Lightweight manufacturing method and system of PET bottle for food packaging

Abstract

The invention discloses a light-weight manufacturing method and system for a PET bottle for food packaging, particularly relates to the technical field of PET bottle blank manufacturing, and aims to solve the problem that the subsequent blow molding quality is affected due to the fact that complex internal stress is generated in a bottle blank due to a rapid cooling process after injection molding in the prior art. Image information and temperature distribution data of a bottle blank after injection molding are obtained, geometric features and cooling rate differences are analyzed and recognized based on multi-physics coupling, a stress cause weight evaluation model is established to generate a stress distribution map, and then structural entropy increase factors under different temperature adjustment paths are simulated and calculated according to thermodynamic parameters, so that the temperature adjustment precision is improved. And carrying out partitioned temperature adjustment treatment on the bottle blank, and finally, carrying out constant-temperature and constant-humidity standing to sufficiently relax the stress. The internal stress distribution of the bottle blank can be effectively controlled, and the wall thickness uniformity and the mechanical property consistency of the light-weight PET bottle are improved.

Description

Technical Field

[0001] This invention relates to the field of PET preform manufacturing technology, and more specifically, to a lightweight manufacturing method and system for PET bottles used in food packaging. Background Technology

[0002] Lightweight manufacturing of PET bottles for food packaging is a significant industry trend. Currently, to achieve lightweighting, the industry focuses on modifying and reinforcing polyethylene terephthalate (PET) materials and optimizing the geometry of preforms (bottle blanks). In the manufacturing process, producing bottle blanks with specific weight and wall thickness distribution through injection molding is a crucial upstream step. To pursue high efficiency and high output, industrial production typically involves rapid cooling of the bottle blanks after injection molding and immediate transfer to storage for future use, aiming to shorten the production cycle.

[0003] However, due to the significant temperature gradient formed inside the preform during rapid cooling after injection molding, the material undergoes uneven shrinkage, resulting in complex and anisotropic internal stresses within the final preform. This stress state is maintained during subsequent storage and handling. When such preforms are heated before the blow molding process, the stress release process is coupled with the material's thermal expansion and softening, causing unpredictable deformation of the preform. This severely interferes with the control of the blow molding stretching initiation state, ultimately resulting in a deviation of the lightweight bottle's wall thickness distribution from the design expectations and a decrease in the consistency of the product's mechanical properties. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a lightweight manufacturing method and system for PET bottles for food packaging to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for lightweighting PET bottles for food packaging, including: S1. Acquire image information of the preform after injection molding, and simultaneously collect temperature distribution data on the surface of the preform; S2. Identify the geometric features of the preform based on image information, and calculate the difference in cooling rate in each region of the preform by combining temperature distribution data; S3. Analyze the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density in different geometric feature regions of the preform during the cooling process, and generate stress origin weight coefficients for the corresponding regions based on the evaluation results of the synergistic effect of the coupling relationship and the difference in cooling rate. S4. Evaluate the internal stress distribution of the preform based on the difference in cooling rate and the stress cause weighting coefficient, and generate a stress distribution map characterizing the stress magnitude and cause attributes. S5. Based on the stress distribution spectrum and combined with the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the calculation of material thermodynamic parameters, the preform is subjected to zoned temperature control. S6. After the preform has been subjected to zoned temperature regulation, let it stand in a constant temperature and humidity environment for a predetermined time.

[0006] Furthermore, image information of the preform after injection molding is acquired, and temperature distribution data on the surface of the preform is collected, including: Multispectral imaging devices are used to collect reflectance distribution information of different wavelength bands on the surface of the preform to form image information; Simultaneously, an infrared thermal imager was used to scan the outer surface of the preform and record the temperature distribution data. The reflectivity distribution information acquired by the multispectral imaging device is registered with the temperature distribution data recorded by the infrared thermal imager according to spatial coordinates to form an image-temperature dataset with a positional correspondence.

[0007] Furthermore, the geometric features of the preform are identified based on image information, and the differences in cooling rates in different regions of the preform are calculated in conjunction with temperature distribution data, including: The wall thickness gradient distribution and radius of curvature distribution of the preform are extracted from the image information as geometric features; Based on the changes in temperature distribution data over a continuous time series, calculate the temperature drop at each measurement point on the surface of the preform per unit time. The wall thickness gradient distribution, radius of curvature distribution, and temperature drop per unit time are superimposed and analyzed according to spatial coordinates to generate a quantitative table of cooling rate differences per region.

[0008] Furthermore, the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density in different geometric regions of the preform during cooling is analyzed. Based on the synergistic effect evaluation results of the coupling relationship and the difference in cooling rate, stress origin weight coefficients for the corresponding regions are generated, including: The degree of geometric constraint experienced by each region of the preform during shrinkage is quantified based on the wall thickness gradient distribution and the radius of curvature distribution. Based on temperature distribution data and mold thermal conductivity parameters, the local heat flux density distribution in each region of the preform surface is derived. Establish a spatial mapping relationship between the quantified value of geometric constraint degree and the local heat flux density distribution, and perform multi-field coupling analysis in conjunction with the quantified table of cooling rate difference; Based on the regional distribution characteristics of the dominant effects of geometric constraints and heat flux density in the multi-field coupling analysis results, a stress origin weight coefficient mapping table is generated.

[0009] Furthermore, the internal stress distribution of the preform is evaluated based on the differences in cooling rates and the stress origin weighting coefficient, generating a stress distribution map characterizing the magnitude and origin of stress, including: Based on the stress origin weight coefficient mapping table, the stress dominance type in each region is determined to be either geometric constraint dominance or heat flux density dominance. The baseline stress level for each region is determined based on the data in the cooling rate difference quantification table. The reference stress level is corrected according to the stress dominance type, with nonlinear strengthening correction applied to the geometric constraint dominance region and linear proportional correction applied to the heat flux density dominance region. The corrected stress level is combined with the corresponding dominant stress type to generate a stress distribution map that includes both stress magnitude and causal attributes.

[0010] Furthermore, based on the stress distribution spectrum and combined with the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the changes in material thermodynamic parameters, the preform is subjected to zoned temperature control, including: Based on the stress magnitude level and stress origin attribute markings in the stress distribution spectrum, the surface of the preform is divided into a geometrically constrained stress zone and a heat flux density-dominated stress zone. For each stress zone, at least three candidate temperature control paths are selected, and the Gibbs free energy change of the preform system under each path is simulated and calculated based on the material specific heat capacity and thermal expansion coefficient parameters. The entropy component of the Gibbs free energy change that is closest to the theoretical equilibrium state is extracted as the structural entropy increase factor. By comparing the magnitudes of structural entropy increase factors corresponding to different stress zones, the preform is subjected to zoned temperature regulation.

[0011] Furthermore, by comparing the magnitudes of the structural entropy increase factors corresponding to different stress zones, the preform undergoes zoned temperature regulation treatment, including: For the geometrically constrained stress region, assign a gradient heating and slow cooling path corresponding to the maximum structural entropy increase factor; for the heat flux density-dominated stress region, assign a step heating path corresponding to the second largest structural entropy increase factor.

[0012] Furthermore, for each stress zone, at least three candidate temperature control paths were selected, and the Gibbs free energy change of the preform system under each path was simulated and calculated based on the material's specific heat capacity and coefficient of thermal expansion parameters, including: Linear heating path, stepped heating path, and gradient heating and slow cooling path were selected as candidate temperature control paths. The enthalpy change component is calculated for each path based on the material's specific heat capacity parameter, and the volume work component is calculated for each path based on the thermal expansion coefficient parameter. The enthalpy change component and the volume work component are combined according to the basic thermodynamic equations to obtain the Gibbs free energy change for each path.

[0013] Furthermore, the preforms, after undergoing zoned temperature control, are left to stand in a constant temperature and humidity environment for a predetermined time, including: Transfer the preforms that have undergone gradient heating and slow cooling or stepped heating processes to a curing space with controlled environmental parameters. Maintain constant temperature and relative humidity within the curing space; The settling time is determined based on the relaxation characteristics of the preform material and the stress magnitude of the highest stress level region in the stress distribution diagram. Keep the preform stationary in the curing space until the required settling time is reached.

[0014] On the other hand, the present invention provides a lightweight manufacturing system for PET bottles for food packaging, comprising: The data acquisition module is used to acquire image information of the preform after injection molding, and at the same time, acquire temperature distribution data on the surface of the preform; The feature analysis module is used to identify the geometric features of the preform based on image information and calculate the cooling rate differences in different regions of the preform by combining temperature distribution data. The coupling analysis module is used to analyze the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density of different geometric feature regions of the preform during the cooling process. Based on the evaluation results of the synergistic effect of the coupling relationship and the difference in cooling rate, the stress origin weight coefficient of the corresponding region is generated. The stress assessment module is used to assess the internal stress distribution of the preform based on the difference in cooling rate and the stress cause weighting coefficient, and to generate a stress distribution map that characterizes the magnitude and cause of stress. The zoned temperature control module is used to perform zoned temperature control on the preform based on the stress distribution spectrum and the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the introduction of material thermodynamic parameters. The static curing module is used to allow preforms that have undergone zoned temperature regulation to stand in a constant temperature and humidity environment for a predetermined time.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By establishing a precise mapping relationship between the stress formation mechanism of injection-molded preforms and subsequent control treatments, systematic control of residual stress during the manufacturing process of lightweight PET bottles was achieved. By simultaneously acquiring surface image information and temperature distribution data of the preforms, and combining geometric feature recognition and cooling rate difference analysis, the non-equilibrium shrinkage behavior formed in various regions of the preforms during cooling can be accurately quantified. Furthermore, by establishing a coupling model between the degree of geometric constraint and local heat flux density, the dominant regions of different stress causes were accurately identified, providing a scientific basis for subsequent targeted treatments. This multi-physics coupling analysis method overcomes the limitations of traditional single-parameter control, achieving a comprehensive characterization of the complex stress state inside the preform.

[0016] 2. A zoned temperature control strategy based on thermodynamic principles was proposed. By introducing material thermodynamic parameters to simulate and calculate the Gibbs free energy changes under different temperature control paths, and extracting the structural entropy increase factor as an optimization index, optimal temperature control schemes can be formulated for different stress-causing regions. Combined with subsequent isothermal and humidity-controlled static treatment, the internal stress of the preform is fully relaxed while avoiding the stress solidification problem caused by traditional rapid cooling processes. This not only effectively improves the deformation stability of the preform during subsequent blow molding but also ensures the uniformity of the lightweight bottle wall thickness distribution and the consistency of mechanical properties. Attached Figure Description

[0017] Figure 1 This is a flowchart of a lightweight manufacturing method for PET bottles used in food packaging according to the present invention; Figure 2 This is a schematic diagram of the lightweight manufacturing system for PET bottles used in food packaging according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Figure 1 The present invention provides a lightweight manufacturing method for PET bottles used in food packaging, comprising: S1. Acquire image information of the preform after injection molding, and simultaneously collect temperature distribution data on the surface of the preform; S2. Identify the geometric features of the preform based on image information, and calculate the difference in cooling rate in each region of the preform by combining temperature distribution data; S3. Analyze the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density in different geometric feature regions of the preform during the cooling process, and generate stress origin weight coefficients for the corresponding regions based on the evaluation results of the synergistic effect of the coupling relationship and the difference in cooling rate. S4. Evaluate the internal stress distribution of the preform based on the difference in cooling rate and the stress cause weighting coefficient, and generate a stress distribution map characterizing the stress magnitude and cause attributes. S5. Based on the stress distribution spectrum and combined with the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the calculation of material thermodynamic parameters, the preform is subjected to zoned temperature control. S6. After the preform has been subjected to zoned temperature regulation, let it stand in a constant temperature and humidity environment for a predetermined time.

[0020] When acquiring image information of the preform after injection molding and collecting temperature distribution data on the preform surface, a multispectral imaging device is first used to collect reflectance distribution information of different bands on the preform surface to form image information. This multispectral imaging device adopts a filter rotation system that includes visible light bands and near-infrared bands. Specific band ranges include, for example, the blue light band from 450 nm to 500 nm, the green light band from 550 nm to 600 nm, and the near-infrared band from 800 nm to 850 nm. The reflectance distribution data of the preform surface under each band is acquired sequentially through time-division spectral acquisition. The acquisition time interval for each band is controlled within 100 milliseconds to ensure data temporal consistency. The obtained reflectance distribution information is stored in the form of a pixel matrix, where each pixel contains the reflection intensity value under the corresponding band. Simultaneously, an infrared thermal imager is used to scan the outer surface of the preform and record the temperature distribution data. The infrared thermal imager operates in the 8-14 micrometer band, with a spatial resolution of no less than 320 pixels × 240 pixels and a temperature measurement accuracy of ±0.5 degrees Celsius. During the scanning process, the infrared thermal imager continuously acquires thermal radiation data of the preform surface at a rate of 25 frames per second, and performs real-time temperature calibration through a built-in blackbody radiation reference source, converting the thermal radiation data into a temperature value matrix, with each temperature value corresponding to the temperature value at a specific location on the preform surface. The reflectance distribution information acquired by the multispectral imaging device and the temperature distribution data recorded by the infrared thermal imager are registered according to spatial coordinates. The registration process first identifies at least four common feature points in the multispectral image and the thermal image as registration references. Feature points include positions with clear geometric features such as the threaded end of the bottle preform mouth and the abrupt change in curvature of the bottle body. Then, an affine transformation algorithm is used to spatially align the pixel coordinate system of the multispectral image with the pixel coordinate system of the thermal image. The affine transformation parameters are obtained by fitting the correspondence of feature point coordinates using the least squares method. Finally, an image-temperature dataset with positional correspondence is formed. This dataset is stored in the form of a two-dimensional data table. Each data record contains a spatial coordinate number, reflectance values ​​of each multispectral band, and the temperature measurement value of the corresponding coordinate point.

[0021] In the specific implementation of the multispectral imaging device, the filter rotation system is driven by a stepper motor. The center wavelength deviation of each filter is controlled within ±2 nanometers, and the half-width is 10 nanometers. During image acquisition, an automatic adjustment function for integral time is used to ensure that the dynamic range of reflectivity signals in each band can cover more than 70% of the full range. During the temperature measurement data acquisition process of the infrared thermal imager, environmental radiation interference is eliminated through an environmental temperature compensation algorithm. Specifically, a dual blackbody reference source is set in the normal temperature range and the high temperature range for segmented calibration. The calibration temperature points include three reference points: 20 degrees Celsius, 50 degrees Celsius, and 80 degrees Celsius. During the spatial coordinate registration process, the feature point identification adopts a scale-invariant feature transformation algorithm. This algorithm detects the position of feature points by constructing a Gaussian difference pyramid and generates a 128-dimensional feature vector for feature matching. The matching accuracy reaches the sub-pixel level, and the image overlap error after affine transformation is less than 0.5 pixels. The final image-temperature dataset is stored in a hierarchical data format. The first layer is a spatial coordinate mapping table, the second layer is a multispectral reflectance data block, and the third layer is a temperature data block. The data in each layer are linked by a unified spatial index number.

[0022] To ensure data acquisition synchronization, the multispectral imaging device and the infrared thermal imager use the same external trigger signal to start acquisition. This trigger signal is generated by a programmable logic controller (PLC) with a rise time of less than 1 microsecond. After both devices complete acquisition, data is read via a hardware interrupt signal. During multispectral image acquisition, to eliminate the influence of ambient light variations, a built-in reference whiteboard is used for real-time reflectivity correction. The standard reflectivity of the reference whiteboard is 98%, and the correction frequency is once every 10 minutes. For temperature data acquisition, to reduce measurement errors caused by variations in the emissivity of the preform surface, a dual-wavelength thermometry method is used for compensation calculations. The true temperature value is derived by calculating the ratio of radiation intensity in the 8-micrometer and 10-micrometer bands. During data registration, when the number of feature points is less than four, edge contour matching is used as an auxiliary registration method. First, the outer contour curves of the preform in the visible light image and the thermal image are extracted. Then, the rotation and translation matrix between the contours is calculated using an iterative nearest-point algorithm, ultimately achieving coordinate system unification.

[0023] Quality control of the image-temperature dataset includes data integrity checks and outlier removal. Integrity checks are performed by verifying that each spatial coordinate point contains reflectance and temperature data for all bands. Outlier removal uses a three-standard-deviation criterion: if the reflectance or temperature value of a point deviates from the average of its eight neighboring points by more than three standard deviations, that point is marked as an outlier and replaced with linear interpolation. Before storage, the dataset undergoes data normalization. Reflectance data is normalized to between 0 and 1, and temperature data is normalized to between 0 and 100 degrees Celsius. Normalization parameters, including minimum and maximum values, are dynamically calculated from the data in the current acquisition batch. The final image-temperature dataset will serve as the foundational input data for subsequent geometric feature recognition and cooling rate calculation.

[0024] When identifying the geometric features of a preform based on image information and calculating the cooling rate differences in different regions of the preform using temperature distribution data, the wall thickness gradient distribution and radius of curvature distribution of the preform are first extracted from the image information as geometric features. The extraction of the wall thickness gradient distribution is based on the reflectivity differences in different bands of multispectral image data. Thickness calculation is achieved by establishing a correspondence between reflectivity and material thickness. For example, in the near-infrared band range of 800 nm to 850 nm, there is an approximately linear correspondence between reflectivity and material thickness. The range of reflectivity values ​​corresponding to different thicknesses is determined through calibration experiments, forming a thickness-reflectivity lookup table. Then, the thickness value of each pixel is inferred from its reflectivity value, ultimately obtaining the thickness distribution matrix of the entire preform surface. The calculation of the radius of curvature distribution is achieved through edge contour information in multispectral images. First, the Canny edge detection algorithm is used to extract the inner and outer contour lines of the preform. Then, the radius of curvature at each point on the contour line is calculated by the cubic spline curve fitting algorithm. Specifically, the moving least squares method is used to perform local polynomial fitting on the contour line. Then, the radius of curvature value is calculated based on the first and second derivatives of the fitted curve. Finally, the radius of curvature distribution map at each position on the surface of the preform is generated.

[0025] To calculate the temperature drop per unit time at each measurement point on the preform surface based on the changes in temperature distribution data over a continuous time series, it is necessary to acquire temperature distribution data at multiple consecutive time points. For example, temperature data at 10 consecutive time points can be collected at 1-second intervals. The temperature difference between adjacent time points is calculated for each measurement point, and then divided by the time interval to obtain the instantaneous temperature change rate. Finally, the average of the temperature change rates over all time intervals is taken as the temperature drop per unit time at that measurement point. The calculation of the temperature drop takes into account the thermal conductivity characteristics of the material. For areas with abnormally drastic temperature changes, a moving average filtering algorithm is used for smoothing. The size of the filtering window is determined based on the thermal relaxation time of the material; for example, a 3×3 filtering window is typically used for PET materials.

[0026] When analyzing the superposition of wall thickness gradient distribution, radius of curvature distribution, and temperature drop per unit time using spatial coordinates, the three types of data are first unified to the same spatial coordinate system. Coordinate mapping ensures a one-to-one correspondence between the three types of data at each spatial location. The superposition analysis employs a weighted fusion algorithm, correcting the temperature drop value based on the wall thickness gradient and radius of curvature values. For example, in regions with large wall thickness gradients, due to significant changes in heat capacity, the temperature drop value needs to be multiplied by a correction factor greater than 1. Conversely, in regions with small radii of curvature, due to the increased surface area to volume ratio and faster heat dissipation, the temperature drop value needs to be multiplied by another correction factor greater than 1. The specific value of the correction factor is determined experimentally, for example, by measuring the actual cooling rate in regions with different wall thicknesses and curvatures using thermal imaging experiments, and then determining the optimal value of the correction factor through regression analysis.

[0027] When generating a quantification table of cooling rate differences by region, the preform surface is first divided into several regions based on geometric similarity and temperature variation consistency. For example, regions with a wall thickness gradient of less than 5% / mm and a radius of curvature greater than 10mm are grouped into one region, while regions with a wall thickness gradient greater than 10% / mm and a radius of curvature less than 5mm are grouped into another. The cooling rate difference value for each region is characterized by calculating the standard deviation of the corrected temperature drop values ​​at all measurement points within that region; a larger standard deviation indicates a greater cooling rate difference within that region. The final quantification table of cooling rate differences is in two-dimensional tabular form. The rows of the table represent different region numbers, and the columns represent the range of geometric characteristic parameters, the average cooling rate value, and the cooling rate difference value for that region.

[0028] To ensure calculation accuracy, a data validity check was implemented during geometric feature extraction. When the reflectivity value of a pixel exceeded the calibrated range, its thickness value was calculated using bilinear interpolation of the thickness values ​​of its eight neighboring pixels. When calculating temperature drop, outlier temperature values ​​were removed; an outlier was defined as a difference from the temperature values ​​of surrounding pixels exceeding three standard deviations. A minimum region area threshold was set during region partitioning; for example, regions smaller than 1% of the total area were merged into adjacent similar regions. Data in the cooling rate difference quantification table was stored in floating-point format with precision retained to three decimal places for subsequent analysis.

[0029] All parameters used in the calculations were determined experimentally. For example, the sampling interval for wall thickness gradient calculation was set to 0.1 mm, the moving window size for radius of curvature calculation was set to 5 pixels, and the time interval for temperature drop calculation was determined based on the material's thermal conductivity; for PET materials, a 1-second time interval was typically used. Parallel computing techniques were employed to improve processing efficiency during data processing. For instance, the preform image was segmented into multiple sub-regions for parallel processing, and the results were then merged. The generated cooling rate difference quantification table contains not only numerical data but also spatial location information for each region, enabling precise positioning and analysis in subsequent steps.

[0030] When analyzing the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density in different geometric feature regions of the preform during cooling, the quantitative value of the degree of geometric constraint experienced by each region of the preform during shrinkage is first calculated based on the wall thickness gradient distribution and the radius of curvature distribution. The quantitative value of the degree of geometric constraint is calculated by establishing a composite function of the wall thickness change rate and the curvature change. Specifically, the product of the absolute value of the wall thickness gradient and the reciprocal of the radius of curvature is used as the basic formula, which is then multiplied by the material shrinkage coefficient to obtain the quantitative value of the degree of geometric constraint. The material shrinkage coefficient is obtained through laboratory measurements; for example, for PET material, it is typically taken in the range of 0.02 to 0.04. During the calculation, the wall thickness gradient distribution and radius of curvature distribution data are normalized to ensure that all values ​​are between 0 and 1. The normalization method uses a minimum-maximum normalization algorithm, that is, each parameter value is subtracted from the minimum value and then divided by the difference between the maximum and minimum values, ensuring that parameters of different dimensions can be effectively calculated. The weight coefficients of each parameter in the formula for calculating the degree of geometric constraint are determined experimentally. For example, by using orthogonal experimental design, the degree of agreement between the calculated results and the actual measured values ​​under different weight combinations is examined, and finally the weight combination that minimizes the error is selected.

[0031] When deriving the local heat flux density distribution in various regions of the preform surface based on temperature distribution data and mold thermal conductivity parameters, Fourier's law of thermal conductivity is used for calculation. The local heat flux density is equal to the product of the temperature gradient and the material's thermal conductivity. The temperature gradient is calculated through spatial difference of the temperature distribution data. For example, at a certain measurement point, the temperature gradient is equal to the difference between the temperature value at that point and the temperature values ​​at adjacent points divided by the distance between the two points. The central difference method is used for difference calculation to improve accuracy.

[0032] The thermal conductivity parameters of the mold include the thermal conductivity and specific heat capacity of the mold material. These parameters are obtained from the technical data provided by the material supplier. For example, the thermal conductivity of mold steel is approximately 30 W / (m·K), and its specific heat capacity is 460 J / (kg·K). The calculation process considers the effect of temperature on the material's thermal conductivity, using interpolation to determine the corresponding thermal conductivity value based on the actual temperature. The calculation of local heat flux density distribution also considers the effect of contact thermal resistance. The contact thermal resistance between the mold and the preform is measured experimentally and corrected in the calculation.

[0033] To establish a spatial mapping relationship between the quantified value of geometric constraint degree and the local heat flux density distribution, and to conduct multi-field coupling analysis in conjunction with a quantified table of cooling rate differences, the coordinate systems of the three datasets are first uniformly registered. Nearest neighbor interpolation is used to ensure that each spatial point has a corresponding quantified value of geometric constraint degree, local heat flux density value, and cooling rate difference value. The spatial mapping relationship is achieved by establishing a multidimensional data table, where each row records the three parameter values ​​and their spatial coordinates for a spatial point. Principal component analysis is used for multi-field coupling analysis to calculate the pairwise correlation coefficients of the three parameters. A correlation coefficient greater than 0.7 is considered a strong correlation. The analysis also considers the nonlinear relationship between parameters by introducing quadratic and interaction terms to capture coupling effects. For example, a regression model is established that includes the square term of the quantified value of geometric constraint degree and the product term of the quantified value of geometric constraint degree and the local heat flux density. The output of the coupling analysis includes the coupling strength value and the dominant factor type for each spatial point. The coupling strength value is obtained by calculating the weighted sum of the three parameters, and the weight coefficients are determined by factor analysis.

[0034] Based on the regional distribution characteristics of the dominant effects of geometric constraints and heat flux density in the multi-field coupling analysis results, when generating the stress origin weight coefficient mapping table, the criteria for determining the dominant effect are first defined. For example, if the quantified value of the degree of geometric constraint in a certain region is greater than a set threshold and its correlation coefficient with the difference in cooling rate is greater than the correlation coefficient between the local heat flux density and the difference in cooling rate, the region is marked as a geometric constraint dominant region. The assignment rules for the stress origin weight coefficients are based on the multi-field coupling analysis results. For geometric constraint dominant regions, the weight coefficient is between 0.8 and 1.0; for heat flux density dominant regions, the weight coefficient is between 0 and 0.2; and for mixed effect regions, the weight coefficient is between 0.3 and 0.7. The specific values ​​of the weight coefficients are determined using the Delphi method. Ten domain experts were invited to score the stress origin of typical regions, and the average value was taken as the final weight coefficient after three rounds of feedback. The weight coefficient mapping table is stored in the form of a two-dimensional matrix. The rows and columns of the matrix correspond to the spatial positions on the preform surface, and each element stores the weight coefficient value and dominant type label for that position.

[0035] To ensure calculation accuracy, a numerical range check was implemented in the calculation of the geometric constraint degree quantification value. If the calculation result is less than 0 or greater than 1, a recalculation was performed, and the input parameters were checked to ensure they were within a reasonable range. Directionality was considered in the local heat flux density calculation; only the heat flux component perpendicular to the preform surface was calculated. This perpendicular component was determined by calculating the angle between the surface normal vector and the temperature gradient direction. During the multi-field coupling analysis, outlier data was removed. Outlier data was judged based on a deviation from other data exceeding three standard deviations. After removal, the K-nearest neighbor algorithm was used for data imputation.

[0036] After the stress origin weight coefficient mapping table is generated, it undergoes smoothing to eliminate abrupt changes. A 3×3 mean filter is used to smooth the weight coefficient matrix, and the filter window size is determined using cross-validation. All threshold parameters used in the calculation process are determined experimentally; for example, the threshold for determining geometric constraint dominance is calculated by taking the 75th percentile after analyzing 100 sample data. Data storage adopts a hierarchical format: the first layer stores spatial coordinate information, the second layer stores parameter values, and the third layer stores coupling analysis results. The generated stress origin weight coefficient mapping table includes the weight coefficient value and dominance type label for each region for use in subsequent steps. An iterative optimization method is used in data processing. By repeatedly adjusting the weight coefficient assignment rules, the root mean square error between the final result and the experimental measurement value is minimized. The optimization algorithm uses gradient descent with a learning rate of 0.01 and 1000 iterations. The final generated stress origin weight coefficient mapping table also includes quality assessment indicators, such as the confidence interval and uncertainty estimate for each weight coefficient. These indicators are calculated using the Bootstrap method with 1000 resampling iterations.

[0037] When assessing the internal stress distribution of the preform based on differences in cooling rates and stress origin weighting coefficients, and generating stress distribution maps characterizing stress magnitude and causal attributes, the first step is to determine whether the dominant stress type in each region is geometrically constrained or heat flux density-dominated, based on a stress origin weighting coefficient mapping table. A threshold segmentation method is used for this determination: when the stress origin weighting coefficient of a region is greater than 0.7, it is classified as a geometrically constrained dominant region; when the coefficient is less than 0.3, it is classified as a heat flux density-dominated region; and regions with weighting coefficients between 0.3 and 0.7 are classified as mixed-effect regions. The thresholds of 0.7 and 0.3 were determined through extensive experimental data analysis, such as analyzing photoelastic measurement data from 1000 sample regions to find the correspondence between stress origin weighting coefficients and actual stress types, ultimately selecting the threshold that yields the highest classification accuracy. Regional continuity is also considered during the dominant type determination process. Adjacent regions of the same type are merged using a region growing algorithm. The similarity threshold for region growing is 0.1, and the maximum region area is limited to 20% of the total area to ensure the rationality of region division.

[0038] When determining the baseline stress level for each region based on the data in the cooling rate difference quantification table, a piecewise linear mapping method is used to convert the cooling rate difference values ​​into baseline stress levels. The baseline stress levels are divided into 10 levels, with level 1 representing the minimum stress and level 10 representing the maximum stress. The mapping relationship is established through experimental calibration. For example, 100 sample regions with different cooling rate difference values ​​are selected, and their actual stress values ​​are measured using photoelasticity experiments. Then, the correspondence between the cooling rate difference values ​​and stress levels is fitted using the least squares method. Material properties are also considered in the baseline stress level calculation; different materials use different mapping curves. For example, the slope of the mapping curve for PET is higher than that for PP, and the specific slope value is determined through material mechanical property testing. After the baseline stress level for each region is calculated, a regional consistency check is performed. If the stress level of a region differs from that of adjacent regions by more than 3 levels, the region is recalculated. The recalculation uses a neighborhood weighted average method, with the weights inversely proportional to the distance.

[0039] When correcting the baseline stress level according to the stress dominance type, a nonlinear strengthening correction is applied to regions dominated by geometric constraints. The correction function is the baseline stress level multiplied by a strengthening coefficient greater than 1. The magnitude of the strengthening coefficient is positively correlated with the quantified value of the geometric constraint degree. For example, when the quantified value of the geometric constraint degree is greater than 0.8, the strengthening coefficient is 1.5; when the quantified value of the geometric constraint degree is between 0.5 and 0.8, the strengthening coefficient is 1.2. The specific value of the strengthening coefficient is determined through regression analysis, establishing a relationship model between the quantified value of the geometric constraint degree and the actual stress measurement value. For regions dominated by heat flux density, a linear proportional correction is applied. The correction method is the baseline stress level multiplied by a scaling factor. The magnitude of the scaling factor is proportional to the local heat flux density value. For example, when the local heat flux density value is greater than 1000 W / m², the scaling factor is 1.1; when the local heat flux density value is between 500 and 1000 W / m², the scaling factor is 1.0. The scaling factor is determined based on thermodynamic simulation results, using finite element analysis to calculate the stress distribution under different heat flux densities. The correction process also takes into account the area factor. For small areas with an area of ​​less than 1 cm², an additional area correction factor is used. The area correction factor is determined experimentally to avoid stress estimation bias.

[0040] When generating a stress distribution map that combines the corrected stress levels with the corresponding dominant stress types, containing both stress magnitude and causal attributes, the RGB color space is used for visualization encoding. Stress magnitude is represented by color depth; for example, stress level 1 is represented by light blue, level 10 by dark red, and intermediate levels are determined by linear interpolation. Stress causal attributes are represented by hue; regions dominated by geometric constraints are represented by red tones, regions dominated by heat flux density by blue tones, and regions of mixed effects by purple tones. Specific hue values ​​are determined using the HSV color model, with saturation fixed at 100%, and brightness correlated with the stress level. Smoothing was performed during map generation using a 5×5 Gaussian filter kernel with a standard deviation of 1.0 to eliminate abrupt changes. The final stress distribution map contains pixel-level stress level values ​​and causal type information, stored simultaneously in PNG and CSV formats. The PNG format is used for visualization, while the CSV format is used for subsequent data analysis. The CSV file contains the coordinates, stress level, dominant stress type, and confidence level of each pixel.

[0041] To ensure the accuracy of the assessment, a confidence check was implemented during the dominant type determination process. When the weight coefficient of a region approached a threshold (e.g., between 0.65 and 0.75), the region was marked as a low-confidence region and annotated with a special color in the graph. The proportion of low-confidence regions was controlled to be within 5% of the total area. Temperature compensation was used in the calculation of the baseline stress level, correcting for differences in cooling rates based on actual ambient temperature. The compensation coefficient was experimentally determined; for example, the compensation coefficient increased by 0.01 for every 1°C increase in temperature. Upper and lower limit protections were set during the correction process, ensuring that the corrected stress level did not exceed level 10 and was not lower than level 1. When the calculation result exceeded the range, the boundary value was used. After the graph was generated, a verification check was performed. 10% of the region was randomly selected for photoelastic experiments to ensure that the stress estimation error was within 20%. If the verification failed, the parameters were readjusted. All parameter settings were determined through experimental optimization. For example, the threshold parameter was determined through ROC curve analysis, and the correction coefficient was determined through response surface methodology optimization. The optimization objective was to minimize the estimation error. The final stress distribution map also includes metadata information, such as generation time, material type, and process parameters, for traceability and analysis. The metadata is stored in JSON format and saved together with the image data.

[0042] Based on the stress distribution map and the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths through simulation calculations using material thermodynamic parameters, the preform surface is first divided into geometrically constrained and heat flux density-dominated stress zones according to the stress magnitude and stress origin attribute markers in the stress distribution map. The division process employs a threshold-based region segmentation method. For example, if a region's stress origin attribute is marked as geometrically constrained and its stress magnitude is greater than or equal to 5, that region is classified as a geometrically constrained dominant stress zone; if the stress origin attribute is marked as heat flux density-dominated and its stress magnitude is greater than or equal to 5, it is classified as a heat flux density-dominated stress zone. Regions with stress magnitude levels below 5 are not included in the zonal temperature control process. Spatial continuity is also considered during region segmentation, using morphological closing operations to process region boundaries, with a structural element size of 3×3 pixels to ensure the smoothness and continuity of region boundaries. A minimum region area threshold is set during the segmentation process; for example, if the area of ​​a region is less than 1% of the total area, it is merged into adjacent similar type regions.

[0043] For each stress zone, at least three candidate temperature control paths were selected. Based on the material's specific heat capacity and coefficient of thermal expansion, the Gibbs free energy change of the preform system under each path was simulated and calculated. The candidate temperature control paths included linear heating, stepped heating, and gradient heating with slow cooling. The temperature change curves for each path were described parametrically. For example, the heating rate of the linear heating path was 2-10℃ / min; the stepped heating path consisted of 2-5 temperature plateaus with a heating rate of 5-15℃ / min between plateaus; and the gradient heating with slow cooling path included a heating section and a slow cooling section, with a heating rate of 3-8℃ / min and a slow cooling rate of 1-3℃ / min. The material's specific heat capacity was obtained using differential scanning calorimetry (DSC) at a temperature range of 20-150℃ and a measurement point interval of 5℃. The coefficient of thermal expansion was obtained using a thermomechanical analyzer at a temperature range of 20-120℃ and a measurement point interval of 10℃. The calculation of the Gibbs free energy change is divided into two parts: First, the enthalpy change component under each path is calculated based on the material's specific heat capacity parameter, and the heat change during the temperature change process is calculated by integration, with the integration interval being the entire temperature regulation process; second, the volume work component under each path is calculated based on the thermal expansion coefficient parameter, and the expansion work or compression work is calculated by integration, taking stress constraint conditions into account; finally, the enthalpy change component and the volume work component are combined according to the basic thermodynamic relations to obtain the Gibbs free energy change corresponding to each path, taking into account the influence of temperature on material parameters during the combination.

[0044] The entropy change component of the Gibbs free energy change corresponding to the path closest to the theoretical equilibrium state is extracted as the structural entropy increase factor. The theoretical equilibrium state is determined by the minimum Gibbs free energy principle; the system is considered to have reached theoretical equilibrium when the Gibbs free energy change approaches zero, with the criterion being an absolute value of the Gibbs free energy change less than 10 J / mol. The entropy change component is extracted based on thermodynamic relations, obtained by dividing the difference between the Gibbs free energy change and the enthalpy change component by the absolute temperature, which is the average temperature of the temperature adjustment process. The calculation of the structural entropy increase factor considers temperature dependence, using a weighted average method to calculate the entropy change component over the entire temperature range, with the weighting coefficient proportional to the duration of the temperature range. The numerical range of the structural entropy increase factor is controlled between 0 and 1 through normalization. The normalization method is to divide the original entropy change component by the maximum possible entropy change value, which is determined by the material's maximum entropy change characteristics.

[0045] The structural entropy increase factors corresponding to different stress zones were compared to perform zoned temperature control on the preform. A ranking method was used, sorting the structural entropy increase factors of the three temperature control paths for each stress zone from largest to smallest. For the geometrically constrained stress zone, a gradient heating and slow cooling path corresponding to the largest structural entropy increase factor was assigned, as this region requires slow temperature changes to release internal stress. The parameters for this gradient heating and slow cooling path were, for example, a heating rate of 3℃ / min, a holding time of 10min, and a slow cooling rate of 2℃ / min. For the heat flux density-dominated stress zone, a stepped heating path corresponding to the second largest structural entropy increase factor was assigned, as this region requires stepped temperature changes to balance heat flux distribution. The parameters for this stepped heating path were, for example, three temperature plateaus with a heating rate of 8℃ / min between plateaus and a holding time of 5min for each plateau. The feasibility of the path implementation was also considered during the allocation process, such as checking whether the accuracy of the temperature control equipment met the path requirements, whether the heating rate was within the equipment's allowable range, and ensuring the equipment control accuracy reached ±0.5℃.

[0046] When implementing zoned temperature control, a multi-zone independent temperature control system is used, with each stress zone corresponding to an independent temperature control unit. The temperature control accuracy of the temperature control unit reaches ±0.5℃, and the heating rate control accuracy reaches ±0.1℃ / min. During the temperature control process, the temperature changes in each zone are monitored in real time, with a monitoring frequency of once per second, and compared with the target path. When the deviation exceeds a set threshold, dynamic adjustments are made, with the deviation threshold set at ±1℃. After the temperature control is completed, the effect is evaluated by measuring the actual temperature distribution using an infrared thermal imager and comparing it with the target temperature distribution to ensure the accuracy of the temperature control process, allowing a temperature deviation within ±2℃. The data records for the entire zoned temperature control process include the temperature control path parameters of each zone, the actual temperature change curve, and the structural entropy increase factor value, etc. The data is saved in CSV format, including timestamps, temperature values, control parameters, and other information, for subsequent analysis and optimization.

[0047] To ensure the treatment effect, small-scale trials were conducted before implementation. Typical stress areas were selected to test the actual effects of different temperature control paths. Stress release effects were verified using a photoelastic method with a measurement accuracy of ±5 MPa. The parameters of the temperature control path were adjusted according to material characteristics. For example, for highly crystalline PET materials, the heating rate was appropriately reduced to 2-5℃ / min; for thicker areas, the holding time was extended to 10-15 min. A safety protection mechanism was implemented during the treatment process. The cooling system was automatically activated when the temperature abnormally increased. The abnormal temperature was judged as exceeding the set temperature by more than 10℃. After activation, the cooling system reduced the temperature at a rate of 5-10℃ / min. The final zoned temperature control treatment plan also included suggestions for subsequent treatment, such as whether secondary temperature control or supplementary treatment was needed. These suggestions were determined based on the stress assessment results after temperature control. The stress assessment used an ultrasonic stress measurement method with a measurement accuracy of ±3 MPa. All parameter settings and adjustment rules were experimentally verified to ensure the repeatability and stability of the treatment effect.

[0048] After the preforms have undergone zoned temperature and humidity treatment, they are left to stand in a constant temperature and humidity environment for a predetermined time. First, the preforms, which have completed the gradient heating and slow cooling path or step heating path treatment, are transferred to a curing space with controlled environmental parameters. The transfer process is completed using an automated conveyor system, with the conveyor speed controlled within the range of 0.5 m / s to 1.0 m / s. For example, a variable frequency speed-controlled belt conveyor is used, with the conveyor belt surface covered with anti-slip material to prevent the preforms from sliding or colliding during transport. The curing space is a sealed structure with 304 stainless steel inner walls, equipped with multiple sets of temperature and humidity sensors to monitor environmental parameters in real time. The sensors are spaced 2 m to 3 m apart, distributed at different heights to ensure comprehensive monitoring; for example, sensors are placed in the upper, middle, and lower parts of the space. An air curtain isolation device is installed at the entrance of the curing space, with the air curtain velocity maintained within the range of 1.5 m / s to 2.5 m / s. The temperature difference between the air curtain and the internal temperature of the curing space is controlled within ±2℃ to prevent external environmental interference.

[0049] To maintain constant temperature and relative humidity within the maintenance space, a precision temperature and humidity control system is employed, achieving a temperature control accuracy of ±0.5℃ and a relative humidity control accuracy of ±2%. Temperature control is achieved through a PID adjustment algorithm. The heating element utilizes infrared radiation heating with a power density of 2W / cm² to 5W / cm², while the cooling system employs compressor refrigeration with an adjustable cooling capacity of 5kW to 10kW. Humidity control is achieved through a combination of an ultrasonic humidifier and a dehumidifier, with humidification capacity adjustable from 0.5L / h to 5L / h and dehumidification capacity adjustable from 3L / h to 10L / h. Environmental parameter monitoring data is recorded in real time, stored once per minute, and retained for at least 30 days. The monitored data includes parameters such as temperature, humidity, and the operating status of the control equipment.

[0050] The settling time was determined based on the relaxation characteristics of the preform material and the stress magnitude of the highest stress level region in the stress distribution spectrum. Material relaxation characteristics were obtained through stress relaxation experiments covering a temperature range of 20℃ to 80℃. The relaxation time spectrum was obtained by fitting the experimental data, and the relaxation time constant was obtained through an exponential decay model. The stress magnitude of the highest stress level region in the stress distribution spectrum was extracted using an image processing algorithm. The algorithm identified regions with stress levels greater than or equal to 8 and calculated the average stress value of these regions. The settling time was calculated by multiplying the relaxation time constant by a stress correction factor. The relaxation time constant was obtained from the material relaxation characteristic curve, and the stress correction factor was determined based on the stress magnitude of the highest stress level region. For example, when the stress value was greater than 50 MPa, the correction factor was 1.5; when the stress value was between 30 MPa and 50 MPa, the correction factor was 1.2; and when the stress value was less than 30 MPa, the correction factor was 1.0. The unit for the settling time calculation was uniformly set to hours, with a minimum accuracy of 0.1 hours.

[0051] To maintain the preforms in a static state within the curing space until the required curing time is reached, a dedicated support made of PTFE (polytetrafluoroethylene) with a thermal conductivity below 0.5 W / (m·K) is used to secure the preforms, preventing thermal bridging. The preforms are spaced 1.5 to 2.0 times their diameter to ensure uniform airflow; for example, for preforms with a diameter of 50 mm, the spacing is 75 mm to 100 mm. Environmental parameters are continuously monitored during curing, and automatic adjustments are made when temperature or humidity deviations exceed set thresholds: ±1℃ for temperature and ±3% for humidity. After the curing time is reached, a gradual discharge method is used. First, the curing space temperature is slowly reduced to ambient temperature at a rate of 2℃ / h to 5℃ / h. Then, the discharge gate is opened, and the discharge rate is controlled at 5 to 10 preforms per minute to avoid sudden temperature changes.

[0052] To ensure effective settling, regular sampling and testing are conducted during the settling process, with a sampling frequency of once every 4 hours and a sampling ratio of no less than 5%. Testing items include preform dimensional stability and stress distribution changes. Dimensional measurements are performed using a laser rangefinder with an accuracy of ±0.01mm, and stress is measured using the polarized light method. Test results are compared with data before settling to assess stress release. When the stress release rate is below 80%, the settling time is appropriately extended by 20% to 50% of the original time. All process parameters and test results are recorded in a quality tracking sheet, including the start and end times of settling for each batch, environmental parameter records, and test data. The record format uses standardized tables containing timestamps, measured values, equipment numbers, etc., for quality traceability and process optimization. The curing space is calibrated and verified regularly, such as a comprehensive calibration every quarter. Calibration items include temperature uniformity testing, humidity uniformity testing, and sensor accuracy verification to ensure the accuracy and stability of environmental parameter control.

[0053] Example 2: Figure 2 A schematic diagram of the lightweight manufacturing system for PET bottles used in food packaging according to the present invention is provided. The lightweight manufacturing system for PET bottles used in food packaging includes: The data acquisition module is used to acquire image information of the preform after injection molding, and at the same time, acquire temperature distribution data on the surface of the preform; The feature analysis module is used to identify the geometric features of the preform based on image information and calculate the cooling rate differences in different regions of the preform by combining temperature distribution data. The coupling analysis module is used to analyze the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density of different geometric feature regions of the preform during the cooling process. Based on the evaluation results of the synergistic effect of the coupling relationship and the difference in cooling rate, the stress origin weight coefficient of the corresponding region is generated. The stress assessment module is used to assess the internal stress distribution of the preform based on the difference in cooling rate and the stress cause weighting coefficient, and to generate a stress distribution map that characterizes the magnitude and cause of stress. The zoned temperature control module is used to perform zoned temperature control on the preform based on the stress distribution spectrum and the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the introduction of material thermodynamic parameters. The static curing module is used to allow preforms that have undergone zoned temperature regulation to stand in a constant temperature and humidity environment for a predetermined time.

[0054] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0055] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0056] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0057] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0058] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0059] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0061] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0063] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for lightweight manufacturing of PET bottles for food packaging, characterized in that, include: S1. Acquire image information of the preform after injection molding, and simultaneously collect temperature distribution data on the surface of the preform; S2. Identify the geometric features of the preform based on image information, and calculate the difference in cooling rate in each region of the preform by combining temperature distribution data; S3. Analyze the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density in different geometric feature regions of the preform during the cooling process, and generate stress origin weight coefficients for the corresponding regions based on the evaluation results of the synergistic effect of the coupling relationship and the difference in cooling rate. S4. Evaluate the internal stress distribution of the preform based on the difference in cooling rate and the stress cause weighting coefficient, and generate a stress distribution map characterizing the stress magnitude and cause attributes. S5. Based on the stress distribution spectrum and combined with the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the calculation of material thermodynamic parameters, the preform is subjected to zoned temperature control. S6. After the preform has been subjected to zoned temperature regulation, let it stand in a constant temperature and humidity environment for a predetermined time.

2. The lightweight manufacturing method for PET bottles for food packaging according to claim 1, characterized in that, Acquire image information of the preform after injection molding, and simultaneously collect temperature distribution data on the surface of the preform, including: Multispectral imaging devices are used to collect reflectance distribution information of different wavelength bands on the surface of the preform to form image information; Simultaneously, an infrared thermal imager was used to scan the outer surface of the preform and record the temperature distribution data. The reflectivity distribution information acquired by the multispectral imaging device is registered with the temperature distribution data recorded by the infrared thermal imager according to spatial coordinates to form an image-temperature dataset with a positional correspondence.

3. The lightweight manufacturing method for PET bottles for food packaging according to claim 2, characterized in that, Based on image information, the geometric features of the preform are identified, and the differences in cooling rates in different regions of the preform are calculated by combining temperature distribution data, including: The wall thickness gradient distribution and radius of curvature distribution of the preform are extracted from the image information as geometric features; Based on the changes in temperature distribution data over a continuous time series, calculate the temperature drop at each measurement point on the surface of the preform per unit time. The wall thickness gradient distribution, radius of curvature distribution, and temperature drop per unit time are superimposed and analyzed according to spatial coordinates to generate a quantitative table of cooling rate differences per region.

4. The lightweight manufacturing method for PET bottles for food packaging according to claim 3, characterized in that, This study analyzes the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density in different geometric regions of the preform during cooling. Based on the synergistic effect assessment results of the coupling relationship and the difference in cooling rate, stress origin weight coefficients for the corresponding regions are generated, including: The degree of geometric constraint experienced by each region of the preform during shrinkage is quantified based on the wall thickness gradient distribution and the radius of curvature distribution. Based on temperature distribution data and mold thermal conductivity parameters, the local heat flux density distribution in each region of the preform surface is derived. Establish a spatial mapping relationship between the quantified value of geometric constraint degree and the local heat flux density distribution, and perform multi-field coupling analysis in conjunction with the quantified table of cooling rate difference; Based on the regional distribution characteristics of the dominant effects of geometric constraints and heat flux density in the multi-field coupling analysis results, a stress origin weight coefficient mapping table is generated.

5. The lightweight manufacturing method for PET bottles for food packaging according to claim 4, characterized in that, The internal stress distribution of the preform is assessed based on differences in cooling rates and stress origin weighting coefficients, generating stress distribution maps characterizing the magnitude and origin of stresses, including: Based on the stress origin weight coefficient mapping table, the stress dominance type in each region is determined to be either geometric constraint dominance or heat flux density dominance. The baseline stress level for each region is determined based on the data in the cooling rate difference quantification table. The reference stress level is corrected according to the stress dominance type, with nonlinear strengthening correction applied to the geometric constraint dominance region and linear proportional correction applied to the heat flux density dominance region. The corrected stress level is combined with the corresponding dominant stress type to generate a stress distribution map that includes both stress magnitude and causal attributes.

6. The lightweight manufacturing method for PET bottles for food packaging according to claim 5, characterized in that, Based on the stress distribution spectrum and combined with the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the changes in material thermodynamic parameters, the preform is subjected to zoned temperature control, including: Based on the stress magnitude level and stress origin attribute markings in the stress distribution spectrum, the surface of the preform is divided into a geometrically constrained stress zone and a heat flux density-dominated stress zone. For each stress zone, at least three candidate temperature control paths are selected, and the Gibbs free energy change of the preform system under each path is simulated and calculated based on the material specific heat capacity and thermal expansion coefficient parameters. The entropy component of the Gibbs free energy change that is closest to the theoretical equilibrium state is extracted as the structural entropy increase factor. By comparing the magnitudes of structural entropy increase factors corresponding to different stress zones, the preform is subjected to zoned temperature regulation.

7. The lightweight manufacturing method for PET bottles for food packaging according to claim 6, characterized in that, Comparing the structural entropy increase factors corresponding to different stress zones, the preform undergoes zoned temperature regulation treatment, including: For the geometrically constrained stress region, assign a gradient heating and slow cooling path corresponding to the maximum structural entropy increase factor; for the heat flux density-dominated stress region, assign a step heating path corresponding to the second largest structural entropy increase factor.

8. The lightweight manufacturing method for PET bottles for food packaging according to claim 6, characterized in that, For each stress zone, at least three candidate temperature control paths are selected, and the Gibbs free energy change of the preform system under each path is simulated and calculated based on the material's specific heat capacity and coefficient of thermal expansion parameters. Linear heating path, stepped heating path, and gradient heating and slow cooling path were selected as candidate temperature control paths. The enthalpy change component is calculated for each path based on the material's specific heat capacity parameter, and the volume work component is calculated for each path based on the thermal expansion coefficient parameter. The enthalpy change component and the volume work component are combined according to the basic thermodynamic equations to obtain the Gibbs free energy change for each path.

9. The lightweight manufacturing method for PET bottles for food packaging according to claim 6, characterized in that, The preforms, after being subjected to zoned temperature regulation, are left to stand in a constant temperature and humidity environment for a predetermined time, including: Transfer the preforms that have undergone gradient heating and slow cooling or stepped heating processes to a curing space with controlled environmental parameters. Maintain constant temperature and relative humidity within the curing space; The settling time is determined based on the relaxation characteristics of the preform material and the stress magnitude of the highest stress level region in the stress distribution diagram. Keep the preform stationary in the curing space until the required settling time is reached.

10. A lightweight manufacturing system for PET bottles for food packaging, used to implement the lightweight manufacturing method for PET bottles for food packaging according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire image information of the preform after injection molding, and at the same time, acquire temperature distribution data on the surface of the preform; The feature analysis module is used to identify the geometric features of the preform based on image information and calculate the cooling rate differences in different regions of the preform by combining temperature distribution data. The coupling analysis module is used to analyze the coupling relationship between the degree of geometric constraint and the corresponding local heat flux density of different geometric feature regions of the preform during the cooling process. Based on the evaluation results of the synergistic effect of the coupling relationship and the difference in cooling rate, the stress origin weight coefficient of the corresponding region is generated. The stress assessment module is used to assess the internal stress distribution of the preform based on the difference in cooling rate and the stress cause weighting coefficient, and to generate a stress distribution map that characterizes the magnitude and cause of stress. The zoned temperature control module is used to perform zoned temperature control on the preform based on the stress distribution spectrum and the structural entropy increase factor derived from the Gibbs free energy change of the system under different temperature control paths by simulating the introduction of material thermodynamic parameters. The static curing module is used to allow preforms that have undergone zoned temperature regulation to stand in a constant temperature and humidity environment for a predetermined time.

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