameter template and combining the actual production data to adjust the mold temperature range, resin flow rate, vacuum degree and pressure parameters, the risk of possible product defects can be reduced and stable production operation can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of the data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming of the present invention;

[0034] Figure 2 It is a structural schematic diagram of the data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] like Figure 1-Figure 2 As shown, the present invention provides a technical solution for a data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming, the method comprising the following steps:

[0037] S100, collecting process parameters during the negative pressure forming process of the glass fiber reinforced plastics, and preprocessing the collected process parameters;

[0038] S200, based on the pre-processed data, extract the characteristic parameters that affect the FRP negative pressure forming process, analyze the bending modulus and the resin impregnation speed, and establish a process parameter optimization model;

[0039] S300, analyzing the parameter stability of the process parameter optimization model, and constructing a process performance objective function to comprehensively evaluate the process performance based on the bending modulus, resin impregnation speed and stability index;

[0040] S400, set the initial process parameter template, and dynamically adjust the process parameter template according to product quality and equipment operation status feedback.

[0041] In step S100, the specific steps include:

[0042] S101, collecting process parameters during the negative pressure molding of FRP, including mold temperature, resin flow, vacuum, pressure and heating rate, wherein the mold temperature is monitored by temperature sensors installed on the mold surface and inside, the resin flow is collected by a flow sensor at the resin injection port, the vacuum and pressure are measured by pressure sensors in the mold cavity and vacuum pipeline, and the heating rate is monitored by a power sensor in the heating system;

[0043] S102, preprocessing the collected data, including data cleaning, denoising, format unification and outlier detection, using a smoothing filter algorithm to remove high-frequency noise from the mold temperature, resin flow, vacuum, pressure and heating rate data, correcting the heating rate temperature data to eliminate abnormal temperature fluctuations caused by external environment temperature fluctuations, converting the collected process parameters of the glass fiber reinforced plastic negative pressure molding process into the same time series format, using a Min-Max normalization algorithm to normalize all collected data, cleaning the collected data based on rules, and removing outliers and noise.

[0044] In step S200, the specific steps include:

[0045] S201, based on the pre-processed data, extract characteristic parameters that affect the glass fiber reinforced plastic negative pressure forming process, including temperature data, resin flow data and pressure data generated by vacuum degree, synchronize the extracted characteristic data, store the processed data in a database, and establish a characteristic data table;

[0046] S202. Analyze the bending modulus, pay attention to the influence of the temperature distribution of the mold and the fluidity of the resin on the bending performance, and the calculation formula is as follows: E b =d+e*T1+f*T2+g*Q r ; Among them, E b represents the bending modulus, d represents the reference value related to the inherent bending modulus characteristics of the material itself under ideal conditions without considering the influence of mold temperature and resin flow rate changes, T1 and T2 represent the temperatures at different positions of the mold, e and f represent the sensitivity of the temperature changes at different positions of the mold to the bending modulus, Q r represents the resin flow rate, g represents the correlation coefficient of the influence of resin flow rate on bending modulus, and the calculation formula for analyzing the resin impregnation effect is as follows: imp = k*ΔP / μ; where v imp represents the resin impregnation speed, k represents the permeability of the fiber preform, ΔP represents the pressure difference caused by the vacuum degree, and μ represents the viscosity of the resin, thereby establishing a process parameter optimization model, which is defined as follows: F = w1*E b +w2*v imp ; Where F represents the objective function of the process parameter optimization model, Eb represents the bending modulus, v imp represents the resin impregnation speed, w1 and w2 represent the weight coefficients of the bending modulus and the resin impregnation speed in the objective function, respectively.

[0047] In step S300, the specific steps include:

[0048] S301. Measure the stability of process parameters and calculate the quality performance indicators of different process cycles. The quality performance indicators are defined as follows:

[0049]

[0050] Among them, Q t represents the mass deviation of the tth cycle, P i represents the performance index value of the i-th product, P target represents the target performance value, and N represents the number of products in a single cycle;

[0051] S302, according to the bending modulus, resin impregnation speed and stability index of process parameters, the process performance objective function is obtained, which is defined as follows: J = E b / E max +v i / v max +Q t ; Among them, J represents the index for comprehensive evaluation of process performance, E max Indicates the target maximum bending modulus within the process allowable range, v max It represents the maximum impregnation speed that can be achieved based on material properties and mold structure, Q t It represents the stability index of process parameters.

[0052] In step S400, the specific steps include:

[0053] S401, setting an initial process parameter template based on historical FRP negative pressure molding result data, material properties and product requirements, wherein the initial process parameter template includes mold temperature range, resin flow rate, vacuum degree, pressure range and heating rate;

[0054] S402. After the production process is completed, the initial process parameter template is adjusted by evaluating the product quality monitoring data and the equipment operation status. The product quality monitoring data includes surface quality and dimensional accuracy. Real-time detection and quality evaluation are used to determine whether the current process parameters meet the product requirements. The equipment operation status includes pressure stability, heating rate consistency and equipment failure rate. The process is evaluated by monitoring the equipment performance to determine whether it is in the best state. The initial process parameter template is dynamically adjusted based on the feedback data of product quality and equipment operation status. When the product quality monitoring data shows that the surface quality has bubbles and cracks, and the dimensional accuracy exceeds the allowable tolerance range, the mold temperature and resin flow rate parameters are adjusted in a targeted manner based on the process parameter adjustment experience corresponding to similar problems in the historical data. When the equipment operation status monitoring finds that the pressure stability is poor and the fluctuation exceeds the normal range, the vacuum system and pressure control system are checked and the vacuum setting value is adjusted. When the heating rate consistency is poor, resulting in large differences in the degree of curing of different parts of the product, the control parameters of the heating equipment are adjusted and the optimized template is updated to the database to improve the subsequent production process.

[0055] A data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming, the system comprising a data acquisition module, an analysis module, a process performance evaluation module and a process parameter management module, the data acquisition module being used to collect process parameters in the glass fiber reinforced plastic negative pressure forming process and preprocessing the collected process parameters, the analysis module being used to extract characteristic parameters affecting the glass fiber reinforced plastic negative pressure forming process based on the preprocessed data, analyzing the bending modulus and the resin impregnation speed, and establishing a process parameter optimization model, the process performance evaluation module being used to analyze the parameter stability of the process parameter optimization model, constructing a process performance objective function to comprehensively evaluate the process performance based on the bending modulus, the resin impregnation speed and the stability index, and the process parameter management module being used to set an initial process parameter template, and dynamically adjusting the process parameter template based on product quality and equipment operation status feedback.

[0056] The data acquisition module includes a process parameter acquisition unit and a data preprocessing unit. The process parameter acquisition unit is responsible for real-time acquisition of process parameters, including mold temperature, resin flow, vacuum degree, pressure and heating rate, through sensors during the negative pressure forming process of FRP. The data preprocessing unit is used to clean the process parameter data acquired by the process parameter acquisition unit, remove abnormal values ​​caused by external environment temperature fluctuations, and uniformly convert the collected process parameters during the negative pressure forming process of FRP into the same time series format. The Min-Max normalization algorithm is used to normalize all collected data, and the collected data is cleaned based on rules to eliminate abnormal values ​​and noise. The output end of the process parameter acquisition unit is connected to the input end of the data preprocessing unit, and the output end of the data preprocessing unit is connected to the input end of the analysis module.

[0057] The analysis module includes a feature extraction unit, a bending modulus analysis unit, a resin infusion speed calculation unit and a process parameter optimization model construction unit. The feature extraction unit is used to extract feature parameters affecting the glass fiber reinforced plastic negative pressure forming process based on the preprocessed data, including temperature data, resin flow data and pressure data generated by vacuum degree; the bending modulus analysis unit is used to analyze the influence of the temperature distribution of the mold and the fluidity of the resin on the bending performance; the resin infusion speed calculation unit is used to analyze the influence of the pressure generated by the vacuum degree on the resin infusion speed; the process parameter optimization model construction unit is used to construct a process parameter optimization model according to the bending modulus obtained by the bending modulus analysis unit and the resin infusion speed calculation unit, the output end of the feature extraction unit is connected to the input end of the bending modulus analysis unit and the resin infusion speed calculation unit, and the output end of the bending modulus analysis unit and the resin infusion speed calculation unit is connected to the input end of the process parameter optimization model construction unit.

[0058] The process performance evaluation module includes a quality performance index calculation unit and a process performance target function construction unit. The quality performance index calculation unit is used to measure the stability of process parameters and calculate the quality performance indexes of different process cycles; the process performance target function construction unit is used to obtain the process performance target function according to the bending modulus, resin infusion speed and stability index of process parameters. The output ends of the quality performance index calculation unit, the bending modulus analysis unit and the resin infusion speed calculation unit are connected to the input end of the process performance target function construction unit, and the output end of the process performance target function construction unit is connected to the output end of the process parameter management module.

[0059] The process parameter management module includes an initial parameter template setting unit and an adjustment unit. The initial parameter template setting unit is used to set the initial process parameter template based on historical FRP negative pressure forming result data, material properties and product requirements. The adjustment unit is used to dynamically adjust the process parameter template based on product quality and equipment operating status feedback. The output end of the initial parameter template setting unit is connected to the input end of the adjustment unit.

[0060] In the embodiment: the process parameters of the glass fiber reinforced plastic negative pressure molding process, including mold temperature, resin flow, vacuum, pressure and heating rate, are collected by sensors, and temperature sensors with an accuracy of ±0.5°C are installed at key positions on the mold surface and inside. It is monitored that during a glass fiber reinforced plastic negative pressure molding process, the temperatures at different positions of the mold are as follows: the temperature T1 near the resin injection port is 55°C at the beginning of molding, stabilizes at 60°C in the middle of molding, and drops to 50°C in the late molding; the temperature T2 at the center of the mold cavity fluctuates relatively little during the whole process and is always maintained at 58°C; the flow sensor accuracy of the resin injection port is ±0.1mL / s, and the resin flow Qr is collected at different times. The stages are: 10.0mL / s at the beginning, adjusted to 8.5mL / s in the middle stage as the molding process progresses, and stabilized at 7.0mL / s in the later stage; the pressure sensor accuracy in the mold cavity and the vacuum pipeline is ±0.05kPa, and the vacuum degree is measured to be maintained at -95kPa during the molding process, and the pressure is 0.5MPa and 1.2MPa in the mold opening and closing stages respectively; the power sensor in the heating system monitors that the heating rate is 2.0℃ / min in the initial stage, stabilized at 1.5℃ / min in the middle stage, and reduced to 1.0℃ / min in the later stage. The collected data is preprocessed, and the temperature data T1 and T2 and the resin flow data Q are extracted from the preprocessed data r The pressure data generated by the vacuum degree -95kPa is used as the characteristic parameter. Through a large number of experiments and historical data regression analysis, the inherent bending modulus reference value of the material itself in the bending modulus calculation formula is determined as d=100GPa, the sensitivity of the temperature change at the T1 position to the bending modulus is e=0.5GPa / ℃, and the temperature at this position is 60℃ in the medium term, the sensitivity of the temperature change at the T2 position is f=0.3GPa / ℃, and the temperature at this position is 58℃ in the medium term, the correlation coefficient of the influence of the resin flow on the bending modulus is g=0.2GPa*s / ml, and the resin flow is 8.5mL / s. According to the bending modulus calculation formula, the bending modulus E is obtained b =149.1 GPa; for the analysis of resin impregnation effect, it is known that the permeability of the fiber preform k = 0.05 mL / (kPa*s), the viscosity of the resin μ = 1 Pa*s, and the pressure difference ΔP generated by the vacuum degree = 95 kPa. According to the formula v imp = k*ΔP / μ to calculate the resin impregnation speed v imp =4.75mL / s; set the weight coefficients of bending modulus and resin impregnation speed in the objective function w1=0.6, w2=0.4, and establish the process parameter optimization model F=0.6E b +0.4v imp ; Five products were produced in one production cycle, and the bending strength and dimensional accuracy of each product were tested. The target bending strength P target =300MPa, bending strength P of 5 productsi The mass deviation Q of this cycle is calculated according to the quality performance index formula. t =8.4; target maximum bending modulus E within the known process allowable range max =350GPa, based on the material properties and mold structure, the target maximum impregnation speed v max =6mL / s, combined with the calculated bending modulus E b is 149.1GPa, and the resin impregnation speed v imp =4.75mL / s and quality performance index Q t =8, construct the process performance objective function J = 250 / 350 + 4.75 / 6 + 8 ≈ 9.53; according to the historical FRP negative pressure molding result data, material properties and product requirements, set the initial process parameter template as follows: mold temperature range is 50 to 65 ° C, resin flow rate is 7 to 10mL / s, vacuum degree is -90 to -100kPa, pressure range is 0.4 to 1.5MPa, heating rate is 1 to 2.5 ° C / min; product quality monitoring data show that some products have a small amount of bubbles on the surface quality, that is, no more than 3 bubbles per square centimeter, and the dimensional accuracy exceeds the allowable tolerance range, that is, 10% of the product dimensional deviation is greater than 0.5mm. Combined with the process parameter adjustment experience corresponding to similar problems in historical data, the mold temperature T1 is adjusted to 58 ° C, The resin flow rate Qr was adjusted to 8mL / s. The equipment operation status monitoring found that the pressure stability was poor and the fluctuation was beyond the normal range, that is, the pressure fluctuation amplitude reached ±0.15MPa. After checking the vacuum system and pressure control system, the vacuum setting value was adjusted to -98kPa. At the same time, the proportional valve of the pressure control system was calibrated to control the pressure fluctuation within ±0.05MPa. The heating rate consistency was poor, resulting in large differences in the degree of curing in different parts of the product. That is, the hardness test found that the hardness deviation of different parts reached ±5HRA. The control parameters of the heating equipment were adjusted to reduce the fluctuation range of the heating rate at different stages to ±0.2℃ / min, and the temperature zoning control function was added to ensure that the temperature of different parts of the mold is more uniform. The optimized template was updated to the database.

[0061] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and range of equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming, characterized by: The method comprises the following steps: S100, collecting process parameters during the negative pressure forming process of the glass fiber reinforced plastics, and preprocessing the collected process parameters; S200, based on the pre-processed data, extract the characteristic parameters that affect the FRP negative pressure forming process, analyze the bending modulus and the resin impregnation speed, and establish a process parameter optimization model; S300, analyzing the parameter stability of the process parameter optimization model, and constructing a process performance objective function to comprehensively evaluate the process performance based on the bending modulus, resin impregnation speed and stability index; S400, set the initial process parameter template, and dynamically adjust the process parameter template according to product quality and equipment operation status feedback.

2. The data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming according to claim 1, characterized in that: In step S100, the specific steps include: S101, collecting process parameters during the negative pressure molding of FRP, including mold temperature, resin flow, vacuum, pressure and heating rate, wherein the mold temperature is monitored by temperature sensors installed on the mold surface and inside, the resin flow is collected by a flow sensor at the resin injection port, the vacuum and pressure are measured by pressure sensors in the mold cavity and vacuum pipeline, and the heating rate is monitored by a power sensor in the heating system; S102, preprocessing the collected data, including data cleaning, denoising, format unification and outlier detection, using a smoothing filter algorithm to remove high-frequency noise from the mold temperature, resin flow, vacuum, pressure and heating rate data, correcting the heating rate temperature data to eliminate abnormal temperature fluctuations caused by external environment temperature fluctuations, converting the collected process parameters of the glass fiber reinforced plastic negative pressure molding process into the same time series format, using a Min-Max normalization algorithm to normalize all collected data, cleaning the collected data based on rules, and removing outliers and noise.

3. The data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming according to claim 2, characterized in that: In step S200, the specific steps include: S201, based on the pre-processed data, extract characteristic parameters that affect the glass fiber reinforced plastic negative pressure forming process, including temperature data, resin flow data and pressure data generated by vacuum degree, synchronize the extracted characteristic data, store the processed data in a database, and establish a characteristic data table; S202. Analyze the bending modulus, pay attention to the influence of the temperature distribution of the mold and the fluidity of the resin on the bending performance, and the calculation formula is as follows: E b =d+e*T1+f*T2+g*Q r ; Among them, E b represents the bending modulus, d represents the reference value related to the inherent bending modulus characteristics of the material itself under ideal conditions without considering the influence of mold temperature and resin flow rate changes, T1 and T2 represent the temperatures at different positions of the mold, e and f represent the sensitivity of the temperature changes at different positions of the mold to the bending modulus, Q r represents the resin flow rate, g represents the correlation coefficient of the influence of resin flow rate on bending modulus, and the calculation formula for analyzing the resin impregnation effect is as follows: imp = k*ΔP / μ; where v imp represents the resin impregnation speed, k represents the permeability of the fiber preform, ΔP represents the pressure difference caused by the vacuum degree, and μ represents the viscosity of the resin, thereby establishing a process parameter optimization model, which is defined as follows: F = w1*E b +w2*v imp ; Where F represents the objective function of the process parameter optimization model, E b represents the bending modulus, v imp represents the resin impregnation speed, w1 and w2 represent the weight coefficients of the bending modulus and the resin impregnation speed in the objective function, respectively.

4. The data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming according to claim 3, characterized in that: In step S300, the specific steps include: S301. Measure the stability of process parameters and calculate the quality performance indicators of different process cycles. The quality performance indicators are defined as follows: Among them, Q t represents the mass deviation of the tth cycle, P i represents the performance index value of the i-th product, P target represents the target performance value, and N represents the number of products in a single cycle; S302, according to the bending modulus, resin impregnation speed and stability index of process parameters, the process performance objective function is obtained, which is defined as follows: J = E b / E max +v i / v max +Q t ; Among them, J represents the index for comprehensive evaluation of process performance, E max Indicates the target maximum bending modulus within the process allowable range, v max It represents the maximum impregnation speed that can be achieved based on material properties and mold structure, Q t It represents the stability index of process parameters.

5. The data analysis and process simulation method for glass fiber reinforced plastic negative pressure forming according to claim 4, characterized in that: In step S400, the specific steps include: S401, setting an initial process parameter template based on historical FRP negative pressure molding result data, material properties and product requirements, wherein the initial process parameter template includes mold temperature range, resin flow rate, vacuum degree, pressure range and heating rate; S402. After the production process is completed, the initial process parameter template is adjusted by evaluating the product quality monitoring data and the equipment operation status. The product quality monitoring data includes surface quality and dimensional accuracy. Real-time detection and quality evaluation are used to determine whether the current process parameters meet the product requirements. The equipment operation status includes pressure stability, heating rate consistency and equipment failure rate. The equipment performance is monitored to evaluate whether the process is in the best state, and the initial process parameter template is dynamically adjusted based on the feedback data of product quality and equipment operation status.

6. Data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming, characterized by: The system includes a data acquisition module, an analysis module, a process performance evaluation module and a process parameter management module. The data acquisition module is used to collect process parameters in the glass fiber reinforced plastic negative pressure forming process and preprocess the collected process parameters. The analysis module is used to extract characteristic parameters that affect the glass fiber reinforced plastic negative pressure forming process based on the preprocessed data, analyze the bending modulus and the resin impregnation speed, and establish a process parameter optimization model. The process performance evaluation module is used to analyze the parameter stability of the process parameter optimization model, and construct a process performance objective function to comprehensively evaluate the process performance based on the bending modulus, the resin impregnation speed and the stability index. The process parameter management module is used to set an initial process parameter template, and dynamically adjust the process parameter template according to product quality and equipment operation status feedback.

7. The data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming according to claim 6, characterized in that: The data acquisition module includes a process parameter acquisition unit and a data preprocessing unit. The process parameter acquisition unit is responsible for real-time acquisition of process parameters, including mold temperature, resin flow, vacuum degree, pressure and heating rate, through sensors during the negative pressure forming process of FRP. The data preprocessing unit is used to clean the process parameter data acquired by the process parameter acquisition unit, remove abnormal values ​​caused by external environment temperature fluctuations, uniformly convert the collected process parameters during the negative pressure forming process of FRP into the same time series format, use the Min-Max normalization algorithm to normalize all collected data, clean the collected data based on rules, and eliminate abnormal values ​​and noise. The output end of the process parameter acquisition unit is connected to the input end of the data preprocessing unit, and the output end of the data preprocessing unit is connected to the input end of the analysis module.

8. The data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming according to claim 7, characterized in that: The analysis module includes a feature extraction unit, a bending modulus analysis unit, a resin impregnation speed calculation unit and a process parameter optimization model construction unit. The feature extraction unit is used to extract feature parameters affecting the glass fiber reinforced plastic negative pressure forming process based on the pre-processed data, including temperature data, resin flow data and pressure data generated by vacuum degree; the bending modulus analysis unit is used to analyze the temperature distribution of the mold and the influence of the fluidity of the resin on the bending performance; The resin infusion speed calculation unit is used to analyze the influence of the pressure generated by the vacuum degree on the resin infusion speed; the process parameter optimization model construction unit is used to construct a process parameter optimization model according to the bending modulus obtained by the bending modulus analysis unit and the resin infusion speed calculation unit, the output end of the feature extraction unit is connected to the input end of the bending modulus analysis unit and the resin infusion speed calculation unit, and the output end of the bending modulus analysis unit and the resin infusion speed calculation unit is connected to the input end of the process parameter optimization model construction unit.

9. The data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming according to claim 8, characterized in that: The process performance evaluation module includes a quality performance index calculation unit and a process performance objective function construction unit. The quality performance index calculation unit is used to measure the stability of process parameters and calculate the quality performance indexes of different process cycles; The process performance objective function construction unit is used to obtain the process performance objective function according to the bending modulus, the resin infusion speed and the stability index of the process parameters. The output ends of the quality performance index calculation unit, the bending modulus analysis unit and the resin infusion speed calculation unit are connected to the input end of the process performance objective function construction unit, and the output end of the process performance objective function construction unit is connected to the output end of the process parameter management module.

10. The data analysis and process simulation system for glass fiber reinforced plastic negative pressure forming according to claim 9, characterized in that: The process parameter management module includes an initial parameter template setting unit and an adjustment unit. The initial parameter template setting unit is used to set the initial process parameter template based on historical FRP negative pressure forming result data, material properties and product requirements. The adjustment unit is used to dynamically adjust the process parameter template based on product quality and equipment operation status feedback. The output end of the initial parameter template setting unit is connected to the input end of the adjustment unit.

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