Lightweight Glass Container Structure Optimization Design Method Based on 3D Simulation Model

Abstract

The present invention relates to the technical field of optimizing the structure of glass containers, and particularly to a method for optimizing the design of the structure of lightweight glass containers based on a three-dimensional simulation model. The method includes the following steps: constructing a continuous surface model of the glass container; constructing a geometric variant library of the glass container based on the continuous surface model of the glass container; performing multi-condition simulations on the geometric variant library of the glass container and extracting stress distribution characteristics to obtain a set of container stress distribution characteristics; constructing a set of constraints for the container forming process; and identifying NURBS control points of the glass container according to the set of constraints for the container forming process to obtain a set of NURBS control points of the constrained container. The present invention can maximize the lightweight design on the premise of ensuring the mechanical properties and manufacturing feasibility of the glass container.

Description

Technical Field

[0001] The present invention relates to the technical field of optimizing the structure of glass containers, and in particular to a method for optimizing the design of a lightweight glass container structure based on a three-dimensional simulation model. Background Art

[0002] As an important packaging form for food, medicine and chemical products, the structural design of glass containers needs to consider lightweight, mechanical properties and manufacturing feasibility. In recent years, with the popularization of computer-aided design (CAD) and finite element analysis (FEA) technologies, the traditional experience-driven manual design mode has gradually been replaced by parametric modeling and simulation verification. However, there are still significant bottlenecks in the existing methods when dealing with the coupled effects of glass material properties and complex working conditions. Glass is a typical brittle material, and its fracture behavior is extremely sensitive to local stress concentration. In lightweight design, traditional uniform wall thinning or simple topology optimization methods can reduce the material usage to a certain extent, but it is difficult to effectively suppress stress peaks. This results in that in actual use, glass containers are prone to rupture due to excessive local stress, seriously affecting their reliability and safety. In addition, the forming process of glass containers also has strict requirements on geometric features such as wall thickness gradient and curvature transition. Existing simulation-driven multi-objective optimization models often lack the dynamic integration of these manufacturability indicators, resulting in a large deviation between the simulation results and the performance of the actually manufactured products. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for optimizing the design of a lightweight glass container structure based on a three-dimensional simulation model to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for optimizing the design of a lightweight glass container structure based on a three-dimensional simulation model includes the following steps:

[0005] Step S1: Construct a continuous surface model of the glass container; construct a geometric variant library of the glass container based on the continuous surface model of the glass container; perform multi-condition simulations on the geometric variant library of the glass container, and extract stress distribution characteristics to obtain a container stress distribution characteristic set;

[0006] Step S2: Construct a set of container forming process constraints; identify NURBS control points of the glass container according to the set of container forming process constraints to obtain a set of NURBS control points of the constrained container; perform gradient conservation deformation regulation on the set of NURBS control points of the constrained container to obtain a container wall thickness gradient constraint model; generate a distribution map of container stress-sensitive regions based on the container wall thickness gradient constraint model;

[0007] Step S3: Construct the objective function for minimizing the mass of the glass container, the objective function for maximizing the strength, and the manufacturing feasibility function; construct a multi-objective optimization model for forming based on the objective function for minimizing the mass, the objective function for maximizing the strength, and the manufacturing feasibility function; solve the multi-objective optimization model for forming to obtain the design parameter set of the glass container;

[0008] Step S4: Construct a parametric three-dimensional model of the glass container according to the design parameter set of the glass container; conduct a forming performance evaluation on the parametric three-dimensional model of the glass container to obtain forming simulation performance data; conduct a parameter perturbation analysis on the design parameter set of the glass container to obtain the evaluation result of the robustness index;

[0009] Step S5: Generate design feedback adjustment suggestions based on the forming simulation performance data and the evaluation result of the robustness index; generate a lightweight glass container structure design scheme according to the design feedback adjustment suggestions.

[0010] By constructing a continuous surface model and a parametric modeling function set of the glass container, and generating a geometric variant library based on this for multi-condition simulation, the present invention can accurately extract the stress distribution eigenvector set, thereby providing a detailed and comprehensive stress data basis for subsequent optimization design, effectively solving the problems of difficult suppression of stress peaks and insufficient structural reliability in the prior art due to the lack of in-depth analysis of the coupling effect between the characteristics of glass materials and complex working conditions. By constructing a set of forming process constraints for the container, and accordingly conducting NURBS control point identification and gradient-conserved deformation regulation, a wall thickness gradient constraint model and a stress-sensitive area distribution map are generated, realizing the accurate control of the forming process constraints of the glass container, effectively making up for the defect that the existing simulation-driven multi-objective optimization model lacks the dynamic integration of manufacturability indicators, and significantly reducing the deviation between the simulation results and the actual product performance. By constructing a multi-objective optimization model for forming and solving to obtain the design parameter set, comprehensively considering mass minimization, strength maximization, and manufacturing feasibility, the present invention can maximize the lightweight design on the premise of ensuring the mechanical properties and manufacturing feasibility of the glass container, effectively balancing the relationship between lightweight, performance, and manufacturability. By generating design feedback adjustment suggestions based on the forming simulation performance data and the evaluation result of the robustness index, and accordingly adjusting the structure of the parametric three-dimensional model of the glass container, the design scheme is further optimized, the robustness and reliability of the design are improved, and the stability and durability of the glass container in actual use are ensured. Description of the Drawings

[0011] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description with reference to the accompanying drawings:

[0012] Figure 1 The step flow schematic diagram of the lightweight glass container structure optimization design method based on a three-dimensional simulation model according to an embodiment is shown.

[0013] Figure 2 Shows a detailed step - by - step schematic diagram of step S17 of an embodiment. Specific embodiments

[0014] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work fall within the scope of protection of the present invention.

[0015] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0016] It should be understood that although terms such as "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0017] To achieve the above - mentioned purpose, please refer to Figures 1 to 2 , the present invention provides a lightweight glass container structure optimization design method based on a three - dimensional simulation model, including the following steps:

[0018] Step S1: Construct a continuous surface model of the glass container; construct a geometric variant library of the glass container based on the continuous surface model of the glass container; perform multi - condition simulations on the geometric variant library of the glass container and extract stress distribution characteristics to obtain a container stress distribution characteristic set;

[0019] Step S2: Construct a set of container forming process constraints; perform NURBS control point identification on the glass container according to the set of container forming process constraints to obtain a set of NURBS control points of the constrained container; perform gradient - conservation deformation regulation on the set of NURBS control points of the constrained container to obtain a container wall - thickness gradient constraint model; generate a container stress - sensitive area distribution map based on the container wall - thickness gradient constraint model;

[0020] Step S3: Construct the objective function for minimizing the mass, the objective function for maximizing the strength, and the manufacturing feasibility function of the glass container; construct a multi-objective optimization model for forming based on the objective function for minimizing the mass, the objective function for maximizing the strength, and the manufacturing feasibility function; solve the multi-objective optimization model for forming to obtain the design parameter set of the glass container;

[0021] Step S4: Construct a three-dimensional parametric model of the glass container according to the design parameter set of the glass container; conduct a forming performance evaluation on the three-dimensional parametric model of the glass container to obtain forming simulation performance data; conduct a parameter perturbation analysis on the design parameter set of the glass container to obtain the evaluation result of the robustness index;

[0022] Step S5: Generate design feedback adjustment suggestions based on the forming simulation performance data and the evaluation result of the robustness index; generate a structural design scheme for the lightweight glass container according to the design feedback adjustment suggestions.

[0023] In this embodiment, a continuous surface model of the glass container is constructed using Rhinoceros software in combination with the Grasshopper plug-in. By importing the 2D sketch or point cloud data of the glass container, a smooth 3D surface model is created using the modeling function of Rhinoceros. Based on this model, a set of parametric modeling functions is defined, including the Bezier curve function for controlling the bottle body contour, the radial thickness function for describing the wall thickness distribution, and the spline interpolation function for defining the transition region. Using these functions, a geometric variant library containing multiple design variants is generated, covering parameter variations such as different bottle body heights, bottleneck wall thicknesses, and bottom curvatures of the bottle. The models in the geometric variant library are subjected to multi-condition simulations using ANSYS Mechanical, including conditions such as vertical compression, lateral extrusion, internal pressure, and thermal shock. The stress distribution characteristics of each variant are extracted through finite element analysis to generate a set of stress distribution characteristic vectors. At the same time, in combination with the production process data provided by the glass container manufacturer, MATLAB is used to construct a set of container forming process constraints, covering glass forming fluidity, mold demolding conditions, and thermal gradient constraints. Based on these constraint conditions, NURBS control point identification is performed on the glass container in Rhinoceros to obtain a set of NURBS control points for the constrained container, and a container wall thickness gradient constraint model is generated through gradient-conserved deformation regulation, and further a stress-sensitive region distribution map is generated. MATLAB is used to construct the objective functions of minimizing the mass of the glass container, maximizing the strength, and the manufacturing feasibility function. By integrating these objective functions, a multi-objective optimization model for forming is constructed, and the optimization toolbox of MATLAB (such as fmincon or gamultiobj) is used for solution to obtain an optimized set of design parameters for the glass container. Based on these parameters, a parametric 3D model of the glass container is constructed in Rhinoceros, and computational fluid dynamics simulation is performed using ANSYS Fluent, combined with structural analysis using ANSYS Mechanical to obtain the forming simulation performance data. At the same time, MATLAB is used to perform parameter perturbation analysis on the set of design parameters to evaluate the robustness of the design scheme and generate the evaluation results of the robustness index. Finally, according to the forming simulation performance data and the evaluation results of the robustness index, Excel and MATLAB are used to generate suggestions for design feedback adjustment. These suggestions include adjusting the bottleneck wall thickness, optimizing the bottle body height, etc. According to these suggestions, the parametric 3D model of the glass container is structurally adjusted in Rhinoceros and Grasshopper, and finally a lightweight and excellent-performance glass container structural design scheme is obtained.

[0024] Preferably, step S1 includes the following steps:

[0025] Step S11: Obtain a list of common glass containers; collect the surface topography of each common glass container according to the list of common glass containers to obtain a glass container point cloud dataset;

[0026] Step S12: Filter out the noise points from the glass container point cloud dataset to obtain a smooth glass container point cloud dataset;

[0027] Step S13: Perform NURBS surface fitting and reconstruction based on the smooth glass container point cloud dataset to obtain a continuous surface model of the glass container;

[0028] Step S14: Partition the structure of the continuous surface model of the glass container to obtain surface blocks for the bottleneck region, bottle shoulder region, bottle body region, and bottle bottom region;

[0029] Step S15: Extract the characteristic dimensions of the surface blocks in the bottleneck region, bottle shoulder region, bottle body region, and bottle bottom region to obtain a set of geometric characteristic parameters of the glass container;

[0030] Step S16: Construct a glass container modeling function set according to the set of geometric characteristic parameters of the glass container. The glass container modeling function set includes a thermal-mechanical coupling gradient function for the bottleneck region, a rheological stress transfer function for the bottle shoulder region, a wall thickness gradient constraint function for the bottle body region, and a contact stress attenuation function for the bottle bottom region. The specific construction process of the glass container modeling function set is as follows:

[0031] Identify the thermal stress concentration regions in the bottleneck region of the glass container to obtain the thermal stress distribution data of the bottleneck region;

[0032] Layout the mold cooling channels according to the thermal stress distribution data of the bottleneck region to obtain the bottleneck-mold cooling channel mapping data;

[0033] Perform thermal-mechanical coupling gradient modeling according to the bottleneck-mold cooling channel mapping data to obtain the thermal-mechanical coupling gradient function of the bottleneck region;

[0034] Identify the transition curvature of the bottle shoulder region of the glass container and perform inverse blowing process pressure field inversion to obtain the transition curvature field of the bottle shoulder region and the bottle shoulder blow molding pressure gradient parameters;

[0035] Constrain the surface rheological characteristics of the glass container according to the transition curvature field of the bottle shoulder region and the bottle shoulder blow molding pressure gradient parameters to obtain the rheological stress transfer function of the bottle shoulder region;

[0036] Decompose the stacking load direction vector field of the bottle body region of the glass container to obtain the load direction vector diagram of the bottle body region;

[0037] Locate the mold parting line of the bottle body region of the glass container to obtain the spatial topology data of the bottle body-mold parting line;

[0038] Based on the load direction vector diagram of the bottle body area and the spatial topological data of the bottle body-mold parting line, a dynamic coupling model of wall thickness-parting line is established to obtain the wall thickness gradient constraint function of the bottle body area;

[0039] Identify the contact stress of the ejector pin distribution in the bottom area of the glass container to obtain the ejector pin distribution constraint data in the bottom area of the bottle;

[0040] Based on the ejector pin distribution constraint data in the bottom area of the bottle, a stress attenuation gradient model is established to obtain the contact stress attenuation function in the bottom area of the bottle;

[0041] Step S17: Construct a geometric variant library of the glass container based on the glass container modeling function set; perform multi-condition simulations on the geometric variant library of the glass container, and extract the stress distribution characteristics to obtain the container stress distribution characteristic set.

[0042] In this embodiment, a list containing common glass wine bottles, beverage bottles, and medicine bottles on the market is obtained. The surface topography of each common glass container is collected using a 3D scanner. Taking the glass wine bottle as an example, it is placed on the working platform of the scanner, the scanner is started, and an appropriate scanning resolution (such as 0.1 mm) is set. The scanner performs an all-round scan of the wine bottle surface through laser or structured light technology, and high-precision point cloud data is collected. These point cloud data are stored in a standard point cloud file format (such as.ply or.pcd), forming a glass container point cloud data set. After obtaining the glass container point cloud data set, a point cloud processing software (such as CloudCompare) is used to filter out noise points from the data. Taking the point cloud data of the glass beverage bottle as an example, the software is opened and the point cloud file is imported. In the software, the "filtering" function module is selected, the filtering algorithm is set to "voxel grid filtering", and the voxel size is set to 0.5 mm to remove discrete noise points caused by the accuracy limitation of the scanning device or environmental interference. After processing by this method, a smooth glass beverage bottle point cloud data set is obtained. Based on the smoothed glass container point cloud data set, a NURBS surface fitting and reconstruction tool (such as the NURBS modeling function in Rhinoceros software) is used to construct a continuous surface model of the glass container. Taking the glass medicine bottle as an example, the smoothed point cloud data is imported into Rhinoceros software, and the "create surface from point cloud" function is selected. The fitting accuracy is set to 0.05 mm, and the software automatically fits out a smooth and continuous NURBS surface model through calculation. This model can accurately reflect the geometric shape of the medicine bottle, including the surface characteristics of parts such as the bottleneck, bottle body, and bottle bottom. After obtaining the continuous surface model of the glass container, a 3D modeling software (such as SolidWorks) is used to perform structural partitioning on it. Taking the glass wine bottle as an example, the continuous surface model is imported into SolidWorks software. Using the "split surface" function of the software, according to the geometric characteristics of the wine bottle, the surface blocks of the bottleneck area, bottle shoulder area, bottle body area, and bottle bottom area are manually or automatically identified and divided. During the division process, by setting the position and direction of the split line, it is ensured that the division of each area is accurate. After completing the structural partitioning of the glass container, a 3D measurement software (such as Geomagic Control) is used to extract the characteristic dimensions of each area. Taking the glass beverage bottle as an example, the partitioned surface model is imported into this software. In the software, the "measurement" function is selected, and the characteristic dimensions such as the diameter of the bottleneck area, the radian of the bottle shoulder area, the height of the bottle body area, and the thickness of the bottle bottom area are accurately measured. The measurement results are displayed in numerical form on the software interface and are recorded, forming a glass container geometric feature parameter set.

[0043] Use ANSYS Mechanical to identify the thermal stress concentration areas in the bottleneck region of glass containers. Taking a glass wine bottle as an example, first import the geometric feature parameters in ANSYS Mechanical and set the material properties (such as the thermal expansion coefficient and elastic modulus of glass). Then, define the thermal boundary conditions to simulate the thermal stress distribution caused by temperature changes during the glass forming process. Through finite element analysis, obtain the thermal stress distribution contour map of the bottleneck region, clearly identifying the stress concentration areas. For example, it is found that the peak thermal stress at the transition between the bottleneck and the bottle body reaches 50 MPa, while the thermal stress in other regions is generally below 30 MPa. Based on the thermal stress distribution data of the bottleneck region, use SolidWorks to layout the mold cooling channels. Taking a glass medicine bottle as an example, open the geometric model of the bottleneck region in SolidWorks and import the thermal stress distribution data. According to the location of the stress concentration area, design the layout of the cooling channels to ensure that the coolant can flow through the parts with higher thermal stress preferentially. For example, design a spiral cooling channel with a diameter of 5 mm at the transition between the bottleneck and the bottle body, and the channel spacing is 10 mm. The inlet and outlet positions of the cooling channels are also optimized to ensure the flow efficiency of the coolant. According to the bottleneck-mold cooling channel mapping data, use MATLAB to perform thermo-mechanical coupling gradient modeling. Taking a glass beverage bottle as an example, import the cooling channel layout data and the thermal stress distribution data into MATLAB. Utilize the numerical analysis function of MATLAB to establish a thermo-mechanical coupling gradient model. The model considers the heat conduction effect of the cooling channels and the thermal expansion characteristics of the glass material. By solving the heat conduction equation and the stress balance equation, obtain the thermo-mechanical coupling gradient function of the bottleneck region. For example, the model predicts that near the cooling channels, the thermal stress gradually decays from 50 MPa to 30 MPa, and the decay gradient is 2 MPa / mm. This function is saved as a MATLAB function file. Use ANSYS Mechanical to identify the transition curvature in the bottle shoulder region and perform inverse analysis of the pressure field in the blow molding process. Taking a glass wine bottle as an example, import the geometric model of the bottle shoulder region in ANSYS Mechanical and set the boundary conditions of the blow molding process (such as blowing pressure, blowing temperature, etc.). Through finite element analysis, obtain the transition curvature field and the pressure field distribution in the bottle shoulder region. For example, it is found that the maximum transition curvature in the bottle shoulder region is 0.05 mm⁻¹, and the corresponding blow molding pressure gradient is 0.5 MPa / mm. According to the transition curvature field and the blow molding pressure gradient parameters in the bottle shoulder region, use MATLAB to perform surface rheological property constraint modeling. Taking a glass medicine bottle as an example, import the transition curvature field and the pressure gradient parameters into MATLAB. Utilize the symbolic calculation function of MATLAB to establish the rheological stress transfer function of the bottle shoulder region. This function considers the flow characteristics of the glass and the pressure transfer effect during the blow molding process. By solving the rheological equation, obtain the rheological stress distribution in the bottle shoulder region.For example, the model predicts that in the bottle shoulder region, the flow stress gradually increases from 1.2 MPa at the blow molding opening to 1.5 MPa in the middle of the bottle shoulder. The ANSYS Mechanical is used to decompose the vector field of the stacking load direction in the bottle body region. Taking a glass beverage bottle as an example, the geometric model of the bottle body region is imported into ANSYS Mechanical, and the boundary conditions of the stacking load (such as stacking pressure, contact conditions, etc.) are set. Through finite element analysis, the distribution of the load direction vector field in the bottle body region is obtained. For example, it is found that the load direction in the bottle body region is mainly along the axial direction of the bottle body, but there is a certain transverse component at the transition between the bottle body and the bottle bottom. SolidWorks is used to locate the mold parting line in the bottle body region. Taking a glass medicine bottle as an example, the geometric model of the bottle body region is opened in SolidWorks, and the load direction vector field data is imported. According to the load direction and the requirements of mold design, the parting line is designed in the bottle body region. For example, the parting line is arranged along the axial direction of the bottle body, but it is optimized at the transition between the bottle body and the bottleneck to avoid stress concentration. According to the load direction vector diagram of the bottle body region and the spatial topology data of the bottle body-mold parting line, MATLAB is used to build the wall thickness gradient constraint model. Taking a glass wine bottle as an example, the load direction vector diagram and the parting line data are imported into MATLAB. Using the numerical analysis function of MATLAB, the wall thickness gradient constraint function is established. This function considers the influence of the load direction and the parting line position on the wall thickness distribution. By solving the wall thickness distribution equation, the wall thickness gradient constraint function of the bottle body region is obtained. For example, the model predicts that in the bottle body region, the wall thickness gradually increases from 2 mm at the bottleneck to 3 mm at the bottle bottom, with a gradient of 0.1 mm / mm. The ANSYS Mechanical is used to identify the contact stress distribution of the mold ejector pins in the bottle bottom region. Taking a glass beverage bottle as an example, the geometric model of the bottle bottom region is imported into ANSYS Mechanical, and the distribution and contact conditions of the mold ejector pins are set. Through finite element analysis, the contact stress distribution in the bottle bottom region is obtained. For example, it is found that the maximum contact stress in the ejector pin contact region is 80 MPa, while the contact stress in other regions is generally below 50 MPa. According to the ejector pin distribution constraint data in the bottle bottom region, MATLAB is used to build the stress attenuation gradient model. Taking a glass medicine bottle as an example, the ejector pin distribution constraint data is imported into MATLAB. Using the numerical analysis function of MATLAB, the contact stress attenuation function in the bottle bottom region is established. This function considers the ejector pin distribution and the attenuation characteristics of the contact stress. By solving the stress attenuation equation, the contact stress attenuation function of the bottle bottom region is obtained. For example, the model predicts that in the ejector pin contact region, the contact stress gradually decays from 80 MPa to 50 MPa, with an attenuation gradient of 3 MPa / mm. Through the combination of these functions, a complete set of glass container modeling functions is constructed.

[0044] The present invention collects the surface topography of common glass containers to generate a point cloud data set, and then through noise filtering and smoothing processing, it provides high-quality basic data for subsequent modeling, ensuring the accuracy and reliability of the model. Through NURBS surface fitting reconstruction, a continuous and smooth glass container surface model can be generated. By structurally partitioning the surface model and extracting the geometric feature parameters of each region, the details of the model are further refined, making the subsequent parametric modeling more targeted and adaptable. The constructed parametric modeling function set covers key features such as the bottle body contour, wall thickness distribution, and transition region, and can flexibly adjust the geometric shape and structural characteristics of the glass container, providing a powerful tool for generating diverse geometric variants. Through multi-condition simulation and stress distribution feature extraction of the geometric variant library constructed based on the parametric modeling function set, a container stress distribution feature set is generated, which not only provides rich data support for optimization design, but also can comprehensively evaluate the mechanical performance of different design schemes in actual use.

[0045] Preferably, step S17 includes the following steps:

[0046] Step S171: Define variables for the glass container modeling function set and construct the dependency constraint relationship between geometric parameters, so as to obtain the glass container geometric parameter association map;

[0047] Step S172: Based on the glass container geometric parameter association map, rank the importance of the parameters in each region of the glass container geometric feature parameter set to obtain the glass container key parameter ranking table;

[0048] Step S173: Determine the glass container core parameter control set according to the glass container key parameter ranking table;

[0049] Step S174: Obtain the value range of each parameter in the glass container core parameter control set and generate a parameter value range table;

[0050] Step S175: Based on the parameter value range table, construct design variants for each parameter in the glass container core parameter control set to obtain the glass container parameter change sequence;

[0051] Step S176: Based on the glass container parameter change sequence, construct a glass container geometric variant library, where the glass container geometric variant library contains no less than 100 design variants;

[0052] Step S177: Conduct multi-condition simulation on the glass container geometric variant library and extract stress distribution features to obtain a container stress distribution feature set.

[0053] In this embodiment, when defining variables for the glass container modeling function set, the parametric modeling tool Grasshopper is used. Taking a glass beverage bottle as an example, first, variables in the Bezier curve function that controls the bottle body contour are defined, including the positions of the control points, weights, and the order of the curve. Then, variables in the radial thickness function that describes the wall thickness distribution are defined, such as the specific values of the wall thickness of the bottle body, bottleneck, and bottle bottom. In addition, variables in the spline interpolation function for the transition region are defined, including the starting point, ending point, and smoothness of the transition curve of the transition region. Through the "parametric component" function of Grasshopper, the dependency constraint relationships between geometric parameters are further constructed. For example, when the wall thickness in the bottleneck region increases, the wall thickness in the bottle body region will decrease accordingly to keep the overall mass unchanged. Finally, through the visualization function of Grasshopper, a geometric parameter correlation map of the glass container is generated, clearly showing the mutual relationships between the parameters. Based on the geometric parameter correlation map of the glass container, the MATLAB software is used to rank the importance of the parameters in each region of the geometric feature parameter set of the glass container. Taking a glass wine bottle as an example, first, the geometric parameter correlation map is imported into MATLAB, and its data processing function is used to perform a sensitivity analysis on the parameters in each region. By calculating the influence degree of each parameter on the overall model quality, strength, and manufacturing feasibility, it is determined that the wall thickness parameter in the bottleneck region, the height parameter in the bottle body region, and the curvature parameter in the bottle bottom region are the key parameters. Then, according to the importance degree of these parameters, a key parameter ranking table of the glass container is generated. For example, the importance of the bottleneck wall thickness is ranked first because it has the greatest impact on the overall strength and stress distribution of the glass container. According to the key parameter ranking table of the glass container, the core parameter control set of the glass container is determined. Taking a glass medicine bottle as an example, referring to the key parameter ranking table, the three most important parameters, namely the bottleneck wall thickness, bottle body height, and bottle bottom curvature, are selected as the core parameter control set. These parameters have the greatest impact on the structural performance and manufacturing feasibility of the medicine bottle, so they are preferentially considered for the construction of subsequent design variants. The names of these core parameters and their corresponding ranking positions are recorded in an Excel table. To obtain the value ranges of each parameter in the core parameter control set of the glass container, industry standards and actual production data are referred to. Taking a glass beverage bottle as an example, by consulting the standard specifications of the glass container manufacturing industry, it is determined that the value range of the bottleneck wall thickness is from 1.0 mm to 2.0 mm, the value range of the bottle body height is from 150 mm to 200 mm, and the value range of the bottle bottom curvature is from 10 mm to 20 mm. These value ranges are determined comprehensively according to the physical properties of the glass material, the limitations of the forming process, and the actual usage requirements of the product. These value ranges are sorted into a parameter value range table and stored in an Excel file. According to the parameter value range table, Grasshopper is used to construct design variants for each parameter in the core parameter control set of the glass container.Taking a glass wine bottle as an example, first, set the value range of the bottleneck wall thickness from 1.2 mm to 1.8 mm with a step size of 0.2 mm in Grasshopper; the value range of the bottle body height from 180 mm to 220 mm with a step size of 10 mm; the value range of the bottle bottom curvature from 12 mm to 18 mm with a step size of 2 mm. Then, through the "parametric drive" function of Grasshopper, design variants under all potential parameter combinations are automatically generated. For example, when the bottleneck wall thickness is 1.2 mm, the bottle body height is 180 mm, and the bottle bottom curvature is 12 mm, a design variant is generated; when the bottleneck wall thickness is 1.4 mm, the bottle body height is 190 mm, and the bottle bottom curvature is 14 mm, another design variant is generated. Finally, a sequence of design variants containing multiple parameter combinations is obtained. Based on the glass container parameter change sequence, a geometric variant library of glass containers is constructed using Rhinoceros and Grasshopper software. Taking a glass medicine bottle as an example, import the parameter change sequence into Grasshopper, and utilize its powerful parametric modeling function to generate the corresponding three-dimensional geometric models according to each parameter combination. By adjusting the variable values in the parametric modeling function set, 150 different geometric variants of glass medicine bottles are generated. These variants differ in terms of bottleneck wall thickness, bottle body height, and bottle bottom curvature, covering different design possibilities. Export these geometric variants into a standard three-dimensional model file format and store them in a dedicated folder, forming a geometric variant library of glass containers containing no less than 100 design variants. When performing multi-condition simulations on the geometric variant library of glass containers, use the finite element analysis software ANSYS Mechanical. Taking a glass beverage bottle as an example, first import each model in the geometric variant library into ANSYS Mechanical and configure multiple working conditions according to the actual usage scenarios of the beverage bottle. For example, in the vertical top compression condition, apply a load of 150 N and constrain the bottle bottom; in the lateral extrusion condition, apply a load of 80 N and constrain the bottleneck; in the internal pressure condition, apply an internal pressure of 0.6 MPa; in the thermal shock condition, the temperature change range is from -10 °C to 50 °C; in the vibration transportation condition, the acceleration is 1.5 g and the frequency is 12 Hz; in the drop impact condition, the drop height is 1.2 m. Perform finite element solutions for each geometric variant separately to obtain the stress and strain data under multiple working conditions. Through the post-processing function of ANSYS Mechanical, extract the maximum principal stress point, maximum shear stress point, and the region with the largest stress gradient of the beverage bottle under each working condition, and organize these data into a container stress distribution feature set.

[0054] Through constructing a geometric parameter correlation map, the present invention clarifies the dependency relationships among various parameters, provides a clear logical framework for subsequent optimization design, and avoids blindness during the parameter adjustment process. By determining the core parameter control set based on the key parameter sorting table, it further focuses on the parameters that have the greatest impact on the design, improving the pertinence and efficiency of design optimization. By generating a parameter value range table and constructing design variants, it can systematically explore design solutions under different parameter combinations, greatly enriching the design space and providing more possibilities for finding the optimal design solution. By constructing a geometric variant library containing no less than 100 design variants and conducting multi-condition simulations and stress distribution feature extraction, it not only provides comprehensive performance evaluation data for design optimization, but also enables in-depth analysis of the mechanical performance of different design solutions based on the stress distribution feature vector set, thereby achieving precise optimization.

[0055] Preferably, step S2 includes the following steps:

[0056] Step S21: Collect the glass forming process constraint parameter set; normalize the glass forming process constraint parameter set to obtain a standard manufacturing process parameter library;

[0057] Step S22: Identify the glass temperature-viscosity relationship according to the standard manufacturing process parameter library to obtain glass temperature-viscosity relationship data;

[0058] Step S23: Evaluate the material flow characteristics based on the glass temperature-viscosity relationship data to obtain the glass forming fluidity constraint conditions;

[0059] Step S24: Identify the demolding constraint conditions for the typical glass container production mold to obtain the mold demolding constraint conditions;

[0060] Step S25: Obtain the glass physical property data; identify the thermal stress distribution law according to the glass physical property data to obtain the glass thermal gradient constraint conditions;

[0061] Step S26: Integrate the glass forming fluidity constraint conditions, the mold demolding constraint conditions, and the glass thermal gradient constraint conditions to obtain the container forming process constraint set;

[0062] Step S27: Identify the NURBS control points of the glass container according to the container forming process constraint set to obtain the constrained container NURBS control point set;

[0063] Step S28: Perform gradient conservation deformation regulation on the constrained container NURBS control point set to obtain the container wall thickness gradient constraint model; generate a container stress sensitive area distribution map based on the container wall thickness gradient constraint model.

[0064] In this embodiment, 5 glass container manufacturers can be contacted, including Company A, Company B, Company C, Company D, and Company E. Detailed production process data are collected from each company, including key parameters such as glass melting temperature, forming speed, mold temperature, and blowing pressure. These data are provided in the form of an Excel table, covering the production processes of different types of glass containers (such as wine bottles, beverage bottles, and medicine bottles). Python scripts are used to normalize these data, scaling all parameter values to the range of 0 to 1 to eliminate the influence of different dimensions and numerical ranges. Finally, a standard manufacturing process parameter library is generated. Based on the standard manufacturing process parameter library, MATLAB software is used to identify the glass temperature-viscosity relationship. Taking the production process data provided by Company A as an example, the glass melting temperature and the corresponding viscosity data are extracted. In MATLAB, the curve fitting toolbox is used to fit these data, and the Arrhenius equation is selected as the fitting model. By adjusting the model parameters, the glass temperature-viscosity relationship curve is obtained, and the fitting result is exported as a data file. According to the glass temperature-viscosity relationship data, ANSYS Fluent software is used to evaluate the material flow characteristics during the glass forming process. Taking the glass wine bottle produced by Company B as an example, a three-dimensional model of glass forming is established in ANSYS Fluent, and the functional relationships between the glass melting temperature and viscosity with temperature are set. By simulating the flow process of the glass in the mold, the fluidity of the glass under different temperature and pressure conditions is evaluated. The simulation results show that at a forming temperature of 1100 °C, the fluidity of the glass is the best, and it can uniformly fill the mold. According to the simulation results, the glass forming fluidity constraint conditions are determined, including the minimum flow velocity and the maximum viscosity range, and these conditions are recorded in the process constraint parameter library. Reverse engineering parameter extraction is carried out on the production molds of typical glass containers. Taking the glass beverage bottle mold produced by Company C as an example, the mold is scanned with high precision using a three-dimensional scanner to obtain the point cloud data of the mold. Geomagic Control software is used to process the point cloud data and extract the key parameters of the mold, including the size, shape, demolding slope, and draft angle of the mold. By analyzing these parameters, the mold demolding constraint conditions are determined. For example, the demolding slope should be greater than 2° to ensure that the glass container can be smoothly demolded from the mold. Glass physical property data are obtained from Company D, including the thermal expansion coefficient, elastic modulus, and thermal conductivity of the glass, etc. MATLAB software is used to analyze these data to identify the thermal stress distribution law of the glass at different temperatures. By calculating the temperature gradient during the glass forming process, the glass thermal gradient constraint conditions are determined. For example, during the glass forming process, the temperature gradient should be controlled to not exceed 10 °C per centimeter to avoid glass breakage caused by excessive thermal stress. The glass forming fluidity constraint conditions, mold demolding constraint conditions, and glass thermal gradient constraint conditions are integrated to generate a container forming process constraint set.Taking the glass medicine bottles produced by Company E as an example, the above three constraint conditions are integrated into a unified process constraint model. Use Excel sheets to organize these constraint conditions to ensure that each constraint condition has a clear parameter range and calculation formula. For example, the fluidity constraint condition requires that the viscosity of the glass remain below 10^5 Pa·s during the forming process, the demolding constraint condition requires that the draft angle of the mold be greater than 3°, and the thermal gradient constraint condition requires that the temperature gradient not exceed 8 °C / cm. According to the set of container forming process constraints, use Rhinoceros software to identify the NURBS control points of the glass container. Taking the glass medicine bottle as an example, first import the three-dimensional model of the medicine bottle into Rhinoceros. Using the "NURBS control point extraction" function of Rhinoceros, a control point grid is evenly arranged on the surface of the medicine bottle. The control point density in the bottle body and bottom areas is set to one control point per 10 mm × 10 mm, and the control point density in the bottleneck and transition areas is encrypted to one control point per 5 mm × 5 mm. By adjusting the weight coefficients and knot vectors of the control points, a complete NURBS control point set is obtained. Then, according to the constraint conditions in the set of process constraints, constraint settings are made for these control points to ensure that the design of the medicine bottle meets the forming process requirements. Finally, a set of NURBS control points for the constrained container is obtained. Perform gradient-conserving deformation regulation on the set of NURBS control points for the constrained container to generate a container wall thickness gradient constraint model and a stress-sensitive area distribution map. Taking the glass beverage bottle as an example, use the Grasshopper plug-in to perform deformation regulation on the set of NURBS control points. First, evaluate the parameter sensitivity of the control points to determine the degree of influence of each control point on the wall thickness and stress distribution. According to the glass forming fluidity constraint condition and the thermal gradient constraint condition, construct a control point motion chain transfer rule. Set dynamic damping constraints in Grasshopper to ensure that the wall thickness gradient and stress distribution meet the process requirements during the deformation process. Through gradient-conserving deformation regulation, a wall thickness gradient constraint model is obtained, and a stress-sensitive area distribution map is generated based on this model. This distribution map clearly shows the stress concentration in different areas of the beverage bottle.

[0065] The present invention constructs a standard manufacturing process parameter library by collecting production process data from multiple manufacturers and performing normalization processing. Through comprehensive analysis of the glass temperature-viscosity relationship, material flow characteristics, mold demolding conditions, and thermal stress distribution law, a set of forming process constraints covering glass forming flow, mold demolding, and thermal gradient constraints is generated, comprehensively covering the key process requirements in the glass container manufacturing process. Further, through NURBS control point identification and gradient conservation deformation regulation, a wall thickness gradient constraint model and a stress-sensitive area distribution map are established, realizing precise control of the geometric features of the glass container, ensuring that the design scheme can effectively meet the strict requirements of the actual manufacturing process while meeting the lightweight requirements, significantly reducing the deviation between the simulation design and the actual production, and improving the practicality and reliability of the design.

[0066] Preferably, step S27 includes the following steps:

[0067] Step S271: Construct a parametric process boundary condition for glass forming based on the set of container forming process constraints;

[0068] Step S272: Construct a constraint violation penalty rule for the parametric process boundary condition of glass forming to obtain a process feasibility evaluation rule;

[0069] Step S273: Perform non-uniform rational B-spline control grid division on the glass container mesh model to obtain the NURBS control grid of the glass container. Specifically, the non-uniform rational B-spline control grid division is as follows: Uniformly arrange a control point grid on the surface of the glass container, with a control point density of one control point per 10mm×10mm area in the bottle body and bottom areas, and encrypted to one control point per 5mm×5mm area in the bottleneck and transition areas;

[0070] Step S274: Set the weight coefficients and knot vectors for the NURBS control grid of the glass container to obtain the complete NURBS control points of the glass container;

[0071] Step S275: Set constraints on the complete NURBS control points of the glass container based on the process feasibility evaluation rule to obtain a set of constrained container NURBS control points.

[0072] In this embodiment, MATLAB software is used. Taking a glass wine bottle as an example, key process parameters are first extracted from the set of process constraints for container forming, including the glass forming temperature range (1050 °C to 1200 °C), the mold demolding angle (minimum 3°), the wall thickness gradient (maximum 0.5 mm / cm), and the thermal gradient (maximum 10 °C / cm). In MATLAB, the upper and lower limits of these parameters are defined and they are parameterized. For example, the forming temperature is defined as a variable T_form, whose value range is from 1050 °C to 1200 °C. Through the above steps, a parametric process boundary condition model containing all key process parameters is constructed. Based on the MATLAB software, a penalty rule for constraint violation of the glass forming parametric process boundary conditions is constructed. Taking a glass beverage bottle as an example, the penalty function for each process parameter is first defined. For example, for the forming temperature T_form, when it exceeds the range of 1050 °C to 1200 °C, the penalty function will linearly increase the penalty value according to the size of the exceeded range. Specifically, if the temperature is lower than 1050 °C, the penalty value is (1050 - T_form) * 10; if the temperature is higher than 1200 °C, the penalty value is (T_form - 1200) * 10. Similarly, for the mold demolding angle, if the angle is less than 3°, the penalty value is (3 - demolding angle) * 5. Through these penalty rules, a complete process feasibility evaluation rule is obtained. The Rhinoceros software is used to perform non-uniform rational B-spline (NURBS) control mesh generation on the glass container mesh model. Taking a glass medicine bottle as an example, the three-dimensional model of the medicine bottle is first imported into Rhinoceros. In the software, the "Mesh Generation" function is selected, and the control point density in the bottle body and bottom regions is set to one control point per 10 mm × 10 mm, and the control point density in the bottleneck and transition regions is encrypted to one control point per 5 mm × 5 mm. Through the above steps, a control point grid is evenly arranged on the surface of the medicine bottle, generating a non-uniform rational B-spline control mesh. This control mesh can accurately capture the geometric features of the medicine bottle. Especially in the bottleneck and transition regions, the encrypted control points can better reflect the geometric changes in these regions. The Rhinoceros software is used to set the weight coefficients and knot vectors for the glass container NURBS control mesh. Taking a glass wine bottle as an example, the "NURBS Control Point Editing" function is first selected in Rhinoceros. For each control point, the weight coefficient is set according to its importance in the geometric model. For example, the weight coefficient of the control points in the bottleneck region is set to 1.5 to ensure the geometric accuracy of the bottleneck; the weight coefficient of the control points in the bottle body region is set to 1.0. At the same time, the knot vector is set according to the topological structure of the geometric model to ensure the accurate position of the control points in the geometric model. Through these settings, a complete glass container NURBS control point is obtained. Constraint settings are performed on the complete glass container NURBS control points based on the process feasibility evaluation rule.Taking a glass beverage bottle as an example, first, import the process feasibility evaluation rules into the Rhinoceros software. In the software, according to the weight coefficients and knot vectors of each control point, apply the process feasibility evaluation rules to set constraints for the control points. For example, for the forming temperature constraint, set the temperature value of each control point to be between 1050 °C and 1200 °C. If the temperature value of a certain control point exceeds this range, the software will automatically adjust the position or weight of this control point to meet the process requirements. Through the above steps, obtain the constrained container NURBS control point set.

[0073] By defining the process boundary conditions and the constraint violation penalty rules, the present invention can effectively evaluate the process feasibility of the design scheme, ensure that the design parameters are within the allowable range of the actual manufacturing process, thereby improving the manufacturability of the design. At the same time, by using non-uniform rational B-spline control mesh division, especially densifying the control points in the bottleneck and transition regions, the geometric features and stress distribution of the glass container can be captured more accurately. By setting the weight coefficients and knot vectors for the NURBS control mesh and performing constraint settings based on the process feasibility evaluation rules, the finally obtained constrained container NURBS control point set can effectively guide the shape adjustment and optimization of the glass container, ensuring that while meeting the process constraints, the design scheme achieves an optimal balance between lightweight and structural performance.

[0074] Preferably, step S28 includes the following steps:

[0075] Step S281: Evaluate the control point parameter sensitivity of the constrained container NURBS control point set to obtain the control point parameter sensitivity distribution data;

[0076] Step S282: Based on the control point parameter sensitivity distribution data, construct a displacement influence factor table to generate a control point displacement influence factor table;

[0077] Step S283: Construct a control point motion chain transfer rule according to the glass forming fluidity constraint conditions;

[0078] Step S284: Inject dynamic damping constraints into the control point motion chain transfer rule according to the stress sensitive area distribution data to obtain the glass container deformation control strategy;

[0079] Step S285: Adjust the deformation of the complete glass container NURBS control points according to the glass container deformation control strategy to obtain a deformable mesh control point set;

[0080] Step S286: Construct a wall thickness gradient limit model according to the deformable mesh control point set;

[0081] Step S287: Discretize the wall thickness gradient limit model to obtain a wall thickness gradient limit discrete model;

[0082] Step S288: Use the wall thickness gradient constraint discrete model to perform gradient-conserving deformation regulation on the deformable grid control point set to obtain the container wall thickness gradient constraint model;

[0083] Step S289: Calculate the local stress response coefficient for the container wall thickness gradient constraint model according to the container stress distribution feature set to generate stress-sensitive area distribution data; Generate a container stress-sensitive area distribution map based on the stress-sensitive area distribution data.

[0084] In this embodiment, taking a glass medicine bottle as an example, first, the NURBS control point set of the constraint container is imported into MATLAB. Using the symbolic calculation function of MATLAB, sensitivity analysis is performed on the parameters of each control point (such as position, weight, and knot vector). Specifically, the influence degree of each control point parameter on the wall thickness and stress distribution of the glass container is calculated. For example, through differential analysis, it is found that the sensitivity of the wall thickness to the change in the position of the control point in the bottleneck area is 0.05 mm / unit change, while that in the bottle body area is 0.02 mm / unit change. These sensitivity data are sorted into control point parameter sensitivity distribution data and stored as a matrix file. Based on the control point parameter sensitivity distribution data, an Excel table is used to construct a control point displacement influence factor table. Taking a glass wine bottle as an example, the sensitivity data are imported into the Excel table. In the table, a displacement influence factor is defined for each control point, and these factors reflect the influence degree of the control point displacement on the overall geometry and stress distribution of the glass container. For example, the displacement influence factor of the control point in the bottleneck area is set to 1.2, indicating that the displacement in this area has a greater impact on the overall structure; while the displacement influence factor of the bottom area of the bottle is set to 0.8. Through the above steps, a detailed control point displacement influence factor table is generated. According to the glass forming fluidity constraint conditions, the ANSYS Mechanical software is used to construct a control point motion chain transfer rule. Taking a glass beverage bottle as an example, the flow characteristics of the glass during the forming process are simulated in ANSYS Mechanical. According to the simulation results, the motion transfer rule between control points is defined to ensure that the fluidity of the glass meets the process requirements during the deformation process. For example, when the control point in the bottleneck area undergoes displacement, the adjacent control points in the bottle body area will undergo cooperative displacement according to a certain ratio (such as 0.7) to maintain the continuity and uniformity of the glass flow. These rules are integrated into the deformation analysis module of ANSYS Mechanical. According to the stress-sensitive area distribution data, the ANSYS Mechanical is used to inject dynamic damping constraints into the control point motion chain transfer rule. Taking a glass medicine bottle as an example, first, the stress-sensitive area distribution data are imported into ANSYS Mechanical, and these data identify the stress concentration conditions in different areas of the medicine bottle. According to the distribution of the stress-sensitive areas, dynamic damping constraints are injected into the control point motion chain transfer rule. For example, in the stress-sensitive bottleneck area, a larger damping coefficient (such as 1.5) is set to slow down the motion speed of the control point and avoid excessive deformation in the stress concentration area. Through the above steps, a deformation control strategy for the glass container is obtained. According to the glass container deformation control strategy, the Rhinoceros software is used to adjust the deformation of the NURBS control points of the complete glass container. Taking a glass wine bottle as an example, the deformation control strategy is imported into Rhinoceros. In the software, precise displacement adjustment is performed on each control point according to the displacement influence factor and dynamic damping constraint of the control point.For example, for the control points in the bottleneck area, according to the deformation control strategy, they are moved inward by 0.5 mm. At the same time, according to the motion chain transfer rule, the control points in the adjacent bottle body area are moved inward by 0.35 mm. Through the above steps, a set of deformable mesh control points is obtained. Based on the set of deformable mesh control points, a wall thickness gradient limit model is constructed using MATLAB. Taking a glass beverage bottle as an example, first, the set of deformable mesh control points is imported into MATLAB. Using the numerical analysis function of MATLAB, the wall thickness gradient corresponding to each control point is calculated, and the wall thickness gradient limit condition is set according to the process requirements (such as the maximum wall thickness gradient not exceeding 0.5 mm / cm). For example, for the control points in the bottleneck area, the calculated wall thickness gradient is 0.4 mm / cm, which meets the process requirements; while for some control points in the bottle body area, the calculated wall thickness gradient is 0.6 mm / cm, exceeding the limit value, so adjustment is needed. Through the above steps, a wall thickness gradient limit model is obtained. Taking a glass medicine bottle as an example, first, the wall thickness gradient limit model is imported into ANSYS Mechanical. In the software, the geometric model of the medicine bottle is divided into finite element meshes, and each mesh element corresponds to a wall thickness gradient value. Through discretization, a wall thickness gradient limit discrete model is obtained. For example, the bottleneck area of the medicine bottle is divided into 10 mesh elements, and the wall thickness gradient values of each element are 0.35 mm / cm, 0.38 mm / cm, etc. These discrete values can accurately reflect the wall thickness gradient distribution of the medicine bottle in different areas. Using the wall thickness gradient limit discrete model, the gradient conservation deformation regulation of the set of deformable mesh control points is carried out using Rhinoceros and Grasshopper plugins. Taking a glass wine bottle as an example, first, the wall thickness gradient limit discrete model is imported into Grasshopper. In Grasshopper, according to the wall thickness gradient value of each mesh element, the corresponding control points are finely adjusted. For example, if the wall thickness gradient of a certain mesh element exceeds the limit value, the position of the control point corresponding to this element will be appropriately adjusted to reduce the wall thickness gradient. Through the above steps, a container wall thickness gradient constraint model is obtained. According to the set of container stress distribution characteristics, MATLAB is used to calculate the local stress response coefficients of the container wall thickness gradient constraint model. Taking a glass beverage bottle as an example, first, the stress distribution characteristic vector set is imported into MATLAB. Using the numerical analysis function of MATLAB, the local stress response coefficients corresponding to each control point are calculated, and these coefficients reflect the influence degree of the change in the control point position on the local stress distribution. For example, the local stress response coefficient of the control point in the bottleneck area is 0.08, indicating that a 0.1 mm change in the position of this control point will cause a 0.008 MPa change in the local stress. According to these coefficients, stress-sensitive area distribution data is generated, and a stress-sensitive area distribution map is generated using the visualization tool of MATLAB.

[0085] Through the sensitivity evaluation of the NURBS control point set of the constraint container, the present invention can clarify the influence degree of each control point parameter on the overall structure. Through the displacement influence factor table constructed based on the sensitivity data and the control point motion chain transfer rule, the dynamic regulation of the deformation process of the glass container is further realized, ensuring the optimization and adjustment of the structure on the premise of meeting the requirements of glass forming fluidity and stress distribution. Through the injection of dynamic damping constraints and the regulation of gradient-conserved deformation, a wall thickness gradient limit model and a stress-sensitive area distribution map are constructed, which can effectively identify and optimize the stress concentration area, ensuring that the wall thickness distribution is reasonable and the stress distribution is uniform during the lightweight design of the glass container, thus significantly improving the structural strength and reliability of the glass container.

[0086] Preferably, step S3 includes the following steps:

[0087] Step S31: Perform material density distribution on the glass container mesh model to obtain the glass container voxel density data;

[0088] Step S32: Calculate the mesh element volume of the glass container mesh model based on the glass container voxel density data to obtain the glass container unit mass distribution map; perform global integration on the glass container unit mass distribution map to obtain the glass container total mass value;

[0089] Step S33: Establish a relationship model between mass and design parameters based on the glass container parametric modeling function and the glass container total mass value to obtain the mass minimization objective function;

[0090] Step S34: Extract the stress peak points from the container stress distribution feature set to obtain the glass container key stress evaluation point set; perform Weibull distribution fitting on the glass container key stress evaluation point set to obtain the glass container failure probability distribution data;

[0091] Step S35: Calculate the overall structure failure probability of the glass container based on the glass container failure probability distribution data to obtain the glass container overall structure failure probability;

[0092] Step S36: Perform inverse transformation on the glass container overall structure failure probability to obtain the strength maximization objective function;

[0093] Step S37: Evaluate the manufacturing process complexity according to the process feasibility evaluation rules to obtain the glass container process difficulty index;

[0094] Step S38: Perform cost mapping on the glass container process difficulty index to obtain the glass container manufacturing cost prediction data; establish a manufacturing feasibility function based on the glass container manufacturing cost prediction data and the glass container process difficulty index;

[0095] Step S39: Construct a forming multi-objective optimization model based on the quality minimization objective function, strength maximization objective function, and manufacturing feasibility function; solve the forming multi-objective optimization model to obtain the glass container design parameter set.

[0096] Particularly importantly, step S39 further includes the following steps:

[0097] Step S391: Conduct multi-objective weight design for the quality minimization objective function, strength maximization objective function, and manufacturing feasibility function to obtain an initial weight configuration scheme;

[0098] Step S392: Construct a glass container comprehensive performance evaluation function based on the initial weight configuration scheme;

[0099] Step S393: Obtain multi-objective optimization iteration historical data;

[0100] Step S394: Conduct target conflict identification based on the multi-objective optimization iteration historical data to obtain glass container manufacturing target conflict data;

[0101] Step S395: Construct a target weight adjustment strategy based on the glass container manufacturing target conflict data;

[0102] Step S396: Integrate the glass container comprehensive performance evaluation function and the target weight adjustment strategy to obtain a forming multi-objective optimization model;

[0103] Step S397: Solve the forming multi-objective optimization model to obtain the glass container design parameter set.

[0104] In this embodiment, taking a glass wine bottle as an example, first import the mesh model of the wine bottle into ANSYS Mechanical. In the material property setting module, select the glass material and input its density value (such as 2.5 g / cm³). Then, distribute this density value to each cell of the mesh model to ensure that each cell has the same material density. Through the above steps, the density data of the glass container volume elements is obtained. Based on the density data of the glass container volume elements, use ANSYS Mechanical to calculate the volume of the mesh cells. Taking a glass beverage bottle as an example, select the "mesh cell volume calculation" function in ANSYS Mechanical, and the software automatically calculates the volume of each mesh cell, and combines with the volume element density data to obtain the mass of each cell. Use the post-processing function of ANSYS Mechanical to perform global integration on the masses of all cells to obtain the total mass value of the glass beverage bottle. For example, the calculation results show that the total mass of the beverage bottle is 300 grams. Through the above steps, the mass distribution map of the glass container cells and the total mass value are obtained. Import the parametric modeling function of the glass container and the total mass value into MATLAB. Use the symbolic calculation function of MATLAB to establish a relationship model between the mass and the design parameters. For example, assume that the relationship between the mass M of the medicine bottle and the wall thickness of the bottle neck, the wall thickness of the bottle body, and the wall thickness of the bottle bottom is:

[0105] ;

[0106] where 、 and They are the volumes of the bottleneck, the bottle body, and the bottle bottom respectively. MATLAB is used to extract the stress peak points from the stress distribution feature set of the container. Taking a glass wine bottle as an example, first, the stress distribution feature vector set is imported into MATLAB. Using the data analysis function of MATLAB, the stress peak points are identified. For example, through analysis, it is found that the maximum principal stress in the bottleneck area is 50 MPa, and the maximum shear stress is 30 MPa; the maximum principal stress in the bottle body area is 40 MPa, and the maximum shear stress is 20 MPa. These stress peak points are extracted to form the key stress assessment point set of the glass container. Then, the curve fitting toolbox of MATLAB is used to fit a Weibull distribution to these key stress assessment points to obtain the failure probability distribution data of the glass container. Through the above steps, the failure probability distribution of the glass container is obtained. Based on the failure probability distribution data of the glass container, MATLAB is used to calculate the overall structure failure probability. Taking a glass beverage bottle as an example, first, the failure probability distribution data is imported into MATLAB. Using the numerical integration function of MATLAB, the failure probability of the entire structure is calculated. For example, assume that the failure probability distribution function is: 1 - ; where, is the stress, is the characteristic stress, and m is the Weibull modulus. Through numerical integration, the overall structure failure probability of the glass beverage bottle is obtained as 0.05%. Through the above steps, the overall structure failure probability of the glass container is obtained. According to the process feasibility evaluation rules, MATLAB is used to evaluate the manufacturing process complexity. Taking a glass wine bottle as an example, the process feasibility evaluation rules are imported into MATLAB. Using the data analysis function of MATLAB, the design parameters of the glass wine bottle (such as wall thickness gradient, mold demolding angle, etc.) are evaluated. For example, according to the process requirements, the wall thickness gradient does not exceed 0.5 mm / cm, and the mold demolding angle is not less than 3°. Through evaluation, the process difficulty index of the glass wine bottle is obtained as 0.7 (with a full score of 1, and the lower the value, the greater the process difficulty). Through the above steps, the process difficulty index of the glass container is obtained. The process difficulty index is imported into MATLAB. Using the data analysis function of MATLAB, based on historical data and empirical formulas, the process difficulty index is mapped to the manufacturing cost prediction data. For example, assume that the relationship between the manufacturing cost C and the process difficulty index I is C = a×I + b, where a and b are constants. Through cost mapping, the manufacturing cost prediction data of the glass beverage bottle is obtained. Based on the manufacturing cost prediction data and the process difficulty index, a manufacturing feasibility function is constructed. MATLAB is used to allocate weights to the mass minimization objective function, the strength maximization objective function, and the manufacturing feasibility function. Taking a glass wine bottle as an example, first, the initial weight configuration scheme of the three objective functions is defined in MATLAB. Assume that the weight of the mass minimization objective function is 0.4, the weight of the strength maximization objective function is 0.4, and the weight of the manufacturing feasibility function is 0.2. These weight values are allocated according to the importance of the design objectives and actual requirements. Through the above steps, the initial weight configuration scheme is obtained.Based on the initial weight configuration scheme, use MATLAB to construct a comprehensive performance evaluation function for glass containers. Taking a glass beverage bottle as an example, import the initial weight configuration scheme into MATLAB. Utilize the symbolic calculation function of MATLAB to perform weighted summation of the three objective functions according to the weights to obtain the comprehensive performance evaluation function. For example, assume the comprehensive performance evaluation function F is: ; where , and are the objective function of minimizing mass, the objective function of maximizing strength, and the manufacturing feasibility function respectively. Through the above steps, the comprehensive performance evaluation function of the glass container is obtained. Obtain the historical data of multi-objective optimization iteration, and use the multi-objective optimization toolbox of MATLAB (such as gamultiobj). Taking a glass medicine bottle as an example, run the multi-objective optimization function in MATLAB and record the objective function values and design parameter values of each iteration. For example, during the iteration of the genetic algorithm, record the objective function values of each generation of population and the corresponding wall thickness parameters.

[0107] The present invention obtains an accurate objective function of minimizing mass by performing material density distribution and mass distribution calculation on the glass container grid model, providing a quantitative basis for lightweight design. By extracting stress peak points and analyzing failure probability based on the stress distribution eigenvector set, the objective function of maximizing strength is further obtained, ensuring that the glass container has sufficient structural strength while being lightweight. By evaluating the complexity of the manufacturing process and cost mapping, a manufacturing feasibility function is constructed, closely integrating the design with the actual manufacturing process, and ensuring that the design scheme has good manufacturability while meeting the performance requirements. Finally, by solving the multi-objective optimization model, an optimized set of design parameters for the glass container is obtained, achieving the best balance among mass, strength, and manufacturing feasibility.

[0108] Preferably, step S4 includes the following steps:

[0109] Step S41: Construct a three-dimensional detailed model of the glass container based on the glass container design parameter set; perform a CAD model integrity check on the three-dimensional detailed model of the glass container to obtain a model quality evaluation report;

[0110] Step S42: Repair the surface of the three-dimensional detailed model of the glass container according to the model quality evaluation report to obtain a repaired three-dimensional model of the glass container;

[0111] Step S43: Perform a mixed tetrahedron and hexahedron mesh division on the repaired three-dimensional model of the glass container to obtain a meshed three-dimensional model of the glass container; configure material parameters for the meshed three-dimensional model of the glass container to obtain a parameterized three-dimensional model of the glass container;

[0112] Step S44: Simulate the filling process of the parametric 3D model of the glass container to obtain the hydrodynamic load distribution data;

[0113] Step S45: Perform a structural force response analysis on the parametric 3D model of the glass container according to the hydrodynamic load distribution data to obtain the coupled stress distribution field; Simulate the stacking load on the parametric 3D model of the glass container to obtain the static load-bearing capacity data;

[0114] Step S46: Calculate the thermal stress of the temperature gradient on the parametric 3D model of the glass container to obtain the thermal stability evaluation data; Calculate the internal pressure load-bearing capacity of the parametric 3D model of the glass container to obtain the internal pressure resistance performance data;

[0115] Step S47: Perform modal decomposition based on the hydrodynamic load distribution data and the coupled stress distribution field to obtain the dynamic characteristic data of the glass container;

[0116] Step S48: Perform a drop impact dynamic simulation and evaluation on the parametric 3D model of the glass container to obtain the impact resistance performance data; Record the static load-bearing capacity data, thermal stability evaluation data, internal pressure resistance performance data, and impact resistance performance data as the forming simulation performance data;

[0117] Step S49: Perform a parameter perturbation analysis on the glass container design parameter set according to the dynamic characteristic data of the glass container to obtain the evaluation result of the robustness index.

[0118] Particularly importantly, Step S49 further includes the following steps:

[0119] Step S491: Identify the main geometric parameters of the glass container design parameter set according to the dynamic characteristic data of the glass container to obtain the key geometric parameter set;

[0120] Step S492: Perform Monte Carlo random perturbation on the glass container design parameter set according to the key geometric parameter set to obtain the glass container parameter perturbation sample set;

[0121] Step S493: Construct a 3D model set of glass container parameter perturbations based on the glass container parameter perturbation sample set;

[0122] Step S494: Quantify the statistical robustness of the 3D model set of glass container parameter perturbations to obtain the evaluation result of the robustness index.

[0123] In this embodiment, the Rhinoceros software is used to construct a three-dimensional detailed model based on the glass container design parameter set. Taking a glass wine bottle as an example, an accurate three-dimensional model is created in Rhinoceros according to the design parameters (such as the bottleneck wall thickness of 1.2 mm, the bottle body height of 180 mm, and the bottle bottom curvature of 15 mm). After completion, SolidWorks is used to check the integrity of the CAD model of the three-dimensional detailed model. The inspection contents include geometric continuity, surface gaps, and topology, etc. The inspection results show that there are a small number of surface gap problems in the model, and a model quality assessment report is generated accordingly. According to the model quality assessment report, the Geomagic Wrap software is used to repair the surface of the three-dimensional detailed model of the glass container. Taking a glass beverage bottle as an example, after importing the model, the "Surface Repair" tool of Geomagic Wrap is used to automatically fill the detected surface gaps and smooth the surface connection. After the repair is completed, the repaired three-dimensional model of the glass container is exported. ANSYS Meshing is used to perform tetrahedron and hexahedron hybrid mesh division on the repaired three-dimensional model of the glass container. Taking a glass medicine bottle as an example, the meshing parameters are set in ANSYS Meshing, and hexahedron meshes are selected in the bottle body and bottle bottom areas to improve the calculation accuracy, while tetrahedron meshes are used in the bottleneck and transition areas to adapt to complex geometries. After the meshing is completed, ANSYS Material Database is used to configure the material parameters of the meshed model, and parameters such as the elastic modulus of glass being 70 GPa and the Poisson's ratio being 0.2 are set to obtain a parameterized three-dimensional model of the glass container. ANSYS Fluent is used to perform fluid dynamics simulation on the parameterized three-dimensional model of the glass container. Taking a glass wine bottle as an example, the fluid properties (such as the density and viscosity of water) are set in ANSYS Fluent, and the boundary conditions of the filling process (such as the inlet flow rate and outlet pressure) are defined. Through simulation calculations, the flow state and load distribution data of the fluid inside the bottle are obtained, including the pressure distribution and velocity field of the fluid on the bottle wall. According to the fluid dynamic load distribution data, ANSYS Mechanical is used to perform a structural force response analysis on the parameterized three-dimensional model of the glass container. Taking a glass beverage bottle as an example, the fluid dynamic load data is imported into ANSYS Mechanical, and the material properties and boundary conditions (such as the fixed constraint at the bottle bottom) are set. Through finite element analysis, the coupled stress distribution field is obtained, showing the stress concentration in the bottle body and bottleneck areas. At the same time, a stacking load simulation is performed in ANSYS Mechanical, the stacking pressure is set to 500 N, and the static bearing capacity data is calculated to ensure the safety of the glass container during stacking. ANSYS Mechanical is used to calculate the temperature gradient thermal stress of the parameterized three-dimensional model of the glass container.Taking a glass medicine bottle as an example, set the temperature gradient boundary condition in ANSYS Mechanical (such as the linear change of the bottle body temperature from 20°C to 60°C), and consider material properties such as the thermal expansion coefficient and elastic modulus of the glass. Through simulation calculations, obtain thermal stability evaluation data, including thermal stress distribution and the maximum thermal stress value. At the same time, conduct the calculation of the internal pressure bearing capacity, set the internal pressure to 0.6 MPa, and obtain the anti-internal pressure performance data. According to the fluid dynamic load distribution data and the coupled stress distribution field, use MATLAB for modal decomposition. Taking a glass wine bottle as an example, import the fluid dynamic load and stress distribution data obtained from the simulation into MATLAB, and use the signal processing toolbox of MATLAB for modal decomposition. Through analysis, obtain the dynamic characteristic data of the glass container, including the modal frequencies and vibration modes of each order. Use ANSYS Mechanical to conduct a drop impact dynamic simulation on the parametric three-dimensional model of the glass container. Taking a glass beverage bottle as an example, set the drop height to 1.2 m in ANSYS Mechanical, the ground as a rigid plane, and the bottom of the bottle as the initial contact point. Through dynamic simulation calculations, obtain the anti-impact performance data, including the stress peak value, deformation situation, and energy absorption situation during the impact process. Integrate the static bearing capacity data, thermal stability evaluation data, anti-internal pressure performance data, and anti-impact performance data into the forming simulation performance data. Use MATLAB to analyze the dynamic characteristic data of the glass container. Taking a glass wine bottle as an example, import the dynamic characteristic data into MATLAB, and use the data analysis function of MATLAB to identify the geometric parameters that have a greater impact on the dynamic characteristics, such as the bottleneck length, bottle body diameter, and bottle bottom thickness. These parameters are classified into the key geometric parameter set. According to the key geometric parameter set, use MATLAB to perform Monte Carlo random perturbations on the glass container design parameter set. Taking a glass beverage bottle as an example, set the perturbation range (such as ±5%) and the number of perturbations (such as 1000 times) of the key geometric parameters (such as the bottleneck length, bottle body diameter) in MATLAB. Generate a random perturbation sample set through the Monte Carlo method, and each sample represents a set of design parameters after random changes. Based on the glass container parameter perturbation sample set, use Rhinoceros to construct a three-dimensional model set of glass container parameter perturbations. Taking a glass medicine bottle as an example, import the perturbation sample set into Rhinoceros, and use its parametric modeling function to generate the corresponding three-dimensional model according to each set of perturbation parameters. Finally, obtain a model set containing multiple perturbed models. Use MATLAB to analyze the three-dimensional model set of glass container parameter perturbations. Taking a glass wine bottle as an example, import the perturbed model set into MATLAB, and use the statistical analysis tool of MATLAB to quantitatively analyze the performance indicators (such as stress peak value, deformation amount) of each model. By calculating statistical quantities such as the mean value, standard deviation, and coefficient of variation of the performance indicators, obtain the evaluation result of the robustness index.For example, if the standard deviation of the stress peak is small, it indicates that the design scheme has high robustness to parameter perturbations.

[0124] Through CAD integrity inspection and surface repair of the three-dimensional detailed model, the present invention ensures the high quality and accuracy of the model. By adopting a hybrid mesh division of tetrahedrons and hexahedrons and performing material parameter configuration, an accurate parametric model is generated, improving the accuracy and efficiency of simulation analysis. Through a series of multi-condition simulations such as filling process simulation, structural force response analysis, stacking load simulation, thermal stress calculation, internal pressure bearing capacity calculation, and drop impact simulation, comprehensive performance data of the glass container under different usage scenarios are obtained, including hydrodynamic load distribution, coupled stress distribution field, static bearing capacity, thermal stability, internal pressure resistance performance, and impact resistance performance, etc. Through parametric perturbation analysis based on dynamic characteristic data, the robustness index evaluation result is obtained, further verifying the stability of the design scheme in the face of manufacturing errors and changes in the usage environment.

[0125] Preferably, step S5 includes the following steps:

[0126] Step S51: Extract statistical indicators from the forming simulation performance data to obtain a set of forming performance indicators;

[0127] Step S52: Conduct a reliability assessment on the robustness index evaluation result to obtain a design reliability analysis report;

[0128] Step S53: Conduct a comprehensive performance score on the glass container according to the set of forming performance indicators and the design reliability analysis report to obtain a design scheme evaluation index;

[0129] Step S54: Obtain the initial forming design goal; quantify the difference between the design scheme evaluation index and the initial forming design goal to obtain the glass container performance gap data;

[0130] Step S55: Conduct Sobol global sensitivity calculation on the glass container design parameter set based on the forming simulation performance data to obtain a key parameter influence data table;

[0131] Step S56: Sort the adjustment parameters according to the glass container performance gap data and the key parameter influence data table to obtain a parameter adjustment priority list; generate design feedback adjustment suggestions based on the parameter adjustment priority list;

[0132] Step S57: Generate a lightweight glass container structure design scheme according to the design feedback adjustment suggestions.

[0133] In this embodiment, MATLAB is used to extract statistical indicators from the forming simulation performance data. Taking a glass wine bottle as an example, the static bearing capacity data, thermal stability evaluation data, internal pressure resistance performance data, and impact resistance performance data obtained from the simulation are imported into MATLAB. Using the statistical analysis tools in MATLAB, statistical indicators such as the mean, standard deviation, maximum value, and minimum value of these performance data are calculated. For example, the mean of the static bearing capacity is 550 N, and the standard deviation is 20 N; the maximum value of the impact resistance performance is 1200 J, and the minimum value is 1000 J. These statistical indicators are organized into a table to form a set of forming performance indicators. MATLAB and Minitab software are used. Taking a glass beverage bottle as an example, the robustness index evaluation results are imported into MATLAB, and the reliability analysis toolbox in MATLAB is used for preliminary analysis. The Minitab software is used for hypothesis testing to verify the statistical significance of the robustness index. For example, through hypothesis testing, it is found that the confidence interval of the robustness index is [0.85, 0.95], indicating that the design scheme has high reliability. A design reliability analysis report is generated based on these analysis results, detailing the process and conclusions of the reliability assessment. According to the set of forming performance indicators and the design reliability analysis report, Excel is used to perform a comprehensive performance score for the glass container. Taking a glass medicine bottle as an example, weights are assigned to each performance indicator and reliability indicator in Excel. For example, the weight of the static bearing capacity is 0.25, the weight of the thermal stability is 0.20, the weight of the internal pressure resistance performance is 0.20, the weight of the impact resistance performance is 0.20, and the weight of the reliability is 0.15. Then, the scores are calculated based on the actual values and target values of each indicator, and all scores are weighted and summed to obtain the design scheme evaluation index. For example, the comprehensive performance score of a certain design scheme is 85 points (out of 100), indicating that the overall performance of this design scheme is good. MATLAB and Excel are used. Taking a glass wine bottle as an example, the initial forming design goals are first defined in MATLAB, including the target static bearing capacity of 600 N, the target thermal stability index of 0.9, the target internal pressure resistance performance of 1.2 MPa, etc. The design scheme evaluation index is compared with these initial design goals to calculate the differences. For example, the static bearing capacity of a certain design scheme is 550 N, which is 50 N different from the target value of 600 N; the thermal stability index is 0.88, which is 0.02 different from the target value of 0.9. These differences are quantified as performance gap data and recorded in an Excel table. Based on the forming simulation performance data, Sobol global sensitivity calculations are performed on the glass container design parameter set using SALib (a Python-based global sensitivity analysis library). Taking a glass beverage bottle as an example, the SALib library is first installed and imported in Python. Then, the design parameters (such as the bottleneck wall thickness, bottle body height, and bottle bottom curvature) and the corresponding performance data (such as the static bearing capacity and impact resistance performance) are input into SALib.By running Sobol sensitivity analysis, a key parameter impact data table is obtained, showing that the bottleneck wall thickness has the most significant impact on the static load-bearing capacity, with a sensitivity index of 0.45; the bottle height has the second most significant impact on the thermal stability, with a sensitivity index of 0.30. According to the glass container performance gap data and the key parameter impact data table, Excel is used to adjust the parameter sorting. Taking the glass medicine bottle as an example, the performance gap data and the key parameter impact data are integrated in Excel. For example, for a static load-bearing capacity gap of 50N, the sensitivity index of the bottleneck wall thickness is 0.45, so the bottleneck wall thickness is listed as the primary parameter to be adjusted; for a thermal stability gap of 0.02, the sensitivity index of the bottle height is 0.30, so the bottle height is listed as the secondary parameter to be adjusted. Based on these sortings, a parameter adjustment priority list is generated, and design feedback adjustment suggestions are generated based on this list, such as suggesting to increase the bottleneck wall thickness by 0.1mm to improve the static load-bearing capacity. According to the design feedback adjustment suggestions, Rhinoceros and Grasshopper are used to adjust the structure of the parametric glass container 3D model. Taking the glass wine bottle as an example, first open the parametric model in Rhinoceros. According to the adjustment suggestions, use the Grasshopper plugin to modify the bottleneck wall thickness parameter from 1.2mm to 1.3mm; adjust the bottle height parameter from 180mm to 182mm. After the modification is completed, regenerate the 3D model and perform necessary geometric inspections and optimizations. Finally, a lightweight glass container structure design scheme is obtained.

[0134] Through the extraction of statistical indicators and the reliability evaluation of the robustness index from the forming simulation performance data, the present invention can comprehensively quantify the performance and reliability levels of the design scheme. Through the comprehensive score generated based on the performance indicators and reliability analysis, combined with the differential quantitative analysis of the initial design objectives, the improvement direction and performance improvement space of the design scheme are further clarified. Through Sobol global sensitivity analysis, the key parameters with the greatest impact on the performance are accurately identified, making the design adjustment more targeted and efficient. Finally, according to the feedback adjustment suggestions, the structure of the 3D model is adjusted, realizing the continuous optimization of the design scheme, ensuring that the glass container has excellent mechanical properties and reliability while meeting the lightweight objective.

[0135] Preferably, step S57 includes the following steps:

[0136] Step S571: Update the constraint conditions of the forming multi-objective optimization model according to the design feedback adjustment suggestions to obtain an optimized boundary correction scheme;

[0137] Step S572: Verify the mathematical feasibility of the optimized boundary correction scheme to obtain an updated set of optimization constraint conditions; construct a constraint iterative optimization strategy based on the updated set of optimization constraint conditions;

[0138] Step S573: Record the glass container design parameter set, forming simulation performance data, design feedback adjustment suggestions, and constraint iterative optimization strategy as glass container design case feature data;

[0139] Step S574: Extract design rules and empirical knowledge from the glass container design case feature data to obtain a glass container design knowledge entry set;

[0140] Step S575: Perform semantic annotation and relationship construction on the glass container design knowledge entry set to obtain knowledge graph construction data;

[0141] Step S576: Construct a glass container design knowledge base based on the knowledge graph construction data; conduct knowledge verification and evaluation on the glass container design knowledge base to obtain a knowledge base quality evaluation report;

[0142] Step S577: Supplement and optimize the glass container design knowledge base according to the knowledge base quality evaluation report to obtain a refined glass container design knowledge base;

[0143] Step S578: Conduct knowledge-driven evaluation on the parametric 3D model of the glass container through the refined glass container design knowledge base to obtain a design rationality verification result;

[0144] Step S579: Make final adjustments to the parametric 3D model of the glass container according to the design rationality verification result and the comprehensive performance score to obtain a lightweight glass container structure design scheme.

[0145] In this embodiment, according to the design feedback adjustment suggestions, MATLAB is used to update the constraint conditions of the formed multi-objective optimization model. Taking a glass wine bottle as an example, open the existing multi-objective optimization model in MATLAB, and adjust the constraint conditions of the model according to the feedback suggestions (such as increasing the wall thickness of the bottleneck to improve strength). For example, update the minimum value of the wall thickness of the bottleneck from 1.2 mm to 1.3 mm. By modifying the constraint conditions of the model, an optimized boundary correction scheme is obtained, which provides new boundary conditions for subsequent optimization calculations. To verify the mathematical feasibility of the optimized boundary correction scheme, the optimization toolbox of MATLAB is used for verification. Taking a glass beverage bottle as an example, import the updated constraint conditions into MATLAB and run the multi-objective optimization model. By checking the convergence and feasibility of the model, confirm whether the new constraint conditions meet the requirements of mathematical optimization. For example, if the multi-objective optimization model can find a feasible solution under the new constraint conditions, it is considered that the constraint conditions are feasible. Based on these verification results, an updated set of optimized constraint conditions is obtained, and a constraint iterative optimization strategy is constructed, providing strategy support for subsequent optimization iterations. Use Excel to structurally organize the glass container design parameter set, forming simulation performance data, design feedback adjustment suggestions, and the constraint iterative optimization strategy. Taking a glass medicine bottle as an example, create a worksheet in Excel to record the design parameters (such as the wall thickness of the bottleneck, the height of the bottle body), simulation performance data (such as static load-bearing capacity, impact resistance), feedback adjustment suggestions (such as increasing the wall thickness of the bottleneck), and optimization strategies (such as iteration step size and direction). Through the above steps, the characteristic data of the glass container design case is obtained, providing a data basis for the subsequent extraction of design rules and empirical knowledge. To extract design rules and empirical knowledge, use MATLAB and Excel to analyze the characteristic data of the glass container design case. Taking a glass wine bottle as an example, perform cluster analysis on the data in MATLAB to identify the key parameter combinations that affect performance. For example, it is found that the combination of the wall thickness of the bottleneck and the height of the bottle body has a significant impact on strength. Then, organize these analysis results in Excel to form design rules (such as "when the wall thickness of the bottleneck increases by 0.1 mm, the strength increases by 5%") and a set of empirical knowledge entries, providing a knowledge basis for the subsequent construction of the knowledge graph. Use Neo4j (a graph database) to perform semantic annotation and relationship construction on the glass container design knowledge entry set. Taking a glass beverage bottle as an example, create nodes and relationships in Neo4j, annotate the design rules and empirical knowledge entries as nodes, and define the relationships between them (such as causal relationships, association relationships). For example, create a node "increase in the wall thickness of the bottleneck" and another node "increase in strength", and define the causal relationship between them. Through the above steps, the data for constructing the knowledge graph is obtained, providing structured knowledge data for the subsequent construction of the knowledge base. Based on the data for constructing the knowledge graph, use Neo4j and Python (combined with the py2neo library) to construct a glass container design knowledge base.Taking a glass medicine bottle as an example, knowledge graph data is imported into Neo4j, and a Python script is used for data verification and preliminary evaluation. By checking the integrity and accuracy of the knowledge graph, a knowledge base quality assessment report is generated. For example, the report indicates that the relationships between certain nodes need to be further refined, or some knowledge entries need to be supplemented with more details. Through these evaluations, the quality and usability of the knowledge base are ensured. According to the knowledge base quality assessment report, Neo4j and Python are used to supplement and optimize the glass container design knowledge base. Taking a glass wine bottle as an example, the knowledge base is supplemented and modified according to the problems pointed out in the assessment report. For example, missing knowledge entries are added, the relationships between nodes are refined, or incorrect data is corrected. Through these operations, a refined glass container design knowledge base is obtained, providing high-quality knowledge support for subsequent knowledge-driven evaluations. Using the refined glass container design knowledge base, a knowledge-driven evaluation of the parametric glass container 3D model is carried out using Python (combined with the py2neo library). Taking a glass beverage bottle as an example, a script is written in Python to query relevant knowledge entries in the knowledge base and evaluate the 3D model based on this knowledge. For example, according to the rule in the knowledge base that "the wall thickness of the bottle neck should be greater than 1.3 mm to ensure strength", it is evaluated whether the wall thickness of the current model's bottle neck meets the requirements. Through the above steps, the verification result of the design rationality is obtained, providing a basis for subsequent model adjustment. According to the design rationality verification result and the comprehensive performance score, Rhinoceros and Grasshopper are used to make the final adjustment to the parametric glass container 3D model. Taking a glass medicine bottle as an example, according to the results of the knowledge-driven evaluation (such as the wall thickness of the bottle neck needs to be further increased) and the comprehensive performance score (such as the strength still needs to be improved), the 3D model is opened in Rhinoceros, and the Grasshopper plugin is used to adjust the relevant parameters. For example, the wall thickness of the bottle neck is increased from 1.3 mm to 1.4 mm, and the model is regenerated. Through these adjustments, a final lightweight glass container structural design scheme is obtained, which achieves the lightweight goal while meeting the performance requirements.

[0146] The present invention ensures the scientificity and feasibility of the optimization process by updating and optimizing the constraint conditions of the model according to the design feedback adjustment suggestions and performing mathematical feasibility verification. Further, by structuring and organizing the characteristic data of design cases and extracting knowledge, a glass container design knowledge base is constructed, and semantic annotation and relationship construction of knowledge are carried out based on knowledge graph technology, providing rich knowledge support for design optimization. This not only accumulates design experience but also enables the rationality verification of design schemes through knowledge-driven evaluation, further optimizing the design scheme. Finally, the 3D model is adjusted in combination with the evaluation results of the knowledge base and the comprehensive performance score, ensuring the scientificity, rationality, and efficiency of the design scheme.

[0147] Therefore, in all aspects, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0148] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A lightweight glass container structure optimization design method based on a three-dimensional simulation model, characterized in that It includes the following steps: Step S1: Construct a geometric variant library of glass containers; conduct multi-condition simulations on the geometric variant library of glass containers, and extract stress distribution characteristics to obtain a container stress distribution characteristic set; Step S2: Identify the NURBS control points of the glass container to obtain a set of NURBS control points for the constrained container; perform gradient-conserving deformation regulation on the set of NURBS control points for the constrained container to obtain a container wall thickness gradient constraint model; generate a distribution map of the stress-sensitive areas of the container based on the container wall thickness gradient constraint model; Step S3: Construct an objective function for minimizing the mass of the glass container, an objective function for maximizing the strength, and a manufacturing feasibility function; construct a multi-objective optimization model for forming based on the objective function for minimizing the mass, the objective function for maximizing the strength, and the manufacturing feasibility function; Solve the multi-objective optimization model for forming to obtain a set of design parameters for the glass container; Step S4: Construct a parametric 3D model of the glass container according to the set of design parameters for the glass container; evaluate the forming performance of the parametric 3D model of the glass container to obtain forming simulation performance data; conduct parametric perturbation analysis on the set of design parameters for the glass container to obtain a robustness index evaluation result; Step S5: Generate design feedback adjustment suggestions based on the forming simulation performance data and the robustness index evaluation result; Generate a structural design scheme for the lightweight glass container according to the design feedback adjustment suggestions.

2. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 1, wherein, Step S1 includes the following steps: Step S11: Obtain a list of common glass containers; collect the surface topography of each common glass container according to the list of common glass containers to obtain a point cloud data set of glass containers; Step S12: Filter out the noise points from the point cloud data set of glass containers to obtain a smooth point cloud data set of glass containers; Step S13: Reconstruct the NURBS surface fitting based on the smooth point cloud data set of glass containers to obtain a continuous surface model of the glass container; Step S14: Partition the structure of the continuous surface model of the glass container to obtain surface blocks for the bottleneck region, the bottle shoulder region, the bottle body region, and the bottle bottom region; Step S15: Extract the characteristic dimensions of the surface blocks for the bottleneck region, the bottle shoulder region, the bottle body region, and the bottle bottom region to obtain a set of geometric characteristic parameters of the glass container; Step S16: Construct a modeling function set for the glass container according to the set of geometric characteristic parameters of the glass container. The modeling function set for the glass container includes a thermal-mechanical coupling gradient function for the bottleneck region, a rheological stress transfer function for the bottle shoulder region, a wall thickness gradient constraint function for the bottle body region, and a contact stress attenuation function for the bottle bottom region. The specific construction process of the modeling function set for the glass container is as follows: Identify the thermal stress concentration areas in the bottleneck region of the glass container to obtain the thermal stress distribution data in the bottleneck region; Layout the mold cooling channels according to the thermal stress distribution data in the bottleneck region to obtain the bottleneck-mold cooling channel mapping data; Conduct thermal-mechanical coupling gradient modeling according to the bottleneck-mold cooling channel mapping data to obtain the thermal-mechanical coupling gradient function for the bottleneck region; Identify the transition curvature in the bottle shoulder region of the glass container and perform inverse inversion of the blow molding process pressure field to obtain the transition curvature field in the bottle shoulder region and the bottle shoulder blow molding pressure gradient parameters; The surface rheological characteristics of the glass container are constrained according to the transition curvature field of the bottle shoulder area and the bottle shoulder blow molding pressure gradient parameter to obtain the rheological stress transfer function of the bottle shoulder area; The stacking load direction vector field of the bottle body area of the glass container is decomposed to obtain the load direction vector diagram of the bottle body area; The mold parting line of the bottle body area of the glass container is located to obtain the spatial topology data of the bottle body-mold parting line; Based on the load direction vector diagram of the bottle body area and the spatial topology data of the bottle body-mold parting line, a dynamic coupling model of wall thickness-parting line is established to obtain the wall thickness gradient constraint function of the bottle body area; The contact stress of the mold ejector pin distribution in the bottle bottom area of the glass container is identified to obtain the ejector pin distribution constraint data of the bottle bottom area; Based on the ejector pin distribution constraint data of the bottle bottom area, a stress attenuation gradient model is established to obtain the contact stress attenuation function of the bottle bottom area; Step S17: Construct a geometric variant library of the glass container based on the glass container modeling function set; perform multi-condition simulations on the geometric variant library of the glass container, and extract stress distribution characteristics to obtain a container stress distribution characteristic set.

3. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 2, characterized in that, Step S17 includes the following steps: Step S171: Define variables for the glass container modeling function set and construct the dependency constraint relationship between geometric parameters, so as to obtain the geometric parameter association map of the glass container; Step S172: Based on the geometric parameter association map of the glass container, rank the importance of the regional parameters in the glass container geometric feature parameter set to obtain the key parameter ranking table of the glass container; Step S173: Determine the core parameter control set of the glass container according to the key parameter ranking table of the glass container; Step S174: Obtain the value range of each parameter in the core parameter control set of the glass container and generate a parameter value range table; Step S175: Construct design variants for each parameter in the core parameter control set of the glass container according to the parameter value range table to obtain the parameter change sequence of the glass container; Step S176: Construct a geometric variant library of the glass container based on the parameter change sequence of the glass container, where the geometric variant library of the glass container contains no less than 100 design variants; Step S177: Perform multi-condition simulations on the geometric variant library of the glass container and extract stress distribution characteristics to obtain a container stress distribution characteristic set.

4. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 1, wherein Step S2 includes the following steps: Step S21: Collect the glass forming process constraint parameter set; normalize the glass forming process constraint parameter set to obtain a standard manufacturing process parameter library; Step S22: Identify the glass temperature-viscosity relationship according to the standard manufacturing process parameter library to obtain the glass temperature-viscosity relationship data; Step S23: Evaluate the material flow characteristics based on the glass temperature-viscosity relationship data to obtain the glass forming fluidity constraint conditions; Step S24: Identify the demolding constraint conditions of the typical glass container production mold to obtain the mold demolding constraint conditions; Step S25: Obtain the glass physical property data; identify the thermal stress distribution law according to the glass physical property data to obtain the glass thermal gradient constraint conditions; Step S26: Integrate the glass forming fluidity constraint conditions, the mold demolding constraint conditions and the glass thermal gradient constraint conditions to obtain the container forming process constraint set; Step S27: Identify the NURBS control points of the glass container according to the container forming process constraint set to obtain the constrained container NURBS control point set; Step S28: Perform gradient-conserved deformation regulation on the constrained container NURBS control point set to obtain the container wall thickness gradient constraint model; generate the container stress-sensitive area distribution map based on the container wall thickness gradient constraint model.

5. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 4, characterized in that, Step S27 includes the following steps: Step S271: Construct the glass forming parametric process boundary conditions based on the container forming process constraint set; Step S272: Construct the constraint violation penalty rules for the glass forming parametric process boundary conditions to obtain the process feasibility evaluation rules; Step S273: Perform non-uniform rational B-spline control grid division on the glass container mesh model to obtain the glass container NURBS control grid. Specifically, the non-uniform rational B-spline control grid division is as follows: uniformly arrange the control point grid on the surface of the glass container, with the control point density in the bottle body and bottom regions being one control point per 10mm×10mm area, and the bottleneck and transition regions being encrypted to one control point per 5mm×5mm area; Step S274: Set the weight coefficients and knot vectors for the glass container NURBS control grid to obtain the complete glass container NURBS control points; Step S275: Set constraints on the complete glass container NURBS control points based on the process feasibility evaluation rules to obtain the constrained container NURBS control point set.

6. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 4, characterized in that, Step S28 includes the following steps: Step S281: Evaluate the control point parameter sensitivity of the constrained container NURBS control point set to obtain the control point parameter sensitivity distribution data; Step S282: Construct the displacement influence factor table based on the control point parameter sensitivity distribution data to generate the control point displacement influence factor table; Step S283: Construct the control point motion chain transfer rule according to the glass forming fluidity constraint conditions; Step S284: Inject dynamic damping constraints into the control point motion chain transfer rule according to the stress-sensitive area distribution data to obtain the glass container deformation control strategy; Step S285: Adjust the deformation of the complete glass container NURBS control points according to the glass container deformation control strategy to obtain the deformable grid control point set; Step S286: Construct the wall thickness gradient limit model according to the deformable grid control point set; Step S287: Discretize the wall thickness gradient limit model to obtain the wall thickness gradient limit discrete model; Step S288: Use the wall thickness gradient limit discrete model to perform gradient-conserved deformation regulation on the deformable grid control point set to obtain the container wall thickness gradient constraint model; Step S289: Calculate the local stress response coefficient of the container wall thickness gradient constraint model according to the container stress distribution feature set to generate the stress-sensitive area distribution data; generate the container stress-sensitive area distribution map according to the stress-sensitive area distribution data.

7. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Allocate the material density of the glass container mesh model to obtain the glass container voxel density data; Step S32: Calculate the volume of each grid cell of the glass container grid model based on the voxel density data of the glass container body to obtain the mass distribution map of the glass container cells; perform global integration on the mass distribution map of the glass container cells to obtain the total mass value of the glass container; Step S33: Establish a relationship model between mass and design parameters based on the parametric modeling function of the glass container and the total mass value of the glass container to obtain the objective function for minimizing mass; Step S34: Extract the stress peak points from the set of container stress distribution characteristics to obtain the set of key stress evaluation points of the glass container; perform Weibull distribution fitting on the set of key stress evaluation points of the glass container to obtain the failure probability distribution data of the glass container; Step S35: Calculate the overall structural failure probability of the glass container based on the failure probability distribution data of the glass container to obtain the overall structural failure probability of the glass container; Step S36: Perform inverse transformation on the overall structural failure probability of the glass container to obtain the objective function for maximizing strength; Step S37: Evaluate the complexity of the manufacturing process according to the process feasibility evaluation rules to obtain the process difficulty index of the glass container; Step S38: Map the process difficulty index of the glass container to obtain the predicted data of the manufacturing cost of the glass container; construct a manufacturing feasibility function based on the predicted data of the manufacturing cost of the glass container and the process difficulty index of the glass container; Step S39: Construct a multi-objective optimization model for forming based on the objective function for minimizing mass, the objective function for maximizing strength, and the manufacturing feasibility function; solve the multi-objective optimization model for forming to obtain the set of design parameters of the glass container.

8. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 1, wherein Step S4 includes the following steps: Step S41: Construct a three-dimensional detailed model of the glass container based on the set of design parameters of the glass container; check the integrity of the CAD model of the three-dimensional detailed model of the glass container to obtain a model quality evaluation report; Step S42: Repair the surface of the three-dimensional detailed model of the glass container according to the model quality evaluation report to obtain a repaired three-dimensional model of the glass container; Step S43: Perform hybrid tetrahedral and hexahedral mesh meshing on the repaired three-dimensional model of the glass container to obtain a meshed three-dimensional model of the glass container; configure the material parameters for the meshed three-dimensional model of the glass container to obtain a parametric three-dimensional model of the glass container; Step S44: Simulate the filling process of the parametric three-dimensional model of the glass container to obtain the fluid dynamic load distribution data; Step S45: Analyze the structural force response of the parametric three-dimensional model of the glass container according to the fluid dynamic load distribution data to obtain the coupled stress distribution field; simulate the stacking load of the parametric three-dimensional model of the glass container to obtain the static bearing capacity data; Step S46: Calculate the thermal stress of the temperature gradient of the parametric three-dimensional model of the glass container to obtain the thermal stability evaluation data; calculate the internal pressure bearing capacity of the parametric three-dimensional model of the glass container to obtain the internal pressure resistance performance data; Step S47: Perform modal decomposition according to the fluid dynamic load distribution data and the coupled stress distribution field to obtain the dynamic characteristic data of the glass container; Step S48: Perform a drop impact dynamic simulation and evaluation on the parametric three-dimensional model of the glass container to obtain anti-impact performance data; record the static load-bearing capacity data, thermal stability evaluation data, internal pressure resistance performance data, and anti-impact performance data as the forming simulation performance data; Step S49: Conduct a parametric perturbation analysis on the glass container design parameter set based on the glass container dynamic characteristic data to obtain the evaluation result of the robustness index.

9. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 1, characterized in that Step S5 includes the following steps: Step S51: Extract statistical indicators from the forming simulation performance data to obtain the forming performance index set; Step S52: Conduct a reliability evaluation on the evaluation result of the robustness index to obtain a design reliability analysis report; Step S53: Conduct a comprehensive performance score on the glass container according to the forming performance index set and the design reliability analysis report to obtain a design scheme evaluation index; Step S54: Obtain the initial forming design goal; quantify the difference between the design scheme evaluation index and the initial forming design goal to obtain the glass container performance gap data; Step S55: Perform a Sobol global sensitivity calculation on the glass container design parameter set based on the forming simulation performance data to obtain a key parameter influence data table; Step S56: Adjust the parameter sorting according to the glass container performance gap data and the key parameter influence data table to obtain a parameter adjustment priority list; generate design feedback adjustment suggestions based on the parameter adjustment priority list; Step S57: Generate a lightweight glass container structure design scheme according to the design feedback adjustment suggestions.

10. The lightweight glass container structure optimization design method based on a three-dimensional simulation model according to claim 9, characterized in that, Step S57 includes the following steps: Step S571: Update the constraint conditions of the forming multi-objective optimization model according to the design feedback adjustment suggestions to obtain an optimized boundary correction scheme; Step S572: Conduct a mathematical feasibility verification on the optimized boundary correction scheme to obtain an updated set of optimization constraint conditions; construct a constraint iterative optimization strategy based on the updated set of optimization constraint conditions; Step S573: Record the glass container design parameter set, forming simulation performance data, design feedback adjustment suggestions, and constraint iterative optimization strategy as the glass container design case feature data; Step S574: Extract design rules and empirical knowledge from the glass container design case feature data to obtain a glass container design knowledge item set; Step S575: Conduct semantic annotation and relationship construction on the glass container design knowledge item set to obtain knowledge graph construction data; Step S576: Construct a glass container design knowledge base based on the knowledge graph construction data; conduct a knowledge verification evaluation on the glass container design knowledge base to obtain a knowledge base quality evaluation report; Step S577: Supplement and optimize the glass container design knowledge base according to the knowledge base quality evaluation report to obtain a perfected glass container design knowledge base; Step S578: Conduct a knowledge-driven evaluation on the parametric three-dimensional model of the glass container through the perfected glass container design knowledge base to obtain a design rationality verification result; Step S579: Conduct a final adjustment on the parametric three-dimensional model of the glass container according to the design rationality verification result and the comprehensive performance score to obtain a lightweight glass container structure design scheme.

Images