CN116514614A - Explosive compression molding process parameter optimization method - Google Patents
Explosive compression molding process parameter optimization method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 162
- 230000008569 process Effects 0.000 title claims abstract description 86
- 239000002360 explosive Substances 0.000 title claims abstract description 76
- 238000000748 compression moulding Methods 0.000 title claims abstract description 63
- 238000005457 optimization Methods 0.000 title claims description 33
- 230000004044 response Effects 0.000 claims abstract description 67
- 238000012360 testing method Methods 0.000 claims abstract description 32
- 239000000463 material Substances 0.000 claims abstract description 31
- 238000013461 design Methods 0.000 claims abstract description 22
- 238000000465 moulding Methods 0.000 claims abstract description 12
- 238000003825 pressing Methods 0.000 claims description 37
- 238000004519 manufacturing process Methods 0.000 claims description 19
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- 239000010720 hydraulic oil Substances 0.000 claims description 8
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- 238000012216 screening Methods 0.000 claims description 5
- 230000000754 repressing effect Effects 0.000 claims description 3
- 238000011058 failure modes and effects analysis Methods 0.000 claims 2
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- C—CHEMISTRY; METALLURGY
- C06—EXPLOSIVES; MATCHES
- C06B—EXPLOSIVES OR THERMIC COMPOSITIONS; MANUFACTURE THEREOF; USE OF SINGLE SUBSTANCES AS EXPLOSIVES
- C06B21/00—Apparatus or methods for working-up explosives, e.g. forming, cutting, drying
- C06B21/0033—Shaping the mixture
- C06B21/0075—Shaping the mixture by extrusion
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- C—CHEMISTRY; METALLURGY
- C06—EXPLOSIVES; MATCHES
- C06B—EXPLOSIVES OR THERMIC COMPOSITIONS; MANUFACTURE THEREOF; USE OF SINGLE SUBSTANCES AS EXPLOSIVES
- C06B21/00—Apparatus or methods for working-up explosives, e.g. forming, cutting, drying
- C06B21/0033—Shaping the mixture
- C06B21/0041—Shaping the mixture by compression
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Abstract
The invention discloses a method for optimizing explosive compression molding process parameters, which optimizes the explosive compression molding process parameters by using a response curved surface method, and adopts the final optimized process parameters to enable the quality parameters to reach target values, improve the molding quality of the explosive, reduce the consumption of manpower and material resources, and have the advantages of low cost, high speed, high precision and the like. Meanwhile, compared with the traditional full factor test, the method for optimizing the explosive compression molding process by adopting the response surface method does not need to continuously carry out multiple tests, and under the condition that the factor numbers are the same, the experimental combination number of the response surface method is less than that of the full factor test design, and the experimental efficiency is higher.
Description
Technical Field
The invention relates to the technical field of explosive compression molding, in particular to an explosive compression molding process parameter optimization method for optimizing explosive molding process parameters based on a response surface method.
Background
Explosive is an important component of the destruction efficiency of a weapon, and one of the important factors affecting the destruction efficiency is the charge quality. At present, the press-loading method is a main loading mode of small-caliber armor-piercing bullets and other bullets with uncomplicated shapes, and when the press-loading method is adopted for loading, technological parameters such as material preheating temperature, mould preheating temperature, vacuum degree and the like influence the density and uniformity of the formed charges, and the traditional press-loading method is used for controlling the quality of the formed charges, judging the quality of the charged quality by detecting the density and rebound quantity of the formed grains, and continuously adjusting the technological parameters according to empirical data, so as to obtain the forming technological parameters meeting the requirements.
However, this method is time-consuming and laborious, and the internal stress and the relative density state of the grain cannot be intuitively observed, so that it is difficult to ensure consistency of the quality of the molded grain, and the method of adjusting the process parameters according to experience has high requirements on the technical level of operators.
Therefore, how to provide a method capable of optimizing the process parameters of explosive compression molding to realize the intellectualization and automation of the explosive compression molding production control, improve the molding quality of the explosive and reduce the consumption of manpower and material resources is a technical problem which is urgently needed to be solved by the technicians in the field.
Disclosure of Invention
In view of the above, the present invention provides a method of optimizing parameters of an explosive compression forming process for overcoming or at least partially solving the above problems. The method is applied to the explosive compression molding process, and aims to optimize explosive compression molding process parameters, improve the quality of finished products, reduce the product difference between batches and improve the charging quality of explosive grains.
The invention provides the following scheme:
an optimization method for explosive compression molding process parameters comprises the following steps:
obtaining a plurality of important influencing factors of the corresponding explosive compression molding process when the molding quality is qualified by adopting a single factor variable method; the important influencing factors comprise material preheating temperature, mould preheating temperature and vacuum degree;
taking a plurality of important influencing factors as independent variables and the density of the finished product as a response value, and performing a response surface optimization test to obtain a plurality of sets of response surface optimization test data;
performing multiple regression fitting on multiple sets of response surface optimization test data to obtain a multiple regression equation of the density of the finished product;
respectively solving first-order partial derivatives of a plurality of important influence factors through the multiple regression equation to obtain a ternary primary equation set;
and solving the ternary once equation set to obtain the optimal compression molding process parameters of the compression molding of the explosive.
Preferably: the single factor variable method comprises the following steps:
searching relevant factors related to forming quality in the explosive pressing process:
the related factors comprise material preheating temperature, mould preheating temperature, pressure rising rate, pressing speed, displacement, temperature, vacuum degree, pressure compensation, pressing times, flow of cylinder hydraulic oil, pre-pressing and pressure relief re-pressing;
the important influencing factors are screened by a C & E matrix and FMEA factor analysis method.
Preferably: classifying the related factors to obtain a process parameter set and a production parameter set;
the technological parameter set comprises material preheating temperature, mould preheating temperature, pressure rising rate, pressing speed, displacement, vacuum degree, pressure compensation and pressing times;
the production parameter set comprises the flow, pre-pressing and pressure relief and repressing of cylinder hydraulic oil;
and screening the important influencing factors through a C & E matrix and FMEA factor analysis method by combining the process parameter set and the production parameter set.
Preferably: the Design of the response surface optimization test and the multiple regression fit were performed by Design experert 11.0.4 software.
Preferably: performing response surface optimization tests to obtain multiple sets of response surface optimization test data; comprising the following steps:
and taking a plurality of important influencing factors as input values, taking the density of the finished product as a response value, setting the numerical ranges of the input values and the response values, obtaining a required experimental design through Plackett-Burmen design, determining an experimental sequence and completing the experiment to obtain a plurality of groups of response curve optimization test data.
Preferably: the input values are the end point values and the middle value of the value range of each single important influence factor.
Preferably: the numerical range of the input value comprises 77-80 ℃ of material preheating temperature, 77-80 ℃ of die preheating temperature and 0.6-0.8 kilopascal of vacuum degree.
Preferably: determining the significance level of each important influence factor and the fitting effect of the model through significance analysis;
interaction among the important influencing factors is obtained through response surface analysis, so that the model is modified to remove insignificant factors and fitting is carried out again.
Preferably: predicting the experimental data by adopting the multiple regression equation to obtain a predicted value of the experimental data;
and comparing the predicted value with the actual value, and verifying the fitting effect of the model.
Preferably: generating target control parameters by utilizing the optimal compression molding process parameters to control the production process, wherein the target control parameter generation method comprises the following steps:
when production begins, the target control parameter is the optimal compression molding process parameter plus 10%;
when the explosive density reaches 70% of the predicted value, the target control parameter is the optimal compression molding process parameter minus 5%;
and when the density of the explosive reaches 85% of the predicted value, the target control parameter is the optimal compression molding process parameter.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the explosive compression molding process parameter optimization method, the response curved surface method is utilized to optimize the explosive compression molding process parameters, and finally optimized process parameters are adopted, so that the quality parameters can reach the target values, the explosive molding quality is improved, the manpower and material resource consumption is reduced, and the explosive compression molding process parameter optimization method has the advantages of being low in cost, fast in speed, high in precision and the like.
Meanwhile, compared with the traditional full factor test, the method for optimizing the explosive compression molding process by adopting the response surface method does not need to continuously carry out multiple tests, and under the condition that the factor numbers are the same, the experimental combination number of the response surface method is less than that of the full factor test design, and the experimental efficiency is higher.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a system block diagram of an explosive compression molding process parameter optimization method provided by an embodiment of the invention;
FIG. 2 is a graph comparing model prediction results with actual results provided by an embodiment of the present invention;
FIG. 3 is a graph of relative density residuals provided by an embodiment of the invention;
FIG. 4 is a residual profile provided by an embodiment of the present invention;
FIG. 5 is a graph showing the density of the finished product, the preheating temperature of the material and the preheating temperature of the die according to the embodiment of the invention;
FIG. 6 is a graph showing the density of the finished product, the preheating temperature of the material and the vacuum degree according to the embodiment of the invention;
FIG. 7 is a graph showing the density of the final product, the degree of vacuum and the preheating temperature of the mold according to the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the invention, fall within the scope of protection of the invention.
Referring to fig. 1, a method for optimizing parameters of an explosive compression molding process according to an embodiment of the present invention, as shown in fig. 1, may include:
obtaining a plurality of important influencing factors of the corresponding explosive compression molding process when the molding quality is qualified by adopting a single factor variable method; the important influencing factors comprise material preheating temperature, mould preheating temperature and vacuum degree; specifically, the single factor variable method includes:
searching relevant factors related to forming quality in the explosive pressing process:
the related factors comprise material preheating temperature, mould preheating temperature, pressure rising rate, pressing speed, displacement, temperature, vacuum degree, pressure compensation, pressing times, flow of cylinder hydraulic oil, pre-pressing and pressure relief re-pressing;
the important influencing factors are screened by a C & E matrix and FMEA factor analysis method.
Further, classifying the related factors to obtain a process parameter set and a production parameter set;
the technological parameter set comprises material preheating temperature, mould preheating temperature, pressure rising rate, pressing speed, displacement, vacuum degree, pressure compensation and pressing times;
the production parameter set comprises the flow, pre-pressing and pressure relief and repressing of cylinder hydraulic oil;
and screening the important influencing factors through a C & E matrix and FMEA factor analysis method by combining the process parameter set and the production parameter set.
Taking a plurality of important influencing factors as independent variables and the density of the finished product as a response value, and performing a response surface optimization test to obtain a plurality of sets of response surface optimization test data; specifically, the Design of the response surface optimization test and the multiple regression fit were performed by Design experert 11.0.4 software.
Further, performing a response surface optimization test to obtain multiple sets of response surface optimization test data; comprising the following steps:
and taking a plurality of important influencing factors as input values, taking the density of the finished product as a response value, setting the numerical ranges of the input values and the response values, obtaining a required experimental design through Plackett-Burmen design, determining an experimental sequence and completing the experiment to obtain a plurality of groups of response curve optimization test data.
Further, the input values are the end point values and the middle value of the value range of each single important influence factor.
The numerical range of the input value comprises 77-80 ℃ of material preheating temperature, 77-80 ℃ of die preheating temperature and 0.6-0.8 kilopascal of vacuum degree.
In order to correct the model, the embodiment of the application can also provide a method for determining the significance level of each important influence factor and the fitting effect of the model through significance analysis;
interaction among the important influencing factors is obtained through response surface analysis, so that the model is modified to remove insignificant factors and fitting is carried out again.
Performing multiple regression fitting on multiple sets of response surface optimization test data to obtain a multiple regression equation of the density of the finished product; in order to verify the obtained multiple regression equation, the embodiment of the application can provide the method for predicting experimental data by adopting the multiple regression equation to obtain a predicted value of the experimental data;
and comparing the predicted value with the actual value, and verifying the fitting effect of the model.
Respectively solving first-order partial derivatives of a plurality of important influence factors through the multiple regression equation to obtain a ternary primary equation set;
and solving the ternary once equation set to obtain the optimal compression molding process parameters of the compression molding of the explosive.
Further, the optimal press forming process parameters are utilized to generate target control parameters for controlling the production process, and the target control parameter generation method comprises the following steps:
when production begins, the target control parameter is the optimal compression molding process parameter plus 10%;
when the explosive density reaches 70% of the predicted value, the target control parameter is the optimal compression molding process parameter minus 5%;
and when the density of the explosive reaches 85% of the predicted value, the target control parameter is the optimal compression molding process parameter.
The explosive compression molding process parameter optimization method provided by the embodiment of the application comprises the steps of determining possible influencing factors and horizontal range, establishing a model, verifying the model, optimizing process parameters and the like. Determining possible influencing factors and level ranges through preliminary experiments to obtain technological parameters related to explosive compression molding quality parameters and a value range of each technological parameter, obtaining partial experimental design required by factorization analysis through Plackett-Burmen design (BP design), determining experimental sequences and completing the partial experiments to obtain related experimental data, selecting a proper model for fitting, analyzing the effectiveness of the model, determining the significance level of factors and the fitting effect of the model through significance analysis, obtaining the significance level of each factor and interaction among the factors through response surface analysis, modifying the model, removing insignificant factors for fitting again, finally obtaining a multiple regression model, modifying boundary conditions, obtaining optimal factors and maximum response values through response surface optimization analysis, and obtaining optimal technological parameter combinations.
The method for optimizing the compression molding process parameters of the explosive provided by the embodiment of the application is described in detail below by taking the compression molding process parameters of the explosive with the model JO-9159 as an example.
The type of the explosive is JO-9159, the explosive is pressed and molded by adopting a two-way pressing method, and the pressing speed is 1mm/s; other unchanged technological parameters are 12.5MPa of pressing pressure, 7min of vacuum time and 7min of pressure maintaining time; the diameter of the size of the powder charge is 20mm, the length-diameter ratio of the powder charge is 0.8, and the height of the powder charge is 25mm; the initial explosive has a bulk density of half that of the compact, an initial explosive height of 43.236mm and a punch stroke of 18.236mm.
And (one) determining possible influencing factors and a level range.
1. And (5) factor searching:
searching factors related to forming quality in the explosive pressing process:
determining important factors influencing the processing process and the product quality in a research range by performing single factor preliminary experiments or by the characteristics and the process of samples;
the single factor preliminary experiment is to adopt a single factor variable method to obtain each single factor value of the corresponding explosive pressing process when the molding quality is qualified;
the related factors are related technological parameters such as material preheating temperature, mould preheating temperature, pressure rising rate, pressing speed, displacement, temperature, vacuum degree, pressure compensation, pressing times, flow of cylinder hydraulic oil, pre-pressing, pressure relief and re-pressing, and the like.
2. And (5) factor screening:
classifying the searched relevant factors:
technological parameters: material preheating temperature, die preheating temperature, pressure rising rate, pressing speed, displacement, vacuum degree, pressure compensation and pressing times;
production parameters: flow, pre-pressing and pressure relief and repression of cylinder hydraulic oil.
3. Screening important influencing factors:
important influencing factors influencing the forming quality of the explosive, including material preheating temperature, mold preheating temperature and vacuum degree, are screened by a C & E matrix and FMEA factor analysis method.
And (II) establishing a model.
1. Plackett-Burmen design (BP design)
The material preheating temperature, the mould preheating temperature and the vacuum degree are taken as independent variables, the density of a finished product is taken as a response value, a part of experimental design required by factorial analysis is obtained through Plackett-Burmen design (BP design), the experimental sequence is determined, the part of experiments are completed to obtain relevant experimental data, and the experimental data are fitted to obtain a compression molding quality prediction model;
the BP is designed as a three-factor three-level test with design points at the combination of the high and low factor levels and their midpoints.
In specific implementation, the material preheating temperature, the mould preheating temperature and the vacuum degree are taken as input values, the density of a finished product is taken as a response value, the numerical range of the input value and the response value is set, the required experimental design is obtained through Plackett-Burmen design, the experimental sequence is determined, and the part of experiments are completed to obtain relevant experimental data;
the numerical range of the input value and the response value may be: the preheating temperature of the material is 77-80 ℃, the preheating temperature of the die is 77-80 ℃, and the vacuum degree is 0.6-0.8KPa;
the experimental design comprises three-factor three-level experiments, namely a key factor 1 value range, a key factor 2 value range and a key factor 3 value range, a central point experimental scheme is added, and the three-factor three-level experiments are carried out for 15 times (the value of the number M is determined according to the factor number and the factor level).
Specifically, the preheating temperatures of the materials are U1, U2 and U3, the preheating temperatures of the dies are V1, V2 and V3, the vacuum degree is W1, W2 and W3, and an experimental scheme of a central point is added to carry out M experiments. The results are shown in Table 1:
table 1 experimental design table
2. Fitting data:
performing multiple regression fitting on the obtained data of the multiple sets of response surface optimization tests to obtain a multiple regression equation of response parameters;
the multiple regression equation for the final density is:
Y＝-18.04545+0.236259A+0.263315B+0.764167C-0.000333A*B-0.005A*C+0.005B*C-0.001315A 2 -0.001537B 2 -0.545833C 2 。
wherein each letter represents: y is the density of the finished product, A is the preheating temperature of the material, B is the preheating temperature of the mould, and C is the vacuum degree.
3. And (5) model analysis.
Determining the significance level of each factor and the fitting effect of the model through variance analysis, obtaining interaction among the factors through response surface analysis, modifying the model, removing the insignificant factors, and fitting again to obtain a final multiple regression model for predicting molding quality;
(1) double response surface analysis:
based on a response surface method, the interaction of the factors is obtained through double-response surface analysis under the condition that the process standard requirements are met.
The C & E matrix analysis is to obtain a distribution matrix of suspicion factors with higher scores through simple operation, and promote to failure mode and influence analysis (FMEA) to compare and determine priority;
the FMEA factor analysis method is to analyze unqualified products, find out failure modes of the products and identify failure reasons of the products.
The results are shown in fig. 2, 3, 4, 5, 6 and 7.
TABLE 2 regression model analysis of variance table
Note that: p is more than 0.01 and less than 0.05, and the influence factors are obvious; p is more than 0.01 and less than 0.01, and the influence factors are very obvious.
(2) Analysis of variance:
the results of this model analysis of variance are shown in Table 2. As can be seen from table 2, the model has an F value of 59.89, indicating that the model is significant; the correction determination coefficient R2=0.9908 and P < 0.0001 of the model show that the model can explain 99.08% of working condition change; the P value of the mismatch item is 0.5320 and is larger than 0.05, and the signal-to-noise ratio index Adeq precision= 22.6274 measured by the model shows that the mismatch item is not obvious, the model fitting degree and the reliability are good, the experimental error is small, the model is accurate and reliable, and the method can be used for detecting the index in a certain experimental range. In summary, the model has good fitting degree and small experimental error, and the response curved surface model can be used for analyzing and predicting the compression molding quality of the explosive.
It can also be seen from Table 2 that the effect of the modeling primary term (B) is significant, the effects of (B) and (C) are not significant, and the independent factor-influencing order is B > A > C; quadratic term A 2 、B 2 、C 2 Is extremely remarkable; the interaction terms AB, BC, AC are significant. For insignificant factors, the interaction terms and quadratic terms cannot be removed from the model because they are significant.
And (III) verifying a model.
Based on a multiple regression equation of response parameters, predicting experimental data to obtain a predicted value of the experimental data, comparing the predicted value with an actual value, and verifying the fitting effect of the model.
The method comprises the steps of adopting a response surface design model, taking key factors as input values, taking finished product density as output values, determining the significance level of the factors and the fitting effect of the model through variance analysis, obtaining the interaction between the significance level of each factor and each factor through response surface analysis, modifying the model, removing insignificant factors, fitting again, and repeating the steps to obtain a multiple regression model for predicting molding quality; and obtaining a predicted value of the density of the finished product based on a multiple regression equation, and comparing the predicted value with an actual value to verify the accuracy of the model.
Table 3 model validation results table
As can be seen from Table 3, the model prediction results are accurate, and the model fitting errors are small.
And (IV) optimizing process parameters.
Based on the response surface method, the optimal value range of each parameter in each process parameter range is obtained through a response optimizer under the condition that the process standard requirement is met. And respectively solving first-order partial derivatives of the material preheating temperature, the mold preheating temperature and the vacuum degree through the regression equation to obtain a ternary primary equation set, and solving the equation set to obtain the optimal technological parameters of explosive compression molding.
The optimal conditions obtained through analysis of Design Expert software are as follows: the optimal values of the pressing process parameters are as follows: the preheating temperature of the material is 78.6 ℃, the preheating temperature of the die is 78.3 ℃, the vacuum degree is 0.71KPa, and the density of the finished product predicted by the model is 1.8118g/cm 3 。
3 repeated experiments were performed under the experimental conditions determined by the obtained quadratic regression model to verify the reliability of the response surface model, and the experimental results are shown in table 4.
Table 4 shows the results of the verification experiments
The average density of the finished products obtained by three experiments is 1.8112g/cm 3 The method is basically consistent with the model theory predicted value, so that the reliability of the optimal surface modification condition obtained by response curve optimization is higher, and the method has practical reference value.
As can be seen from table 4, the method for optimizing the explosive compression molding process parameters provided by the embodiment of the application has remarkable effect, all process indexes are qualified after application, and the quality of explosive compression molding is remarkably improved.
According to the technical scheme, the optimal compression molding process parameters are obtained by the explosive molding process parameter optimization method based on the response surface method, and according to the obtained process parameters, explosive powder is placed into a molding die to be subjected to compression treatment, so that a product with qualified quality is obtained.
In the process of intelligently controlling explosive compression molding by taking the material preheating temperature, the die preheating temperature and the vacuum degree as control parameters:
the production process is divided into three stages for intelligent control:
when production begins, the control parameters are the optimal values of all the process parameters plus 10%;
when the explosive density reaches 70% of the predicted value, the control parameter is subtracted by 5%;
when the density of the explosive reaches 85% of the predicted value, the control parameter is the optimal value of each process parameter.
In a word, the explosive compression molding process parameter optimization method provided by the application optimizes the explosive compression molding process by adopting a response surface method, simultaneously adopts multiple regression fitting analysis to process data, and establishes a regression equation to display the functional relation between a response value (the density of a finished product) and an independent variable (the preheating temperature of a material, the preheating temperature of a die and the vacuum degree) by adopting a reasonable test method, thereby reflecting the relation between the independent variable and the independent variable under the combined interaction and the response value, being beneficial to visual observation and selection of the optimal explosive compression molding process parameter, solving the problem that the combined effect of the factors of the preheating temperature of the material, the preheating temperature of the die and the vacuum degree influences the explosive compression molding quality in actual engineering, and being capable of rapidly and effectively determining more reasonable compression process parameters when the actual engineering is applied, ensuring the explosive compression molding quality and improving the qualification rate of the finished product quality.
Meanwhile, compared with the traditional full factor test, the method for optimizing the explosive compression molding process by adopting the response surface method does not need to continuously carry out multiple tests, and under the condition that the factor numbers are the same, the experimental combination number of the response surface method is less than that of the full factor test design, and the experimental efficiency is higher.
In addition, the method for optimizing the process parameters of the compression molding of the explosive provided by the embodiment of the application is high in implementation reliability, popular and easy to understand, simple to operate and beneficial to regulating and controlling the process parameters of the compression molding process of the actual explosive.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the description of the embodiments above, it will be apparent to those skilled in the art that the present application may be implemented in software plus the necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in the embodiments or some parts of the embodiments of the present application.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a system or system embodiment, since it is substantially similar to a method embodiment, the description is relatively simple, with reference to the description of the method embodiment being made in part. The systems and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (10)
1. The method for optimizing the parameters of the explosive compression molding process is characterized by comprising the following steps of:
obtaining a plurality of important influencing factors of the corresponding explosive compression molding process when the molding quality is qualified by adopting a single factor variable method; the important influencing factors comprise material preheating temperature, mould preheating temperature and vacuum degree;
taking a plurality of important influencing factors as independent variables and the density of the finished product as a response value, and performing a response surface optimization test to obtain a plurality of sets of response surface optimization test data;
performing multiple regression fitting on multiple sets of response surface optimization test data to obtain a multiple regression equation of the density of the finished product;
respectively solving first-order partial derivatives of a plurality of important influence factors through the multiple regression equation to obtain a ternary primary equation set;
and solving the ternary once equation set to obtain the optimal compression molding process parameters of the compression molding of the explosive.
2. The method of optimizing explosive compression molding process parameters according to claim 1, wherein the single factor variable method comprises:
searching relevant factors related to forming quality in the explosive pressing process:
the related factors comprise material preheating temperature, mould preheating temperature, pressure rising rate, pressing speed, displacement, temperature, vacuum degree, pressure compensation, pressing times, flow of cylinder hydraulic oil, pre-pressing and pressure relief re-pressing;
the important influencing factors are screened by a C & E matrix and FMEA factor analysis method.
3. The method for optimizing the process parameters of the compression molding of the explosive according to claim 2, wherein the related factors are classified to obtain a process parameter set and a production parameter set;
the technological parameter set comprises material preheating temperature, mould preheating temperature, pressure rising rate, pressing speed, displacement, vacuum degree, pressure compensation and pressing times;
the production parameter set comprises the flow, pre-pressing and pressure relief and repressing of cylinder hydraulic oil;
and screening the important influencing factors through a C & E matrix and FMEA factor analysis method by combining the process parameter set and the production parameter set.
4. The method of claim 1, wherein the Design of the response surface optimization test and the multiple regression fit are performed by Design experert 11.0.4 software.
5. The method for optimizing parameters of an explosive compression molding process according to claim 1, wherein the response surface optimization test is performed to obtain a plurality of sets of response surface optimization test data; comprising the following steps:
and taking a plurality of important influencing factors as input values, taking the density of the finished product as a response value, setting the numerical ranges of the input values and the response values, obtaining a required experimental design through Plackett-Burmen design, determining an experimental sequence and completing the experiment to obtain a plurality of groups of response curve optimization test data.
6. The method according to claim 5, wherein the input values are the end values and the middle values of the range of values of each single important influencing factor.
7. The method for optimizing parameters of explosive compression molding process according to claim 6, wherein the numerical range of the input values includes a material preheating temperature of 77-80 ℃, a mold preheating temperature of 77-80 ℃ and a vacuum of 0.6-0.8 kpa.
8. The method for optimizing the parameters of the explosive compression molding process according to claim 5, wherein the significance level of each important influence factor and the fitting effect of the model are determined through significance analysis;
interaction among the important influencing factors is obtained through response surface analysis, so that the model is modified to remove insignificant factors and fitting is carried out again.
9. The method for optimizing explosive compression molding process parameters according to claim 1, wherein,
predicting the experimental data by adopting the multiple regression equation to obtain a predicted value of the experimental data;
and comparing the predicted value with the actual value, and verifying the fitting effect of the model.
10. The method of optimizing explosive press forming process parameters according to claim 1, wherein the production process is controlled by generating target control parameters using the optimal press forming process parameters, the target control parameter generation method comprising:
when production begins, the target control parameter is the optimal compression molding process parameter plus 10%;
when the explosive density reaches 70% of the predicted value, the target control parameter is the optimal compression molding process parameter minus 5%;
and when the density of the explosive reaches 85% of the predicted value, the target control parameter is the optimal compression molding process parameter.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6165295A (en) * | 1999-11-23 | 2000-12-26 | Wagaman; Kerry L. | Gas-generating liquid compositions (PERSOL 1) |
| WO2014074947A2 (en) * | 2012-11-08 | 2014-05-15 | Das, Suman | Systems and methods for additive manufacturing and repair of metal components |
| US20200078847A1 (en) * | 2017-09-11 | 2020-03-12 | Komatsu Industries Corporation | Press system and method of controlling the same |
-
2023
- 2023-05-16 CN CN202310550532.1A patent/CN116514614A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6165295A (en) * | 1999-11-23 | 2000-12-26 | Wagaman; Kerry L. | Gas-generating liquid compositions (PERSOL 1) |
| WO2014074947A2 (en) * | 2012-11-08 | 2014-05-15 | Das, Suman | Systems and methods for additive manufacturing and repair of metal components |
| US20200078847A1 (en) * | 2017-09-11 | 2020-03-12 | Komatsu Industries Corporation | Press system and method of controlling the same |
Non-Patent Citations (5)
| Title |
|---|
| 宋欣;李艳聪;万志生;李艳琳;郑爽爽;: "基于响应面法的鱼类饲料颗粒密度预测模型研究", 饲料工业, no. 03, 10 February 2015 (2015-02-10), pages 27 - 31 * |
| 胡建忠: "黄土高原油用灌木资源开发利用", vol. 1, 31 October 2021, 中国环境出版集团, pages: 177 - 182 * |
| 郭彬: "创造价值的根因分析与问题解决之道", vol. 1, 31 January 2022, 机械工业出版社, pages: 182 - 183 * |
| 霍东兴;闫大庆;高波;: "可变流量固体冲压发动机技术研究进展与展望", 固体火箭技术, no. 01, 28 February 2017 (2017-02-28), pages 7 - 15 * |
| 高剑: "多工位压曲机压制大曲成型工艺优化", 食品工业科技, vol. 43, no. 8, 20 November 2021 (2021-11-20), pages 172 - 179 * |
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