CN113946903B - Optimization test design method for preparation process of heat insulation layer of solid rocket engine - Google Patents
Optimization test design method for preparation process of heat insulation layer of solid rocket engine Download PDFInfo
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Abstract
The invention provides a design method for an optimization test of a preparation process of a heat insulating layer of a solid rocket engine, which comprises the following specific implementation steps: the method comprises the following steps: combing the process factors and the level thereof and the evaluation indexes of the process level; step two: designing and implementing a process factor test scheme; step three: constructing a quantitative relation model of the process factors and the process level; step four: determining the optimal level combination of the process factors; the method comprises the steps of designing and implementing a process factor test scheme according to orthogonal design, further constructing a quantitative relation model between process factors and process levels by using stepwise regression analysis, least square estimation and AIC statistics, and finally determining the optimal level combination of the process factors by using an optimization method to enable the bonding performance of a heat insulation layer to reach an optimal state; the method provided by the invention is simple and convenient to calculate, easy to realize, more suitable for engineering practice, convenient for engineering technicians to master and use, and convenient for application and popularization.
Description
Technical Field
The invention provides a design method for an optimization test of a preparation process of a heat insulation layer of a solid rocket engine, which is an optimization method of the preparation process of the heat insulation layer of the solid rocket engine based on orthogonal design, stepwise regression analysis, least square estimation, AIC statistics and an optimization method. Aiming at a preparation process of a heat insulating layer of a solid rocket engine, carding process factors and process level evaluation indexes, the method develops process optimization test design by using orthogonal design, further constructs a quantitative relation model between the process factors and the process level evaluation indexes by using stepwise regression analysis, least square estimation and AIC statistics on the basis of test data, finally determines the optimal process combination, guides process optimization, and is suitable for the related technical fields of production process design optimization and the like.
Background
The heat insulation layer is a non-metal heat insulation ablation-resistant elastic material which is positioned between the inner surface of the shell and the propellant grain in the solid rocket engine, and the heat insulation layer has the main function of slowing down the speed of transferring heat released by the combustion of the solid propellant to the shell through the actions of heat absorption melting, decomposition and the like of the material in the working process of the solid rocket engine, so that the structural integrity of the shell and the normal work of the solid rocket engine are guaranteed. Because the working environment of the solid rocket motor is very harsh, the high requirement is put forward on the performance of the heat insulating layer, once the heat insulating layer is subjected to the action of external load, the phenomena of debonding and cracking occur, the whole rocket is caused to fail, not only can great economic loss be caused, but also the safety of workers can be endangered. Therefore, the heat insulating layer must have a good preparation process, so that the heat insulating layer not only has the characteristics of uniformity, flexibility and ablation resistance, but also has excellent adhesive property, and can be firmly adsorbed on the inner surface of the shell in the working process.
The adhesive property of the heat insulating layer is determined by the heat insulating layer preparation material and the preparation process, wherein the preparation material ensures that the heat insulating layer has the adhesive property, and the preparation process determines the maximum extent of the adhesive property. However, there are many process factors in the process of manufacturing the thermal insulation layer, and different levels of these factors have a great influence on the adhesion property of the thermal insulation layer. In addition, there are often different degrees of interaction between factors, as shown by the effect of other factors on the adhesion properties of the insulating layer when the levels of some of the factors are adjusted. The optimization of the preparation process of the heat insulation layer faces great difficulty due to multiple process factors and interaction among the factors. Therefore, in order to improve the bonding performance of the heat insulating layer of the solid rocket motor, a process optimization test design needs to be developed, and the influence relationship of process factors on the bonding performance of the heat insulating layer is explored, so that the optimal factor level combination is determined, and the bonding performance of the heat insulating layer reaches the optimal state.
Based on the method, the invention provides a method for designing an optimization test of a preparation process of a heat insulating layer of a solid rocket engine, which is a method for designing an optimization test of a bonding process of the heat insulating layer of the solid rocket engine based on orthogonal design, stepwise regression analysis, least square estimation, AIC statistics and an optimization method.
Disclosure of Invention
(1) The purpose of the invention is as follows:
the invention provides a design method for a preparation process optimization test of a solid rocket motor heat insulating layer, which aims at a series of process optimization problems generated by multiple process factors and interaction among the factors in the preparation process of the solid rocket motor heat insulating layer, and is a preparation process optimization method for the heat insulating layer, which comprises the steps of process factor and level, process level evaluation index carding, process factor test scheme design and implementation, process factor and process level quantitative relation model construction and optimal level combination determination of the process factors; the method comprises the steps of combing the process factors for preparing the heat insulation layer, the level of the process factors and the evaluation indexes of the process level, further developing the design of a process factor test scheme by utilizing orthogonal design, constructing the quantitative relation between the process factors and the process level by utilizing stepwise regression analysis, least square estimation and AIC statistics, and finally determining the optimal level combination of the process factors based on an optimization method to complete the preparation process optimization of the heat insulation layer of the solid rocket engine. The orthogonal design refers to a method for developing a test scheme design by utilizing an orthogonal table; the step-by-step regression analysis refers to a regression analysis method which gradually introduces significant variables and eliminates insignificant variables until a regression model is optimal; the least square estimation refers to a method for realizing regression coefficient estimation by minimizing the sum of squares of errors between real data and estimation data; the AIC statistic is an index for selecting an optimal model by using a mean square error; the "optimization method" refers to a method for solving an optimization problem.
(2) The technical scheme is as follows:
based on the thought, the invention provides a design method for an optimization test of a preparation process of a heat insulating layer of a solid rocket engine, which comprises the following specific implementation steps:
the method comprises the following steps: technological factor and its level, technological level evaluation index carding
The important basis for developing the optimization test design of the preparation process of the heat insulating layer of the solid rocket engine is to determine main process factors and levels thereof which influence the process level and reflect evaluation indexes of the process level;
assume that there are N factors affecting the process level, as determined by the experience of the associated worker, and are denoted as { S } 1 ，S 2 ，…，S N In which the factor S i Has a value range of [ S i，L ，S i，U ]I =1,2, …, N; in order to effectively obtain the influence information of the factors on the process level, each factor at least comprises 3 levels; therefore, based on the factors and their value range information, the factors and their levels are determined, as shown in table 1:
TABLE 1 preparation of the insulating layer Process factors and the level information table
In Table 1, q represents the number of factor levels, S i，l Represents the factor S i L =1,2, …, q;
secondly, for the adhesive property of the heat insulating layer, the tearing strength measures the force required for tearing the heat insulating layer in unit area from the inner surface of the shell, thereby reflecting the adhesive property of the heat insulating layer; the higher the preparation process level of the heat insulation layer is, the higher the tear strength of the heat insulation layer is, the better the bonding performance of the heat insulation layer is, and vice versa; therefore, the tear strength is used as an evaluation index of the production process level of the heat insulation layer;
step two: design and implementation of process factor test scheme
Based on the heat insulation layer preparation process factors and the level information thereof (table 1) determined in the step one, carrying out process factor test scheme design by utilizing orthogonal design; the orthogonal design refers to the design of a test scheme by utilizing an orthogonal table, and the design method adopts the principle of horizontal combination balance, so that the designed test points have the characteristics of uniform dispersion and uniformity;
the design and implementation steps of the heat insulation layer preparation process factor test scheme based on the orthogonal table are as follows:
(1) determining an orthogonal table L with the minimum test times based on the factor number N and the factor level number q n (Q M ) So that N is less than or equal to M and Q is less than or equal to Q, wherein L represents an orthogonal table, N represents the total test times of the orthogonal table, M represents the number of columns of the orthogonal table, and Q represents the number of different levels contained in each column of the orthogonal table;
(2) correspondingly filling the process factors and the levels thereof into an orthogonal table to obtain a heat insulation layer preparation process factor test scheme;
(3) carrying out test implementation according to the heat insulation layer preparation process factor test scheme, and recording the tear strength of each group of tests;
in order to avoid the interference of random errors on the analysis of test data, r (r is more than or equal to 2) samples are arranged for simultaneous development of each group of tests, and the average of r results is taken as the pull-off strength of the group of tests; assuming experimental data were obtained, as shown in table 2:
TABLE 2 Heat insulation layer preparation Process test data
In the context of table 2, the following,
representing factor S 1 ，S 2 ，…，S N Combine the levels at the kth test point, and } at the kth test point>
Denotes the tear strength at the kth test point, jth sample>
And k =1,2, …, n, j =1,2, …, r;
step three: process factor and process level quantitative relation model construction
According to the heat insulation layer preparation process test data (table 2) collected in the second step, the construction of a quantitative relation model between the process factors and the process level is carried out by utilizing stepwise regression analysis, least square estimation and AIC statistics, and the development process is as follows:
(1) complete formal regression model construction
First, considering the interaction between the factors, a complete formal regression model is constructed as
In the formula (1), X represents the tear strength, S i And S h Represents a process factor, a 0 Represents a constant, a i And b i，h Representing a regression coefficient;
(2) regression coefficient least squares estimation
In order to reduce the influence of random factors on the test result, the average value of the tear strengths of all samples at each test point is used as the tear strength of the test point; thus, the processed data is obtained as
Then, based on the least square estimation principle, the coefficient a can be obtained 0 ，a i (i =1,2, …, N) and b i，h (1. Ltoreq. I. Ltoreq. H. Ltoreq.N) is estimated as
Wherein the content of the first and second substances,
represents the coefficient θ = (a) 0 ，a 1 ，…，a N ，b 1，1 ，…，b N，N ) T Is evaluated by the evaluation unit>
(3) Computing AIC statistics
The AIC statistic is an optimal model selection index commonly used in regression analysis, and the smaller the AIC value is, the better the fitting effect of the regression model on data is shown to be; assuming that the coefficient estimation result (3) and the data (2) are substituted into the formula (1), the result of predicting the tear strength at each test point is obtained as
Wherein it is present>
Represents the tear strength prediction at the kth test point; the mean square error is
In the formula (4), MSE represents a mean square error; thus, the value of the AIC statistic is
AIC＝r×ln(MSE)+2E， (5)
In the formula (5), AIC represents the value of AIC statistic, and E represents the number of regression coefficients;
(4) performing stepwise regression analysis based on AIC statistics
The step of developing stepwise regression analysis based on AIC statistics is as follows:
I. complete formal regression model (1) is noted
Let w be the model
The sum of the middle accumulated items is sequentially eliminated>
The 1 st, 2 nd, … …, the w th of (a), form corresponding candidate regression models, and respectively mark as ^ greater than or equal to>
To all
Repeating (2) and (3) to obtain AIC statistical values corresponding to the models, selecting the model with the minimum AIC statistical value from the AIC statistical values, and recording the model as ^ or ^ based on the AIC statistical value>
Comparison IV
AIC statistic and->
If is greater than or equal to>
Is less than ≦ the AIC statistic value of>
Will->
Is recorded as->
And repeating steps II-IV; otherwise, the loop is ended and output->
Finally, the output model
Namely a quantitative relation model of the process factors and the process level;
step four: determination of optimum level combination of technological factors
After a quantitative relation model between the process factors and the process level is obtained, according to the value range of each factor, a heat insulation layer preparation process optimization model is obtained as follows:
in the formula (6), the reaction mixture is,
a quantitative relation model for the process factors and the process level determined in the third step, S i Is the ith process factor, S i，L Is a factor S i Lower limit of (1), S i，U Factor S i I =1,2, …, N;
solving the heat insulation layer preparation process optimization model (6) by using an optimization method (such as a genetic method and the like) to obtain the optimal level combination of process factors;
through the steps, the process factor test scheme is designed and implemented according to orthogonal design, a quantitative relation model between the process factors and the process level is further constructed by using stepwise regression analysis, least square estimation and AIC statistics, and finally an optimization method is used for determining the optimal level combination of the process factors, so that the bonding performance of the heat insulation layer reaches the optimal state; the method is simple and convenient to calculate, easy to realize, more in line with engineering practice, convenient for engineering technicians to master and use and convenient to apply and popularize.
(3) The advantages and the effects are as follows:
the invention provides a design method for an optimization test of a preparation process of a heat insulating layer of a solid rocket engine, which has the advantages that:
(1) aiming at the preparation process of the heat insulating layer of the solid rocket engine, the invention combs process factors and the level and process level evaluation indexes thereof, designs and implements a process factor test scheme according to orthogonal design, further constructs a quantitative relation model between the process factors and the process level by using stepwise regression analysis, least square estimation and AIC statistics, and finally determines the optimal level combination of the process factors by using an optimization method so as to ensure that the bonding performance of the heat insulating layer reaches the optimal state.
(2) The method provided by the invention is simple and convenient to calculate, easy to realize, more in line with engineering practice, convenient for engineering technicians to master and use and convenient to apply and popularize.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following will describe the present invention in further detail by taking the preparation process of a solid engine heat insulating layer as an example and referring to the attached drawing 1.
The preparation process of the heat insulating layer of a certain type of solid engine mainly comprises three stages of grinding, gluing and vulcanizing, wherein a plurality of process factors exist in each stage, including the mesh number of sand paper used in the grinding stage and the placement time after grinding, the primer coating times, the airing time, the adhesive coating times and temperature in the gluing stage, the vulcanizing pressure and the vulcanizing time in the vulcanizing stage and the like, and the factors can obviously influence the bonding performance of the heat insulating layer during molding. In addition, various factors interact with each other to different degrees, so that the optimization of the preparation process of the heat insulating layer becomes extremely complicated.
Therefore, the invention provides a method for designing an optimized test of a preparation process of a heat insulating layer of a solid rocket engine, the operation flow is shown in figure 1, and the method comprises the following specific steps:
the method comprises the following steps: technological factor and its level, technological level evaluation index carding
According to the working experience of related technicians, the total number of factors influencing the preparation process level of the heat insulating layer of the solid rocket engine is determined to be 8, and the names and the value ranges of the factors are respectively as follows:
(1) number of sand paper meshes (S) 1 ) The value range is [100 meshes, 300 meshes]；
(2) After grinding, standing time (S) 2 ) The value range is [1 hour, 20 hours]；
(3) Number of primer applications (S) 3 ) The value range is [2 times, 6 times ]]；
(4) Time of drying the primer (S) 4 ) The value range is [1 hour, 2 ]0 hour]；
(5) Number of times of application of adhesive (S) 5 ) The value range is [2 times, 6 times ]]；
(6) Temperature of adhesive (S) 6 ) The value range is [30 ℃,50℃ ]]；
(7) Vulcanization pressure (S) 7 ) The value range is [40 Nm, 100 Nm]；
(8) Vulcanization time (S) 8 ) The value range is [3 hours, 5 hours]。
Each factor was set to contain 3 levels, and therefore, the heat insulating layer production process factor and its level were determined as shown in table 3. Further, the tear strength was used as an index for evaluating the adhesion performance of the heat insulating layer.
TABLE 3 preparation of the insulating layer Process factors and the level information table
Step two: design and implementation of process factor test scheme
According to the heat insulation layer preparation process factors and the level information thereof provided in the table 3, the design and implementation of the process factor test scheme are carried out by utilizing orthogonal design. The method comprises the following specific steps:
(1) as can be seen from Table 3, the number of process factors is 8 and the number of factor levels is 3. Thus, the orthogonal table is determined to be L 27 (3 9 ，9 1 ) The first 8 columns of (c);
(2) correspondingly filling the process factors and the levels thereof into the selected orthogonal table to obtain a heat-insulating layer preparation process factor test scheme, as shown in table 4:
TABLE 4 test scheme for process factors for the preparation of thermal insulation layers
(3) Carrying out test implementation according to a heat insulation layer preparation process factor test scheme, wherein 5 samples are arranged in each group of tests; the average of the tear strengths of the 5 samples was calculated and taken as the tear strength for this set of tests, resulting in test data as shown in table 5:
TABLE 5 test data for the preparation of the thermal insulation layer
| Test number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| Tear strength | 4.497 | 4.182 | 4.206 | 3.903 | 4.56 | 5.085 | 4.236 | 4.965 | 4.41 |
| Test number | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 |
| Tear strength | 3.801 | 4.605 | 4.353 | 4.332 | 4.176 | 4.311 | 4.668 | 4.08 | 4.833 |
| Test number | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 |
| Tear strength | 4.185 | 5.019 | 4.884 | 5.097 | 4.736 | 4.905 | 4.683 | 4.269 | 3.843 |
Step three: process factor and process level quantitative relation model construction
And (3) according to the heat insulation layer preparation process test data (table 5) collected in the step two, developing process factor and process level quantitative relation model construction by utilizing stepwise regression analysis, least square estimation and AIC statistics, wherein the developing process is as follows:
(1) complete formal regression model construction
First, considering the interaction among the factors, a complete formal regression model is constructed as follows:
wherein X represents a tear strength, S i And S h Representing the process factors listed in Table 3, a 0 Represents a constant, a i And b i，h The regression coefficients are represented.
(2) Regression coefficient least squares estimation
The regression coefficient estimation of the full form regression model (7) was obtained by solving equation (3) based on the combination of the test data listed in table 5 and the factor levels corresponding to the test numbers listed in table 4, and the results are shown in table 6:
TABLE 6 regression coefficient estimation results for the complete form regression model
| Coefficient of performance | a 0 | a 1 | a 2 | a 3 | a 4 | a 5 | a 6 | a 7 | a 8 |
| Estimated value | 9.675 | 0.053 | -0.686 | 1.349 | -0.949 | -0.267 | -1.363 | 0.844 | -3.080 |
| Coefficient of performance | b 1，1 | b 1，2 | b 1，3 | b 1，4 | b 1，5 | b 1，6 | b 1，7 | b 1，8 | b 2，2 |
| Estimated value | 0 | 0 | -0.003 | 0.002 | -0.001 | 0 | 0 | 0 | 0.006 |
| Coefficient of performance | b 2，3 | b 2，4 | b 2，5 | b 2，6 | b 2，7 | b 2，8 | b 3，3 | b 3，4 | b 3，5 |
| Estimated value | 0 | 0.002 | 0 | 0.022 | -0.006 | 0 | -0.096 | 0 | 0 |
| Coefficient of performance | b 3，6 | b 3，7 | b 3，8 | b 4，4 | b 4，5 | b 4，6 | b 4，7 | b 4，8 | b 5，5 |
| Estimated value | 0 | 0 | 0 | 0.024 | 0 | 0 | 0 | 0 | 0.051 |
| Coefficient of performance | b 5，6 | b 5，7 | b 5，8 | b 6，6 | b 6，7 | b 6，8 | b 7，7 | b 7，8 | b 8，8 |
| Estimated value | 0 | 0 | 0 | 0.015 | 0 | 0 | -0.005 | 0 | 0.369 |
(3) Computing AIC statistics
The combinations of the regression coefficient estimation results listed in table (6) and the factor levels listed in table (4) were substituted into the perfect form regression model (7), and the results of predicting the tear strength at each test point are shown in table 7:
TABLE 7 prediction results of tear Strength at test points
Therefore, according to equations (4) and (5), the AIC statistic value of-89.48 of the complete formal regression model (7) is calculated.
(4) Developing stepwise regression analysis based on AIC statistics
Carrying out stepwise regression analysis based on AIC statistics until the AIC value of the model is not reduced any more, and obtaining a quantitative relation model of process factors and process levels as follows:
the corresponding AIC statistic was-93.94.
Step four: determination of optimum level combination of technological factors
Based on the quantitative relation model (8) of the process factors and the process level, the heat insulation layer preparation process optimization model is obtained according to the value range of each factor as follows:
solving the heat insulation layer preparation process optimization model (9) by using a genetic algorithm to obtain the optimal level combination of process factors, as shown in table 8:
TABLE 8 optimum level combinations of Process factors
| Factors of the fact | S 1 | S 2 | S 3 | S 4 | S 5 | S 6 | S 7 | S 8 |
| Level of | 300 mesh | 1 hour | 2 times (one time) | 20 hours | 2 times (one time) | 30℃ | 68 Niu Rice | 3 hours |
In addition, based on the quantitative relationship model (8) between the process factors and the process level, the tear strength at the optimum level combination of the process factors shown in table 8 was calculated to be 13.7673 (MPa), which is improved by 8.6703 (MPa) compared to the tear strength maximum of 5.097 (MPa) in the current experimental data.
In conclusion, the invention relates to a design method for an optimization test of a preparation process of a heat insulating layer of a solid rocket engine. The method aims at the preparation process of the heat insulating layer of the solid rocket engine, a technological factor test scheme is designed and implemented according to orthogonal design through carding technological factors, level and technological level evaluation indexes of the technological factors, a quantitative relation model between the technological factors and the technological level is further constructed by using stepwise regression analysis, least square estimation and AIC statistics, and finally an optimal level combination of the technological factors is determined by using an optimization method, so that the bonding performance of the heat insulating layer reaches an optimal state, and the preparation process design optimization of the heat insulating layer is effectively guided.
Claims (2)
1. A method for optimizing experimental design of a preparation process of a heat insulating layer of a solid rocket engine is characterized by comprising the following steps: the specific implementation steps are as follows:
the method comprises the following steps: combing process factors and levels thereof and process level evaluation indexes:
the important basis for developing the optimization test design of the preparation process of the heat insulating layer of the solid rocket engine is to determine the process factors and the level thereof which influence the process level and reflect the evaluation indexes of the process level;
the factors influencing the process level are determined according to the experience of related workers and are totally N and are marked as { S 1 ，S 2 ，…，S N In which the factor S i Has a value range of [ S i，L ，S i，U ]I =1,2, …, N; in order to effectively obtain the influence information of the factors on the process level, each factor at least comprises 3 levels;
secondly, for the adhesive property of the heat insulating layer, the tearing strength measures the force required for tearing the heat insulating layer in unit area from the inner surface of the shell, thereby reflecting the adhesive property of the heat insulating layer; the higher the preparation process level of the heat insulation layer is, the higher the tear strength is, the better the bonding performance of the heat insulation layer is, and vice versa; therefore, the tear strength is used as an evaluation index of the production process level of the heat insulation layer;
step two: designing and implementing a process factor test scheme:
based on the heat insulation layer preparation process factors and the horizontal information thereof determined in the step one, carrying out process factor test scheme design by utilizing orthogonal design; the orthogonal design refers to the design of a test scheme by utilizing an orthogonal table, and the design method adopts the principle of horizontal combination balance, so that the designed test points have the characteristics of uniform dispersion and uniformity;
step three: establishing a quantitative relation model between the process factors and the process level:
and (3) according to the heat insulation layer preparation process test data collected in the step two, carrying out process factor and process level quantitative relation model construction by utilizing stepwise regression analysis, least square estimation and AIC statistics, wherein the development process is as follows:
(1) complete formal regression model construction
First, considering the interaction between the factors, a complete formal regression model is constructed as
In the formula (1), X represents the tear strength, S i And S h Represents a process factor, a 0 Represents a constant, a i And b i，h Representing a regression coefficient;
(2) regression coefficient least squares estimation
In order to reduce the influence of random factors on the test result, the average value of the tear strengths of all samples at each test point is used as the tear strength of the test point; thus, the processed data is obtained as
Then, based on the least square estimation principle, the coefficient a can be obtained 0 ，a i (i =1,2, …, N) and b i，h (1. Ltoreq. Ih. Ltoreq.N as a result of estimation
Wherein, the first and the second end of the pipe are connected with each other,
represents the coefficient θ = (a) 0 ，a 1 ，…，a N ，b 1，1 ，…，b N，N ) T Is determined by the estimated value of (c),
(3) computing AIC statistics
The AIC statistic is an optimal model selection index commonly used in regression analysis, and the smaller the AIC value is, the better the fitting effect of the regression model on data is shown to be; the coefficient estimation result (3) and the data (2) are substituted into the formula (1), and the prediction result of the tear strength at each test point is obtained as
Wherein it is present>
Represents the tear strength prediction at the kth test point; then the mean square error is
In the formula (4), MSE represents a mean square error; thus, the value of the AIC statistic is
AIC＝r×ln(MSE)+2E, (5)
In the formula (5), AIC represents the value of AIC statistics, and E represents the number of regression coefficients;
(4) developing stepwise regression analysis based on AIC statistics
The step of developing stepwise regression analysis based on AIC statistics is as follows:
I. complete formal regression model (1) is noted
II, let w be the model
The sum of the middle accumulated items is sequentially eliminated>
Clause 1, clause 2, clause … …, clause w ofCorresponding alternative regression models and are each recorded as->
To all
Repeating (2) and (3) to obtain AIC statistical values corresponding to the models, selecting the model with the minimum AIC statistical value from the AIC statistical values, and recording the model as ^ or ^ based on the AIC statistical value>
Comparison IV
AIC statistic and->
If is greater than or equal to>
Is less than
Will->
Is recorded as +>
And repeating steps II-IV; otherwise, the loop is ended and output
Finally, the output model
Namely a quantitative relation model of process factors and process levels;
step four: determination of optimum level combination of technological factors
After a quantitative relation model between the process factors and the process level is obtained, according to the value range of each factor, the preparation process optimization model of the heat insulation layer is obtained as follows:
in the formula (6), the reaction mixture is,
a quantitative relation model for the process factors and the process level determined in the third step, S i Is the ith process factor, S i，L Is a factor S i Lower limit of (2), S i，U Factor S i I =1,2, …, N;
and solving the heat insulation layer preparation process optimization model (6) by using an optimization method to obtain the optimal level combination of process factors.
2. The method for designing the optimized test of the preparation process of the insulating layer of the solid rocket engine according to claim 1, wherein the method comprises the following steps: the design and implementation steps of the heat insulation layer preparation process factor test scheme based on the orthogonal table are as follows:
(1) determining an orthogonal table L with the minimum test times based on the factor number N and the factor level number q n (Q M ) So that N is less than or equal to M and Q is less than or equal to Q, wherein L represents an orthogonal table, N represents the total test times of the orthogonal table, M represents the number of columns of the orthogonal table, and Q represents the number of different levels contained in each column of the orthogonal table;
(2) correspondingly filling the process factors and the levels thereof into an orthogonal table to obtain a heat insulation layer preparation process factor test scheme;
(3) according to the heat insulation layer preparation process factor test scheme, carrying out test implementation, and recording the tear strength of each group of tests;
in order to avoid the interference of random errors on the analysis of test data, r (r is more than or equal to 2) samples are arranged to be developed simultaneously in each group of tests, and the average of r results is taken as the pull-off strength of the group of tests.
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