Method for simulating a refrigerator liner

Abstract

The application provides a simulation method of a refrigerator liner, which obtains a simulation model of the refrigerator liner by performing a plastic uptake forming simulation, specifies a cross section on the simulation model to obtain a simulation thickness distribution along the cross section, simultaneously obtains a measured thickness distribution of a target liner along the same cross section, and compares the two. Since the simulation thickness distribution and the measured thickness distribution both represent the corresponding relationship that the thickness continuously changes along the cross section distance, the thickness distribution of the liner along the entire cross section can be comprehensively reflected, rather than being limited to discrete local points. By comparing the two as a whole, the accuracy of the plastic uptake forming simulation can be accurately evaluated, thereby providing a reliable simulation basis for the liner structure design and process parameter optimization.

Description

Technical Field

[0001] This application pertains to the field of refrigerator manufacturing, and more specifically, relates to a method for simulating the inner liner of a refrigerator. Background Technology

[0002] The refrigerator liner is a core component of a refrigerator, and its thickness uniformity directly affects the product's structural strength and reliability. An excessively thin liner in certain areas can easily lead to quality problems such as deformation and cracking, and once damaged, the liner cannot be repaired, requiring the entire refrigerator to be scrapped. Currently, vacuum forming simulation technology has been applied to predict the thickness of refrigerator liners and optimize the manufacturing process. However, current simulation methods have the limitation of not being able to accurately evaluate the thickness distribution of the refrigerator liner. Summary of the Invention

[0003] The purpose of this application is to provide a simulation method for refrigerator liner, so as to solve the technical problem that existing simulation methods for refrigerator liner cannot accurately evaluate the thickness distribution of the refrigerator liner.

[0004] To achieve the above objectives, the technical solution adopted in this application is: to provide a simulation method for a refrigerator inner liner, the simulation method for the refrigerator inner liner comprising: Perform a vacuum forming simulation of the refrigerator liner to obtain a simulation model of the refrigerator liner; Specify at least one cross section on the refrigerator liner simulation model and obtain the simulation thickness distribution of the refrigerator liner simulation model along the cross section; Obtain the target inner liner, which is the refrigerator inner liner actually obtained in production; Determine a cross section on the target inner liner that is at the same location as the specified cross section, and obtain the measured thickness distribution of the target inner liner along this cross section; The simulated thickness distribution is compared with the measured thickness distribution to evaluate the accuracy of the vacuum forming simulation.

[0005] Optionally, perform a vacuum forming simulation of the refrigerator liner, including setting simulation parameters. The simulation parameters include determined parameters and parameters to be determined. The determined parameters are those that can be preset in the vacuum forming simulation, and the parameters to be determined are those that cannot be directly obtained in the vacuum forming simulation. After comparing the simulated thickness distribution with the measured thickness distribution, the simulation method for the refrigerator liner also includes: determining the parameters to be determined based on the comparison between the simulated thickness distribution and the measured thickness distribution.

[0006] Optionally, based on the comparison between the simulated thickness distribution and the measured thickness distribution, the parameters to be determined are identified, including: Obtain multiple combinations of values ​​for the parameters to be determined; Perform vacuum forming simulations for each set of values ​​to obtain the simulation thickness distribution for each set of simulations. The simulated thickness distribution of each group was compared with the measured thickness distribution to obtain the comparison results for each group; Based on the comparison results of each group, the parameters to be determined for subsequent simulations are determined from the combinations of values ​​of multiple parameters to be determined.

[0007] Optionally, each simulated thickness distribution can be compared with the measured thickness distribution, including: A simulated thickness curve is generated based on the simulated thickness distribution. The simulated thickness curve shows the relationship between thickness and distance along the cross section. A measured thickness curve is generated based on the measured thickness distribution. The measured thickness curve shows the relationship between the thickness and the distance along the cross section. The simulated thickness curve is compared with the measured thickness curve.

[0008] Optionally, the simulated thickness curve is compared with the measured thickness curve, including calculating at least one of the Fraser distance and the Euler distance between the simulated thickness curve and the measured thickness curve, and using the Fraser distance and / or the Euler distance as the evaluation index of the comparison result.

[0009] Optionally, multiple combinations of values ​​for the parameters to be determined can be obtained through orthogonal experimental design; Based on the comparison results of each group, the parameters to be determined for subsequent simulations are determined from multiple combinations of values ​​for the parameters to be determined, including: Based on the comparison results of each group, analyze the influence weight of each parameter to be determined on the simulation accuracy. Based on the influence weights, the parameters to be determined for subsequent simulations are determined from multiple combinations of values ​​for the parameters to be determined.

[0010] Optionally, the parameters to be determined include at least one of mold temperature, heat transfer coefficient, slip coefficient, and penalty coefficient.

[0011] Optionally, before performing the vacuum forming simulation of the refrigerator liner, the simulation method for the refrigerator liner may also include: Establish constitutive models for polymer materials; Establish a finite element model of the thermoforming process for the refrigerator liner; Set the vacuum forming process parameters and perform simulation calculations; Output the thickness distribution cloud map of the refrigerator liner simulation model based on the simulation calculation results; Extract the simulated thickness distribution along a specified cross section from the thickness distribution cloud map.

[0012] Optionally, based on the comparison between the simulated thickness distribution and the measured thickness distribution, the parameters to be determined are identified, including: Adjust the parameters to be determined based on the comparison results; Re-execute the vacuum forming simulation and obtain a new simulated thickness distribution. Compare the new simulated thickness distribution with the measured thickness distribution again. Repeat the adjustment and comparison process until the comparison results meet the preset conditions; The parameters to be determined when the preset conditions are met are determined as the parameters to be determined for subsequent simulations.

[0013] Optionally, before performing the step of simulating the vacuum forming of the refrigerator liner, the following steps are also included: A two-way thermal-fluid-structure interaction calculation was performed on the co-extruded plate to obtain the temperature distribution and geometry after melting and sagging. After the geometry is fused and sags, the facets are stitched and reassembled to form a whole, and the mid-face of the solid unit is extracted. The temperature distribution was converted into a format readable by the vacuum forming simulation software and then imported as the initial conditions for the vacuum forming simulation.

[0014] The beneficial effects of the refrigerator liner simulation method provided in this application are as follows: Compared with the prior art, the embodiments of this application obtain a simulation model of the refrigerator liner by performing vacuum forming simulation, obtain the simulated thickness distribution along a specified cross section on the simulation model, and simultaneously obtain the measured thickness distribution of the target liner along the same cross section, and compare the two. Since both the simulated thickness distribution and the measured thickness distribution represent the corresponding relationship of thickness continuously changing with the distance along the cross section, they can comprehensively reflect the thickness distribution of the liner along the entire cross section, rather than being limited to discrete local points. By comparing the two as a whole, the accuracy of the vacuum forming simulation can be accurately evaluated, thereby providing a reliable simulation basis for the liner structure design and process parameter optimization. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the simulation method for the refrigerator inner liner in the embodiments of this application; Figure 2 This is a flowchart illustrating the process of determining the parameter to be determined based on a combination of multiple parameters in an embodiment of this application. Figure 3 This is a schematic diagram illustrating the comparison process between the simulated thickness curve and the measured thickness curve in the embodiments of this application; Figure 4 This is a thickness distribution cloud map of the refrigerator liner simulation model in this application embodiment; Figure 5 for Figure 4 Comparison of thickness distribution curves at section a; Figure 6 for Figure 4 Comparison of thickness distribution curves at section b; Figure 7 for Figure 4 Comparison of thickness distribution curves at section c; Figure 8 for Figure 4 Comparison of thickness distribution curves at section d; Figure 9 for Figure 4 Comparison of thickness distribution curves at section e; Figure 10 This is a schematic diagram of the process for determining the parameters to be determined based on iterative adjustment in an embodiment of this application; Figure 11 This is a schematic diagram of the vacuum forming simulation and thickness distribution extraction process in the embodiments of this application; Figure 12 This is a schematic diagram of the thermal-fluid-structure interaction and geometric processing flow in the embodiments of this application; Figure 13 This is a schematic diagram of the temperature data conversion and interpolation import process in an embodiment of this application. Detailed Implementation

[0017] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0018] It should be noted that when a component is referred to as being "fixed to" or "set on" another component, it can be directly on or indirectly on that other component. When a component is referred to as being "connected to" another component, it can be directly connected to or indirectly connected to that other component.

[0019] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] The refrigerator liner is a core component, and its thickness uniformity directly affects the product's structural strength and reliability. An excessively thin liner in certain areas can easily lead to quality problems such as deformation and cracking. Furthermore, once the liner is damaged, it cannot be repaired, and the entire refrigerator must be scrapped. Therefore, accurately obtaining the liner thickness distribution and effectively evaluating its uniformity is of great significance for ensuring refrigerator quality and optimizing manufacturing processes.

[0022] Currently, the manufacturing process of refrigerator liners mainly relies on experience-based design and repeated trial molding, resulting in long development cycles, high costs, and high scrap rates. To improve this situation, vacuum forming simulation technology has been applied to the thickness prediction and process optimization of refrigerator liners. However, some simulation methods in related technologies typically evaluate simulation accuracy by comparing single-point numerical values, that is, comparing the thickness values ​​of a few discrete points obtained from the simulation with the measured thickness values. Due to the complex structure of the refrigerator liner and the continuous variation of its thickness distribution along the cross-section, single-point data cannot fully reflect the overall thickness distribution of the liner, leading to inaccurate simulation accuracy evaluation and making it difficult for simulation results to effectively guide mold structure design and process parameter optimization.

[0023] Therefore, there is a problem with the related technology that it is impossible to accurately evaluate the thickness distribution of the refrigerator liner.

[0024] To address the aforementioned problems, this application provides a simulation method for a refrigerator inner liner. Please refer to [link / reference]. Figure 1 The simulation methods for the refrigerator liner include: S110. Perform a vacuum forming simulation of the refrigerator liner to obtain a simulation model of the refrigerator liner. Vacuum forming is a thermoforming process in which heated and softened plastic sheets are adhered to the surface of a mold through vacuum adsorption, and then cooled to form a product of the desired shape. Vacuum forming simulation uses computer numerical simulation technology to reproduce the above physical process in a virtual environment. Through simulation, the deformation, stress distribution, and thickness distribution of the plastic sheet at various moments during the forming process can be calculated. This step, by performing the vacuum forming simulation, ultimately obtains a virtual model containing the geometric shape and thickness distribution information of the liner, i.e., the refrigerator liner simulation model. For example, during the simulation, geometric models of the plastic sheet and the mold can be established, material properties and process parameters can be set, and the entire process of the sheet from heating, stretching, and adhering to the mold to cooling and forming can be simulated by solving the governing equations.

[0025] S120. Specify at least one cross section on the refrigerator liner simulation model and obtain the simulated thickness distribution of the refrigerator liner simulation model along the cross section. On the obtained refrigerator liner simulation model, select at least one cross section as the analysis object. A cross section refers to a sectional surface passing through a certain location on the liner model, such as a longitudinal cross section along the length of the liner, a transverse cross section along the width, or a diagonal cross section. Along this cross section, extract the thickness values ​​of the liner model at different cross section locations from the simulation calculation results. These thickness values ​​form a one-to-one correspondence with the distance to the cross section, thereby obtaining the simulated thickness distribution along the cross section. This distribution reflects the overall situation where the liner thickness continuously changes with the cross section position under virtual molding conditions.

[0026] S130. Obtain the target inner liner, which is the refrigerator inner liner actually produced in production. Obtain the refrigerator inner liner manufactured through the actual production process as the target inner liner. This target inner liner is a physically existing real product, and its thickness distribution reflects the molding result under actual production conditions, which can be used as a benchmark for subsequent evaluation of simulation accuracy.

[0027] S140. Determine a cross-section on the target inner liner that is at the same location as the specified cross-section, and obtain the measured thickness distribution of the target inner liner along this cross-section. On the target inner liner, determine a cross-section at the exact same location as the cross-section specified on the aforementioned refrigerator inner liner simulation model. For example, if the simulation model specifies a transverse cross-section at a certain height from the bottom of the inner liner, then select a transverse cross-section at the same height on the target inner liner. Along this cross-section, collect the actual thickness values ​​at different cross-section locations using a measuring method (such as a thickness gauge). These thickness values ​​form a one-to-one correspondence with the distance from this cross-section, thereby obtaining the measured thickness distribution along this cross-section. This distribution reflects the overall situation where the thickness of the actual product continuously changes with the cross-section location.

[0028] S150. Compare the simulated thickness distribution with the measured thickness distribution to evaluate the accuracy of the vacuum forming simulation. Compare the simulated thickness distribution obtained in step S120 with the measured thickness distribution obtained in step S140 on the same coordinate system. The simulated thickness distribution reflects the variation of thickness with cross-sectional distance calculated in the simulation, while the measured thickness distribution reflects the variation of thickness with cross-sectional distance of the actual product. By observing the degree of agreement between the two in terms of overall trend, local fluctuations, and numerical magnitude, the consistency between the simulation results and the actual production results can be determined. If the two are relatively close, it indicates high simulation accuracy; if there is a significant deviation, it indicates an error in the simulation model or parameter settings, requiring further adjustment.

[0029] In this embodiment, the simulated thickness distribution and the measured thickness distribution along a specified cross section are obtained and compared. During the vacuum forming process, after the plastic sheet is heated and softened, it gradually adheres to the mold surface under vacuum adsorption. Its thickness distribution depends on various factors such as the initial thickness of the sheet, the heating temperature distribution, the stretching deformation path, the mold geometry, and the cooling rate after contact with the mold. The degree of stretching varies at different locations, resulting in the final inner liner thickness exhibiting a continuous change rather than a uniform distribution along the cross section. This continuous change cannot be fully described by the thickness values ​​of a few discrete points. The simulated thickness distribution comes from the vacuum forming simulation calculation, while the measured thickness distribution comes from the measurement of the actual production inner liner. The core of both is that they present the thickness as a continuous change along the cross section distance, transforming the originally discrete point data into a continuous curve. Comparing the two curves in the same coordinate system is essentially comparing the degree of agreement between the two data sets in continuous space. When the two curves are highly consistent, it indicates that the material constitutive model, boundary conditions, process parameters, etc., in the simulation model are consistent with the actual production situation; when there is a significant deviation, it suggests that the simulation parameters need to be adjusted or the mold structure optimized.

[0030] This embodiment overcomes the limitation of single-point data in comprehensively reflecting the thickness of the inner liner by acquiring a continuous thickness distribution along the cross-section. The thickness variations in complex areas such as inner corners, ribs, and lugs in the inner liner structure often exhibit continuous gradients, making it difficult for single-point measurements to capture the thickness variation patterns in these areas. A continuous thickness distribution, however, can completely present the thickness value at every point from the start to the end of the cross-section, allowing the comparison between simulation results and actual products to go beyond a few locations and thus more comprehensively reflect simulation accuracy. This embodiment compares the simulated thickness distribution with the measured thickness distribution as a whole, comprehensively evaluating simulation accuracy from both overall trend and local detail perspectives. The overall trend reflects the consistency between the two at a macroscopic scale, while local details reflect the degree of agreement in characteristic areas such as the transition zone of inner corners and the root of ribs. This multi-level evaluation method can more accurately identify problems in the simulation model. This embodiment provides a reliable evaluation basis for subsequent mold structure optimization and process parameter calibration. Because it can accurately identify the location and degree of deviation between simulation and reality, targeted adjustments can be made to the mold structure or process parameters, and the optimization effect can be verified by comparison again, thereby reducing the blindness of repeated trial molding and improving R&D efficiency.

[0031] In some embodiments of this application, performing a vacuum forming simulation of a refrigerator liner includes setting simulation parameters, which include determined parameters and parameters to be determined. The determined parameters are those that can be preset in the vacuum forming simulation, while the parameters to be determined are those that cannot be directly obtained in the vacuum forming simulation. After comparing the simulated thickness distribution with the measured thickness distribution, the simulation method for the refrigerator liner further includes determining the parameters to be determined based on the comparison between the simulated thickness distribution and the measured thickness distribution.

[0032] In this embodiment, the simulation of the vacuum forming of the refrigerator liner includes setting simulation parameters, which include determined parameters and parameters to be determined. The determined parameters are those that can be preset in the vacuum forming simulation, and the parameters to be determined are those that cannot be directly obtained in the vacuum forming simulation. After comparing the simulated thickness distribution with the measured thickness distribution, the simulation method of the refrigerator liner further includes: determining the parameters to be determined based on the comparison between the simulated thickness distribution and the measured thickness distribution.

[0033] Specifically, when performing vacuum forming simulation, a series of parameters need to be input into the simulation software. These parameters collectively determine the simulation results. Determined parameters are those that can be directly obtained from material handbooks, equipment manuals, or conventional measurement methods, such as the initial thickness of the sheet material, material density, specific heat capacity, and elastic modulus. These parameters are already known before the simulation and do not need to be determined by the simulation itself. Undetermined parameters, on the other hand, are those that cannot be directly obtained through measurement or consulting literature, such as the heat transfer coefficient between the mold and the sheet material, the slip coefficient of the contact surface between the sheet material and the mold, and the penalty coefficient for controlling mesh penetration. The values ​​of these parameters are closely related to actual process conditions, mold surface condition, material batch, and other factors, making them difficult to obtain accurately through conventional methods.

[0034] After comparing the simulated thickness distribution with the measured thickness distribution, this embodiment determines the parameter to be determined based on the comparison result. The principle is that the simulated thickness distribution is a function of the parameter to be determined; different values ​​of the parameter will lead to different simulated thickness distributions. The measured thickness distribution reflects the thickness under actual process conditions and is the target that the simulation needs to approximate. By comparing the simulated thickness distribution with the measured thickness distribution, it can be determined whether the current value of the parameter to be determined is reasonable. If the two match well, it indicates that the value of the parameter to be determined is close to the true value; if there is a deviation, the value of the parameter to be determined can be adjusted according to the direction and degree of the deviation, so that the simulation result approaches the measured result. This process essentially uses the measurable output (measured thickness distribution) to infer the indirectly measurable input (parameter to be determined), thereby achieving the calibration of the simulation parameters.

[0035] This embodiment compares the simulated thickness distribution with the measured thickness distribution and determines the parameters to be determined based on the comparison results, enabling the accurate calibration of simulation parameters that were originally impossible to obtain directly. The calibrated simulation parameters are closer to actual working conditions, significantly improving the accuracy of subsequent simulations. Simultaneously, this method transforms the determination of parameters from empirical guesswork to scientific calibration based on measured data, providing a repeatable operational basis for setting simulation parameters under different molds, material batches, and process conditions.

[0036] Please see Figure 2 In some embodiments of this application, the parameters to be determined are determined based on a comparison between the simulated thickness distribution and the measured thickness distribution, including: S151. Obtain multiple sets of values ​​for the parameters to be determined. These parameters are those that cannot be directly obtained in the vacuum forming simulation, such as mold temperature, heat transfer coefficient, slip coefficient, and penalty coefficient. To determine the optimal values ​​for these parameters, multiple different sets of values ​​need to be pre-defined. Each set of values ​​represents a possible parameter configuration scheme, covering the possible value range of the parameters to be determined. These sets of values ​​can be obtained in various ways, such as uniformly selecting based on empirical ranges, obtaining a balanced distribution using orthogonal experimental design methods, or setting based on historical data. Obtaining multiple sets of values ​​provides sufficient candidate schemes for subsequent screening of the optimal parameters.

[0037] S152. Perform vacuum forming simulations for each set of parameter combinations to obtain the simulation thickness distribution for each set of simulations. Input each set of parameter combinations to be determined obtained in step S151 into the vacuum forming simulation software and perform a complete vacuum forming simulation calculation. Each set of parameter combinations corresponds to an independent simulation calculation. After each simulation, the simulation thickness distribution of the refrigerator liner simulation model along a specified cross-section is obtained. By executing multiple sets of simulations, multiple sets of simulation thickness distribution results under different parameter configurations are obtained, forming a correspondence between parameter values ​​and simulation results.

[0038] S153. Compare each group of simulated thickness distributions with the measured thickness distributions to obtain the comparison results. Compare each of the obtained simulated thickness distributions with the measured thickness distribution of the target inner liner. The measured thickness distribution is the thickness distribution of the inner liner along the same cross-section obtained in actual production, reflecting the thickness under real working conditions. During the comparison, each group of simulated thickness distributions and measured thickness distributions can be compared under the same coordinate system to evaluate the degree of agreement between the two. The comparison results can be qualitative observations or quantitative evaluation indicators, such as the average deviation, maximum deviation, or shape similarity between the two. Through comparison, the simulation accuracy evaluation results corresponding to each combination of parameter values ​​to be determined can be obtained.

[0039] S154. Based on the comparison results of each group, determine the parameters to be determined for subsequent simulations from multiple combinations of parameter values. After obtaining the comparison results for each combination of parameter values, rank the parameters according to their merits. The comparison results reflect the degree of agreement between the simulated thickness distribution and the measured thickness distribution. The higher the degree of agreement, the closer the parameter values ​​are to the actual working conditions. Therefore, select one or more combinations of parameter values ​​with the best comparison results (i.e., the best agreement between simulation and measurement) as the input parameters for subsequent formal simulations. The selected parameters enable the simulation results to more accurately reflect the actual production situation.

[0040] The values ​​of the parameters to be determined directly affect the simulated thickness distribution, while the measured thickness distribution is the result under actual process conditions. By constructing multiple sets of parameter combinations, performing simulations and obtaining the corresponding simulated thickness distributions, and then comparing each set of simulated thickness distributions with the measured thickness distributions, it is equivalent to searching in the parameter space for the parameter points that make the simulation results closest to the measured results. Since the parameters to be determined cannot be directly measured, this method establishes a relationship between the parameter values ​​and the observable output results (i.e., the simulated thickness distribution), and then uses the measured results as an evaluation criterion for screening, thus achieving the goal of inferring the unmeasurable input from the measurable output.

[0041] By acquiring multiple sets of parameter values ​​and conducting multiple rounds of simulations, the randomness and uncertainty inherent in relying on empirical parameters in a single simulation are avoided, making the parameter determination process more systematic. By comparing each set of simulation results with measured results and selecting the optimal parameters based on these comparisons, parameter determination is transformed from subjective guesswork into objective, data-driven decision-making, improving the accuracy of parameter calibration. This method provides a repeatable operational procedure for setting simulation parameters under different molds, material batches, and process conditions, reducing the blindness of repeated trial molding and improving the overall efficiency of simulation development. The calibrated parameters more closely approximate real-world conditions, significantly improving the accuracy of subsequent vacuum forming simulations and providing a reliable basis for inner liner structure optimization and process parameter adjustment.

[0042] Please see Figure 3 In some embodiments of this application, each group of simulated thickness distributions is compared with the measured thickness distributions, including: S1531. Generate a simulation thickness curve based on the simulated thickness distribution. The simulation thickness curve shows the relationship between thickness and distance along the cross-section. The simulated thickness distribution is derived from the calculation results of the vacuum forming simulation, containing thickness data of the refrigerator liner simulation model at various locations on a specified cross-section. Plot these data on a coordinate system with cross-section distance as the abscissa and thickness as the ordinate, creating a continuous curve, i.e., the simulation thickness curve. This curve visually reflects the overall trend and local characteristics of the continuous change in liner thickness from the starting point to the ending point of the cross-section under simulation conditions.

[0043] S1532. Generate a measured thickness curve based on the measured thickness distribution. The measured thickness curve shows the relationship between thickness and distance along the cross-section. The measured thickness distribution comes from actual measurements of the target inner liner, including thickness data at various locations on the same cross-section as the simulation. Similarly, these data are plotted on a coordinate system with cross-sectional distance as the abscissa and thickness as the ordinate, forming a continuous curve, i.e., the measured thickness curve. This curve intuitively reflects the true situation of continuous change in inner liner thickness along the cross-section under actual production conditions.

[0044] S1533. Compare the simulated thickness curve with the measured thickness curve. Observe and analyze the two curves under the same coordinate system, comparing their degree of agreement in terms of overall trend, local fluctuations, and numerical magnitude. The overall trend reflects the consistency of the two at the macroscopic scale, such as whether the thickness gradually increases or decreases; local fluctuations reflect whether the thickness changes are consistent in characteristic areas such as the inner corner transition zone and the root of the fibrous band; the numerical magnitude reflects whether the thickness values ​​of the two at the same cross-sectional location are close.

[0045] In this way, the originally discrete thickness data is transformed into a continuous curve. The simulated thickness curve reflects the prediction of the physical process by the computational model, while the measured thickness curve reflects the actual result of the physical process. Comparing the two curves in the same coordinate system is essentially a holistic comparison of the two data sets in a continuous space, rather than being limited to a numerical comparison of a few discrete points. When the two curves are highly consistent, it indicates that the material constitutive model, boundary conditions, process parameters, etc., in the simulation model are consistent with the actual production situation; when there is a significant deviation, the location and degree of the deviation can be used to determine the problems in the simulation model.

[0046] By converting thickness distribution into curves and comparing them, the limitation of single-point data in comprehensively reflecting the thickness of the inner liner is overcome. The thickness variations in complex areas such as inner corners, ribs, and lugs within the inner liner structure often exhibit continuous gradients. Single-point measurements struggle to capture the thickness variation patterns in these areas, while continuous curves can fully represent the thickness value at every point from the start to the end of the cross-section. This allows the comparison between simulation results and actual products to go beyond a few locations, thus reflecting simulation accuracy more comprehensively. Furthermore, curve comparison can comprehensively evaluate simulation accuracy from both overall trend and local detail perspectives. The overall trend reflects the consistency between the two at a macroscopic scale, while local details reflect the degree of agreement in characteristic areas such as the transition zone of inner corners and the root of ribs. This multi-level evaluation method can more accurately identify problems in the simulation model. In addition, curve comparison provides an intuitive basis for subsequent mold structure optimization and process parameter calibration. When there are deviations between the simulation curve and the measured curve, targeted adjustments to the mold structure or process parameters can be made, and the optimization effect can be verified by comparison again, thereby reducing the blindness of repeated trial molding and improving R&D efficiency.

[0047] by Figures 4 to 9 For example, among which Figure 4 This is a thickness distribution cloud map of a refrigerator liner simulation model. The cloud map visually displays the thickness distribution of the refrigerator liner simulation model within a specified area through color depth. Figures 5 to 9 The thickness distribution curves correspond to five different cross-sectional positions or different structural schemes, namely a, b, c, d, and e.

[0048] The red curve represents the simulated thickness distribution curve, indicating the relationship between thickness and cross-sectional distance calculated through vacuum forming simulation. The purple curve represents the measured thickness distribution curve, indicating the relationship between thickness and cross-sectional distance obtained by measuring the target inner liner obtained from actual production. By comparing the red simulated curve and the purple measured curve on the same coordinate system, the degree of agreement between the two in terms of overall trend, local fluctuations, and numerical magnitude can be observed intuitively. When the red curve and the purple curve basically overlap, it indicates that the simulation accuracy is high; when there is a significant deviation, it indicates that the simulation model or parameter settings need further adjustment.

[0049] In some embodiments of this application, the simulated thickness curve is compared with the measured thickness curve, including calculating at least one of the Fraser distance and the Euler distance between the simulated thickness curve and the measured thickness curve, and using the Fraser distance and / or the Euler distance as the evaluation index of the comparison result.

[0050] The Friesian distance is an indicator of the shape similarity between two curves. It is defined as the minimum and maximum distance required between points on the two curves during parametric traversal. In curve comparison, a smaller Friesian distance indicates that the overall shape of the two curves is closer, meaning that the simulated thickness curve and the measured thickness curve have a high degree of consistency in the trend of thickness change, the position and shape of peaks and troughs. When the Friesian distance is used alone as an evaluation indicator, it can effectively determine whether the simulation results accurately reproduce the actual thickness variation along the cross-section, such as whether the trend of the thickness in the inner corner region first decreasing and then increasing is consistent with the measurement. The Euler distance is an indicator of the numerical deviation between two curves. It is usually expressed as root mean square error or mean absolute error, calculating the average level of the thickness difference between the two curves at the same abscissa position. In curve comparison, a smaller Euler distance indicates that the two curves are numerically closer, meaning that the deviation between the simulated thickness value and the measured thickness value at each cross-sectional position is smaller. When the Euler distance is used alone as an evaluation indicator, it can effectively determine the numerical accuracy of the simulation results, such as whether the overall deviation between the simulated thickness value and the measured thickness value is within an acceptable range.

[0051] When using both Fréchet distance and Euler distance as evaluation metrics, both the similarity of curve shapes and the accuracy of numerical values ​​can be assessed simultaneously. The Fréchet distance reflects the consistency in shape between the simulated and measured curves, ensuring accurate reproduction of key features such as thickness variation trends and extreme locations of characteristic regions. The Euler distance reflects the closeness in numerical values ​​between the simulated and measured curves, ensuring that the absolute thickness deviation at each cross-section is controlled within a reasonable range. Using both together avoids misjudgments that might arise from using either metric alone. If two curves have completely identical shapes but exhibit a systematic overall offset, with a smaller Fréchet distance and a larger Euler distance, relying solely on the Fréchet distance might lead to a misjudgment of high accuracy. Conversely, if two curves have completely different shapes but similar average thicknesses, with a smaller Euler distance and a larger Fréchet distance, relying solely on the Euler distance might also lead to a misjudgment of high accuracy. Only when both conditions of shape similarity and numerical closeness are met can the simulation results be considered truly accurate.

[0052] By using Fraser distance and Euler distance as evaluation metrics, the comparison between simulated and measured curves can be transformed from qualitative observation to quantitative calculation, making the evaluation of simulation accuracy more objective and precise. When Fraser distance is used alone, it accurately evaluates the accuracy of simulation results in terms of thickness variation trends; when Euler distance is used alone, it accurately evaluates the accuracy of simulation results in terms of thickness values; when both are used in combination, simulation accuracy can be comprehensively evaluated from both shape similarity and numerical accuracy dimensions, avoiding the limitations of a single metric and providing a more reliable basis for subsequent parameter optimization and structural design.

[0053] In some embodiments of this application, multiple sets of combinations of values ​​for parameters to be determined are obtained through orthogonal experimental design; based on the comparison results of each set, parameters to be determined for subsequent simulation are determined from the multiple sets of combinations of values ​​for parameters to be determined, including: analyzing the influence weight of each parameter to be determined on the simulation accuracy based on the comparison results of each set; and determining the parameters to be determined for subsequent simulation from the multiple sets of combinations of values ​​for parameters to be determined based on the influence weight.

[0054] Orthogonal experimental design is an efficient method for designing multi-factor experiments. When there are multiple parameters to be determined, and each parameter has multiple possible levels, a full factorial design requires a large number of simulations. However, orthogonal experimental design, by selecting an orthogonal array with balanced distribution characteristics, can cover all combinations of parameter values ​​with fewer experiments, while ensuring that the influence of each parameter at different levels can be analyzed independently. For example, when the parameters to be determined are mold temperature, heat transfer coefficient, slip coefficient, and penalty coefficient, and each parameter has three levels, a full factorial design requires 81 simulations, while an L9(3) orthogonal design requires 100 simulations. 4 Orthogonal arrays only require 9 simulations to obtain effective information on the impact of each parameter on simulation accuracy.

[0055] By using range analysis, the average value of each parameter to be determined is calculated at different levels. Then, the difference between the average value at the highest level and the average value at the lowest level is calculated; this is the range. The magnitude of the range reflects the degree to which changes in the parameter's value affect the simulation results. The larger the range, the more significant the parameter's impact on simulation accuracy, and the higher its influence weight. Through this analysis, we can identify which parameters are the key factors affecting simulation accuracy and which parameters have a relatively smaller impact.

[0056] For parameters with high influence weights, the level value that best yields the comparison results is selected first. For parameters with low influence weights, the selection can be made by comprehensively considering factors such as the rationality of the value range and the feasibility of the process. In this way, a set of parameter combinations that achieves the best match between the simulated thickness distribution and the measured thickness distribution is finally determined as the input parameters for subsequent formal simulations.

[0057] By obtaining multiple combinations of values ​​for the parameters to be determined through orthogonal experimental design, the number of simulation calculations can be significantly reduced, improving the efficiency of parameter calibration. Further analysis of the influence weight of each parameter based on the comparative results clarifies the contribution of each parameter to the simulation accuracy, identifies key and secondary parameters, and makes the parameter determination process more targeted. Determining the parameters based on their influence weights ensures the accuracy of key parameters while also considering the rationality of secondary parameters, avoiding deviations that may arise from blind selection or reliance on empirical guesswork. The parameters calibrated through this process enable subsequent thermoforming simulation results to more closely approximate real-world conditions, providing a reliable simulation basis for inner liner structure design and process parameter optimization.

[0058] In some embodiments of this application, the parameters to be determined include at least one of mold temperature, heat transfer coefficient, slip coefficient, and penalty coefficient.

[0059] Mold temperature refers to the temperature of the mold during the thermoforming process. In actual production, the mold temperature is not a constant value; it varies in different areas due to differences in the arrangement of cooling channels and the contact conditions with the sheet material, and the temperature changes dynamically during the forming process. However, conventional measurement methods can only obtain the temperature at a limited number of points on the mold surface, and cannot obtain the complete temperature field distribution. Therefore, it is difficult to determine the mold temperature directly and accurately when using it as a simulation input parameter.

[0060] The heat transfer coefficient refers to the efficiency of heat exchange between the co-extruded plate and the die. This coefficient depends on factors such as the contact state between the two, the gap size, the surface roughness, and the presence of air bubbles. These factors change in real time during the molding process and are difficult to measure by placing sensors inside the die. Therefore, the heat transfer coefficient cannot be obtained through direct measurement and can only be calibrated by comparing simulation and actual measurement results.

[0061] The slip coefficient refers to the sliding friction characteristics between the co-extruded sheet and the die. This coefficient depends on the viscoelasticity of the material at high temperatures, the surface finish of the die, lubrication conditions, etc., and varies with contact pressure, temperature, and speed. It cannot be directly measured by simple experiments and needs to be determined by reverse derivation.

[0062] The penalty coefficient refers to the spring stiffness coefficient that controls mesh penetration in finite element method (FEM) calculations. This coefficient is a computational parameter in the numerical solution process, not a physical quantity. It does not have a corresponding physical entity and cannot be obtained through experimental measurement. Its value depends on factors such as mesh quality, material stiffness, and contact algorithm, and can only be determined through numerical experiments.

[0063] Of the four parameters mentioned above, mold temperature, heat transfer coefficient, slip coefficient, and penalty coefficient are all parameters that cannot be directly obtained in vacuum forming simulation and need to be determined. In some embodiments of this application, orthogonal simulation experiments are designed to obtain multiple combinations of values ​​for the parameters to be determined. Vacuum forming simulations corresponding to each combination of values ​​are executed to obtain the simulated thickness distribution for each set of simulations. The simulated thickness distributions are then compared with the measured thickness distributions. Based on the comparison results, the influence weight of each parameter to be determined on the simulation accuracy is analyzed. Based on the influence weight, the parameters to be determined for subsequent simulations are determined from multiple combinations of values ​​for the parameters to be determined, thereby determining the mold temperature, heat transfer coefficient, slip coefficient, and penalty coefficient. The following is a parameter table from a specific embodiment of this application.

[0064] Table 1: Orthogonal experimental parameter combinations and simulation evaluation results.

[0065]

[0066] Table 2: Fraser distance range analysis table.

[0067]

[0068] Table 3: Euler distance range analysis table.

[0069]

[0070] Table 1 lists nine orthogonal experimental groups, each corresponding to a set of parameters to be determined, including mold temperature Tmold, heat transfer coefficient alpha, slip coefficient slip, and penalty coefficient pen. After performing vacuum forming simulation for each experimental group, the Fraser distance and Euler distance between the simulated thickness distribution and the measured thickness distribution were calculated as indicators to evaluate the simulation accuracy. The smaller the Fraser distance and Euler distance, the better the simulation results match the measured results.

[0071] Tables 2 and 3 calculate the k-value, average value, and range R of each parameter at different levels for the two evaluation metrics: Fraser distance and Eulerian distance. The range R reflects the degree to which the parameter's value changes affects the simulation accuracy; the larger the range, the more significant the parameter's impact on simulation accuracy.

[0072] Based on the range calculation results, under the Fraser distance evaluation index, the influence weights of each parameter, from highest to lowest, are: penalty coefficient (pen), slip coefficient (slip), heat transfer coefficient (alpha), and mold temperature (Tmold). Under the Euler distance evaluation index, the influence weights of each parameter are ranked the same. Combining the results of the two evaluation indices, it can be determined that the penalty coefficient and slip coefficient are the key parameters affecting simulation accuracy, while the influence of the heat transfer coefficient and mold temperature is relatively small.

[0073] The analysis of the table above identifies the influence weight of each parameter on simulation accuracy, providing a basis for subsequent selection of the optimal parameter combination. For example, for penalty coefficients and slip coefficients with high influence weights, the level values ​​that optimize the evaluation index should be selected first; for parameters with low influence weights, a comprehensive selection within a reasonable range is possible. This table illustrates the complete process of parameter calibration through orthogonal experimental design and range analysis.

[0074] Please see Figure 10 In some embodiments of this application, the parameters to be determined are determined based on a comparison between the simulated thickness distribution and the measured thickness distribution, including: S1511. Adjust the parameters to be determined based on the comparison results. After comparing the current simulated thickness distribution with the measured thickness distribution, adjust the values ​​of the parameters to be determined according to the direction and degree of the deviation between the two. For example, if the simulated thickness distribution is generally too thick, the heat transfer coefficient or mold temperature can be adjusted appropriately; if the deviation is large in a local area, the slip coefficient or penalty coefficient can be adjusted specifically. The adjustment can be based on engineering experience or the adjustment direction obtained from parameter sensitivity analysis.

[0075] S1521. Re-execute the vacuum forming simulation and obtain a new simulated thickness distribution. Compare the new simulated thickness distribution with the measured thickness distribution again. Input the adjusted parameter values ​​into the simulation software and re-execute the complete vacuum forming simulation calculation to obtain an updated simulated thickness distribution. Compare the updated simulated thickness distribution with the measured thickness distribution again to observe whether the deviation has improved. This step verifies whether the parameter adjustment is proceeding in the correct direction.

[0076] S1531. Repeat the adjustment and comparison process until the comparison result meets the preset conditions. Repeat the above process of adjusting parameters, executing simulations, and comparing results multiple times. Each iteration corrects the parameters based on the previous comparison result, gradually approximating the measured thickness distribution. The preset conditions are pre-defined criteria for determining whether the iteration process should terminate. For example, the deviation between the simulated and measured thickness distributions could be set to be less than a certain threshold, or a maximum number of iterations could be set, or the deviation could no longer change significantly after multiple consecutive iterations could be used as a convergence criterion. When the comparison result meets the preset conditions, the iteration process stops.

[0077] S1541. The parameters to be determined when the preset conditions are met are determined as the parameters to be determined for subsequent simulations. When the iteration process terminates, the currently used parameter values ​​are the calibrated parameter values. This set of parameters is used as the input for subsequent formal simulations for inner liner structure optimization, process parameter adjustment, or other design verification work.

[0078] This embodiment uses an iterative adjustment method to gradually approximate the true parameter values ​​without relying on a large number of pre-designed parameter combinations. This method has relatively lenient requirements for initial parameter values; as long as the initial values ​​are within a reasonable range, convergence to a better solution can be achieved through multiple iterations. Simultaneously, each parameter adjustment during the iteration process has clear feedback criteria, making the adjustment process more targeted. By setting preset conditions, the iteration process can have clear termination criteria, avoiding the waste of computational resources caused by infinite iteration, while ensuring that the calibrated simulation parameters meet the predetermined accuracy requirements. This embodiment complements orthogonal experimental design, providing another feasible parameter calibration approach for parameter spaces that are difficult to cover by orthogonal experiments or for situations with a small number of parameters.

[0079] Please see Figure 11 In some embodiments of this application, before performing the vacuum forming simulation of the refrigerator liner, the simulation method for the refrigerator liner further includes: S111. Establish a constitutive model for the polymer material. During thermoforming, the plastic sheet exhibits viscoelastic behavior at high temperatures. Its deformation and flow characteristics depend not only on the instantaneous force but also on temperature, deformation rate, and deformation history. By establishing an accurate material constitutive model, the simulation can be based on the mechanical response of the material at each stage of heating, stretching, and cooling. Therefore, it is necessary to obtain material parameters such as density, coefficient of thermal expansion, specific heat capacity, storage modulus, loss modulus, tensile viscosity, and apparent viscosity of the co-extruded sheet. Density is used to consider the deformation of the co-extruded sheet under gravity; the coefficient of thermal expansion and specific heat capacity are used to describe the material's expansion deformation at different temperatures and its ability to change temperature after contact with the mold; the storage modulus, loss modulus, tensile viscosity, and apparent viscosity are used to describe the co-extruded sheet's ability to resist deformation under external forces and its resistance during flow. The above parameters are generated into a material constitutive file using dedicated fitting software, manually imported during simulation model establishment, and the gravity direction is set.

[0080] S112. Establish a finite element model for the vacuum forming of the refrigerator liner. This finite element model discretizes the geometry of the mold and the sheet metal into mesh elements. Through reasonable mesh generation and quality control, the stability and accuracy of the numerical calculation are ensured. During the model construction process, the mold geometry needs to be cleaned, the contact surfaces and the middle surfaces of the sheet metal need to be extracted, and the mesh elements need to be quality checked to lay the foundation for subsequent solutions.

[0081] S113. Set the vacuum forming process parameters and perform simulation calculations. Vacuum forming process parameters include mold temperature, edge pressure, bubble height, mold movement speed, vacuum forming pressure, and mesh reconstruction parameters. These parameters reflect the actual process conditions. Input the set parameters into the finite element model, start the solver to perform numerical simulation of the entire vacuum forming process, and calculate the deformation, temperature field, stress field, and final thickness distribution of the sheet material at each moment.

[0082] S114. Output the thickness distribution cloud map of the refrigerator liner simulation model based on the simulation calculation results. The cloud map visually presents the thickness distribution of each region of the liner using color mapping, making it easy to identify areas with thinner or thicker thicknesses and the gradient of thickness changes.

[0083] S115. Extract the simulated thickness distribution along a specified cross-section from the thickness distribution contour map. Select one or more cross-sectional lines on the contour map, such as transverse, longitudinal, or diagonal cross-sections, and extract the corresponding thickness values ​​at each location along the cross-section to obtain the correspondence between the thickness and the distance between the cross-sections. This distribution reflects the overall distribution of the inner liner thickness under given material constitutive, finite element model, and process parameters.

[0084] Please see Figure 12 and Figure 13 In some embodiments of this application, before performing the step of simulating the vacuum forming of the refrigerator liner, the simulation method for the refrigerator liner further includes: S101. Perform two-way thermo-fluid-structure interaction (TFISE) calculations on the co-extruded sheet to obtain its temperature distribution and post-sag geometry. Co-extruded sheets are multi-layered composite plastic sheets used in thermoforming, typically high-impact polystyrene in refrigerator liner production. Before thermoforming, the co-extruded sheet needs to be heated to a softened state. In actual production, the heating of the co-extruded sheet is not uniformly distributed; instead, the heating power of the corresponding heating elements is controlled according to the mold shape and the distribution of the stretching area to create a temperature gradient. Areas with a high stretch ratio have lower heating power, while areas with a low stretch ratio have higher heating power. Simultaneously, after softening, the co-extruded sheet will sag under its own gravity, a phenomenon known as sag. Two-way thermo-fluid-structure interaction calculations are a numerical calculation method that comprehensively considers the interaction between temperature field, fluid flow, and solid deformation. Through this calculation, the temperature distribution of the co-extruded sheet after heating and the post-sag geometry due to gravity can be obtained simultaneously, making the initial state of the simulation model closer to actual production conditions.

[0085] S102. After the geometry has sagged, the facets are stitched and reassembled to form a whole, and the mid-surface is extracted from the solid element. The geometry output by thermo-fluid-structure interaction (TFS) calculations is usually presented as a set of facets, i.e., it is composed of a large number of discrete small facets. These facets have gaps and overlaps, and cannot be directly used for subsequent vacuum forming simulations. Therefore, these facets need to be stitched and reassembled to eliminate the gaps between the facets and form a complete continuous geometric surface. Subsequently, the mid-surface is extracted from this geometric solid, i.e., the mid-surface in the thickness direction of the geometric solid is extracted. Since vacuum forming simulations usually use shell elements for modeling, mid-surface extraction is a necessary step to simplify the three-dimensional solid model into a two-dimensional shell, which reduces the amount of computation and ensures the simulation accuracy.

[0086] S103. After converting the temperature distribution to a format readable by the vacuum forming simulation software, import it as the initial condition for the vacuum forming simulation. The temperature data output by the thermo-fluid-structure interaction (TFI) calculation is related to the coordinates of the geometric facets, and its data format differs from the input format required by the vacuum forming simulation software. Therefore, the temperature data needs to be corrected first, and then, using an interpolation algorithm, the temperature value corresponding to the location of the simulation mesh node is calculated based on the known coordinates and temperature values ​​of the facet nodes, generating interpolated temperature data. After converting the interpolated temperature data to a format readable by the vacuum forming simulation software, import it and assign it to the co-extruded plate model at the start of the simulation. This allows the simulation to begin from the initial state of a non-uniform temperature distribution and the geometry after weld sagging, rather than from an idealized uniform temperature and planar geometry.

[0087] This embodiment uses a two-way thermo-fluid-solid coupling calculation to obtain the actual temperature distribution and weld sag geometry of the co-extruded sheet at the end of heating, solving the error problem caused by simplifying the initial state of the co-extruded sheet to uniform temperature and planar geometry in traditional simulations. Through patch stitching and reconstruction and mid-surface extraction, complex patch data is transformed into a continuous geometric model usable for simulation. Temperature data format conversion ensures that temperature distribution information can be correctly transmitted to the vacuum forming simulation software. These processes ensure that the initial conditions of the vacuum forming simulation are highly consistent with actual conditions. By accurately simulating the non-uniform temperature distribution and gravity-induced weld sag during the heating process of the co-extruded sheet, the initial state of the simulation model is closer to actual production conditions, thus significantly improving the accuracy of subsequent vacuum forming simulations. Especially in complex structural areas such as inner corners and inner linings, the initial temperature distribution and geometry have a significant impact on the final thickness distribution. The processing in this embodiment effectively reduces simulation deviations caused by the simplification of the initial state, providing a more reliable data foundation for subsequent thickness distribution comparisons, simulation accuracy evaluation, and inner lining structure optimization.

[0088] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for simulating the inner liner of a refrigerator, characterized in that, The simulation method for the refrigerator inner liner includes: Perform the vacuum forming simulation of the refrigerator liner to obtain a simulation model of the refrigerator liner; At least one cross section is specified on the refrigerator liner simulation model, and the simulation thickness distribution of the refrigerator liner simulation model along the cross section is obtained; Obtain the target inner liner, which is the refrigerator inner liner actually produced; A cross section at the same location as the specified cross section is determined on the target inner liner, and the measured thickness distribution of the target inner liner along this cross section is obtained; The simulated thickness distribution is compared with the measured thickness distribution to evaluate the accuracy of the vacuum forming simulation.

2. The simulation method for the refrigerator inner liner as described in claim 1, characterized in that, The simulation of vacuum forming the refrigerator liner includes setting simulation parameters, which include determined parameters and undetermined parameters. The determined parameters are those that can be preset in the vacuum forming simulation, and the undetermined parameters are those that cannot be directly obtained in the vacuum forming simulation. After comparing the simulated thickness distribution with the measured thickness distribution, the simulation method for the refrigerator liner further includes: determining the parameter to be determined based on the comparison between the simulated thickness distribution and the measured thickness distribution.

3. The simulation method for the refrigerator inner liner as described in claim 2, characterized in that, The step of determining the parameter to be determined based on the comparison between the simulated thickness distribution and the measured thickness distribution includes: Obtain multiple combinations of values ​​for the parameters to be determined; Perform vacuum forming simulations for each set of values ​​to obtain the simulation thickness distribution for each set of simulations. Each group of simulated thickness distributions is compared with the measured thickness distributions to obtain the comparison results for each group; Based on the comparison results of each group, the parameters to be determined for subsequent simulations are determined from the combinations of values ​​of multiple parameters to be determined.

4. The simulation method for the refrigerator inner liner as described in claim 3, characterized in that, The step of comparing each group of simulated thickness distributions with the measured thickness distributions includes: A simulated thickness curve is generated based on the simulated thickness distribution, and the simulated thickness curve is the corresponding relationship between the thickness and the distance along the cross section; A measured thickness curve is generated based on the measured thickness distribution, and the measured thickness curve is the corresponding relationship between the thickness and the distance along the cross section. The simulated thickness curve is compared with the measured thickness curve.

5. The simulation method for the refrigerator inner liner as described in claim 4, characterized in that, The step of comparing the simulated thickness curve with the measured thickness curve includes calculating at least one of the Fraser distance and the Euler distance between the simulated thickness curve and the measured thickness curve, and using the Fraser distance and / or the Euler distance as the evaluation index of the comparison result.

6. The simulation method for the refrigerator inner liner as described in claim 5, characterized in that, The combinations of values ​​for the multiple sets of parameters to be determined were obtained through orthogonal experimental design; The step of determining the parameters to be determined for subsequent simulation from multiple combinations of values ​​of the parameters to be determined based on the comparison results of each group includes: Based on the comparison results of each group, analyze the influence weight of each parameter to be determined on the simulation accuracy. Based on the influence weights, the parameters to be determined for subsequent simulations are determined from multiple combinations of values ​​for the parameters to be determined.

7. The method for simulating a refrigerator liner as described in any one of claims 3 to 6, characterized in that, The parameters to be determined include at least one of mold temperature, heat transfer coefficient, slip coefficient, and penalty coefficient.

8. The simulation method for the refrigerator inner liner as described in claim 2, characterized in that, The step of determining the parameter to be determined based on the comparison between the simulated thickness distribution and the measured thickness distribution includes: Adjust the parameters to be determined based on the comparison results; Re-execute the vacuum forming simulation and obtain a new simulated thickness distribution. Compare the new simulated thickness distribution with the measured thickness distribution again. Repeat the adjustment and comparison process until the comparison results meet the preset conditions; The parameters to be determined when the preset conditions are met are determined as the parameters to be determined for subsequent simulations.

9. The method for simulating the inner liner of a refrigerator as described in claim 1, characterized in that, The simulation of vacuum forming the refrigerator liner includes: Establish constitutive models for polymer materials; Establish a finite element model of the thermoforming process for the refrigerator liner; Set the vacuum forming process parameters and perform simulation calculations; Output the thickness distribution cloud map of the refrigerator liner simulation model based on the simulation calculation results; Extract the simulated thickness distribution along the specified cross section from the thickness distribution cloud map.

10. The method for simulating the inner liner of a refrigerator as described in claim 1, characterized in that, Before performing the step of simulating the vacuum forming of the refrigerator liner, the simulation method for the refrigerator liner further includes: A two-way thermal-fluid-structure interaction calculation was performed on the co-extruded plate to obtain the temperature distribution and geometry of the co-extruded plate after melting and sagging. The geometrically shaped panels after the melting and sagging are stitched and reassembled to form a whole, and the mid-surface of the solid unit is extracted. The temperature distribution is converted into a format readable by the vacuum forming simulation software and then imported as the initial conditions for the vacuum forming simulation.

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