Nozzle structure active design method, nozzle structure and dispensing packaging system

Abstract

The invention relates to a nozzle structure active design method, a nozzle structure and a dispensing packaging system.The nozzle structure active design method comprises the following steps that orthogonal simulation tests under various structure parameters are conducted on the nozzle structure with the optimal dispensing effect, and an initial multiple linear regression model between the dispensing effect and the structure parameters is constructed; evaluating and optimizing the obtained initial multiple linear regression model to obtain an optimized multiple linear regression model; and the design structure parameters of the nozzle structure are obtained according to the actually needed dispensing effect in combination with the optimized multiple linear regression model, and the design method is high in efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of research and development of a glue dispensing packaging system, and in particular to an active design method for a nozzle structure, a nozzle structure, and a glue dispensing packaging system. Background Art

[0002] The statements herein merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] According to the analysis of fluid dynamics theory, when the fluid flows through the self-excited oscillation cavity structure, the flow in the cavity will form periodic oscillations, and this periodic oscillation will make the pressure and speed of the fluid more controllable. According to the fact that the flow velocity of the fluid in the cavity is the product of the oscillation frequency and the characteristic length of the cavity, the uniformity of the fluid is directly affected by the oscillation frequency and the cavity. The flow state of the viscous fluid in the self-excited oscillation cavity can be described by the Navier-Stokes equation. Under the action of the self-excited oscillation cavity, a complex velocity and pressure field will be formed inside the fluid, and the flow characteristics will be improved through periodic disturbances. For the jet dispensing process, the uniformity and flow characteristics of the colloid will directly affect the jet dispensing accuracy (the dispensing accuracy mainly includes the control of the dispensing amount, the dispensing position accuracy and the dispensing consistency), so the self-excited oscillation cavity structure is expected to significantly improve the dispensing performance. Therefore, in order to solve the problem of high-precision dispensing of high-viscosity glue, it is urgent to study a suitable self-excited oscillation effect nozzle structure to meet the field of precision electronic components and optical sensor packaging.

[0004] Since the nozzle has a small structural size (generally 0.03-0.06mm) and a large depth-to-diameter ratio (generally 5-10), the reasonable design of the nozzle structure is crucial for the subsequent convenient manufacturing and processing of the nozzle. The existing nozzle structure design method is mainly a passive design method. For example, the trial and error method repeatedly tries different structural design schemes to meet the design goals. This method is time-consuming and inefficient when facing complex problems such as the high-viscosity jet dispensing nozzle structure design. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a nozzle structure active design method and a nozzle structure and dispensing packaging system, which improves the efficiency of nozzle structure design work and solves the problem of not being able to positively and reasonably select the nozzle structure when dispensing with a single-point diameter (0.15-0.35mm) and a single-point volume (0.5-3.0nl).

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for actively designing a nozzle structure, comprising the following steps:

[0008] Orthogonal simulation tests were conducted on the nozzle structure with the best dispensing effect under various structural parameters to build an initial multivariate linear regression model between the dispensing effect and the structural parameters.

[0009] Evaluate and optimize the obtained initial multiple linear regression model to obtain an optimized multiple linear regression model;

[0010] The design structural parameters of the nozzle structure are obtained based on the actual dispensing effect and the optimized multivariate linear regression model.

[0011] Optionally, the initial multiple linear regression model is evaluated and optimized as follows:

[0012] First, evaluate whether the initial multiple linear regression model meets the requirements. If it does not meet the requirements, add structural parameters and corresponding dispensing result data and refit until the multiple linear regression model can be judged to meet the requirements.

[0013] Optionally, use the mvregress function to construct an initial multiple linear regression model.

[0014] Optionally, the dispensing effect is represented by dispensing diameter and dispensing volume.

[0015] Optionally, a method for obtaining a nozzle structure with the best dispensing effect is:

[0016] The fluid simulation of nozzle structures with various oscillation cavity structures was carried out under the same test conditions to obtain the dispensing effects of nozzle structures with different structural forms;

[0017] The nozzle structure with the best dispensing effect is selected as the nozzle structure with the best oscillation effect.

[0018] Optionally, the dispensing effect is represented by the dispensing volume and the dispensing diameter, and the nozzle structure with the smallest dispensing diameter and the smallest dispensing volume is selected as the nozzle structure with the best dispensing effect.

[0019] Optionally, the specific steps of fluid simulation are:

[0020] Model the nozzle structure;

[0021] The established nozzle structure model is meshed to obtain a fluid simulation model;

[0022] Perform fluid simulation of the dispensing process on the fluid simulation model to obtain the dispensing results.

[0023] Optionally, the model of the nozzle structure is a two-dimensional model.

[0024] In a second aspect, an embodiment of the present invention provides a nozzle structure designed using the nozzle structure active design method described in the first aspect.

[0025] In a third aspect, an embodiment of the present invention provides a dispensing packaging system, which is provided with the nozzle structure described in the second aspect.

[0026] The beneficial effects of the present invention are as follows:

[0027] 1. The design method of the present invention first establishes an initial multiple linear regression model between the nozzle structure parameters and the dispensing results according to the orthogonal simulation test results, obtains an optimized multiple linear regression model after evaluation and optimization, and obtains the required design structure parameters according to the optimized multiple linear regression model combined with the required dispensing results. Compared with the traditional trial and error method, it has higher flexibility and reliability, improves the efficiency of nozzle structure design, and solves the problem of not being able to positively and reasonably select the nozzle structure when dispensing single-point diameter (0.15-0.35mm) and single-point volume (0.5-3.0nl).

[0028] 2. The design method of the present invention uses R 2 The optimized multivariate linear regression model is obtained by evaluating and optimizing the value, P value and residual analysis, which further improves the accuracy of the model. This evaluation system provides a scientific basis for the nozzle structure design, which significantly improves the controllability and repeatability of the dispensing process.

[0029] 3. The design method of the present invention uses the mvregress function to construct an initial multivariate linear regression model, thereby realizing dual-objective optimization of dispensing diameter and dispensing volume, so that the structural design parameters obtained according to the multivariate linear regression model can meet the requirements of dispensing diameter and dispensing volume. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0031] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0032] Figure 2 1 is a structural parameter diagram of the elliptical oscillation cavity nozzle structure of Example 1 of the present invention;

[0033] Figure 3 It is a structural parameter diagram of the rectangular cavity oscillating cavity nozzle structure of Example 1 of the present invention;

[0034] Figure 4 It is a structural parameter diagram of the conical oscillation cavity nozzle structure of Example 1 of the present invention;

[0035] Figure 5This is a simulation test diagram of the elliptical oscillating cavity nozzle structure of Example 1 of the present invention;

[0036] Figure 6 This is a simulation test diagram of the rectangular cavity oscillating cavity nozzle structure of Example 1 of the present invention;

[0037] Figure 7 This is a simulation test diagram of the conical oscillating cavity nozzle structure of Example 1 of the present invention;

[0038] Figure 8 is a fitting diagram of the nozzle diameter of Example 1 of the present invention;

[0039] Fig. 9 is a residual diagram of the nozzle hole diameter of Example 1 of the present invention;

[0040] Fig.10 is a residual probability diagram of the nozzle hole diameter in Example 1 of the present invention;

[0041] Fig.11 is a residual histogram of the nozzle hole diameter in Example 1 of the present invention; DETAILED DESCRIPTION

[0042] Example 1

[0043] This embodiment provides a nozzle structure design method, such as Figure 1 As shown, the following steps are included:

[0044] Step 1: Conduct orthogonal simulation tests under various structural parameters for the nozzle structure with the best dispensing effect, and construct an initial multiple linear regression model between the dispensing effect and the structural parameters, which specifically includes the following steps:

[0045] Step (1) obtains the nozzle structure with the best dispensing effect.

[0046] The method to obtain the nozzle structure with the best dispensing effect is:

[0047] Step a: Perform fluid simulation on nozzle structures with various oscillation cavity structures under the same test conditions to obtain the dispensing effects of nozzle structures with different structural forms;

[0048] Step b: Select the nozzle structure with the best dispensing effect as the nozzle structure with the best oscillation effect.

[0049] During the fluid injection process, the nozzle converts pressure energy into kinetic energy. The self-excited oscillation effect can enhance the stability of the fluid and improve the dispensing accuracy. Therefore, the selection of the oscillation cavity structure and the structural design of the nozzle directly affect the injection performance. Reasonable nozzle structure design can improve the energy conversion efficiency and reduce energy loss along the way. In this embodiment, fluid simulation is performed on nozzle structures with three vibration cavity structures, namely, the nozzle structure of the elliptical oscillation cavity, the nozzle structure of the rectangular cavity oscillation cavity, and the nozzle structure of the conical oscillation cavity. Figure 2-Figure 7 As shown, the main structural parameters of the nozzle structures of the three vibration chambers are:

[0050] The nozzle hole diameter D3, nozzle hole length L3, contact cone angle θ1 between the impactor and the nozzle, the impactor radius R, the upper arc diameter R1 of the elliptical oscillation cavity, the lower arc diameter R2 of the elliptical oscillation cavity, the oscillation cavity inlet diameter D2, the elliptical oscillation cavity arc contact surface diameter D4, the oscillation cavity height L, the upper arc part height L1 of the elliptical oscillation cavity, the lower arc part height L2 of the elliptical oscillation cavity, and the cone angle θ2 of the conical oscillation cavity. In the nozzles of the three structures, the nozzle hole diameter D3, the nozzle hole length L3, the contact cone angle θ1 between the impactor and the nozzle, the impactor radius R, the oscillation cavity inlet diameter D2, and the oscillation cavity height L are the same. The nozzle structure parameters are shown in the following table:

[0051] Table 1: Nozzle structure parameters

[0052]

[0053]

[0054] The dispensing effects of nozzles with different oscillation cavity structures under high viscosity glue conditions are compared and analyzed, and the optimal structure is selected. Specifically, under the same experimental conditions, fluid simulation is performed on nozzles with three oscillation cavity structures, and the nozzle structure with the best dispensing effect is obtained based on the simulation results.

[0055] In step a, the specific steps of fluid simulation are:

[0056] The model of the nozzle structure is established using CAD software. In this embodiment, the established nozzle structure model is a two-dimensional model, which simplifies the difficulty of modeling and analysis and calculation.

[0057] The established two-dimensional model of the nozzle structure is imported into the Meshing software for meshing to obtain a fluid simulation model of the nozzle structure.

[0058] Fluent software was used to simulate the dispensing process of the three nozzle structures to observe the dispensing results.

[0059] In this embodiment, the dispensing results are expressed in terms of dispensing diameter and dispensing volume, and the changes in dispensing diameter and dispensing volume of the three nozzle structures are observed through fluid simulation.

[0060] The simulation results were analyzed using the post-processing software CFD-Post. There were significant differences in the dispensing effects of various nozzle structures under the same conditions. After a comparative analysis of the single-point diameter and single-point volume, it was found that the nozzle with an elliptical self-excited oscillation cavity structure had a better dispensing performance for high-viscosity glue, and its dispensing volume and dispensing diameter were the smallest. At the same time, by changing the initial experimental conditions such as the impact speed of the striker and the glue supply pressure, multiple groups of comparisons were carried out, which verified that the structure had a better effect on high-viscosity jet dispensing. Therefore, a nozzle with an elliptical self-excited oscillation cavity structure was selected for orthogonal experimental research and an active design method for its nozzle structure was established. In the above steps, the dispensing parameters diameter and volume were used to evaluate the performance of each self-excited oscillation cavity nozzle structure in the dispensing process, especially in terms of dispensing stability and controllability. This detailed comparative analysis provides a basis for selecting the optimal oscillation cavity structure, ensuring reliability and efficiency in high-viscosity glue applications.

[0061] Step (2) is to conduct an orthogonal simulation test under various structural parameters for the nozzle structure of the elliptical oscillation cavity, and to construct an initial multivariate linear regression model between the nozzle structural parameters and the dispensing effect based on the orthogonal simulation test results.

[0062] In this embodiment, the influencing factors of the spray hole diameter D3, the spray hole length L3, the contact cone angle θ1 between the impact pin and the nozzle, the impact pin radius R, the upper arc diameter R1 of the elliptical oscillation cavity, the lower arc diameter R2 of the elliptical oscillation cavity, the oscillation cavity inlet diameter D2, the arc contact surface diameter D4 of the elliptical oscillation cavity, the height L1 of the upper arc part of the elliptical oscillation cavity, and the height L2 of the lower arc part of the elliptical oscillation cavity are selected as dependent variables, i.e., target variables, and the dispensing diameter D and the dispensing volume V are selected as independent variables to construct an initial multivariate linear regression model.

[0063]

[0064] in:

[0065] D is the dispensing diameter and V is the dispensing volume as independent variables.

[0066] D3, L3, θ1, R, R1, R2, D2, D4, L1, and L2 are dependent variables (i.e., target variables).

[0067] β1, β2, β3, β4, β5, β6, β7, β8, β9, β 10 , γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, γ9, γ 10It is the multiple linear regression coefficient, which indicates the influence of the independent variable on the dependent variable.

[0068] Simulation experiments were conducted on nozzle structures with various structural parameters, and the corresponding dispensing diameters and dispensing volumes under different structural parameters were recorded to ensure that there was sufficient data for regression analysis.

[0069] The method for the simulation test can be the fluid simulation method in step a, which will not be repeated here.

[0070] After all simulation experiments are completed, all independent variables are normalized or standardized to ensure that variables of different scales will not have too much impact on model fitting.

[0071] All simulation data were imported into Matlab software, and Matlab software was used to construct an initial multivariate linear regression model between nozzle structural parameters and dispensing results. Specifically:

[0072] In Matlab, you can use the mvregress function to build a multiple linear regression model and perform a significance test by outputting the P value, multiple linear regression coefficient, and other statistical information. The mvregress function automatically outputs the P value, regression coefficient, and other statistical information.

[0073] Assume that the experimental data is as follows:

[0074] [DV] is the independent variable matrix, [D3 L3 θ1 R R1 R2 D2 D4 L1 L2] is the dependent variable matrix

[0075] Y=[D3, L3, θ1, R, R1, R2, D2, D4, L1, L2;

[0076] …

[0077] D3, L3, θ1, R, R1, R2, D2, D4, L1, L2];

[0078] 27 groups of structural parameters were selected.

[0079] D=[y1;y2;y3;y4;y5;y6;y7;y8;y9;y 10 ;y 11 ;y 12 ;y 13 ;y 14 ;y 15 ;y 16 ;y 17 ;y 18 ;y 19 ;y 20 ;y 21 ;y 22;y 23 ;y 24 ;y 25 ;y 26 ;y 27 ];

[0080] 27 dispensing diameters are obtained.

[0081] V=[z1;z2;z3;z4;z5;z6;z7;z8;z9;z 10 ; z 11 ; z 12 ; z 13 ; z 14 ; z 15 ; z 16 ; z 17 ; z 18 ; z 19 ; z 20 ; z 21 ; z 22 ; z 23 ; z 24 ; z 25 ; z 26 ; z 27 ];

[0082] This results in 27 dispense volumes.

[0083] X=[DV]

[0084] Create a linear multiple linear regression model

[0085]

[0086] According to the constructed multiple linear regression model, β1, β2, β3, β4, β5, β6, β7, β8, β9, β 10 , γ1, γ2, γ3, γ4, γ5, γ6, γ7, γ8, γ9, γ 10 Specific parameter values ​​are obtained to obtain the functional relationship between the nozzle structure parameters and the dispensing effect.

[0087]

[0088] Estimate the regression coefficient matrix using the least squares method

[0089]

[0090] Step 2: Evaluate and optimize the obtained initial multiple linear regression model to obtain an optimized multiple linear regression model.

[0091] The evaluation method can be based on existing technology, by calculating R 2The goodness of fit of the model can be tested by using the MATLAB software to extract the R 2 The goodness of fit of the initial multiple linear regression model was tested by using the value and P value. Matlab software can automatically extract the R 2 The values ​​and P values ​​are not described in detail here.

[0092] R 2 It can be expressed by the following formula:

[0093]

[0094] R 2 The value is used to measure the fitting degree of the multivariate linear regression model to the experimental data. is the residual sum of squares, which represents the difference between the model predicted value and the actual value. is the total sum of squares, which represents the total difference between the actual value and its mean, y i represents the actual value, is the model prediction value, is the mean of the actual values.

[0095] According to the initial R 2 The adjusted R adj 2 value.

[0096] Adjusted R adj 2 It refers to the correction of the ordinary R 2 The sensitivity to the independent variables is adjusted by considering the sample size and the number of independent variables to better avoid overfitting.

[0097]

[0098] R 2 is the unadjusted coefficient of determination, n is the total number of observed dependent variables (here 27), and p is the number of independent variables (here 10).

[0099] If 1 and R 2 The difference is less than the first set value, and R adj 2 If it is greater than the second set value, it means that the initial multiple linear regression model has a good fitting effect, otherwise, the fitting effect is poor.

[0100] Preferably, the first set value is 0.05, and the second set value is 0.1.

[0101] Perform residual analysis on the fitted model, i.e. the difference between the actual and predicted values.

[0102]

[0103] Then solve the standard error of the regression coefficient The size matrix of the t statistic is solved according to the standard error to further obtain the P value matrix. The P value indicates the significance of the regression coefficient, and the t value is used to test whether the regression coefficient is significantly different from zero.

[0104] Residual analysis can help detect the model's fitting effect and determine whether the model has systematic errors. The residual is the difference between the model's predicted value and the actual value. The following are examples of various analysis graphs generated by Matlab, taking the nozzle diameter as an example. Figure 8 is the fitting diagram of the nozzle diameter, Fig. 9 is the residual diagram of the nozzle diameter, Fig.10 is the residual probability map of the nozzle diameter, Fig.11 is the residual histogram of the nozzle diameter.

[0105] Solving with Matlab will output a complete regression analysis table containing the following information:

[0106] Regression coefficients: β1, β2, β3, β4, β5, β6, β7, β8, β9, β 10

[0107] P value: The P value of each independent variable is used to determine the degree of influence of the independent variable on the dependent variable. If the P value is less than the significance level (set as a), the variable has a significant effect on the dependent variable; if the P value is greater than the significance level, the variable has little effect on the dependent variable.

[0108] SE value: The standard error of each regression coefficient, indicating the range of variation of the estimated value. The smaller the value, the more reliable the estimated value.

[0109] tStar: used to test whether each regression coefficient is significant. The larger the value, the more significant the impact of the structural parameter.

[0110] R 2 Value: goodness of fit of the model. The closer the value is to 1, the better the fit.

[0111] Adjusted R 2 Value: Corrected normal R 2 The sensitivity to the independent variables is adjusted by considering the sample size and the number of independent variables to better avoid overfitting.

[0112] Through residual analysis and various diagnostic charts, verify whether the model conforms to the multivariate linear regression model and judge the fitting effect.

[0113] Residual Analysis and R 2Among the values, as long as one of the verified models is unreasonable, it means that the obtained multivariate linear regression model is unreasonable.

[0114] When the initial multiple linear regression model is unreasonable, the amount of data is increased, that is, the structural parameters and the corresponding dispensing diameter and dispensing volume data are increased, and the fitting is performed again until the requirements are met to obtain the final optimized multiple linear regression model.

[0115] Step 3: Obtain the design structural parameters of the nozzle structure based on the actual dispensing effect and the optimized multivariate linear regression model.

[0116] The actually required dispensing diameter and dispensing volume are obtained, and the actually required dispensing diameter and dispensing volume are brought into the optimized multivariate linear regression model obtained in step 2 to obtain the design structural parameters of the corresponding nozzle structure.

[0117] The design method of this embodiment is firstly to carry out an orthogonal simulation test according to the better nozzle structure, to establish an initial multiple linear regression model between the nozzle structure parameters and the dispensing results, to obtain the optimized multiple linear regression model after evaluation and optimization, and to obtain the required design structure parameters according to the optimized multiple linear regression model combined with the required dispensing results. Compared with the traditional trial and error method, it has higher flexibility and reliability, improves the efficiency of nozzle structure design, solves the problem of not being able to positively and reasonably select the nozzle structure when dispensing single-point diameter (0.15-0.35mm) and single-point volume (0.5-3.0nl), and is applied to the nozzle structure design in the scenario of high-precision jet dispensing packaging of high-viscosity glue (viscosity ≥ 20000cps).

[0118] In a practical application of the method of this embodiment:

[0119] It is known that the target dispensing diameter is 0.25mm and the dispensing volume is 1.2nl. According to the fitted multivariate linear regression model, the structural dimension parameters can be obtained as follows:

[0120] [D3 L3 θ1R R1 R2 D2 D4 L1 L2]=[0.041621065 0.20185082 90.717360.30658454 0.97884478 0.12111122 0.110999 0.32291819 0.07061112 0.161458635

[0121] During the modeling process, the optimization of each dimension can obtain the following structural dimension parameter values: [D3 L3 θ1 R R1R2 D2 D4 L1 L2] = [0.04 0.20 90 0.30 0.98 0.12 0.11 0.32 0.07 0.16]

[0122] The nozzle with the above structural parameters is modeled and simulated, and the results show that its dispensing diameter is 0.2489mm and the dispensing volume is 1.0478nl.

[0123]

[0124] Substituting the data, the relative errors of the dispensing diameter and dispensing volume are calculated to be 0.0044 and 0.1268 respectively, which meet the requirements. The main source of the error is that the structural dimensions are optimized during the modeling process in consideration of actual processing problems.

[0125] Example 2

[0126] This embodiment provides a nozzle structure, which is designed using the nozzle structure design method described in Embodiment 1.

[0127] Example 3

[0128] This embodiment provides a dispensing packaging system, which is provided with the nozzle structure described in Example 2. The remaining structures of the dispensing packaging system can adopt the existing technology and will not be described in detail here.

[0129] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for active nozzle structure design, characterized in that: The following steps are involved: Orthogonal simulation tests were conducted on the nozzle structure with the best dispensing effect under various structural parameters to build an initial multivariate linear regression model between the dispensing effect and the structural parameters. Evaluate and optimize the obtained initial multiple linear regression model to obtain an optimized multiple linear regression model; The design structural parameters of the nozzle structure are obtained based on the actual dispensing effect and the optimized multivariate linear regression model.

2. A nozzle structure active design method as claimed in claim 1, characterized in that: The evaluation and optimization method of the initial multiple linear regression model is: First, evaluate whether the initial multiple linear regression model meets the requirements. If it does not meet the requirements, add structural parameters and corresponding dispensing result data and refit until the multiple linear regression model can be judged to meet the requirements.

3. A nozzle structure design method according to claim 1, characterized in that: Use the mvregress function to construct an initial multiple linear regression model.

4. A nozzle structure active design method as claimed in claim 1, characterized in that: The dispensing effect is expressed by dispensing diameter and dispensing volume.

5. A nozzle structure active design method as claimed in claim 1, characterized in that: The method for obtaining the nozzle structure with the best dispensing effect is: The fluid simulation of nozzle structures with various oscillation cavity structures was carried out under the same test conditions to obtain the dispensing effects of nozzle structures with different structural forms; The nozzle structure with the best dispensing effect is selected as the nozzle structure with the best oscillation effect.

6. A nozzle structure active design method as claimed in claim 5, characterized in that: The dispensing effect is expressed by the dispensing volume and the dispensing diameter. The nozzle structure with the smallest dispensing diameter and dispensing volume is selected as the nozzle structure with the best dispensing effect.

7. A nozzle structure active design method as claimed in claim 5, characterized in that: The specific steps of fluid simulation are: Model the nozzle structure; The established nozzle structure model is meshed to obtain a fluid simulation model; Perform fluid simulation of the dispensing process on the fluid simulation model to obtain the dispensing results.

8. A nozzle structure active design method as claimed in claim 7, characterized in that: The model of the nozzle structure is a two-dimensional model.

9. A nozzle structure, characterized in that: The nozzle structure is designed by the active nozzle structure design method described in any one of claims 1 to 8.

10. A dispensing packaging system, characterized in that: The nozzle structure according to claim 9 is provided.

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