Intelligent control system for composite material forming process

Abstract

The present application relates to the technical field of intelligent control, in particular to a composite material forming process intelligent control system, which comprises a micro-scale deformation detection module, a stress offset correction module, a temperature gradient regulation module, an interlayer curing equalization module and a forming intelligent control execution module.In the present application, the deformation rate mutation area is accurately positioned through micro-scale detection, and stress concentration is found in advance, so that stress adjustment is more targeted, local stress overload influence is reduced, real-time pressure feedback is combined with target pressure offset analysis to optimize the loading mode, the stress uniformity in the curing process is ensured, the defect risk is reduced, high-precision temperature gradient monitoring and thermal diffusion rate analysis improve temperature control precision, interlayer temperature equalization is improved, curing rate change rate calculation and thermal conduction path optimization dynamically adjust the heat dissipation channel, promote uniform curing, stress-temperature distribution balance degree analysis is combined with deformation trend to optimize heating and pressure parameters, and forming stability and quality consistency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and particularly relates to a composite material forming process intelligent control system. BACKGROUND

[0002] The technical field of intelligent control includes automatic adjustment and optimization control methods for complex systems. This technical field involves the use of sensor data collection, control strategy execution, and feedback mechanism adjustment to achieve precise regulation of industrial production, automation equipment, intelligent manufacturing, and other scenarios. The core content includes model predictive control, fuzzy control, adaptive control, and other methods to improve the system's response to environmental changes. It is widely used in intelligent manufacturing, automated production lines, robot control, process industries, and new material processing fields to improve production efficiency, reduce human intervention, and optimize resource allocation.

[0003] Among them, the composite material forming process intelligent control system refers to a control system that automatically monitors and adjusts key process parameters such as temperature, pressure, and fluidity during composite material manufacturing. It uses high-precision sensors to collect real-time process parameter data and calculates target adjustment values based on historical data and real-time measurement results to drive actuators to finely adjust mold temperature, pressure, and resin flow rate. In addition, the system combines adaptive learning mechanisms to dynamically adjust the curing behavior of different materials to ensure the stability and consistency of the forming process, thus providing precise process control means in composite material processing.

[0004] In existing composite material forming processes, stress monitoring mainly relies on overall stress distribution models, lacking precise identification of local micro-scale stress mutations, leading to difficulty in timely adjusting stress concentration areas and easily causing local deformation or cracking. In the pressure control process, the adjustment method is usually based on global average values, which is difficult to adapt to local stress changes, leading to uneven pressure distribution and affecting the final forming quality of the material. Temperature control methods usually rely on global temperature monitoring and fail to accurately identify local temperature gradient mutations, resulting in insufficient temperature adjustment precision, affecting interlayer temperature uniformity, and increasing the risk of uneven curing. The curing rate adjustment lacks optimization of heat conduction paths, making the heat dissipation method fixed and difficult to dynamically adjust according to the curing rate of different parts, affecting the consistency of material curing. In the overall control process, stress adjustment and temperature regulation are not combined, and there is a lack of comprehensive consideration of the stability of the forming process, making process optimization lack precise feedback mechanisms and difficult to adapt to complex manufacturing environments, resulting in a large impact of external interference factors on the performance of the workpiece. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and propose a composite material forming process intelligent control system.

[0006] To achieve the above object, the present application adopts the following technical solutions: a composite material forming process intelligent control system comprises:

[0007] The micro-scale deformation detection module extracts deformation information in the material curing process, calculates the deformation rate of each region, calculates the stress increment of the mutation region, determines the local stress concentration region, and calculates the local stress offset data;

[0008] The stress offset correction module analyzes the local stress distribution trend of the material based on the local stress offset data, judges whether the contact pressure conforms to the target pressure distribution curve, adjusts the pressure loading mode, and obtains local stress correction data;

[0009] The temperature gradient control module collects forming process temperature data, calculates the temperature change rate between layers, compares the change threshold to screen the temperature gradient change significant area, calculates the thermal diffusion rate, and obtains the interlayer temperature gradient data;

[0010] The interlayer curing equalization module calculates the material curing rate change rate based on the interlayer temperature gradient data, analyzes the heat conduction path distribution, adjusts the opening and closing state of the heat dissipation channel, and obtains the curing rate equalization data;

[0011] The forming intelligent control execution module calculates the stress-temperature distribution equalization degree based on the local stress correction data and the curing rate equalization data, analyzes the material deformation trend change, judges the forming stability, dynamically adjusts the forming parameters, and outputs the composite material forming intelligent control scheme.

[0012] As a further scheme of the present application, the micro-scale deformation detection module comprises:

[0013] The optical interference data acquisition sub-module acquires optical interference measurement data, collects the intensity distribution of the interference light field, extracts phase information, analyzes the micro-scale displacement at different positions, calculates the displacement change amount per unit time, and obtains the micro-scale displacement change amount of each region;

[0014] The deformation rate calculation sub-module calculates the displacement gradient based on the micro-scale displacement change amount of each region, solves the relative deformation between adjacent position points in each region, synchronously calculates the deformation rate, and screens the deformation rate mutation region to obtain deformation rate mutation region data;

[0015] The local stress concentration calculation sub-module calculates the stress increment corresponding to the deformation rate change according to the deformation rate mutation region data, calculates the local stress concentration region using the stress distribution in the region, and uses the formula:

[0016]

[0017] Calculate the stress offset value G of each local area to obtain local stress offset data, wherein, Δσ i represents the stress increment of the i-th area, L i represents the deformation path length of the i-th area, S i represents the stress distribution value of the i-th area, A i represents the deformation area of the i-th area, and n is the total number of deformation rate mutation areas.

[0018] As a further scheme of the present application, the stress offset correction module comprises:

[0019] The local stress distribution analysis submodule calculates the stress gradient of each area based on the local stress offset data, analyzes the stress distribution trend, judges the stress concentration area, and extracts the local stress change amplitude to obtain local stress distribution trend data;

[0020] The contact pressure judgment submodule calculates the actual contact pressure according to the local stress distribution trend data in combination with the pressure sensor feedback data, compares it with the target pressure distribution curve, calculates the pressure deviation of each area, and obtains the contact pressure deviation data;

[0021] The pressure loading adjustment submodule adjusts the pressure loading mode in combination with the contact pressure deviation data, and uses the formula:

[0022]

[0023] Calculate the corrected local stress value g to obtain local stress correction data, wherein, g o,i represents the original stress value of the i-th area, P i represents the pressure correction amount of the i-th area, a i represents the contact area of the i-th area, F j represents the force of the j-th area, l j represents the loading path length of the j-th area, M is the total number of correction areas, and m is the total number of loading areas.

[0024] As a further scheme of the present application, the temperature gradient control module comprises:

[0025] The temperature data acquisition submodule acquires the temperature data collected by the micro temperature sensor array, extracts the temperature values of each layer, calculates the temperature change of adjacent layers, analyzes the temperature fluctuation with time, and obtains the interlayer temperature change rate data;

[0026] The temperature gradient screening submodule compares the preset temperature change threshold based on the interlayer temperature change rate data, screens the areas with a change amplitude exceeding the threshold, extracts the temperature change characteristics thereof, and obtains temperature gradient change significant area data;

[0027] The heat diffusion rate calculation sub-module calculates the heat diffusion rate H of each region according to the temperature gradient change significant region data, combines the region temperature difference and the time interval, and uses the formula:

[0028]

[0029] The heat diffusion rate H of each region is calculated to obtain the interlayer temperature gradient data, wherein T i represents the temperature of the i-th layer, t i represents the time corresponding to the i-th layer, D i represents the heat conduction path length of the i-th layer, C i represents the heat capacity of the i-th layer, and N represents the total number of calculation layers.

[0030] As a further scheme of the present application, the interlayer solidification balancing module comprises:

[0031] The solidification rate calculation sub-module calculates the solidification rate of each layer material based on the interlayer temperature gradient data, solves the solidification rate change rate of adjacent layers, analyzes the distribution characteristics of the rate change, and obtains solidification rate change rate data.

[0032] The heat conduction path analysis sub-module identifies the interlayer heat conduction path according to the solidification rate change rate data, calculates the heat flux density, analyzes the distribution law of the heat conduction path, screens the abnormal region of the heat flux density, and obtains heat conduction path distribution data.

[0033] The heat dissipation channel adjustment sub-module reads the heat conduction path distribution data, analyzes the influence of the heat dissipation channel on the solidification rate, adjusts the opening and closing state of the heat dissipation channel according to the temperature gradient and the heat conduction path, and analyzes the heat transfer situation after optimization to obtain solidification rate balancing data.

[0034] As a further scheme of the present application, the forming intelligent control execution module comprises:

[0035] The stress temperature balancing calculation sub-module analyzes the spatial distribution characteristics of the stress offset and the solidification rate based on the local stress correction data and the solidification rate balancing data, and uses the formula:

[0036]

[0037] The stress-temperature distribution balancing degree E of each region in the forming process is calculated to obtain stress-temperature distribution balancing degree data, wherein g i represents the stress value of the i-th region, Q i represents the measurement interval of the i-th region, T j represents the temperature value of the j-th region, W j represents the temperature measurement area of the j-th region.

[0038] The deformation trend analysis submodule analyzes the material deformation trend of different regions based on the stress-temperature balance degree data, calculates the local deformation variable and its change rate, judges the evolution of the deformation variable with time, evaluates the stability of the forming process, and obtains material deformation trend data;

[0039] The intelligent control parameter adjustment submodule adjusts the local heating power and pressure loading parameters in the forming process according to the material deformation trend data, optimizes the temperature control and pressure distribution, calculates the corrected control parameters, and obtains a composite material forming intelligent control scheme.

[0040] Compared with the prior art, the advantages and positive effects of the present application are:

[0041] In the present application, the micro-scale detection means is used to accurately identify the deformation rate mutation region, and the stress distribution calculation is combined to find the stress concentration trend in advance, so that the stress adjustment is more targeted, the influence of local stress overload on the forming quality is reduced, based on real-time pressure feedback, combined with target pressure offset analysis, the loading mode is dynamically adjusted, the local stress environment is optimized, the material is ensured to be uniformly stressed during solidification, the internal defect risk is reduced, a high-precision temperature gradient monitoring system is constructed by using a temperature sensor array, temperature mutation regions are accurately screened, and heat diffusion rate analysis is combined to optimize local temperature distribution, improve the temperature control precision of the forming process, reduce the influence of interlayer temperature imbalance on the consistency of material solidification, calculate the solidification rate change rate, combine heat conduction path optimization, dynamically adjust the heat dissipation channel state, make the material uniformly solidify at different positions, reduce the deformation or internal defects caused by local uneven solidification, based on stress correction data and solidification rate balance data, calculate the stress-temperature distribution balance degree in the forming process, accurately identify the stress offset amount exceeding limit and solidification rate abnormal region, combine deformation trend analysis, dynamically optimize local heating power and pressure loading parameters, improve the forming stability, and improve the stability and consistency of the part quality. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The system flowchart of the present application is shown in the figure;

[0043] Figure 2 The micro-scale deformation detection module flowchart of the present application is shown in the figure;

[0044] Figure 3 The stress offset correction module flowchart of the present application is shown in the figure;

[0045] Figure 4 The temperature gradient control module flowchart of the present application is shown in the figure;

[0046] Figure 5 The interlayer solidification balance module flowchart of the present application is shown in the figure;

[0047] Figure 6The flow chart of the intelligent control execution module of the composite material forming. DETAILED DESCRIPTION

[0048] In order to make the objects, technical solutions and advantages of the present application clearer, the present application 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 application and should not be used to limit the present application.

[0049] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0050] Please refer to Figure 1 A composite material forming process intelligent control system comprises:

[0051] The micro-scale deformation detection module acquires optical interference measurement data, extracts deformation information in the material curing process, calculates the deformation rate of each region, screens the deformation rate mutation region, calculates the stress increment of the mutation region, acquires stress distribution data according to the material constitutive relation, determines the local stress concentration region, and calculates the local stress offset data;

[0052] The stress offset correction module analyzes the local stress distribution trend of the material based on the local stress offset data, combines the feedback data of the pressure sensor, judges whether the contact pressure conforms to the target pressure distribution curve, adjusts the pressure loading mode according to the contact pressure, and acquires local stress correction data;

[0053] The temperature gradient regulation module acquires forming process temperature data through a micro temperature sensor array, calculates the temperature change rate between layers, compares the change threshold to screen the temperature gradient change significant region, calculates the thermal diffusion rate, and acquires the interlayer temperature gradient data;

[0054] The interlayer curing equalization module calculates the material curing rate change rate based on the interlayer temperature gradient data, analyzes the heat conduction path distribution, adjusts the opening and closing state of the heat dissipation channel, and acquires the curing rate equalization data;

[0055] The molding intelligent control execution module calculates the stress-temperature distribution balance during the molding process based on local stress correction data and curing rate balance data, screens areas where stress offset exceeds the balance threshold and curing rate is uneven, analyzes changes in material deformation trends, determines the stability of the molding process, adjusts local heating power and pressure loading parameters, and outputs an intelligent control solution for composite material molding.

[0056] Local stress offset data includes deformation rate distribution, deformation rate mutation area, stress increment distribution data, stress concentration area, and local stress offset data; local stress correction data includes stress distribution trend, target pressure offset, and pressure loading correction parameters; interlayer temperature gradient data includes temperature change rate distribution record, temperature gradient mutation area, and thermal diffusion rate distribution record; curing rate balance data includes curing rate change rate, heat conduction path adjustment parameters, and heat dissipation channel state distribution record. The intelligent control solution for composite material molding includes stress-temperature distribution balance calculation results, stress offset exceeding limit area, curing rate uneven area, material deformation trend, local heating power adjustment parameters, and pressure loading correction parameters.

[0057] See also Figure 2 , the micro-scale deformation detection module includes:

[0058] The optical interference data acquisition submodule acquires optical interference measurement data, collects the intensity distribution of the interference light field, extracts phase information, analyzes the micro-scale displacement at different positions, calculates the displacement change per unit time, and obtains the micro-scale displacement change of each area;

[0059] When obtaining optical interference measurement data, a high-resolution interferometer is used to record the intensity distribution of the light field. The detector of the interferometer collects the phase change information of the light wave on the surface of the material to form a two-dimensional interference fringe pattern. The light field phase is analyzed by Fourier transform, and the phase data of different positions of the material are extracted and converted into micro-scale displacement. Assuming that the phase change of a certain area during the curing process of the material is Δφ = 0.5 radians, according to the phase-displacement conversion formula If the interference light wavelength λ = 632.8nm, the displacement change in this area is To further calculate the displacement change per unit time, high frame rate images are used to record the evolution of the phase over time. The time interval is set to t = 0.01s, and the displacement rate of the area is Similarly, data of multiple regions are collected and calculated to obtain the micro-scale displacement change of each region.

[0060] The deformation rate calculation sub-module calculates the displacement gradient based on the micro-scale displacement change of each region, solves the relative deformation between adjacent position points in each region, synchronously calculates the deformation rate, and screens the deformation rate mutation region to obtain the deformation rate mutation region data.

[0061] Based on the micro-scale displacement change of each region, the displacement gradient is calculated by using the finite difference method, assuming that the displacements between adjacent points are z1=100 nm and z2=150 nm, and the distance between the two points is d=5 μm, The deformation rate can be calculated by time change. If the time interval is still t=0.01 s, The deformation rate mutation region is screened. The setting of the mutation threshold is based on the solidification characteristics and stress-strain relationship of the material. The strain rate change range of the material during the solidification process measured by experiment is used as the setting basis. At the same time, the stress concentration effect in the theoretical calculation is combined. At the stress release stage, the deformation rate usually maintains a stable interval of 0.5 s -1 to 1.0 s -1 . In the stress concentration area, the deformation rate will fluctuate with the stiffness change of the local structure of the material, showing a sharp change in the range of 1.0 s -1 to 1.5 s -1 . Therefore, the upper limit of the deformation rate 1.2 s -1 is taken as the threshold for mutation screening in order to accurately capture the stress mutation characteristics of the local region. All regions are screened. When the deformation rate is higher than the threshold, the region is marked. Finally, the deformation rate mutation region data is obtained.

[0062] The local stress concentration calculation sub-module calculates the stress increment corresponding to the deformation rate change according to the deformation rate mutation region data combined with the constitutive relationship in the material solidification process, calculates the local stress concentration region by using the stress distribution in the region, and uses the formula:

[0063]

[0064] The stress offset value G of each local region is calculated to obtain the local stress offset data, wherein Δσ i represents the stress increment of the i-th region, L i represents the deformation path length of the i-th region, S i represents the stress distribution value of the i-th region, A i represents the deformation area of the i-th region, and n is the total number of deformation rate mutation regions.

[0065] Combined with the deformation rate mutation region data, the stress increment is calculated by using the material constitutive relationship. Assuming that the elastic modulus E=200 GPa and the deformation ∈=0.01, the stress increment Δσ=E·∈=200×109 x 0.01 = 2 x 10 9 Pa, combined with stress distribution calculation local stress concentration area, and calculate the local stress offset, set the stress increment in a certain area Δσ1=2GPa, Δσ2=3GPa, deformation path length L1=10μm, L2=15μm, stress distribution S1=1.5GPa, S2=2.5GPa, deformation area A1=20μm 2 , A2=25μm 2 , then the local stress offset calculation as follows:

[0066]

[0067] Operation to obtain the stress offset value of each local area, the final local stress offset data.

[0068] Table 1 local stress calculation data table

[0069] Region number Stress increment (GPa) Deformation path length (pm) Stress distribution (GPa) Deformation area (pm2) 1 2 10 1.5 20 2 3 15 2.5 25

[0070] As shown in Table 1, by the stress increment of each region, deformation path and area calculation local stress offset, finally obtained G=0.575GPa, the results show that the offset of the local stress concentration area, can be used for further analysis of the stress distribution characteristics of the material solidification process.

[0071] Please refer to Figure 3 , stress offset correction module includes:

[0072] Local stress distribution analysis submodule based on local stress offset data, calculate the stress gradient of each region, analyze the stress distribution trend, judge the stress concentration area, and extract the local stress change amplitude, get local stress distribution trend data;

[0073] Based on the local stress offset data, obtain the stress value distribution of each region, calculate the stress gradient, analyze the stress change of each region, and compare the stress difference between adjacent regions, set the stress change threshold, filter out the local stress mutation point, and then calculate the stress gradient distribution in the region by interpolation, judge the stress concentration area, calculate the stress difference of each point in the region, and calculate the stress distribution characteristics of the whole region. Take a material area as an example, assuming that its stress distribution is as follows:

[0074] Table 2 local area initial stress distribution table

[0075]

[0076]

[0077] According to the data in Table 2, the stress gradient between adjacent points is calculated, for example, the stress gradient of 1 to 2 is calculated as follows:

[0078]

[0079] Assuming the distance between two points is 0.5 mm, then:

[0080]

[0081] Similarly, the gradient values of other points are calculated, and the stress change threshold is set to 1.2 MPa / mm. The threshold is set according to the relationship between the stress carrying capacity and the fatigue limit of the material. Specifically, if the stress gradient of the material under continuous loading exceeds its internal stress release rate, it may cause local stress concentration, which increases the crack propagation rate. Therefore, the value should be lower than the fatigue limit stress gradient of the material to avoid the occurrence of local overload phenomenon. According to the experimental data of the material, if the stress gradient is close to or exceeds the fatigue limit of the material (such as 1.5 MPa / mm for a certain metal alloy), the crack propagation will accelerate, therefore, 80% of the fatigue limit can be used as a reasonable stress change threshold, i.e. 1.2 MPa / mm. Comparing the calculated values, if the stress gradient of a certain region exceeds the threshold, it is determined to be a stress concentration region, for example, the gradient of 4-5:

[0082]

[0083] Greater than the stress change threshold, so point 5 may belong to the stress concentration region. The final calculation of the local stress distribution trend data is obtained.

[0084] The contact pressure judgment submodule calculates the actual contact pressure according to the local stress distribution trend data combined with the pressure sensor feedback data, and compares it with the target pressure distribution curve to calculate the pressure deviation of each region, and obtains the contact pressure deviation data;

[0085] The local stress distribution trend data is called, combined with the pressure sensor feedback data, to measure the contact pressure, calculate the actual contact pressure distribution, compare the measured value with the target pressure curve, calculate the pressure deviation of each region, and screen the regions with larger pressure deviation to analyze whether they meet the set pressure distribution standard. The target pressure distribution curve is set as follows:

[0086] Table 3 Comparison of target and actual contact pressure

[0087] Position point number Target pressure (MPa) Measured pressure (MPa) Pressure deviation (MPa) 1 2.5 2.3 -0.2 2 3.0 2.8 -0.2 3 3.5 3.1 -0.4 4 4.0 3.7 -0.3 5 5.0 4.5 -0.5

[0088] According to the data in Table 3, the maximum pressure deviation is calculated, and the deviation threshold is set to 0.4 MPa. The threshold is set according to the response ability of the material interface contact pressure to the target load distribution. If the pressure deviation in a local area exceeds a certain range, the area may be in a non-uniform stress state, which will affect the overall structural performance. Specifically, the value should be lower than the yield stress of the material (e.g. the yield stress of a certain polymer material is 5 MPa), and based on the interface contact condition, it is set to 8% of the yield stress, i.e. 0.4 MPa. If the deviation exceeds the threshold, it is considered that the pressure deviation in the area is large and needs to be adjusted. For example, the pressure deviation of point 5 is 0.5 MPa, which exceeds the deviation threshold, so the contact pressure of point 5 area needs to be adjusted, and then the contact pressure deviation data is obtained.

[0089] The pressure loading adjustment sub-module combines the contact pressure deviation data to adjust the pressure loading mode, using the formula:

[0090]

[0091] The corrected local stress value g is calculated to obtain the local stress correction data, where g o,i represents the original stress value of the i-th area, P i represents the pressure correction amount of the i-th area, a i represents the contact area of the i-th area, F j represents the force of the j-th area, l j represents the loading path length of the j-th area, M is the total number of correction areas, and m is the total number of loading areas.

[0092] According to the contact pressure deviation data, the pressure loading mode is adjusted, the pressure distribution is optimized, and the corrected local stress value is calculated. The formula is used to calculate the corrected stress value.

[0093] Taking the 5-point area as an example, the corrected pressure is calculated, the corrected force P5 is set to 0.3 MPa, the contact area a5 is set to 5.0 mm 2 , the original stress g o,5 = 4.5 MPa, the loading force F5 is 20 N, and the loading path length l5 is 10 mm. The calculation is as follows:

[0094]

[0095] The local stress correction data is obtained.

[0096] Please refer to Figure 4 The temperature gradient control module includes:

[0097] The temperature data acquisition sub-module acquires temperature data collected by the micro temperature sensor array, extracts temperature values of each layer, calculates temperature changes of adjacent layers, analyzes temperature fluctuations over time, and obtains interlayer temperature change rate data.

[0098] When acquiring temperature data collected by the micro temperature sensor array, the temperature sensors at different positions need to be numbered to ensure the spatial distribution correspondence of the data, for example, a 5x5 sensor matrix is laid out, each sensor records temperature changes at an interval of 1s, assuming that at the initial time t0, the temperature recorded by each sensor is [300.5K, 301.2K, 299.8K, …], and the temperature change values are continuously recorded at subsequent times, these data are stored in a database, the temperature change between adjacent sensors is calculated, and the temperature gradient is calculated using the numerical difference method, that is,

[0099] ΔT i,j = T i,j (t k+1 )-T i,j (t k );

[0100] wherein, ΔT i,j represents the temperature difference of the sensor at position (i, j) at two time points, T i,j (t k ) represents the temperature value of the sensor at time t k . For the calculation of the interlayer temperature change, two adjacent temperature data in the vertical direction of the layer are selected, for example, the temperature sensors corresponding to the same horizontal position of the nth layer and the nth+1 layer are selected, and the temperature change rate is calculated:

[0101] Temperature change rate

[0102] wherein, the temperature change rate T,n of the nth layer represents the temperature change rate of the nth layer, and Δt represents the time interval, which is usually set to 1s. Assuming that the temperature of the first layer increases from 300K to 303K and the temperature of the second layer increases from 302K to 306K in a period of time, the temperature change rate of the first layer is calculated as follows:

[0103] Temperature change rate

[0104] Temperature change rate

[0105] As shown in Table 4, temperature data of different layers is given.

[0106] Table 4 Interlayer temperature change rate

[0107] Layer number Initial temperature (K) End temperature (K) Change rate (K / s) 1 300 303 3 2 302 306 4 3 305 310 5

[0108] As shown in Table 4, the temperature change rate of each layer reflects the trend of heat conduction between layers, and the interlayer temperature change rate data is calculated.

[0109] The temperature gradient screening sub-module compares the preset temperature change threshold with the interlayer temperature change rate data, screens the region whose change amplitude exceeds the threshold, extracts the temperature change characteristics, and obtains the temperature gradient change significant region data.

[0110] Based on the interlayer temperature change rate data, first, set the temperature gradient change threshold, which can be set according to the material thermal expansion coefficient, processing environment temperature change range and other parameters. For example, for a certain material, the temperature gradient exceeding 5K / s may lead to thermal expansion imbalance, so set the temperature gradient change threshold = 5K / s, for each interlayer temperature change rate data, judge whether it exceeds the threshold:

[0111] If the temperature change rate T,n is greater than the temperature gradient change threshold, it is marked as a temperature gradient change significant region.

[0112] For example, assume that the interlayer temperature change rate of a certain region is calculated as follows:

[0113] The temperature change rate T,1 = 3K / s;

[0114] The temperature change rate T,2 = 4K / s;

[0115] The temperature change rate T,3 = 6K / s;

[0116] Since the temperature change rate T,3 > 5K / s, this region is marked as a temperature gradient change significant region. The region number and position information of the screened region are stored in the data table, as shown in Table 5.

[0117] Table 5 Temperature gradient change significant region

[0118] Region number Position coordinates (x, y) Temperature change rate (K / s) 1 (2,3) 6 2 (4,5) 7

[0119] As shown in Table 5, the temperature gradient change in the screened region is large, and the temperature gradient change significant region data is calculated.

[0120] The heat diffusion rate calculation sub-module calculates the heat diffusion rate H of each region according to the temperature gradient change significant region data, combines the region temperature difference and time interval, and uses the formula:

[0121]

[0122] The interlayer temperature gradient data is obtained, where T i represents the temperature of the i-th layer, ti D represents the time corresponding to the ith layer i C represents the thermal conduction path length of the ith layer i N represents the total number of layers calculated

[0123] When the temperature gradient change significant area data is called, the heat diffusion rate is calculated, and the influence of heat diffusion is considered based on the temperature change characteristics in the area, for example, assuming that the temperature data of a certain area is as follows:

[0124] T1 = 300 K

[0125] T2 = 305 K

[0126] t1 = 0 s

[0127] t2 = 2 s

[0128] D = 0.01 m

[0129] C = 0.5 J / (kg·K)

[0130] Then the heat diffusion rate is calculated:

[0131]

[0132] As shown in the calculation, the heat diffusion rate of this area is 0.05. The interlayer temperature gradient data is calculated.

[0133] The results show that the heat diffusion rate of this area plays a key role in the interlayer heat conduction process, which can be used for subsequent analysis of temperature uniformity and possible thermal stress distribution.

[0134] Please refer to Figure 5 The interlayer curing uniformity module includes:

[0135] The curing rate calculation submodule calculates the curing rate of each layer based on the interlayer temperature gradient data, solves the curing rate change rate of adjacent layers, analyzes the distribution characteristics of the rate change, and obtains the curing rate change rate data.

[0136] Based on the interlayer temperature gradient data, the temperature change of each layer is obtained, the temperature difference between adjacent layers is analyzed, and the curing rate is calculated in combination with the time variable in the curing process. First, multiple measurement points are set for each layer, and it is assumed that the material has multiple curing stages during manufacturing, and the time interval of each stage is set to 5 minutes. Temperature measurement points are arranged at the center and four corners of each layer, and the temperature change is recorded, as shown in Table 6.

[0137] Table 6 Interlayer temperature data collection table

[0138] Layer number Measurement point Initial temperature (°C) Final temperature (°C) Time (min) Temperature difference (°C) 1 Center 30 80 5 50 1 Corner 28 76 5 48 2 Center 32 82 5 50 2 Corner 30 78 5 48

[0139] As shown in Table 6, the temperature change rate of each layer can be calculated by measuring point data, and the solidification rate change of each measuring point can be obtained by using the ratio of temperature difference to time interval. For example, the temperature change rate of the center of layer 1 can be calculated as 50℃ / 5min=10℃ / min, and the temperature change rate of the corner of layer 1 is 48℃ / 5min=9.6℃ / min. By calculating the temperature change rate of adjacent layers and combining the heat conduction parameters of the solidified material, the solidification uniformity can be evaluated, and finally the solidification rate change rate data is obtained.

[0140] The heat conduction path analysis submodule identifies the interlayer heat conduction path according to the solidification rate change rate data, calculates the heat flux density, analyzes the distribution rule of the heat conduction path, screens the abnormal area of the heat flux density, and obtains the heat conduction path distribution data;

[0141] The solidification rate change rate data is called to analyze the heat conduction path of each measuring point, and the heat flux density is calculated according to the rate and spatial distribution of temperature change of the measuring point. Assuming that the distance between the measuring points is fixed at 10mm, and the thermal conductivity of the material is set to 0.25W / (m·K), the heat flux density between each two measuring points is calculated to evaluate the effectiveness of the heat conduction path.

[0142] Table 7 Heat flux density calculation table

[0143] Measurement point x Measurement point y Temperature difference (°C) Distance (mm) Thermal conductivity (W / m·K) Heat flux (W / m 2 )]]> 1st layer center 1st layer corner 2 10 0.25 50 2nd layer center 2nd layer corner 4 10 0.25 100

[0144] As shown in Table 7, the heat flux density is calculated according to the temperature difference, distance and thermal conductivity of the measuring point. For example, the heat flux density between the center and the corner of layer 1 is (2℃ / 10mm)×0.25W / m·K=50W / m 2 , and the heat flux density between the center and the corner of layer 2 is (4℃ / 10mm)×0.25W / m·K=100W / m 2 By comparing the heat flux densities between different layers, the areas with higher or lower heat flux densities are screened out, and the abnormal situation of the heat conduction path distribution is identified, and finally the heat conduction path distribution data is obtained.

[0145] The heat dissipation channel adjustment submodule reads the heat conduction path distribution data, analyzes the influence of the heat dissipation channel on the solidification rate, adjusts the opening and closing state of the heat dissipation channel according to the temperature gradient and the heat conduction path, and analyzes the heat transfer after optimization to obtain the solidification rate balance data;

[0146] According to the heat conduction path distribution data, the influence of the opening and closing state of the heat dissipation channel on the solidification rate is analyzed, and the local heat dissipation is adjusted. In the manufacturing process, the heat dissipation channel can change the temperature gradient by controlling the air flow rate, assuming that the air flow rate of the heat dissipation channel is adjustable in the range of 0-5 m / s, and the opening and closing state of the heat dissipation channel is set according to the temperature difference in the heat conduction path. For example, in the abnormal heat flux density area (such as the area of 100 W / m 2 in Table 2), the air flow rate is appropriately increased to 3 m / s to reduce the phenomenon of local overheating, while in the low heat flux density area (50 W / m 2 ), the air flow rate is appropriately reduced to 1 m / s to maintain the uniform distribution of the solidification rate, and finally the solidification rate balance data is obtained.

[0147] Please refer to Figure 6 , the intelligent control execution module for forming includes:

[0148] The stress temperature balance calculation sub-module analyzes the spatial distribution characteristics of the stress offset and the solidification rate based on the local stress correction data and the solidification rate balance data, and uses the formula:

[0149]

[0150] The stress-temperature distribution balance degree E of each area in the forming process is calculated, and the stress-temperature balance degree data is obtained, wherein g i represents the stress value of the i-th area, Q i represents the measurement interval of the i-th area, T j represents the temperature value of the j-th area, and W j represents the temperature measurement area of the j-th area.

[0151] Based on the local stress correction data and the solidification rate balance data, the stress-temperature distribution balance degree of each area in the forming process is calculated. First, the stress data g i and the temperature data T j of each area need to be extracted, wherein the stress data is obtained by monitoring the corrected local stress value, and the temperature data is derived from the temperature gradient in the solidification rate balance data. The measurement points are arranged in different areas of the material forming with a fixed interval Q i , for example, in a 1 m 2 composite material forming area, a measurement point is set every 50 mm, a total of 400 stress measurement points and 400 temperature measurement points are arranged, and the measurement data of each point is collected at the same time. All data is stored and normalized to ensure balanced data distribution and avoid the influence of single abnormal points on calculation. The normalization uses the minimum-maximum normalization method to map the data to the 0-1 interval.

[0152] To analyze the stress-temperature balance, the stress variation g between adjacent regions needs to be calculated i+1 -g i and the temperature variation T j+1 -T j The calculation method of the stress variation is as follows: assuming that the initial stress of a region is 30 MPa and the stress of the adjacent region is 35 MPa, the stress variation of the region is 35-30=5 MPa. This calculation process is performed for all adjacent regions to form a stress variation matrix. Similarly, the calculation method of the temperature variation is as follows: assuming that the initial temperature of a region is 180℃ and the temperature of the adjacent region is 190℃, the temperature variation is 190-180=10℃. After the calculation of all regions, a temperature variation matrix is formed. Next, these data need to be normalized for subsequent calculation.

[0153] To determine the balance of stress and temperature during the molding process, the balance degree of the stress gradient and the temperature gradient needs to be calculated. The calculation of the stress gradient uses local second-order difference calculation, that is, For example, if the stress variation of a region is 5 MPa and the measurement interval is 50 mm, the calculation result of the stress gradient balance degree is:

[0154]

[0155] Similarly, the calculation of the temperature gradient balance degree uses Assuming that the temperature variation is 10℃ and the measurement area is 2500 mm 2 , then:

[0156]

[0157] The above calculation is performed for all measurement points, and the sum of the balance degrees is calculated to obtain the overall stress-temperature balance degree index.

[0158] As shown in Table 8, the stress and temperature variation data of each measurement point are calculated to form balance degree data.

[0159] Table 8 Stress-temperature balance degree calculation data

[0160]

[0161] Table 8 lists the stress-temperature balance calculation results of some measurement points. The higher the stress gradient value, the greater the stress change in the region. The higher the temperature gradient value, the more intense the temperature change in the region. By calculating the data of all measurement points, the final stress-temperature balance data is obtained. The results show that the higher the stress-temperature balance value, the greater the fluctuation of stress and temperature during the forming process, which needs to be adjusted. The lower the balance value, the more stable the forming process, so the subsequent process adjustment needs to optimize the local heating power or pressure loading parameters of the higher balance area.

[0162] The deformation trend analysis submodule analyzes the material deformation trend in different regions based on the stress-temperature balance data, calculates the local deformation and its change rate, judges the evolution of the deformation with time, evaluates the stability of the forming process, and obtains the material deformation trend data.

[0163] To analyze the material deformation trend in different regions, the stress and temperature data of each region need to be extracted and their change rates with time need to be calculated. The analysis of the deformation trend mainly focuses on the matching of the stress gradient and the solidification rate to determine whether there is a problem of local deformation unevenness in the material during the forming process.

[0164] In practical applications, the stress data is obtained from the local stress corrected measurement value, and the temperature data is provided by the solidification rate balance data. All measurement data is stored in time series to calculate the change trend of the deformation. The stress and temperature change rates of each region need to be compared with the critical value. If the stress change rate of a region is higher than the average value, but the temperature change rate is lower, then the region may have local deformation aggregation, which affects the final forming precision. Conversely, if the temperature change rate is higher, but the stress change rate is lower, then there may be a problem of too fast local solidification rate, which increases the residual stress in the material.

[0165] To further analyze the stability of the forming process, the overall deformation distribution of the material needs to be calculated, and the regions with deformation change rates exceeding the set threshold value need to be selected. For example, if the stress change rate of multiple measurement points in some region exceeds the preset range, it indicates that there is a potential deformation risk in that region, which may cause stress concentration points in the composite material, affecting the final mechanical properties. Therefore, during the deformation trend analysis process, statistical analysis of the measurement data is needed to extract abnormal change regions for subsequent intelligent control adjustment.

[0166] Finally, through the analysis of the material deformation trend, it can be determined which regions have a larger deformation change rate, and the key factors that may cause uneven deformation can be identified. The data will serve as the basis for intelligent control parameter adjustment and provide a basis for the optimization of local heating power and pressure loading parameters.

[0167] The intelligent control parameter adjustment submodule adjusts the local heating power and pressure loading parameters in the molding process according to the material deformation trend data, optimizes the temperature control and pressure distribution, calculates the corrected control parameters, and obtains the intelligent control scheme for composite material molding;

[0168] In combination with the material deformation trend data, the local heating power and pressure loading parameters in the molding process are adjusted. First, the region with a large change rate of deformation variable needs to be determined, and the stress-temperature matching condition is analyzed. In the adjustment process, if the change rate of deformation variable in a certain region is too high, the pressure loading rate of the region needs to be reduced to reduce the additional deformation of the material. At the same time, if the temperature change of the region is fast, the local heating power may need to be reduced to avoid the accumulation of internal stress caused by uneven thermal expansion.

[0169] In actual control process, the adjustment of heating power and pressure needs to be combined with molding process parameters. For example, during the interlaminar curing of composite materials, the pressure loading needs to be maintained within a certain range to ensure uniform curing of the material, while the local temperature control needs to be optimized according to the feedback data of the curing rate. Therefore, according to the stress, temperature, and deformation variable data of each measurement point, the output power of the local heating device can be adjusted, and the pressure loading program can be optimized to make the deformation variable of the material uniformly distributed in different regions.

[0170] In addition, when adjusting the control parameters, the thermal expansion coefficient and stress relaxation characteristics of the material also need to be considered. Different materials will exhibit different thermal response behaviors during curing. For example, high-temperature curing materials have high internal residual stress when the temperature changes greatly, so the heating rate and pressure application method need to be accurately controlled. Low-temperature curing materials are relatively stable and can allow greater temperature fluctuations. In actual operation, the temperature and stress distribution of different regions can be compared based on the measurement data, and dynamic adjustment can be made based on the deformation trend data to optimize the balance of local stress and temperature distribution in the molding process.

[0171] Finally, by optimizing the local heating power and pressure loading parameters, an intelligent control scheme for composite material molding is formed, which can be used to real-time control the stress distribution and curing rate in the molding process to ensure the overall structural stability of the material after molding and reduce the influence of local uneven deformation.

[0172] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in other forms. Any person skilled in the art can make changes or modifications to the above disclosed technical contents into equivalent embodiments with equivalent changes, and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, without departing from the technical solution content of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. An intelligent control system for composite material forming process, characterized in that: The system comprises: The micro-scale deformation detection module extracts deformation information during the material solidification process, calculates the deformation rate of each area, calculates the stress increment in the mutation area, determines the local stress concentration area, and calculates the local stress offset data; The stress offset correction module analyzes the local stress distribution trend of the material based on the local stress offset data, determines whether the contact pressure conforms to the target pressure distribution curve, adjusts the pressure loading mode, and obtains local stress correction data; The temperature gradient control module collects temperature data during the molding process, calculates the temperature change rate between each layer, compares the change threshold to screen the area with significant temperature gradient change, calculates the heat diffusion rate, and obtains the temperature gradient data between layers; The interlayer curing balance module calculates the material curing rate change rate based on the interlayer temperature gradient data, analyzes the heat conduction path distribution, adjusts the opening and closing status of the heat dissipation channel, and obtains the curing rate balance data; The molding intelligent control execution module calculates the stress-temperature distribution balance based on the local stress correction data and the curing rate balance data, analyzes the change in material deformation trend, determines the molding stability, dynamically adjusts the molding parameters, and outputs the composite material molding intelligent control solution.

2. The intelligent control system for composite material forming process according to claim 1, characterized in that: The local stress offset data includes deformation rate distribution, deformation rate mutation area, stress increment distribution data, stress concentration area, and local stress offset data; the local stress correction data includes stress distribution trend, target pressure offset, and pressure loading correction parameter; The interlayer temperature gradient data includes temperature change rate distribution records, temperature gradient mutation areas, and thermal diffusion rate distribution records; the curing rate balance data includes curing rate change rate, heat conduction path adjustment parameters, and heat dissipation channel state distribution records; the composite material forming intelligent control solution includes stress-temperature distribution balance calculation results, stress offset exceeding limit areas, curing rate uneven areas, material deformation trends, local heating power adjustment parameters, and pressure loading correction parameters.

3. The intelligent control system for composite material forming process according to claim 1, characterized in that: The micro-scale deformation detection module includes: The optical interference data acquisition submodule acquires optical interference measurement data, collects the intensity distribution of the interference light field, extracts phase information, analyzes the micro-scale displacement at different positions, calculates the displacement change per unit time, and obtains the micro-scale displacement change of each area; The deformation rate calculation submodule calculates the displacement gradient based on the micro-scale displacement change of each area, solves the relative deformation between adjacent points in each area, simultaneously calculates the deformation rate, and screens the deformation rate mutation area to obtain the deformation rate mutation area data; The local stress concentration calculation submodule calculates the stress increment corresponding to the change in deformation rate based on the deformation rate mutation area data and the constitutive relationship of the material during the solidification process. It calculates the local stress concentration area using the stress distribution in the area using the formula: Calculate the stress offset value G of each local area to obtain the local stress offset data, where Δσ i represents the stress increment of the ith region, L i represents the deformation path length of the i-th region, S i Represents the stress distribution value of the i-th region, A i represents the deformation area of ​​the i-th region, and n is the total number of regions with sudden changes in deformation rate.

4. The intelligent control system for composite material forming process according to claim 1, characterized in that: The stress offset correction module includes: The local stress distribution analysis submodule calculates the stress gradient of each region based on the local stress offset data, analyzes the stress distribution trend, determines the stress concentration area, and extracts the local stress variation amplitude to obtain local stress distribution trend data; The contact pressure judgment submodule calculates the actual contact pressure based on the local stress distribution trend data and the pressure sensor feedback data, compares it with the target pressure distribution curve, calculates the pressure deviation of each area, and obtains the contact pressure deviation data; The pressure loading adjustment submodule adjusts the pressure loading mode in combination with the contact pressure deviation data, using the formula: Calculate the corrected local stress value g and obtain the local stress correction data, where g o,i represents the original stress value of the i-th region, P i Represents the pressure correction amount of the i-th area, a i represents the contact area of ​​the ith region, F j represents the force acting on the jth region, l j represents the loading path length of the jth region, M is the total number of correction regions, and m is the total number of loaded regions.

5. The intelligent control system for composite material forming process according to claim 1, characterized in that: The temperature gradient control module includes: The temperature data acquisition submodule obtains the temperature data collected by the micro temperature sensor array, extracts the temperature value of each layer, calculates the temperature change of adjacent layers, analyzes the temperature fluctuation over time, and obtains the temperature change rate data between layers; The temperature gradient screening submodule compares the interlayer temperature change rate data with a preset temperature change threshold, screens areas where the change amplitude exceeds the threshold, extracts the temperature change characteristics, and obtains data on areas with significant temperature gradient changes; The heat diffusion rate calculation submodule uses the formula: Calculate the thermal diffusion rate H of each region and obtain the interlayer temperature gradient data, where T i represents the temperature of the i-th layer, t i represents the time corresponding to the i-th layer, D i represents the heat conduction path length of layer i, C i represents the heat capacity of the i-th layer, and N represents the total number of calculation layers.

6. The intelligent control system for composite material forming process according to claim 1, characterized in that: The interlayer curing balancing module includes: The curing rate calculation submodule calculates the curing rate of each layer of material based on the interlayer temperature gradient data, solves the curing rate change rate of adjacent layers, analyzes the distribution characteristics of the rate change, and obtains the curing rate change rate data; The heat conduction path analysis submodule identifies the interlayer heat conduction path based on the curing rate change rate data, calculates the heat flux density, analyzes the distribution pattern of the heat conduction path, screens the abnormal heat flux density area, and obtains the heat conduction path distribution data; The heat dissipation channel adjustment submodule reads the heat conduction path distribution data, analyzes the impact of the heat dissipation channel on the curing rate, adjusts the opening and closing state of the heat dissipation channel according to the temperature gradient and the heat conduction path, and analyzes the optimized heat transfer to obtain the curing rate balance data.

7. The intelligent control system for composite material forming process according to claim 1, characterized in that: The molding intelligent control execution module includes: The stress temperature equilibrium calculation submodule analyzes the spatial distribution characteristics of stress offset and solidification rate based on the local stress correction data and the solidification rate equilibrium data, using the formula: Calculate the stress-temperature distribution balance E of each area during the forming process and obtain the stress-temperature balance data, where g i represents the stress value of the ith region, Q i represents the measurement spacing of the ith region, T j Represents the temperature value of the jth region, W j represents the temperature measurement area of ​​the jth region; The deformation trend analysis submodule analyzes the material deformation trends in different regions based on the stress-temperature balance data, calculates the local deformation amount and its rate of change, determines the evolution of the deformation amount over time, evaluates the stability of the forming process, and obtains material deformation trend data; The intelligent control parameter adjustment submodule adjusts the local heating power and pressure loading parameters during the molding process according to the material deformation trend data, optimizes the temperature control and pressure distribution, calculates the corrected control parameters, and obtains an intelligent control solution for composite material molding.

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