CN117316356B - Feedforward compensation regulation and control method for composite material component autoclave molding process parameters - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 68
- 239000002131 composite material Substances 0.000 title claims abstract description 44
- 230000008569 process Effects 0.000 title claims abstract description 44
- 238000000465 moulding Methods 0.000 title claims abstract description 15
- 238000013528 artificial neural network Methods 0.000 claims abstract description 36
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000012360 testing method Methods 0.000 claims abstract description 4
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- 238000004321 preservation Methods 0.000 claims description 8
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- 230000008878 coupling Effects 0.000 description 2
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- 238000005859 coupling reaction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 229920000049 Carbon (fiber) Polymers 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000004917 carbon fiber Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000003822 epoxy resin Substances 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 230000001537 neural effect Effects 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 229920000647 polyepoxide Polymers 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 229920005989 resin Polymers 0.000 description 1
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Abstract
The invention provides a feedforward compensation regulation and control method for molding process parameters of an autoclave made of composite material components, which comprises the following steps: s1: constructing a neural network inverse model, wherein the neural network inverse model comprises an input value, a hidden layer, an output layer and an output value; s2: determining input parameters, output parameter samples and input parameter samples of the neural network inverse model; s3: determining the proportion of a training set, a verification set and a test set, and training a neural network inverse model; s4: and (3) connecting the finite element positive model in series to complete feedforward compensation regulation and control of the forming process parameters of the autoclave of the composite material component of the neural network inverse model. According to the invention, the temperature compensation feedforward inverse model is established through the neural network, so that the temperature hysteresis phenomenon in the existing composite material autoclave molding process can be effectively improved, and the curing quality of the composite material component is improved.
Description
Technical Field
The invention belongs to the technical field of temperature regulation of industrial autoclave, and particularly relates to a feedforward compensation regulation method for molding process parameters of an autoclave made of composite material components.
Background
The dosage proportion of the composite material in the aerospace field is gradually increased, an autoclave molding process is used as a main method for manufacturing the fiber reinforced resin matrix composite material component at present, and the temperature field distribution in the curing process is a very important process control factor. At present, the most remarkable problems of the autoclave molding process are represented by non-uniformity of a temperature field and hysteresis of the temperature field. The autoclave adopts a thermocouple feedback mode to control the temperature in the component curing process, and a PLC logic processor is used for controlling the temperature. The traditional PLC temperature control method has poor control precision due to the adoption of a passive mode temperature control, and meanwhile, the traditional autoclave temperature controller can generate local overheating in the temperature control process, so that the problem of uncontrollable temperature of the composite material component in the curing process is caused.
Disclosure of Invention
In view of the above, the present invention aims to overcome the shortcomings of the prior art, and provides a method for feedforward compensation and regulation of autoclave molding process parameters of a composite material member, which can effectively improve the temperature hysteresis phenomenon in the current autoclave molding process of the composite material and improve the curing quality of the composite material member by establishing a temperature compensation feedforward inverse model through a neural network.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
A feedforward compensation regulation and control method for forming process parameters of an autoclave of a composite material component comprises the following steps:
s1: constructing a neural network inverse model, wherein the neural network inverse model comprises an input value, a hidden layer, an output layer and an output value;
s2: determining input parameters, output parameter samples and input parameter samples of the neural network inverse model;
S3: determining the proportion of a training set, a verification set and a test set, and training a neural network inverse model;
s4: and (3) connecting the finite element positive model in series to complete feedforward compensation regulation and control of the forming process parameters of the autoclave of the composite material component of the neural network inverse model.
Further, the neural network inverse model hidden layer is provided with 15 neurons, and the output layer is provided with 50 neurons.
Further, the input parameter is a temperature curve of a hysteresis thermocouple on the composite material member, and the temperature curve is scattered into a temperature value point to serve as the input parameter.
Further, the output parameter is a curing process curve of the composite material prepreg arranged in the autoclave control system, and the curing process curve is scattered into temperature value points.
Furthermore, the output parameter sample adopts a Latin hypercube random sampling method to randomly combine the heating rate K with the heat preservation temperature T, the value range of the heating rate is 0.01-0.05K/s, the value range of the heat preservation temperature is 429-493K, and 100 combinations are extracted as the output parameter sample of the neural network inverse model.
Furthermore, the input parameter sample is an input parameter sample of which the autoclave equipment is replaced by adopting a finite element model and the output result of finite element simulation is used as a neural network inverse model.
Further, in the step S4, the data after discretizing the prepreg process curve is substituted as input into a plurality of trained neural network inverse models, then a plurality of groups of data calculated by the inverse models are averaged according to the corresponding point sequence, then the averaged data points are input into an autoclave finite element model, and finally the transient temperature field of the composite material component, which is subjected to feedforward compensation by the inverse models, is obtained through the finite element model calculation.
Further, the finite element positive model is built using an ABAQUS/STANDARD and CFD solver.
Compared with the prior art, the feedforward compensation regulation and control method for the composite material component autoclave molding process parameters has the following advantages:
By the feedforward compensation regulation and control method for the composite material component autoclave molding process parameters based on the inverse model, the current situation that the temperature of the composite material component is greatly different from the process temperature in the autoclave molding process of the composite material component can be greatly improved, the curing quality of the composite material component in the autoclave process is improved, and the problem of great temperature hysteresis in the curing process of the composite material component is solved. In the prior production practice, the main method for solving the problem is a trial-and-error method, a large number of experiments are needed to solve the problem, the curing process time and the monetary cost of the autoclave are high, and the method can greatly save time and monetary cost.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of an inverse neural network model of the present invention;
FIG. 3 is an exemplary graph of input parameters and output parameters of an inverse neural network model in an embodiment of the present invention;
FIG. 4 is a regression diagram of the inverse model of a neural network in an embodiment of the present invention;
FIG. 5 is a graph of the effect of inverse model feedforward compensation in an embodiment of the invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", etc. may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art in a specific case.
The invention will be described in detail below with reference to the drawings in connection with embodiments.
Because of more sample data, only one group of corresponding sample data is selected for embodiment display, and the sample data selected by the invention are similar in rule.
1-2, The invention provides a feedforward compensation regulation method for forming process parameters of an autoclave of a composite material component, wherein the forming process parameters of the autoclave of the composite material component are the forming temperature of the autoclave of the composite material component; the composite material is a unidirectional tape prepreg formed by carbon fiber filaments with the brand name of AS4 and 3501-6 epoxy resin, and the method specifically comprises the following steps:
step one: determining input parameters of a neural network inverse model;
The input parameter is the temperature profile of the hysteresis thermocouple on the composite member. In order to meet the requirement of the establishment of the neural network model, the temperature curve is discretized into 50 temperature value points to serve as input parameters. Since the composite member has completed curing before the cool-down phase of the curing process profile, the temperature profile only considers the warm-up and warm-up phases, with a time of 15000 seconds. The curve labeled as input parameter in fig. 3 is a selected set of input parameter sample data, which is discretized into 50 temperature value points at time sequence.
Step two: determining output parameters of the neural network inverse model;
The output parameters are curing process curves of the composite material prepreg arranged in the autoclave control system, and the curing process curves are scattered into 50 temperature value points. Just the heating and holding phases are considered as the input parameters. The curve labeled output parameter in fig. 3 is a selected set of output parameter sample data, which is 50 temperature value points corresponding to the input parameter sample data. In fig. 3, it is evident that there is a significant temperature hysteresis in the curing process curve of the composite prepreg.
Step three: determining an output parameter sample;
The output parameter samples are randomly combined with the temperature rising rate K and the heat preservation temperature T by using a Latin hypercube random sampling method, the value range of the temperature rising rate is 0.01-0.05K/s, the value range of the heat preservation temperature is 429-493K, 100 combinations are extracted as output samples of the neural network model, the temperature rising rate K of the output parameter samples in the figure 3 is 0.03K/s, and the heat preservation temperature T is 454K. The invention obtains output parameter samples with various different characteristics by random value
Step four: obtaining an input parameter sample;
if the input parameter sample is obtained by adopting the traditional autoclave process, the cost is higher, therefore, a finite element model is adopted to replace autoclave equipment, and the output result of finite element simulation is used as the input sample of the neural network model. The sample data marked as input parameters in fig. 3 is the output result of the finite element simulation.
The finite element model is established by using an ABAQUS/STANDARD and CFD solver, and can be equivalently replaced with autoclave equipment by adopting a thermal-flow-solid multi-physical coupling simulation technology.
The input and output parameters in the present invention are 50 parameter values each. The sample represents a plurality of sets of 50 parameter values, 100 sets being provided in the present invention.
Step five: determining the type and structure of a neural network inverse model;
The neural network inverse model is built by using an APP (application) in a MATLAB tool box (Neural NET FITTING), the number of neurons of an hidden layer is set to be 15, the training set is set to be 80%, the verification set is set to be 10%, and the testing set is set to be 10%. The structure diagram of the neural network inverse model is shown in fig. 2, and the model comprises an input value, a hidden layer, an output layer and an output value, wherein the hidden layer has 15 neurons, and the output layer has 50 neurons.
Step six: training a neural network inverse model;
After training, a regression diagram of the neural network can be obtained, as shown in fig. 4. In fig. 4, the closer the value of R to 1, the higher the fitting degree of the neural network after training, demonstrating the higher the accuracy of the neural network inverse model.
Step seven: and (3) connecting a finite element positive model in series, and verifying the feedforward compensation regulation and control effect of the autoclave molding process parameters of the composite material component based on the inverse model. In the embodiment, a curing process curve with the heating rate K of 0.03K/s and the heat preservation temperature T of 454K is selected, and is substituted into a series model of a neural network inverse model and a CAE model, and a curing temperature curve of a composite material component based on inverse model feedforward compensation regulation is obtained through calculation; and then the curing process curve is calculated only through a CAE model to obtain the curing temperature curve of the composite material component. The feedforward compensation effect of the inverse model can be judged by comparing the fitting degree of the two composite material curing temperature curves and the curing process curve, the feedforward compensation effect is shown as a figure 5, and the temperature hysteresis phenomenon is obviously reduced by the curing temperature curve of the composite material component after the feedforward compensation regulation of the inverse model can be obviously found in the figure.
Specifically, the finite element model is built by using an ABAQUS/STANDARD and CFD solver, and the finite element model can be equivalently replaced with autoclave equipment by adopting a thermal-flow-solid multi-physical coupling simulation technology.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (3)
1. A feedforward compensation regulation and control method for forming process parameters of an autoclave made of composite material components is characterized by comprising the following steps of: the method comprises the following steps:
s1: constructing a neural network inverse model, wherein the neural network inverse model comprises an input value, a hidden layer, an output layer and an output value;
S2: determining input parameters, output parameter samples and input parameter samples of the neural network inverse model; the input parameters are temperature curves of hysteresis thermocouples on the composite material component, and the temperature curves are scattered into temperature value points to serve as the input parameters; the output parameters are curing process curves of the composite material prepreg arranged in the autoclave control system, and the curing process curves are scattered into temperature value points; the output parameter samples are randomly combined with a temperature rising rate K and a heat preservation temperature T by using a Latin hypercube random sampling method, the value range of the temperature rising rate is 0.01-0.05K/s, the value range of the heat preservation temperature is 429-493K, and 100 combinations are extracted as output parameter samples of a neural network inverse model; the input parameter sample is an input parameter sample of which the autoclave equipment is replaced by adopting a finite element model, and the output result of finite element simulation is used as a neural network inverse model;
S3: determining the proportion of a training set, a verification set and a test set, and training a neural network inverse model;
s4: the finite element positive model is connected in series to complete feedforward compensation regulation and control of the forming process parameters of the autoclave of the composite material component of the neural network inverse model;
The method comprises the steps of substituting discretized data of a prepreg process curve as input into a plurality of trained neural network inverse models, averaging a plurality of groups of data calculated by the inverse models according to corresponding point sequences, inputting the averaged data points into an autoclave finite element model, and finally calculating by the finite element model to obtain a transient temperature field of the composite material component subjected to feedforward compensation by the inverse models.
2. The method for feedforward compensation regulation and control of the autoclave molding process parameters of the composite material component according to claim 1, which is characterized in that: the neural network inverse model hidden layer is provided with 15 neurons, and the output layer is provided with 50 neurons.
3. The method for feedforward compensation regulation and control of the autoclave molding process parameters of the composite material component according to claim 1, which is characterized in that: the finite element positive model was built using an ABAQUS/STANDARD and CFD solver.
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