Plastic packaging bottle precise forming method driven by three-dimensional simulation analysis

Abstract

The invention relates to the technical field of plastic packaging bottle processing, and provides a plastic packaging bottle precise forming method driven by three-dimensional simulation analysis. The method comprises the steps that processing orders are interacted, and basic forming information is extracted to construct a three-dimensional simulation model; fitting production line equipment precision to generate a first forming influence coefficient; setting a molding tolerance bandwidth; deriving a three-dimensional simulation model based on the influence coefficient and the tolerance bandwidth; processing the sample according to preset parameters; a multi-point image is used for collecting a simulation forming sample; matching the derivative model to obtain a target model; performing twinborn analysis to generate adjustment parameters; and forming of the target plastic bottle is controlled. The technical problem that in the prior art, adjustment is conducted only according to a three-dimensional simulation model of a finished product during machining, the influence of the machining capacity of a machining production line is ignored, and consequently the control efficiency is low is solved, the machining capacity of the machining production line and the finished product quality tolerance interval are comprehensively considered, forming adjustment parameters are determined, and the control efficiency is improved. Therefore, the control efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plastic processing, specifically to the technical field of plastic packaging bottle processing, and particularly to a three-dimensional simulation analysis driven plastic packaging bottle precise forming method. BACKGROUND

[0002] With the rapid development of the medical industry and the increasing demand of patients for drug safety and quality, the processing and forming control technology of plastic packaging bottles for infusion plays a crucial role in the field of medical packaging. However, with the continuous expansion of the infusion product market and the increasing concern of consumers for the safety of drug packaging, the traditional plastic packaging bottle processing and forming control method has been difficult to meet the high standard requirements of modern medical industry for high efficiency, precision and safety. When dealing with packaging bottles for infusion, the traditional plastic packaging bottle processing and forming control method usually faces problems such as low control precision, slow response speed and poor stability. These problems not only affect the production efficiency of packaging bottles for infusion, but also may pose potential threats to the quality and safety of drugs. For example, insufficient control precision may lead to uneven bottle wall thickness, thereby affecting the shelf life and stability of the drug; slow response speed may make it difficult for the production process to respond to unexpected situations, increasing the production risk. SUMMARY

[0003] The present application provides a three-dimensional simulation analysis driven plastic packaging bottle precise forming method, which aims to solve the technical problem of low control efficiency caused by the fact that the existing technology only adjusts according to the three-dimensional simulation model of the finished product, ignoring the influence of the processing capacity of the production line itself.

[0004] In view of the above problems, the present application provides a three-dimensional simulation analysis driven plastic packaging bottle precise forming method.

[0005] The application provides a three-dimensional simulation analysis driven plastic packaging bottle precise forming method, which comprises the following steps: interacting with a target processing order, extracting basic forming information of a target plastic packaging bottle, and constructing a three-dimensional simulation model of the target plastic packaging bottle according to the basic forming information; fitting the production line processing precision according to the control precision of a plurality of processing equipment of a target processing forming production line, generating a first forming influence coefficient; configuring a forming tolerance bandwidth of the target plastic packaging bottle, wherein the forming tolerance bandwidth is set according to a forming index qualified parameter interval in the basic forming information; deriving and expanding the three-dimensional simulation model based on the first forming influence coefficient and the forming tolerance bandwidth to obtain a three-dimensional derived simulation model set; processing a sample of the target plastic packaging bottle according to a preset forming control parameter to generate a plurality of forming samples; using a multi-point image acquisition device to perform three-dimensional point cloud simulation on the plurality of forming samples to generate a plurality of forming sample simulation models; using the plurality of forming sample simulation models to perform similarity matching on the three-dimensional derived simulation model set, taking the three-dimensional derived simulation model with the highest matching degree as a target three-dimensional simulation model; performing twin analysis based on the plurality of forming sample simulation models and the target three-dimensional simulation model, generating an adjusted forming control parameter according to the analysis result, and using the adjusted forming control parameter to control the processing and forming of the target plastic packaging bottle.

[0006] One or more technical solutions provided in the application have at least the following technical effects or advantages: The above-mentioned three-dimensional simulation analysis driven plastic packaging bottle precise forming method receives and analyzes the target processing order, extracts the basic forming information of the plastic packaging bottle, and constructs a three-dimensional simulation model accordingly. This model provides intuitive and accurate reference for the subsequent production process. Then, according to the control accuracy of each processing equipment on the production line, the processing capacity of the whole production line is simulated and evaluated, and a first forming influence coefficient is generated. This coefficient reflects the potential capacity and limitation of the production line in processing the plastic packaging bottle. Then, a forming tolerance bandwidth is configured for the target plastic packaging bottle. This bandwidth is set based on the qualified parameter interval in the basic forming information, ensuring that the product still meets the quality requirements even in slight deviation. With the first forming influence coefficient and the forming tolerance bandwidth, the initial three-dimensional simulation model is derived and expanded to generate a set of three-dimensional derivative simulation models containing multiple possibilities. These models represent the possible product forms under different conditions. Then, according to the preset forming control parameters, the actual sample is processed, and a multi-point image acquisition device is used to simulate the three-dimensional point cloud of the sample, generating multiple forming sample simulation models. These simulation models are highly consistent with the actual sample, providing accurate data for subsequent matching and analysis. Then, the simulation model of the actual sample is matched with the set of three-dimensional derivative simulation models, and the model with the highest matching degree is selected as the target three-dimensional simulation model. This model best represents the product form that can be obtained under actual production conditions. Finally, the multiple forming sample simulation models and the target three-dimensional simulation model are analyzed to reveal the deviation and potential problems in the actual production process, and the adjusted forming control parameters are generated accordingly. These adjusted parameters will be used to guide the subsequent production process, ensuring that the plastic packaging bottle is processed more precisely, efficiently and safely.

[0007] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0009] Figure 1 The flowchart of the three-dimensional simulation analysis driven plastic packaging bottle precise forming method in an embodiment.

[0010] Figure 2A flowchart for generating a first forming influence coefficient of a three-dimensional simulation analysis driven plastic packaging bottle precision forming method in an embodiment is shown. DETAILED DESCRIPTION

[0011] The embodiment of the present application provides a three-dimensional simulation analysis driven plastic packaging bottle precision forming method, and solves the technical problem of low control efficiency caused by the fact that the prior art only adjusts according to a three-dimensional simulation model of a finished product and ignores the influence of the processing capacity of a processing line.

[0012] The technical solutions in the embodiments of the present application will be clearly and completely described in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0013] It should be noted that the terms “comprising” and “having” and any change thereof are intended to cover the non-exclusive inclusion, for example, a process, a method, a system, a product or a server including a series of steps or units does not have to be limited to the clearly listed steps or units, but can include other steps or modules that are not clearly listed or inherent to the process, the method, the product or the device.

[0014] An embodiment, as shown in the figure, the present application provides a three-dimensional simulation analysis driven plastic packaging bottle precision forming method, the method comprises: Figure 1 interacting with a target processing order, extracting basic forming information of a target plastic packaging bottle, and constructing a three-dimensional simulation model of the target plastic packaging bottle according to the basic forming information.

[0015] In the current plastic packaging bottle processing industry for infusion, with the continuous growth of demand for pharmaceuticals, higher requirements are put forward for the manufacturing precision and quality control of the packaging bottle for infusion. However, the traditional processing method often depends on the adjustment of the three-dimensional simulation model of the finished product. Although this method can ensure the appearance and quality of the product to a certain extent, it ignores the influence of the processing capacity of the processing line. This practice often leads to low control efficiency in the production process, and it is difficult to meet the demand of the medical industry for high-precision and high-quality pharmaceutical packaging.

[0016] ​In the embodiments of the present application, the system terminal interacts with the transaction platform to obtain a target processing order, which contains information such as the specifications and quantity of plastic packaging bottles for infusion that need to be processed. This plastic packaging bottle for infusion is the target plastic packaging bottle. Subsequently, the system terminal extracts the specifications of the target plastic packaging bottle, i.e. the basic forming information, from the target processing order, which includes key parameters such as size, shape, and material requirements. Once these basic forming information is obtained, the system terminal activates the three-dimensional modeling component, and inside the three-dimensional modeling component, the system terminal uses a similar basic geometric body as a starting point to begin constructing a basic model of the target plastic packaging bottle according to the extracted basic forming information. Then, according to the size information of the bottle, the size parameters of the basic model are adjusted to match the actual size of the target plastic packaging bottle. Based on the basic model, the system terminal adds the detailed features of the bottle, such as the mouth, bottom, label area, etc. These detailed features are based on actual needs and design requirements. After that, the basic model with detailed features is polished, such as adjusting the surface smoothness, adding texture, etc., to improve the realism of the basic model. Then, according to the material requirements in the basic forming information, the corresponding material properties of the basic model are set, such as plastic type, color, transparency, etc. These properties will affect the rendering effect of the basic model and the appearance of the final product. When the material properties are set, the system terminal constructs a three-dimensional simulation model of the target plastic packaging bottle. This three-dimensional simulation model can help the system terminal understand the final form of the product and possible problems more intuitively. In this way, a series of verification and optimization can be carried out before formal production, to ensure that the final produced target plastic packaging bottle can meet the requirements and standards of the target processing order.

[0017] According to the control accuracy of the plurality of processing equipment of the target processing forming production line, the production line processing precision fitting is performed to generate a first forming influence coefficient.

[0018] In one embodiment, on the target processing and forming production line, each processing equipment has its specific control accuracy, which directly determines the processing accuracy they can achieve in the production process. In order to evaluate the processing accuracy of the entire production line, the system terminal performs production line processing accuracy fitting. The core of this fitting process is to comprehensively consider the control accuracy of each device and analyze how they jointly affect the forming quality of the final product. Specifically, the system terminal first acquires a plurality of processing record data sets of a plurality of processing devices in the target processing and forming production line, and then constructs a plurality of processing box plots of the plurality of processing devices based on these sets. Subsequently, discrete analysis is performed on the plurality of processing box plots to obtain a plurality of control accuracies of the plurality of processing devices. These accuracy data include the positioning accuracy, cutting accuracy, forming error, etc. of the equipment. Then, the system terminal sets a corresponding weight for the control accuracy data of each device according to the importance of each device on the production line and the degree of influence on the forming quality of the final product. The weight is determined according to historical experience, expert advice and historical data. The system terminal will give a higher weight to the devices that have a greater impact on the final product quality. Then, the control accuracy data of each device is multiplied by the corresponding weight, and the results are added to obtain a weighted sum. This weighted sum is the first forming influence coefficient, which represents the processing accuracy level of the entire production line, can help production managers understand the performance of the production line, find potential problems, and provide a basis for subsequent optimization and improvement.

[0019] Further, as shown in Figure 2 The present application provides a method for fitting the processing accuracy of a production line according to the control accuracy of a plurality of processing devices of a target processing and forming production line, generating a first forming influence coefficient, the method further comprising: Acquiring a plurality of processing record data sets of a plurality of processing devices in the target processing and forming production line; retrieving the plurality of processing record data sets with processing deviation values as indexes to generate a plurality of processing deviation value sets.

[0020] Preferably, when evaluating the processing accuracy of the target processing and forming production line, the system terminal first acquires the processing record data sets of the plurality of processing devices in the production line. These data sets contain information about the processing results of the devices under different times and conditions. Subsequently, the processing record data sets are retrieved with processing deviation values as the key index. The processing deviation value is set based on the actual production requirements and describes the difference between the actual processing result and the expected result, reflecting the processing accuracy and stability of the device. By indexing retrieval with processing deviation values, the system terminal can extract a subset of data related to a specific deviation value from the vast amount of processing record data, forming a plurality of processing deviation value sets. The data in these sets will help the system terminal more clearly understand the processing performance of the device within a specific deviation range, and further analyze the control accuracy of the production line.

[0021] constructing a plurality of processing box plots of the plurality of processing devices based on the plurality of sets of processing deviation values; performing discrete analysis on the plurality of processing box plots to obtain a plurality of control precisions of the plurality of processing devices.

[0022] Preferably, when evaluating the control precision of the plurality of processing devices, the system terminal constructs a processing box plot of each device based on the collected plurality of sets of processing deviation values. The box plot is a commonly used statistical graph for displaying the dispersion of a set of data, especially the distribution range, median, and outliers of the data. For each processing device, the system terminal processes its corresponding set of processing deviation values according to the construction rules of the box plot, and draws a box plot containing the minimum value, the first quartile (Q1), the median (Q2), the third quartile (Q3), and the maximum value. This box plot intuitively shows the distribution of the processing results of the device, especially the dispersion degree and possible outliers of the data. Subsequently, discrete analysis is performed on the constructed plurality of processing box plots. Discrete analysis mainly focuses on the dispersion degree and distribution characteristics of the data, including the range and interquartile range (IQR) of the data. The interquartile range reflects the dispersion degree of the data set, especially the dispersion degree of the middle 50% of the data in the data set. The interquartile range measures the distance between the first quartile and the third quartile, i.e., the third quartile minus the first quartile. By analyzing these statistical quantities, the control precision of each processing device can be evaluated. If the box of the box plot is narrow, it indicates that the processing results of the device are concentrated. In this case, the system terminal calculates the mean of each set of processing deviation values and performs a ratio operation with the sum of the deviation values of each set of processing deviation values to obtain a plurality of processing control precisions. Conversely, if the box is wide, it indicates that the processing results of the device are dispersed. In this case, the system terminal extracts a plurality of deviation values between the first quartile and the third quartile, and performs control precision analysis on these deviation values to generate a plurality of processing control precisions. Through this method, the system terminal can construct a processing box plot based on the set of processing deviation values and obtain the control precision evaluation results of the plurality of processing devices through discrete analysis. This provides an important basis for subsequent process adjustment.

[0023] Further, the present application provides that when the plurality of interquartile ranges is less than or equal to a preset interquartile range threshold, the method comprises: extracting a plurality of interquartile ranges of the plurality of processing box plots and performing discrete verification on the plurality of interquartile ranges; when the plurality of interquartile ranges is less than or equal to a preset interquartile range threshold, performing mean calculation on the plurality of sets of processing deviation values, and performing a ratio operation on the calculation results and the sum of the deviation values of the plurality of sets of processing deviation values to obtain a plurality of processing control precisions.

[0024] Optionally, when evaluating the control accuracy of multiple processing steps, the system terminal extracts multiple interquartile ranges from these processing box plots. These interquartile ranges are then discretely validated to check if they meet preset interquartile range thresholds. The purpose of discrete validation is to determine if the data is sufficiently concentrated to meet the expected processing control requirements. The preset interquartile range thresholds are set based on historical data and industry standards. When multiple interquartile ranges are less than or equal to the preset thresholds, it indicates that the datasets for these processing steps are relatively concentrated, and the processing control is relatively stable. Next, the system terminal calculates the mean of multiple processing deviation value sets, and then divides each mean by the sum of the deviation values ​​in the corresponding processing deviation value set to obtain multiple measures of processing control accuracy. This step aims to evaluate the stability and accuracy of the processing steps by comparing the relative magnitude of the mean and the sum of deviations. In this way, the control accuracy of different processing steps can be quantified, providing important reference for production management and quality control.

[0025] Furthermore, this application provides a method that further includes, when the plurality of interquartile ranges are greater than the preset interquartile range threshold: When the multiple interquartile ranges are greater than the preset interquartile range threshold, multiple screening processing deviation values ​​located between the first quartile and the third quartile in the multiple processing box plots are extracted and added to multiple target processing deviation value sets; based on the multiple target processing deviation value sets, control accuracy analysis is performed to generate multiple processing control accuracies.

[0026] Optionally, when the interquartile range of multiple extracted processing box plots exceeds a preset interquartile range threshold, this indicates significant dispersion in the data, potentially due to noise or outliers. To more accurately assess the control precision of the processing, this noise needs to be removed. Specifically, the system terminal extracts multiple processing deviation values ​​located between the first and third quartiles from each processing box plot, serving as multiple filtered processing deviation values. These filtered values ​​are considered relatively concentrated data points less affected by noise and are therefore added to multiple target processing deviation value sets. Subsequently, control precision analysis is performed based on these target processing deviation value sets. The system terminal randomly selects one target processing deviation value set from the multiple target processing deviation value sets and then sorts this target processing deviation value set in ascending order. Next, the median is extracted from this ascending sequence, the absolute deviation of each deviation value from this median is calculated, and the median of these deviations is taken to obtain the processing control precision of this target processing deviation value set. Then, the system terminal repeats the above process to perform control precision analysis on the remaining target processing deviation value sets, thereby deriving multiple processing control precisions.

[0027] Configure the molding tolerance bandwidth of the target plastic packaging bottle, wherein the molding tolerance bandwidth is set according to the qualified parameter range of the molding index in the basic molding information.

[0028] In one embodiment, in order to obtain more accurate molding control parameters, the system terminal is configured with a molding tolerance bandwidth for plastic packaging bottles. This molding tolerance bandwidth refers to the range of fluctuations in some process parameters allowed during the manufacturing process to ensure product quality. It is a qualified parameter range set based on the molding indicators of the target plastic packaging bottle, such as size, weight, and strength.

[0029] Based on the first molding influence coefficient and the molding tolerance bandwidth, the three-dimensional simulation model is derived and expanded to obtain a set of three-dimensional derived simulation models.

[0030] In one embodiment, the system terminal uses the obtained first molding influence coefficient and molding tolerance bandwidth to derive and expand the initially constructed 3D simulation model. This derive and expansion process involves a series of adjustments and optimizations to the original 3D simulation model to simulate the bottle structure under different molding conditions. This adjustment and optimization process is iterative until a preset number of iterations is met. Through this derive and expansion method, the system terminal can obtain a set of multiple 3D derived simulation models. Each model in this set represents the bottle structure under specific molding conditions and conforms to the set molding tolerance bandwidth.

[0031] Furthermore, this application provides a method for obtaining the aforementioned three-dimensional derived simulation model set, which further includes: The basic forming index is adjusted multiple times according to a preset adjustment method to obtain multiple initial adjusted forming indexes. The preset adjustment method is to increase or decrease different basic forming indices according to a preset adjustment step size. The forming tolerance bandwidth is used to constrain the multiple initial adjusted forming indices to obtain multiple adjusted forming indices.

[0032] Preferably, to ensure that the manufactured target plastic packaging bottle meets the requirements and conditions, the system terminal adjusts the basic molding parameters. This adjustment is performed according to a preset adjustment method. Specifically, the system terminal increases or decreases different basic molding parameters according to a preset adjustment step size. This yields multiple initial adjusted molding parameters, representing potential parameter combinations under different molding conditions. For example, setting the preset adjustment step size for density to 0.1 g / cm³, the system terminal increases or decreases this preset adjustment step size for the density in the basic molding parameters to obtain initial adjusted densities among multiple initial adjusted molding parameters. These densities correspond to the lightweight, durable, etc., characteristics of the target plastic packaging bottle. Subsequently, the system terminal uses a molding tolerance bandwidth to constrain the initial adjusted molding parameters, ensuring that these adjusted parameters remain within an acceptable range. During the constraint process, the system terminal checks each initial adjusted molding parameter to determine if it exceeds the molding tolerance bandwidth. If a parameter exceeds this range, the system terminal discards it. Thus, the system terminal obtains a set of parameters that fully meet the molding tolerance bandwidth requirements, i.e., multiple adjusted molding parameters. These adjusted molding parameters serve as important references for subsequently generating an adjusted 3D simulation model.

[0033] Based on the first molding influence coefficient and the multiple adjustment molding indicators, a three-dimensional simulation is performed to generate an adjustment three-dimensional simulation model set; the historical processing data set of the target processing and molding production line is called to evaluate the frequency of the adjustment three-dimensional simulation model set, and the adjustment three-dimensional simulation model set is iteratively updated according to the evaluation results until the preset number of iterations is met, thereby obtaining the three-dimensional derivative simulation model set.

[0034] Preferably, to optimize the target plastic packaging bottle, the system terminal uses a first molding influence coefficient and multiple adjustment molding indices to adjust the 3D simulation model. First, in the 3D simulation component, the system terminal adjusts the 3D simulation model according to the influence of the first molding influence coefficient on the shape and performance of the target plastic packaging bottle. Specifically, to avoid problems such as dents and bulges caused by insufficient control precision, the 3D simulation model is pre-adjusted in the opposite direction, thus offsetting some of the problems caused by insufficient control precision. Subsequently, multiple adjustment molding indices are used to fine-tune the 3D simulation model. These adjustments simulate product shapes under different molding conditions by changing the model's parameters. After adjustment, the system terminal obtains a set of adjusted 3D simulation models, where each model corresponds to an adjustment molding index. Then, the system terminal calls the historical processing data set of the target processing and molding production line to evaluate the frequency of this set of adjusted 3D simulation models, obtaining multiple adjustment frequency factors. Based on a subset of the larger adjustment frequency factors, clustering is performed to obtain multiple follow-stage adjustment clusters. Larger frequencies are selected because higher frequencies better represent the average processing level. Then, these adjustment clusters are adjusted according to a preset adjustment method, and iterative updates are performed based on the adjustment results until a preset number of iterations is met, resulting in a set of three-dimensional derived simulation models. These models not only consider various influencing factors in the molding process, but also undergo fine adjustments and optimizations, providing important references for subsequent production.

[0035] Furthermore, this application provides basic forming indicators, and the method also includes: The basic forming indicators include appearance quality, tensile strength, sealing performance, and density.

[0036] Optionally, basic molding parameters are key factors in ensuring product quality and performance. These parameters include appearance quality, tensile strength, sealing performance, and density. Appearance quality refers to the visual appearance of the target plastic packaging bottle, including its transparency, surface smoothness, absence of bubbles, impurities, and scratches. Good appearance quality not only concerns the product's aesthetics but, more importantly, ensures that the liquid inside the bottle can be clearly seen during use, which is crucial for ensuring the accuracy and safety of the injected medication. Tensile strength refers to the target plastic packaging bottle's ability to resist breakage under tensile force. This relates to the various pressures the bottle may withstand during transportation, storage, and injection. High tensile strength ensures that the bottle will not crack or deform during normal use, thus preventing leakage or contamination. Sealing performance refers to the target plastic packaging bottle's ability to prevent the entry of external substances when sealed. Good sealing performance is key to ensuring that the medication remains sterile and pure during storage and transportation. Density is an important physical parameter of the target plastic packaging bottle, reflecting the material's compactness and weight. Appropriate density ensures that the bottle has sufficient strength and durability while remaining lightweight and easy to handle.

[0037] Furthermore, this application provides a method for conducting frequency evaluation, which also includes: The historical processing data set of the target processing and forming production line is called to evaluate the frequency of the adjustment three-dimensional simulation model set, and multiple adjustment frequency factors are obtained. The multiple adjustment frequency factors are sorted in descending order, and the M adjustment forming indicators corresponding to the M adjustment frequency factors in the first m positions are taken as the M adjustment directions.

[0038] Optionally, to determine which adjustment directions are most important and frequent, the system terminal uses historical processing data to evaluate the frequency of adjustments to the 3D simulation model set. The core of this process lies in analyzing the frequency of different 3D simulation models appearing in historical processing. Higher frequencies indicate that these models or the forming indicators they represent are more common in actual production, and therefore more likely to represent the average processing level or key adjustment points. Subsequently, frequency statistics are performed on each model in the adjustment 3D simulation model set within the historical processing data set, generating multiple adjustment frequency factors. These adjustment frequency factors represent the frequency of the corresponding model's appearance. These adjustment frequency factors are then sorted in descending order, placing the most frequently occurring models at the top. Then, the top m adjustment frequency factors are selected; the M adjustment forming indicators corresponding to these factors are considered the most important M adjustment directions. Here, m is set based on historical experience, and M is the same value as m. These adjustment directions represent the aspects that require the most attention in the production process because they appear most frequently in actual processing and have the greatest impact on overall processing efficiency and product quality.

[0039] Using the M adjustment and shaping indicators as cluster centers, the remaining multiple adjustment and shaping indicators are clustered to generate M follow-up adjustment clusters; based on the M adjustment directions, the M follow-up adjustment clusters are adjusted according to the preset adjustment method to obtain M follow-up stage adjustment clusters.

[0040] Optionally, after determining the M adjustment molding indicators for the target processing and molding production line, the system terminal uses these adjustment molding indicators as cluster centers. Then, it uses Euclidean distance to calculate the distance between each remaining adjustment molding indicator and the M cluster centers. Subsequently, each remaining adjustment molding indicator is assigned to the cluster containing the cluster center with the smallest distance, forming M follow-up adjustment clusters. Then, based on the determined M adjustment directions, the adjustment molding indicators in each follow-up adjustment cluster are adjusted. This adjustment is based on a preset adjustment method, that is, adding or subtracting the adjustment molding indicator from a preset adjustment step size according to the adjustment direction. In this way, each follow-up adjustment cluster will be improved according to the corresponding adjustment direction, thus forming M follow-up stage adjustment clusters.

[0041] Furthermore, this application provides a method for obtaining a set of three-dimensional derived simulation models, which also includes: The M adjustment directions are iteratively updated using the M following stage adjustment clusters to generate M updated adjustment directions and M updated following adjustment clusters; the M updated following adjustment clusters are adjusted according to the preset adjustment method based on the M updated adjustment directions, and iteratively updated according to the adjustment results until the preset number of iterations is met to obtain M target adjustment directions; the M target adjustment three-dimensional simulation models corresponding to the M target adjustment directions are used as the set of three-dimensional derived simulation models.

[0042] Optionally, the system terminal first associates the stage adjustment shaping index in each follow-stage adjustment cluster with the corresponding adjustment direction. For each follow-stage adjustment cluster, the distance between each stage adjustment shaping index within the cluster and the cluster center is calculated using Euclidean distance. Simultaneously, the differences between different clusters are calculated to ensure that the updated adjustment direction can distinguish between different clusters. Subsequently, based on the distribution and characteristics of the stage adjustment shaping index within the cluster, the corresponding adjustment direction is fine-tuned. This involves moving and scaling the adjustment direction. The purpose of fine-tuning is to ensure that the updated adjustment direction better represents the main trends and characteristics of the stage adjustment shaping index within the cluster. Based on the updated adjustment direction, the stage adjustment shaping index is re-clustered to generate M new follow-stage adjustment clusters. This clustering process is similar to the previous one, except that the updated adjustment direction is used as the new cluster center. Afterward, the system terminal uses the same adjustment method again to adjust the M updated follow-stage adjustment clusters according to the updated adjustment direction and the preset adjustment method. Then, the above clustering and adjustment process is repeated until the preset number of iterations is reached. When the preset number of iterations is reached, the system terminal outputs the current M updated adjustment directions to obtain M target adjustment directions. These target adjustment directions are derived from a large amount of historical data and multiple iterative optimizations, thus possessing high reliability and guiding value. Finally, the adjustment 3D simulation models corresponding to these M target adjustment directions are compiled into a set of 3D derived simulation models. This set of 3D derived simulation models can intuitively demonstrate the impact and effect of each target adjustment direction on the production line, providing a clear reference and basis for optimizing the production of target plastic packaging bottles.

[0043] The target plastic packaging bottle is processed according to the preset molding control parameters to generate multiple molded samples; the multiple molded samples are simulated in three dimensions using a multi-point image acquisition device to generate multiple molded sample simulation models.

[0044] In one embodiment, during the production of plastic packaging bottles, to verify and optimize the molding effect of the product, the system terminal manufactures samples of the target plastic packaging bottle according to preset molding control parameters, generating multiple molded samples. Once these molded samples are manufactured, the system terminal uses a multi-point image acquisition device to acquire three-dimensional point cloud data of these molded samples to simulate and generate multiple molded sample simulation models. These molded sample simulation models correspond one-to-one with the molded samples.

[0045] The set of three-dimensional derivative simulation models is matched with the multiple molded sample simulation models, and the three-dimensional derivative simulation model with the highest matching degree is taken as the target three-dimensional simulation model.

[0046] In one embodiment, the system terminal randomly selects a molded sample simulation model from multiple molded sample simulation models, and then performs iterative nearest-point matching between this model and each model in the set of 3D derived simulation models. In each iteration, the iterative nearest-point algorithm finds the closest point pairs between two point clouds and calculates a rigid transformation to align these points. The iteration process continues until the maximum number of iterations. At the end of each iteration, the system terminal calculates the root mean square error between the aligned point clouds and uses this error as a similarity score. The above process is repeated for the remaining molded sample simulation models, obtaining multiple similarity scores for each model. Subsequently, the 3D derived simulation models are sorted according to the similarity scores. The lower the score, the higher the matching degree. Then, the 3D derived simulation model with the lowest similarity score is selected as the target 3D simulation model. This model represents the adjustment parameters most similar to the actual molded sample.

[0047] Based on the simulation models of the multiple molded samples and the target three-dimensional simulation model, a twin analysis is performed. Based on the analysis results, molding control parameters are generated and adjusted. The molding control parameters are then used to control the processing and molding of the target plastic packaging bottle.

[0048] In one embodiment, after obtaining the target 3D simulation model, the system terminal performs a twin analysis on multiple molded sample simulation models based on this target 3D simulation model. During this twin analysis, the system terminal extracts the similarity scores between the target 3D simulation model and the corresponding 3D derived simulation model and these molded sample simulation models. These similarity scores represent the errors between the target 3D simulation model and the multiple molded sample simulation models. Subsequently, the system terminal calculates the average difference of these errors. Then, the system terminal inputs this average difference and preset molding control parameters into a pre-constructed difference control network. Based on learned knowledge, the difference control network generates adjusted molding control parameters. Finally, the system terminal uses the adjusted molding control parameters to control the processing and molding of the target plastic packaging bottle. By applying these adjusted molding control parameters in actual production, plastic packaging bottles that more closely resemble the target 3D simulation model can be obtained.

[0049] For the differential control network, the system terminal first acquires a large number of sample differential averages, preset sample shaping control parameters, and sample adjusted shaping control parameters. These sample data are then divided into training and testing sets. Subsequently, based on the complexity of the problem and the characteristics of the data, a neural network structure is constructed, including the number of neurons in the input, hidden, and output layers. Next, the weights and biases of the neural network are initialized using random numbers, and the initialized neural network is trained using the training set. After each iteration, the system terminal uses the mean squared error loss function to calculate the error between the predicted adjusted shaping control parameters and the sample adjusted shaping control parameters. Then, based on the calculated error, the Adam optimizer is used to update the weights of the neural network. This process is repeated until the maximum number of iterations is met. When the maximum number of iterations is met, the system terminal evaluates the trained neural network using the testing set and calculates the accuracy of the neural network. If the accuracy is higher than expected, the current neural network is output, generating the differential control network. Otherwise, the neural network is optimized by increasing its depth, modifying its structure, etc., and retrained until the expected accuracy is met.

[0050] In summary, the embodiments of this application have at least the following technical effects: This application's embodiments interactively acquire target processing orders and extract basic molding information of target plastic packaging bottles, then construct a three-dimensional simulation model based on this information. Subsequently, the control precision of processing equipment in the production line is analyzed, a first molding influence coefficient is generated through data fitting, and a molding tolerance bandwidth is set based on the qualified parameter range of the basic molding information. Then, these parameters are used to derive and expand the three-dimensional simulation model, resulting in a set of derived three-dimensional simulation models. To verify and adjust these simulation models, samples are processed according to preset molding control parameters, and a multi-point image acquisition device is used to perform three-dimensional point cloud simulation, generating a molded sample simulation model. By performing similarity matching between these simulation models and the set of derived simulation models, the three-dimensional simulation model that best matches the target is found. After obtaining the target three-dimensional simulation model, twin analysis is performed using multiple molded sample simulation models to identify the control parameters that need adjustment. Simultaneously, combined with historical processing data from the production line, the molding indicators are iteratively updated and optimized until a preset number of iterations is met. These technologies collectively solve the problem of low control efficiency caused by existing technologies that adjust based solely on the three-dimensional simulation model of the finished product during processing, ignoring the influence of the processing line's own processing capacity. They achieve the effect of comprehensively considering the processing line's processing capacity and the tolerance range of the finished product's quality to determine the molding adjustment parameters, thereby improving control efficiency.

[0051] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0052] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0053] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis, characterized in that, The method includes: Interact with the target processing order, extract the basic molding information of the target plastic packaging bottle, and construct a three-dimensional simulation model of the target plastic packaging bottle based on the basic molding information; Based on the control accuracy of multiple processing equipment in the target processing and forming production line, the processing accuracy of the production line is fitted to generate the first forming influence coefficient. Configure the molding tolerance bandwidth of the target plastic packaging bottle, wherein the molding tolerance bandwidth is set according to the qualified parameter range of the molding index in the basic molding information; Based on the first molding influence coefficient and the molding tolerance bandwidth, the three-dimensional simulation model is derived and expanded to obtain a set of three-dimensional derived simulation models. The target plastic packaging bottle is processed according to the preset molding control parameters to generate multiple molded samples; A multi-point image acquisition device is used to perform three-dimensional point cloud simulation on the multiple molded samples to generate multiple molded sample simulation models. The set of three-dimensional derivative simulation models is similarly matched using the multiple molded sample simulation models, and the three-dimensional derivative simulation model with the highest matching degree is taken as the target three-dimensional simulation model. Based on the simulation models of the multiple molded samples and the target three-dimensional simulation model, a twin analysis is performed. Based on the analysis results, molding control parameters are generated and adjusted. The molding control parameters are then used to control the processing and molding of the target plastic packaging bottle.

2. The method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis as described in claim 1, characterized in that, Based on the control precision of multiple processing equipment in the target processing and forming production line, the processing precision of the production line is fitted to generate a first forming influence coefficient. The method includes: Obtain a set of processing record data from multiple processing devices in the target processing and forming production line; Using the processing deviation value as an index, the multiple processing record data sets are retrieved to generate multiple processing deviation value sets; Multiple processing box plots are constructed for the multiple processing equipment based on the multiple sets of processing deviation values; Discrete analysis is performed on the multiple processing box diagrams to obtain multiple control accuracies for the multiple processing equipment.

3. The method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis as described in claim 2, characterized in that, The method includes: Extract multiple interquartile distances from the multiple processing box plots, and perform discrete verification on the multiple interquartile distances; When the multiple interquartile ranges are less than or equal to a preset interquartile range threshold, the average of the multiple processing deviation value sets is calculated, and the calculation result is compared with the sum of the deviation values ​​of the multiple processing deviation value sets to obtain multiple processing control accuracies.

4. The method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis as described in claim 3, characterized in that, The method includes: When the multiple interquartile ranges are greater than the preset interquartile range threshold, multiple screening processing deviation values ​​located between the first quartile and the third quartile in the multiple processing box plots are extracted and added to the multiple target processing deviation value set. Based on the set of multiple target machining deviation values, control accuracy analysis is performed to generate multiple machining control accuracies.

5. The method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis as described in claim 1, characterized in that, The method includes: The basic forming index is adjusted multiple times according to a preset adjustment method to obtain multiple initial adjusted forming indices. The preset adjustment method is to increase or decrease different basic forming indices according to a preset adjustment step size. By using the molding tolerance bandwidth to constrain the multiple initial adjusted molding indices, multiple adjusted molding indices are obtained; Based on the first molding influence coefficient and the multiple adjusted molding indices, a three-dimensional simulation is performed to generate a set of adjusted three-dimensional simulation models. The frequency of the adjusted three-dimensional simulation model set is evaluated by calling the historical processing data set of the target processing and forming production line. The adjusted three-dimensional simulation model set is iteratively updated according to the evaluation results until the preset number of iterations is met, and the three-dimensional derivative simulation model set is obtained.

6. The method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis as described in claim 5, characterized in that, The method includes: The frequency of adjustment of the three-dimensional simulation model set is evaluated by calling the historical processing data set of the target processing and forming production line to obtain multiple adjustment frequency factors; The multiple adjustment frequency factors are sorted in descending order, and the M adjustment shaping indicators corresponding to the M adjustment frequency factors in the first m positions are taken as the M adjustment directions. Using the M adjustment and shaping indices as cluster centers, the remaining adjustment and shaping indices are clustered to generate M follow-up adjustment clusters; Based on the M adjustment directions, the M following adjustment clusters are adjusted according to the preset adjustment method to obtain M following stage adjustment clusters.

7. The method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis as described in claim 6, characterized in that, The method includes: The M adjustment directions are iteratively updated using the M follow-stage adjustment clusters to generate M update adjustment directions and M update follow-stage adjustment clusters; Based on the M update adjustment directions, the M update following adjustment clusters are adjusted according to the preset adjustment method, and iterative updates are performed based on the adjustment results until the preset number of iterations is met, thereby obtaining the M target adjustment directions; The three-dimensional simulation models corresponding to the M target adjustment directions are used as the set of three-dimensional derived simulation models.

8. The method for precise molding of plastic packaging bottles driven by three-dimensional simulation analysis as described in claim 5, characterized in that, The basic forming indicators include appearance quality, tensile strength, sealing performance, and density.

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