CN113693272A - Cigarette weight control method - Google Patents
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- CN113693272A CN113693272A CN202111029044.3A CN202111029044A CN113693272A CN 113693272 A CN113693272 A CN 113693272A CN 202111029044 A CN202111029044 A CN 202111029044A CN 113693272 A CN113693272 A CN 113693272A
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- A—HUMAN NECESSITIES
- A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
- A24C—MACHINES FOR MAKING CIGARS OR CIGARETTES
- A24C5/00—Making cigarettes; Making tipping materials for, or attaching filters or mouthpieces to, cigars or cigarettes
- A24C5/32—Separating, ordering, counting or examining cigarettes; Regulating the feeding of tobacco according to rod or cigarette condition
- A24C5/34—Examining cigarettes or the rod, e.g. for regulating the feeding of tobacco; Removing defective cigarettes
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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Abstract
The invention discloses a cigarette weight control method, which comprises the following steps: taking the standard deviation of the cigarette weight as a response variable, taking parameters influencing the cigarette weight as independent variables, and carrying out optimization design on the parameters of the response curved surface to obtain a response curved surface regression model; and according to the response surface regression model, performing feedback adjustment on the cigarette weight by adopting an exponential weighted moving average method. The cigarette weight control method of the invention takes the standard deviation of the cigarette weight as a response variable, optimizes and designs the parameters of the response curved surface to obtain a response curved surface regression model, and then adopts an exponential weighted moving average method to perform feedback adjustment on the cigarette weight according to the response curved surface regression model.
Description
Technical Field
The invention relates to the technical field of cigarette control, in particular to a cigarette weight control method.
Background
For the cigarette industry, quality is a competitive power of enterprises. As a first line of product manufacture, cigarette enterprises directly relate to the survival of the cigarette enterprises in terms of the quality and processing level of products. The circumference of the cigarette plays a very important role in stabilizing the internal quality of the cigarette.
In the cigarette rolling process, the weight of a single cigarette is usually calculated by detecting the density of a cigarette rod through microwave or infrared rays, and then the reciprocating movement of the position of the leveler is controlled through PID feedback to realize the regulation of the weight of the cigarette.
Therefore, a method for controlling the weight of cigarettes is needed.
Disclosure of Invention
The invention aims to provide a cigarette weight control method, which aims to solve the problems in the prior art, improve the precision of cigarette weight control and ensure the stability of cigarette weight.
The invention provides a cigarette weight control method, which comprises the following steps:
taking the standard deviation of the cigarette weight as a response variable, taking parameters influencing the cigarette weight as independent variables, and carrying out optimization design on the parameters of the response curved surface to obtain a response curved surface regression model;
and according to the response surface regression model, performing feedback adjustment on the cigarette weight by adopting an exponential weighted moving average method.
The method for controlling cigarette weight as described above, wherein preferably, the parameters affecting cigarette weight include: the blade clearance of the leveler, and/or the wire throwing rotation speed and/or the air pressure of the small fan.
The method for controlling cigarette weight as described above, preferably, the step of optimally designing the parameters of the response surface by using the standard deviation of the cigarette weight as the response variable and the parameters affecting the cigarette weight as the independent variables to obtain the regression model of the response surface includes:
respectively setting a low-level parameter and a high-level parameter for each independent variable;
performing a full factor test according to the parameter corresponding to each independent variable;
estimating the coefficient of the regression equation according to the test result to obtain a response surface fitting equation;
verifying the significance of each coefficient in the response surface fitting equation;
obtaining a final response surface regression model according to the significance verification result;
and carrying out optimization design on each independent variable according to the final response surface regression model.
The method for controlling cigarette weight as described above, wherein preferably, the estimating the coefficients of the regression equation according to the test result to obtain a response surface fitting equation specifically includes:
and according to the calculation result of the test scheme, estimating each regression coefficient of the multivariate quadratic regression model of the response surface by adopting a least square method to obtain a response surface fitting equation.
The method for controlling cigarette weight as described above, wherein preferably, the verifying the significance of each coefficient in the response surface fitting equation specifically includes:
and verifying the significance of each coefficient in the response surface fitting equation by a residual error analysis method.
The method for controlling cigarette weight as described above, wherein preferably, the optimizing each independent variable according to the regression model of the final response curved surface specifically includes:
and according to the final response surface regression model, in the value range of each independent variable, with the minimum value of the response variable as a target, searching the optimal solution of each independent variable and the response variable through a genetic algorithm.
The method for controlling cigarette weight as described above, preferably, the feedback adjustment of cigarette weight is performed by using an exponential weighted moving average method according to the response surface regression model, and specifically includes:
calculating the offset of the weight of a single cigarette by adopting an exponential weighted moving average method according to the detection result of the comprehensive test table on the weight of the cigarette;
and adjusting the position of the leveler according to the offset of the weight of a single cigarette so as to perform feedback adjustment on the weight of the cigarette.
The method for controlling cigarette weight as described above, wherein preferably, the calculating the deviation of the weight of a single cigarette by using an exponential weighted moving average method according to the detection result of the comprehensive test bench on the cigarette weight specifically includes:
the offset of the weight of a single cigarette is calculated by the following formula,
wherein the content of the first and second substances,is a process deviation estimate, X, for the current detection time ttIs a single-branch weight value detected by the comprehensive test bench at the current moment T, T is a single-branch weight target value, lambda is a smoothing coefficient,is the estimated value of the process deviation at the last detection time t-1, and the initial value thereof
The method for controlling cigarette weight as described above, wherein preferably, the adjusting the position of the leveler according to the offset of the weight of a single cigarette to perform feedback adjustment on the cigarette weight specifically includes:
if it isThe position of the leveler is adjusted to be 0.8mm-1.2mm lower and then taken outRe-weighting calculation, i.e. at the next detection instant t +1Then, carrying out iterative calculation according to a formula (1);
if it isThe position of the leveler is increased by 0.8mm-1.2mm, and the leveler is taken out after adjustmentRe-weighting calculation, i.e. at the next detection instant t +1Then, carrying out iterative calculation according to a formula (1);
if it isThe position of the leveler is not changed and the iterative calculation is continued to the next time according to the formula (1).
The invention provides a cigarette weight control method, which takes the standard deviation of cigarette weight as a response variable, optimizes and designs the parameters of a response curved surface to obtain a response curved surface regression model, and then adopts an exponential weighted moving average method to perform feedback adjustment on the cigarette weight according to the response curved surface regression model.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings, in which:
fig. 1 is a flowchart of an embodiment of a method for controlling cigarette weight according to the present invention.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is in no way intended to limit the disclosure, its application, or uses. The present disclosure may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that: the relative arrangement of parts and steps, the composition of materials, numerical expressions and numerical values set forth in these embodiments are to be construed as merely illustrative, and not as limitative, unless specifically stated otherwise.
As used in this disclosure, "first", "second": and the like, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element preceding the word covers the element listed after the word, and does not exclude the possibility that other elements are also covered. "upper", "lower", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
In the present disclosure, when a specific component is described as being located between a first component and a second component, there may or may not be intervening components between the specific component and the first component or the second component. When it is described that a specific component is connected to other components, the specific component may be directly connected to the other components without having an intervening component, or may be directly connected to the other components without having an intervening component.
All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
As shown in fig. 1, the method for controlling the weight of a cigarette provided by this embodiment specifically includes the following steps in an actual implementation process:
and step S1, taking the standard deviation of the cigarette weight as a response variable, taking the parameters influencing the cigarette weight as independent variables, and carrying out optimization design on the parameters of the response curved surface to obtain a response curved surface regression model.
In the cigarette rolling process, there are many factors influencing the control of the cigarette weight, and in the invention, the parameters influencing the cigarette weight comprise: the blade clearance of the leveler, and/or the wire throwing rotation speed and/or the air pressure of the small fan. Preferably, in the embodiment of the invention, the blade clearance of the leveler, the wire throwing rotation speed and the air pressure of the small fan are used as independent variables. In order to improve the precision of weight control and reduce the standard deviation of the cigarette weight, the standard deviation of the cigarette weight is firstly used as a response variable, and parameters of a response curved surface are optimally designed by taking the blade clearance of a leveler, the wire percolation rotating speed, the air pressure of a small fan and the like as independent variables. As a method for parameter optimization design, the response surface can not only consider the linear correlation relationship between the independent variable and the response variable, but also consider the relationship between the square term of the independent variable and the response variable and the relationship between interaction among a plurality of independent variables and the response variable, so that a multiple quadratic regression fitting model of the shape of the surface can be formed, and the response surface is called. In an embodiment of the cigarette weight control method of the present invention, the step S1 may specifically include:
step S11, setting a low level parameter and a high level parameter for each argument, respectively.
According to the practical situation, the design of the central composite surface is selected, a higher-level parameter and a lower-level parameter are selected for each independent variable of three independent variables of the blade gap of the leveler, the wire throwing rotation speed and the small fan air pressure, and the lower-level parameter of the independent variable of the blade gap of the leveler is set to be 0.5, and the high-level parameter is set to be 1.5; setting the low-level parameter of the independent variable of the spinning speed to be 40 and setting the high-level parameter to be 60; the low level parameter of the independent variable of the small fan air pressure is set to 1000, and the high level parameter is set to 1600.
And step S12, performing full factor test according to the parameters corresponding to each independent variable.
The method comprises the steps of carrying out full factor tests according to test parameters in a table 1, wherein a first column is a standard sequence, the standard sequence is the design of a test scheme, 20 groups of tests are required to be carried out according to the number of independent variables and different value levels of the independent variables, different factor level combinations of the 20 groups of tests are obtained according to a factor change sequence one by one, the standard sequence of the first column is constructed, and the test sequence is randomly rearranged to obtain the actual test sequence, namely the second column running sequence, because the inertia of the test result caused by the fact that only one factor level is changed but other factors are not changed in two consecutive tests is easily caused by the test carried out in the sequence. The third column to the fifth column are values of independent variables corresponding to each test, the third column is a value of the blade clearance of the leveler during each test, the fourth column is a value of the wire throwing rotation speed during each test, the fifth column is a value of the small fan air pressure during each test, and the last column is a standard deviation value calculated according to weight detection data obtained during each test.
TABLE 1 test parameter Table for full factor test
And S13, estimating the coefficients of the regression equation according to the test result to obtain a response surface fitting equation.
Specifically, according to the calculation result of the test scheme, each regression coefficient of the multiple quadratic regression model of the response surface is estimated by adopting a least square method, and a response surface fitting equation is obtained.
According to the calculation result of the test scheme, estimating each regression coefficient of the multivariate quadratic regression model of the response surface by adopting a least square method to obtain the following response surface fitting equation:
standard deviation of cigarette weight is 0.097705+0.310219 blade gap-0.118686 filament percolation speed-0.170231 small fan air pressure +0.0003903 blade gap +1.02134E-05 filament percolation speed +2.47770E-08 small fan air pressure-0.078645 blade gap filament percolation speed-0.026429 blade gap small fan air pressure +1.81072E-07 filament percolation speed.
And step S14, verifying the significance of each coefficient in the response surface fitting equation.
In the invention, the significance of each coefficient in the response surface fitting equation is verified by a residual error analysis method.
TABLE 2 residual analysis table of each coefficient in response surface fitting equation
Item(s) | Coefficient of performance | Standard error of coefficient | P |
Constant quantity | 0.097705 | 0.001019 | 0.000 |
Blade clearance | 0.310219 | 0.000938 | 0.088 |
Rotating speed of throwing | -0.118686 | 0.000938 | 0.027 |
Pressure of small fan | -0.170231 | 0.000938 | 0.087 |
BladeClearance of blade | 0.0003903 | 0.001788 | 0.597 |
Spinning speed of spinning | 1.02134E-05 | 0.001788 | 0.581 |
Air pressure of small fan | 2.47770E-08 | 0.001788 | 0.241 |
Blade gap and spinning speed | -0.078645 | 0.001048 | 0.015 |
Blade gap small fan air pressure | -0.026429 | 0.001048 | 0.013 |
Air pressure of small fan at spinning speed of spring silk | 1.81072E-07 | 0.001048 | 0.616 |
The standard error of the coefficients in table 2 is the standard error existing when estimating the coefficients. The P value is a main index for judging the significance of residual analysis, and the smaller the P value is, the more significant the regression term is.
And step S15, obtaining a final response surface regression model according to the significance verification result.
Analysis table 2 shows that the P values of the constant term, the blade clearance, the filament percolation speed, the small fan air pressure, the interaction between the blade clearance and the filament percolation speed, and the interaction between the blade clearance and the small fan air pressure are less than 0.1, and the influence of the constant term, the blade clearance, the filament percolation speed, and the interaction between the blade clearance and the small fan air pressure on the weight standard deviation is considered to be significant, and the influence of other terms is not significant and can not be considered. A final response surface regression model is then obtained:
standard deviation of cigarette weight is 0.097705+0.310219 blade gap-0.118686 filament percolation speed-0.170231 small fan air pressure-0.078645 blade gap filament percolation speed-0.026429 blade gap small fan air pressure.
And step S16, carrying out optimization design on each independent variable according to the final response surface regression model.
Specifically, according to the final response surface regression model, in the value range of each independent variable, the optimal solution of each independent variable and the response variable is found through a genetic algorithm with the minimum value of the response variable as a target.
Because the standard deviation of the cigarette weight is expected to be small, namely the smaller the standard deviation is, the better the standard deviation is, according to the obtained final response surface regression model, the optimal solution is found through a genetic algorithm between the value range of the blade clearance of 0.5 to 1.5, the value range of the filament percolation rotation speed of 40-60 and the value range of the small fan air pressure of 1000-1600 respectively, and when the blade clearance is 0.5505, the filament percolation rotation speed of 48.2828 and the small fan air pressure of 1351.5152, the standard deviation of the cigarette weight can reach the minimum value, and the minimum value is 0.0196.
And step S2, according to the response surface regression model, performing feedback adjustment on the cigarette weight by using an exponential Weighted Moving-Average (EWMA) method.
In step S1, after the optimal regression model of the response surface is obtained through parameter design, step S2 is performed to perform feedback adjustment according to the variation of the production result in the actual production of the device, which is beneficial to improving the accuracy of cigarette weight control and further ensuring the stability of cigarette weight. In an embodiment of the cigarette weight control method of the present invention, the step S2 may specifically include:
and step S21, calculating the offset of the weight of a single cigarette by adopting an exponential weighted moving average method according to the detection result of the comprehensive test bench on the weight of the cigarette.
Specifically, the offset of the weight of a single cigarette is calculated by the following formula,
wherein the content of the first and second substances,is a process deviation estimate, X, for the current detection time ttIs a single-branch weight value detected by the comprehensive test bench at the current moment T, T is a single-branch weight target value, lambda is a smoothing coefficient,is the estimated value of the process deviation at the last detection time t-1, and the initial value thereofTo ensure the sensitivity of the smoothing coefficient to the current changes, λ may be 0.35.
And step S22, adjusting the position of the leveler according to the offset of the weight of a single cigarette so as to perform feedback adjustment on the weight of the cigarette.
The offset according to the weight of a single cigaretteThe position of adjustment evener to carry out feedback adjustment to a cigarette weight, specifically include:
if it isThe position of the leveler is lowered by 0.8mm to 1.2mm, for example, by 1mm, and the leveler is adjusted and removedRe-weighting calculation, i.e. at the next detection instant t +1Then, the iterative calculation is performed according to the formula (1).
If it isThe position of the leveler is then adjusted by 0.8mm to 1.2mm, e.g., 1mm, and the leveler is adjusted and removedRe-weighting calculation, i.e. at the next detection instant t +1Then, the iterative calculation is performed according to the formula (1).
If it isThe position of the leveler is not changed and the iterative calculation is continued to the next time according to the formula (1).
The cigarette weight control method provided by the embodiment of the invention takes the standard deviation of the cigarette weight as a response variable, optimizes and designs the parameters of the response curved surface to obtain a response curved surface regression model, and then adopts an exponential weighted moving average method to perform feedback adjustment on the cigarette weight according to the response curved surface regression model.
Thus, various embodiments of the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be understood by those skilled in the art that various changes may be made in the above embodiments or equivalents may be substituted for elements thereof without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (9)
1. A cigarette weight control method is characterized by comprising the following steps:
taking the standard deviation of the cigarette weight as a response variable, taking parameters influencing the cigarette weight as independent variables, and carrying out optimization design on the parameters of the response curved surface to obtain a response curved surface regression model;
and according to the response surface regression model, performing feedback adjustment on the cigarette weight by adopting an exponential weighted moving average method.
2. The cigarette weight control method according to claim 1, wherein the parameters affecting the weight of the cigarette comprise: the blade clearance of the leveler, and/or the wire throwing rotation speed and/or the air pressure of the small fan.
3. The cigarette weight control method according to claim 2, wherein the optimization design is performed on the parameters of the response surface by using the standard deviation of the cigarette weight as a response variable and using the parameters affecting the cigarette weight as independent variables to obtain a response surface regression model, and specifically comprises:
respectively setting a low-level parameter and a high-level parameter for each independent variable;
performing a full factor test according to the parameter corresponding to each independent variable;
estimating the coefficient of the regression equation according to the test result to obtain a response surface fitting equation;
verifying the significance of each coefficient in the response surface fitting equation;
obtaining a final response surface regression model according to the significance verification result;
and carrying out optimization design on each independent variable according to the final response surface regression model.
4. The cigarette weight control method according to claim 3, wherein the estimating of the coefficients of the regression equation according to the test results to obtain a response surface fitting equation specifically comprises:
and according to the calculation result of the test scheme, estimating each regression coefficient of the multivariate quadratic regression model of the response surface by adopting a least square method to obtain a response surface fitting equation.
5. The cigarette weight control method according to claim 3, wherein the verifying the significance of each coefficient in the response surface fitting equation specifically comprises:
and verifying the significance of each coefficient in the response surface fitting equation by a residual error analysis method.
6. The cigarette weight control method according to claim 3, wherein the optimal design of each independent variable according to the regression model of the final response surface specifically comprises:
and according to the final response surface regression model, in the value range of each independent variable, with the minimum value of the response variable as a target, searching the optimal solution of each independent variable and the response variable through a genetic algorithm.
7. The cigarette weight control method according to claim 1, wherein the feedback adjustment of the cigarette weight is performed by an exponential weighted moving average method according to the response surface regression model, and specifically comprises:
calculating the offset of the weight of a single cigarette by adopting an exponential weighted moving average method according to the detection result of the comprehensive test table on the weight of the cigarette;
and adjusting the position of the leveler according to the offset of the weight of a single cigarette so as to perform feedback adjustment on the weight of the cigarette.
8. The cigarette weight control method according to claim 7, wherein the method of calculating the shift of the weight of a single cigarette by using an exponential weighted moving average according to the detection result of the comprehensive test bench on the weight of the cigarette specifically comprises:
the offset of the weight of a single cigarette is calculated by the following formula,
wherein the content of the first and second substances,is a process deviation estimate, X, for the current detection time ttIs a single-branch weight value detected by the comprehensive test bench at the current moment T, T is a single-branch weight target value, lambda is a smoothing coefficient,is the estimated value of the process deviation at the last detection time t-1, and the initial value thereof
9. The cigarette weight control method according to claim 8, wherein the position of the leveler is adjusted according to the offset of the weight of a single cigarette to perform feedback adjustment on the weight of the cigarette, specifically comprising:
if it isThe position of the leveler is adjusted to be 0.8mm-1.2mm lower and then taken outRe-weighting calculation, i.e. at the next detection instant t +1Then, carrying out iterative calculation according to a formula (1);
if it isThe position of the leveler is increased by 0.8mm-1.2mm, and the leveler is taken out after adjustmentRe-weighting calculation, i.e. at the next detection instant t +1Then, carrying out iterative calculation according to a formula (1);
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