A method and system for predicting deformation of plastic parts

Abstract

The present invention discloses a method and system for predicting the deformation of plastic components, which relates to the field of manufacturing, and includes: a data acquisition module; a data preprocessing module; a feature extraction module; a deformation prediction model construction module; a prediction result output module; a user interaction interface module. The deformation prediction model construction module of the present invention uses machine learning algorithms to construct an accurate prediction model based on these key factors, and can effectively predict the deformation of plastic components. The present invention also includes a model verification sub-module and a model optimization sub-module, which can strictly verify and optimize the prediction model to ensure the prediction accuracy and generalization ability of the model. By adopting advanced machine learning algorithms such as the support vector machine algorithm and combining the comparison formula and the optimization formula, the present invention can continuously optimize the prediction model and improve the accuracy and reliability of the prediction.

Description

Technical Field

[0001] The present invention relates to the field of manufacturing industry, and in particular to a deformation prediction method and system for plastic parts. Background Art

[0002] In the manufacturing industry, the deformation problem of plastic parts has always been one of the key factors affecting product quality and production efficiency. During the processing, storage and use of plastic parts, deformation often occurs due to the influence of various factors such as material properties, processing conditions and external environment, resulting in a decrease in dimensional accuracy, which in turn affects the assembly performance and functional realization of the product.

[0003] Traditional deformation prediction methods for plastic parts mainly rely on empirical formulas and experimental data. These methods are not only time-consuming and labor-intensive, but also have limited prediction accuracy, and it is difficult to meet the modern manufacturing industry's demand for high precision and high efficiency. With the rapid development of machine learning technology, its application in various fields is becoming more and more extensive, providing new ideas and methods for deformation prediction of plastic parts.

[0004] However, there is currently no system on the market that can comprehensively consider the original size data, material property data, processing condition data, and external environment data of plastic parts, and use machine learning algorithms to perform high-precision deformation prediction. Therefore, how to develop an efficient and accurate plastic part deformation prediction method and system has become a technical problem that needs to be urgently solved in the current manufacturing industry.

[0005] Based on this, the present invention aims to provide a deformation prediction method and system for plastic parts, so as to achieve accurate prediction of the deformation of plastic parts and provide strong technical support for the manufacturing industry. Summary of the invention

[0006] Based on this, the purpose of the present invention is to provide a deformation prediction method and system for plastic parts to solve the technical problem that there is currently no system on the market that can comprehensively consider the original size data, material property data, processing condition data and external environment data of plastic parts and use machine learning algorithms to perform high-precision deformation prediction.

[0007] In view of the above problems, the present application provides a deformation prediction method and system for plastic parts.

[0008] In the first aspect of the present application, a deformation prediction system for plastic components is provided, including: a data acquisition module for acquiring the original dimension data, material property data, processing condition data, and external environment data of the plastic components; a data preprocessing module for cleaning, format conversion, and standardization processing of the data acquired by the data acquisition module; a feature extraction module for extracting key factors affecting the deformation of the plastic components from the preprocessed data, including but not limited to temperature, pressure, time, material elastic modulus, and Poisson's ratio; a deformation prediction model construction module for constructing a prediction model for the deformation of the plastic components based on the key factors extracted by the feature extraction module using machine learning algorithms; a prediction result output module for visually displaying or storing the prediction results output by the deformation prediction model construction module; and a user interaction interface module for receiving operation instructions input by the user and displaying the system status, prediction results, and error messages.

[0009] The present invention is further configured such that the deformation prediction model construction module further includes: a model training sub-module for training an initial prediction model using historical deformation data, where the historical deformation data includes the actual deformation amounts of the plastic components under different conditions; a model verification sub-module for verifying the trained prediction model using a verification data set to evaluate the prediction accuracy and generalization ability of the model; and a model optimization sub-module for adjusting the parameters or optimizing the structure of the prediction model according to the verification results to improve the prediction accuracy.

[0010] The present invention is further configured to further include: a deformation warning module for issuing a deformation warning signal when the predicted deformation amount exceeds a preset threshold according to the prediction results output by the prediction result output module; and an optimization suggestion generation module for generating optimization suggestions for processing conditions or material properties according to the deformation warning signal to reduce the deformation amount of the plastic components.

[0011] In the second aspect of the present application, a method for deformation prediction using a deformation prediction system for plastic components is provided, including the following steps: Step 1: Using the data acquisition module to acquire the original data of the plastic components; Step 2: Using the data preprocessing module to preprocess the original data; Step 3: Using the feature extraction module to extract the key factors affecting the deformation of the plastic components; Step 4: Using the deformation prediction model construction module to construct a prediction model for the deformation of the plastic components; Step 5: Using the prediction result output module to output the prediction results; Step 6: According to the prediction results, using the deformation warning module and the optimization suggestion generation module to generate deformation warnings and optimization suggestions.

[0012] In summary, the present invention mainly has the following beneficial effects:

[0013] Through the data acquisition module, the present invention can comprehensively collect the original dimensional data, material property data, processing condition data, and external environment data of plastic parts, providing a rich information basis for subsequent deformation prediction. The data preprocessing module ensures the accuracy, consistency, and availability of the data, providing reliable data guarantee for deformation prediction. The feature extraction module can extract the key factors affecting the deformation of plastic parts from the preprocessed data, such as temperature, pressure, time, material elastic modulus, and Poisson's ratio. These factors are crucial for deformation prediction. The deformation prediction model construction module uses machine learning algorithms to build an accurate prediction model based on these key factors, enabling effective prediction of the deformation of plastic parts. The present invention also includes a model verification sub-module and a model optimization sub-module, which can strictly verify and optimize the prediction model to ensure the prediction accuracy and generalization ability of the model. By adopting advanced machine learning algorithms such as support vector machine algorithms and combining comparison formulas and optimization formulas, the present invention can continuously optimize the prediction model and improve the accuracy and reliability of prediction. The deformation warning module can, according to the prediction result, issue a deformation warning signal in a timely manner when the predicted deformation amount exceeds the preset threshold, reminding the production personnel to take corresponding measures. The optimization suggestion generation module can generate optimization suggestions for processing conditions or material properties according to the deformation warning signal to reduce the deformation amount of plastic parts and improve product quality and production efficiency. The deformation prediction system for plastic parts provided by the present invention has a friendly user interface, which can receive the operation instructions input by the user and display the system status, prediction results, and error information, enabling the user to easily operate the system. At the same time, the prediction method and process of the system are simple and clear, and are easy to promote and apply. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flowchart of the method for deformation prediction using the deformation prediction system for plastic parts of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0016] Next, the embodiments of the present invention will be described according to the overall structure of the present invention.

[0017] A deformation prediction system for plastic parts includes:

[0018] The data acquisition module is a key component in the plastic part deformation prediction system. The main function of this module is to collect various data of plastic parts, including but not limited to original dimension data, material property data, processing condition data, and external environment data. These data are important bases for subsequent data processing, feature extraction, and deformation prediction.

[0019] The original dimension data refers to the initial dimension information of plastic parts before manufacturing or processing. These data are crucial for understanding the initial state of the parts and predicting their deformation under different conditions. The material property data involves various physical and chemical properties of the materials used in plastic parts. These properties include but are not limited to the elastic modulus, Poisson's ratio, coefficient of thermal expansion, etc. of the materials, and they have important influences on the deformation behavior of the parts. The processing condition data refers to various process parameters involved in the manufacturing or processing of plastic parts. These parameters include temperature, pressure, time, etc., and they directly affect the processing quality and deformation of the parts. The external environment data refers to the environmental conditions in which plastic parts are located during storage, transportation, or use. These conditions may include temperature, humidity, light, etc., and they also have certain influences on the deformation of the parts.

[0020] The data preprocessing module, whose main task is to perform a series of processing on the original data collected by the data acquisition module to ensure the accuracy and effectiveness of subsequent analysis. These processing steps include data cleaning, format conversion, and normalization processing.

[0021] Data cleaning is the first step in data preprocessing. Its purpose is to identify and correct errors, anomalies, or missing values in the data. In the deformation prediction of plastic parts, the original data may contain incorrect data due to equipment failures, sensor errors, or human operation mistakes. The data cleaning process will check this data and take corresponding measures to correct or delete it to ensure the accuracy of subsequent analysis. Format conversion refers to converting data from one format to another to meet the requirements of subsequent analysis. In the deformation prediction system of plastic parts, the data acquisition module may collect data from different sources and different devices, and these data may have different formats and units. The data preprocessing module will perform format conversion on these data to unify them into a standard format that the system can recognize, thus facilitating subsequent data processing and analysis. Standardization processing refers to scaling or converting data according to certain rules to eliminate the dimensional differences between different data and make them have the same scale. In the deformation prediction of plastic parts, the original data may contain various different types of data such as temperature, pressure, time, material elastic modulus, and Poisson's ratio. These data may have large differences in numerical range and units. If directly used for analysis, it may lead to inaccurate results. Therefore, the data preprocessing module will perform standardization processing on these data to convert them into data with the same scale, thus ensuring the accuracy and reliability of subsequent analysis.

[0022] The feature extraction module, whose main task is to extract the key factors that have a significant impact on the deformation of plastic parts from the data that has been cleaned, format-converted, and standardized by the data preprocessing module. These factors, as the basis for constructing the subsequent deformation prediction model, are directly related to the prediction accuracy and reliability of the model, including but not limited to temperature, pressure, time, material elastic modulus, and Poisson's ratio.

[0023] Temperature is one of the important factors affecting the deformation of plastic parts. During the processing, temperature changes will cause the redistribution of internal stress in the material, thereby affecting the dimensional stability and shape accuracy of the parts. The feature extraction module can accurately capture and record the temperature data at different processing stages, providing key input for subsequent model construction. Pressure also has a significant impact on the deformation of plastic parts. In molding processes such as injection molding and pressing, the size and distribution of pressure directly determine the flow behavior and final molding state of the material. Therefore, the feature extraction module needs to accurately measure and record the pressure data during the processing. The time factor cannot be ignored in the deformation prediction of plastic parts. Long-term heating, cooling or pressure holding processes may cause changes in material properties, thereby affecting the deformation behavior of parts. The feature extraction module will record the key time points or time periods in the processing process so that the subsequent model can accurately reflect the effect of time on deformation. Material properties are intrinsic factors that affect the deformation of plastic parts. The elastic modulus reflects the ability of the material to resist deformation, while the Poisson's ratio describes the degree of lateral deformation of the material when subjected to force. These material property data are usually provided by material suppliers or obtained through experimental measurements. The feature extraction module will take these key material property data into consideration to build a more accurate prediction model.

[0024] The feature extraction module is usually implemented through technical means such as data analysis and machine learning algorithms. It can deeply mine and analyze the preprocessed data and identify those characteristic variables that are closely related to the deformation of plastic parts. In practical applications, the feature extraction module may combine multiple algorithms and technical means to improve the accuracy and efficiency of feature extraction.

[0025] The deformation prediction model building module is the core part of the entire system. It is responsible for building a prediction model for the deformation of plastic parts based on the key factors provided by the feature extraction module and using machine learning algorithms. This model can predict the deformation of plastic parts under different conditions and provide data support for subsequent deformation warnings and optimization suggestions.

[0026] When building the prediction model, the module uses a machine learning algorithm. Machine learning is a technology that can automatically learn from data and improve its performance. It is very suitable for dealing with complex and changeable problems such as deformation of plastic parts. By training a large amount of historical data, the machine learning algorithm can learn the complex relationship between the key factors affecting the deformation of plastic parts and the deformation amount, and build a prediction model based on this relationship. The feature extraction module has extracted the key factors affecting the deformation of plastic parts from the preprocessed data, including but not limited to temperature, pressure, time, material elastic modulus and Poisson's ratio. These factors are the basis for building a prediction model, and they can reflect the deformation law of plastic parts under different conditions.

[0027] The deformation prediction model construction module further includes:

[0028] A model training sub-module, whose main task is to train the initial prediction model using historical deformation data. These historical deformation data contain the actual deformation amounts of plastic parts under different conditions and are the basis for the model to learn. Historical deformation data: includes the actual deformation amounts of plastic parts under different temperatures, pressures, times, material properties, and external environmental conditions.

[0029] Obtain historical deformation data from the data acquisition module, preprocess the data to ensure the quality and consistency of the data, construct an initial prediction model using machine learning algorithms (such as support vector machines, neural networks, etc.), input the historical deformation data as the training set into the initial prediction model for model training. The trained prediction model can predict the deformation amount of plastic parts according to the input feature vectors (such as temperature, pressure, time, etc.).

[0030] The prediction formula of the support vector machine algorithm adopted by the model training sub-module is:

[0031]

[0032] Among them, x is the input feature vector, which is the input of the model and contains the key factors affecting the deformation of plastic parts, such as temperature, pressure, time, material elastic modulus, and Poisson's ratio. These factors are extracted from the preprocessed data and are the basis for the model to make predictions.

[0033] sign is a sign function, which is a function used to determine the positive or negative of the expression inside the parentheses. In SVM (support vector machine), it is usually related to the decision boundary and is used to judge which category the input feature vector belongs to (in this case, whether significant deformation will occur).

[0034] n is the number of support vectors. Support vectors are the key data points used to define the decision boundary in the SVM algorithm. Its quantity represents the number of these key points, and they are crucial for the construction and prediction ability of the model.

[0035] i is an index variable used to traverse all support vectors. During the training process of SVM, it is necessary to continuously adjust the model parameters to find the optimal decision boundary, and the index variable helps the algorithm to complete this task.

[0036] y i is the label of the training sample, which is the actual deformation situation in the historical deformation data and is used to train the model. The positive or negative or numerical size of the label reflects the deformation degree of plastic parts under different conditions.

[0037] α iThe weights corresponding to the support vectors. The weights are an important parameter in the SVM model, which represents the influence degree of each support vector on the decision boundary. By adjusting the weights, the algorithm can optimize the prediction ability of the model;

[0038] K(x i , x) is the kernel function, which is a key concept in the SVM algorithm. It is used to map the input feature vectors to a high-dimensional space so as to find the optimal decision boundary in this space. The choice of the kernel function has an important impact on the performance and prediction ability of the model;

[0039] b is the bias term, which is another important parameter in the SVM model. It is used to adjust the position of the decision boundary. By adjusting the bias term, the algorithm can further optimize the prediction accuracy of the model.

[0040] In the deformation prediction system of plastic parts, this formula uses the support vector machine algorithm to train the initial prediction model with historical deformation data. By continuously adjusting the model parameters (such as weights, bias terms, etc.), the algorithm can find the optimal decision boundary, thus realizing the effective prediction of the deformation of plastic parts.

[0041] The model verification sub-module uses the verification data set to verify the trained prediction model, which is used to evaluate the prediction accuracy and generalization ability of the trained prediction model. It uses a verification data set independent of the training set for testing to ensure that the model can not only perform well on the known data, but also maintain stable prediction performance on the unknown data. Verification data set: It contains the actual deformation amounts of plastic parts under different conditions, but does not overlap with the training set.

[0042] Obtain the verification data set from the data acquisition module or external data source, input the verification data set into the trained prediction model for prediction, calculate the difference between the prediction result and the actual deformation amount, evaluate the prediction accuracy of the model, and use evaluation metrics (such as the coefficient of determination R 2 , mean square error MSE, etc.) to quantify the generalization ability of the model. Verification results: Include prediction accuracy evaluation metrics, generalization ability evaluation metrics, and possible model performance analysis reports.

[0043] The comparison formula used by the model verification sub-module is:

[0044]

[0045] Among them, R 2 is the coefficient of determination (also known as the goodness of fit or coefficient of determination), which is used to quantify the fitting degree of the model to the data;

[0046] n is the number of samples, that is, the total number of sample points in the data set used for verification;

[0047] i is the index of the sample point, used to traverse all sample points;

[0048] y i is the actual deformation of the i-th sample point;

[0049] is the predicted deformation of the i-th sample point, that is, the deformation predicted by the model based on the input features; is the average value of the actual deformations, that is, the arithmetic mean of the actual deformations of all sample points.

[0050] The numerator part: represents the sum of squares of prediction errors, that is, the sum of squares of the differences between the model prediction values and the actual values. The smaller this value is, the smaller the prediction error of the model and the higher the prediction accuracy; The denominator part: represents the total sum of squares, that is, the sum of squares of the differences between the actual deformations of all sample points and their average value. This value reflects the degree of data fluctuation itself. By calculating the ratio of the sum of squares of prediction errors to the total sum of squares and subtracting this ratio from 1, the coefficient of determination R 2 is obtained. The closer the value of R 2 is to 1, the higher the prediction accuracy of the model, that is, the model can better fit the actual data; The smaller the value of R 2 is (but usually not less than 0), the lower the prediction accuracy of the model, that is, the greater the difference between the model and the actual data.

[0051] In the model verification sub-module, this formula is used to evaluate the performance of the trained prediction model. By comparing the prediction results of the model on the validation dataset with the actual results, the coefficient of determination R 2 can be calculated, so as to judge the prediction accuracy and generalization ability of the model. If the value of R 2 is relatively high (such as close to 1), it means that the model performs well on the validation dataset and has high prediction accuracy and generalization ability; On the contrary, if the value of R 2 is relatively low, it means that the model may have problems such as overfitting or underfitting and needs to be further adjusted and optimized.

[0052] The model optimization sub-module adjusts the parameters or optimizes the structure of the prediction model according to the verification results of the model verification sub-module to improve the prediction accuracy. It may involve adjusting the hyperparameters of the model, changing the model structure or adopting more advanced machine learning algorithms. The verification results include prediction accuracy evaluation metrics, generalization ability evaluation metrics, etc.

[0053] Analyze the verification results to identify the deficiencies of the model. According to the analysis results, adjust the model parameters (such as the C parameter and kernel function parameters of the support vector machine) or the structure (such as adding hidden layers, changing the number of neurons, etc.), retrain the model, and verify it again to evaluate the optimization effect. Repeat the above steps until satisfactory prediction accuracy and generalization ability are achieved. Optimized prediction model: A model with higher prediction accuracy and better generalization ability after parameter adjustment or structure optimization.

[0054] The optimization formula adopted by the model optimization sub-module is:

[0055]

[0056] where J(θ) is the optimization objective function;

[0057] (h θ (x (i) ) is the output of the prediction model, that is, the deformation amount of the plastic part predicted by the model based on the input features;

[0058] y (i) is the actual deformation amount, which is the actual deformation situation of the plastic part under different conditions and is used to compare with the predicted value to evaluate the prediction accuracy of the model;

[0059] θ is the model parameter, including the weights and bias terms of the support vector machine, etc. These parameters are optimized during the model training process to minimize the prediction error;

[0060] γ is the regularization coefficient, which is used to control the complexity of the model and prevent overfitting. The regularization term is usually proportional to the sum of the squares of the model parameters. By adjusting the size of γ, a balance can be achieved between the prediction accuracy and generalization ability of the model;

[0061] m is the number of training samples, that is, the number of historical deformation data used to train the model;

[0062] n is the number of features, that is, the number of key factors affecting the deformation of the plastic part, such as temperature, pressure, time, material elastic modulus, and Poisson's ratio, etc.;

[0063] j is an index variable used to traverse all the parameters in the parameter vector θ;

[0064] i is an index variable used to traverse all the training samples.

[0065] In the model optimization sub-module, the purpose of this formula is to minimize the difference between the output of the prediction model and the actual deformation by adjusting the model parameters θ, while considering the regularization term to control the complexity of the model. This usually involves an iterative process, where the value of θ is continuously adjusted through optimization algorithms such as gradient descent until a satisfactory prediction accuracy and generalization ability are achieved.

[0066] In practical applications, the optimization objective function may consist of multiple parts, such as the sum of squared prediction errors, regularization terms, etc. These parts are combined through weighted summation and other methods to form a comprehensive optimization objective. The model optimization sub-module will, based on this optimization objective, adopt appropriate optimization algorithms and strategies to adjust the parameters or optimize the structure of the prediction model to improve its prediction accuracy and generalization ability.

[0067] Once the prediction model is constructed, verified, and optimized, it can be used to predict the deformation of plastic parts under different conditions. These prediction results can be visually displayed or stored through the prediction result output module for users to refer to and make decisions.

[0068] The prediction result output module, whose main function is to visually display or store the prediction results output by the deformation prediction model construction module. This means that once the prediction model has completed the prediction of the deformation of plastic parts, the prediction result output module will receive these prediction data and present them in an intuitive manner or save them to a specified storage medium.

[0069] Visual display is usually in the form of charts, images, or animations to make the prediction results more intuitive and understandable. Through forms such as charts and images, users can quickly understand the deformation of plastic parts under different conditions, which helps users discover deformation patterns, thereby optimizing and adjusting processing conditions or material properties to reduce the amount of deformation; storage is to save the prediction data in a database, file, or other storage systems for subsequent analysis, comparison, or reference. Users can retrieve the stored prediction data at any time for further analysis or comparison, which helps users track the deformation of plastic parts, promptly discover potential problems, and take corresponding measures.

[0070] The user interaction interface module is used to receive operation instructions input by the user and display the system status, prediction results, and error messages. The main function of this module is to receive operation instructions input by the user, which may include starting the system, selecting a prediction model, setting prediction parameters, etc. The system will perform corresponding operations according to these instructions. At the same time, the user interaction interface module will also display the current status of the system in real time, such as data collection progress, model training status, prediction result output, etc., so that the user can clearly understand the operation of the system. In addition, this module will also display relevant prediction results and error messages during or after the prediction result output. The prediction results may include predicted deformation values of plastic parts under different conditions, while the error messages may include data collection errors, model training failures, abnormal prediction results, etc. These messages help users discover and solve problems in a timely manner.

[0071] The system also includes:

[0072] The deformation warning module is used to issue a deformation warning signal when the predicted deformation amount exceeds a preset threshold according to the prediction results output by the prediction result output module. The deformation warning module first receives the prediction results from the prediction result output module, which contain the predicted deformation amounts of plastic parts under different conditions. Next, the deformation warning module will compare the received predicted deformation amounts with the preset deformation threshold. This preset threshold is set based on factors such as the usage requirements, process specifications, or industry experience of plastic parts to determine whether the deformation is within the acceptable range. If the predicted deformation amount exceeds the preset threshold, the deformation warning module will immediately issue a deformation warning signal. This signal can be in the form of sound, light, screen display, or other forms of notification to attract the attention of production personnel.

[0073] The optimization suggestion generation module is used to generate optimization suggestions for processing conditions or material properties based on the deformation warning signal to reduce the deformation amount of plastic parts. The optimization suggestion generation module first receives the warning signal from the deformation warning module, which indicates the possible deformation situation of plastic parts under specific conditions. Next, the optimization suggestion generation module will analyze the predicted deformation amount and try to find out the main factors causing the deformation, such as processing temperature, pressure, time, or material properties. Based on the analysis results, the optimization suggestion generation module will generate a series of optimization suggestions for processing conditions or material properties. These suggestions are aimed at reducing the deformation amount of plastic parts by adjusting processing parameters or replacing materials. Finally, the optimization suggestion generation module will output the generated optimization suggestions to the user or relevant systems. These suggestions can be presented in the form of text, charts, or other forms for easy understanding and implementation by the user.

[0074] The optimization formula adopted by the optimization suggestion generation module is:

[0075]

[0076] Among them, T opt and P opt are the optimized temperature and pressure, which are the two key variables that the formula tries to solve, that is, it is hoped that the deformation of plastic parts can be reduced by adjusting these two parameters;

[0077] T base and P base are the base temperature and pressure, respectively, which are the initial conditions before any optimization suggestions are made and are usually determined based on historical data or experience;

[0078] ΔP and ΔT are the differences between the predicted deformation and the reference deformation, respectively. These differences reflect the gap between the deformation of the plastic component under the current conditions and the expected state;

[0079] Δt is the change in processing time, which is also an important factor affecting the deformation of plastic parts, but in this formula it is used as an adjustment factor rather than directly as an optimization variable;

[0080] E new and E base are the elastic modulus of the new material and the benchmark material, respectively. The elastic modulus is an important indicator of the material's ability to resist deformation, and is therefore a key factor to be considered during the optimization process;

[0081] V new and V base They are the Poisson’s ratio of the new material and the reference material, respectively. The Poisson’s ratio describes the degree of lateral deformation of the material when subjected to force, and also has an important influence on the deformation of plastic parts.

[0082] WB is the influencing factor of external environmental factors on deformation. It is a composite index that combines multiple external factors such as humidity, temperature, and light, and is used to reflect the influence of the external environment on the deformation of plastic parts.

[0083] β1, β2, β3, β4, β5 and μ1, μ2, μ3, μ4, μ5 are the adjustment coefficients of each factor respectively. These coefficients need to be determined through experiments or data analysis. They reflect the weight and sensitivity of different factors on the deformation of plastic parts. In practical applications, these coefficients can be adjusted and optimized according to specific circumstances to obtain better prediction effects and optimization suggestions.

[0084] By comprehensively considering multiple key factors affecting the deformation of plastic parts and presenting the calculation methods for optimized temperature and pressure, in practical applications, based on the difference between the predicted deformation amount and the reference deformation amount, as well as the adjustment coefficients of various factors, the optimized temperature and pressure values can be calculated, thus providing optimization suggestions for production personnel regarding processing conditions or material properties to reduce the deformation amount of plastic parts and improve product quality and production efficiency.

[0085] A method for predicting deformation using a plastic part deformation prediction system includes the following steps:

[0086] S1: Use the data acquisition module to collect the original data of plastic parts.

[0087] The data acquisition module is a key component of the plastic part deformation prediction system. Its main function is to comprehensively collect various data of plastic parts, including but not limited to original dimension data, material property data, processing condition data, and external environment data. These data are important bases for subsequent data processing, feature extraction, and deformation prediction. The original dimension data reflects the initial state of the part, the material property data involves the physical and chemical properties of the material used for the part, the processing condition data includes process parameters such as temperature, pressure, and time, and the external environment data takes into account the environmental conditions where the part is located during storage, transportation, or use.

[0088] S2: Use the data preprocessing module to preprocess the original data.

[0089] The main task of the data preprocessing module is to perform a series of processes on the original data collected by the data acquisition module to ensure the accuracy and effectiveness of subsequent analysis. These processing steps include data cleaning, format conversion, and standardization processing. Data cleaning aims to identify and correct errors, anomalies, or missing values in the data to ensure data accuracy. Format conversion is to convert the data from one format to another to meet the requirements of subsequent analysis. Standardization processing is to scale or transform the data according to certain rules to eliminate the dimensional differences between different data and make them have the same scale, thus ensuring the reliability of subsequent analysis.

[0090] S3: Use the feature extraction module to extract the key factors affecting the deformation of plastic parts.

[0091] The main task of the feature extraction module is to extract from the data processed by the data preprocessing module those key factors that have a significant impact on the deformation of plastic parts. These factors serve as the basis for constructing the subsequent deformation prediction model and are directly related to the prediction accuracy and reliability of the model. In the deformation prediction of plastic parts, the key factors may include temperature, pressure, time, material elastic modulus, Poisson's ratio, etc. The feature extraction module deeply mines and analyzes the preprocessed data through technical means such as data analysis and machine learning algorithms to identify the feature variables closely related to the deformation of plastic parts.

[0092] S4: Use the deformation prediction model construction module to construct a prediction model for the deformation of plastic parts.

[0093] The deformation prediction model construction module is the core part of the entire system. It is responsible for constructing a prediction model for the deformation of plastic parts based on the key factors provided by the feature extraction module using machine learning algorithms. This model can predict the deformation of plastic parts under different conditions and provide data support for subsequent deformation warning and optimization suggestions. When constructing the prediction model, this module uses machine learning algorithms such as support vector machines and neural networks. By training a large amount of historical data, the machine learning algorithms can learn the complex relationship between the key factors affecting the deformation of plastic parts and the deformation amount, and construct a prediction model based on this relationship.

[0094] S5: Use the prediction result output module to output the prediction results.

[0095] Once the prediction model is constructed, verified, and optimized, it can be used to predict the deformation of plastic parts under different conditions. The prediction result output module is responsible for visually displaying or storing the prediction results output by the deformation prediction model construction module. In this way, users can intuitively understand the deformation of plastic parts under different conditions and provide a reference for subsequent production and quality control.

[0096] S6: According to the prediction results, use the deformation warning module and the optimization suggestion generation module to generate deformation warnings and optimization suggestions.

[0097] The deformation warning module and the optimization suggestion generation module are important components of the plastic part deformation prediction system. The deformation warning module can, based on the prediction results output by the prediction result output module, issue a deformation warning signal in a timely manner when the predicted deformation amount exceeds the preset threshold. In this way, production personnel can take timely measures to avoid or reduce the deformation of plastic parts. The optimization suggestion generation module can generate optimization suggestions for processing conditions or material properties based on the deformation warning signal. These suggestions aim to reduce the deformation amount of plastic parts and improve product quality and production efficiency by adjusting processing conditions or selecting more suitable material properties.

[0098] Those of ordinary skill in the art can understand that the various numerical numbers such as the first and second involved in this application are only for the convenience of description and are not used to limit the scope of this application, nor do they represent the order of precedence. "And / or" describes the relationship between related objects and indicates that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one" means one or more. At least two means two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one (item, kind) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0099] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0100] The steps of the methods or algorithms described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of the two. The software units can be stored in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be provided in an ASIC, and the ASIC can be provided in a terminal. Optionally, the processor and the storage medium can also be provided in different components of the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate computer-implemented processing, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 a process or multiple processes and / or blocks Figure 1 a block or multiple blocks.

[0101] Although the present application has been described in connection with specific features and their embodiments, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the present application. Accordingly, the present specification and the drawings are merely illustrative of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A deformation prediction system for plastic parts, characterized in that: include: Data acquisition module, used to collect original size data, material property data, processing condition data and external environment data of plastic parts; A data preprocessing module is used to clean, convert and standardize the data collected by the data collection module; Feature extraction module, used to extract key factors affecting the deformation of plastic parts from preprocessed data, including temperature, pressure, time, material elastic modulus and Poisson's ratio; A deformation prediction model building module, using a machine learning algorithm to build a prediction model for deformation of plastic parts based on the key factors extracted by the feature extraction module; A prediction result output module, used for visually displaying or storing the prediction result output by the deformation prediction model building module; A user interaction interface module is used to receive operation instructions input by the user and display system status, prediction results and error information; A deformation warning module, configured to issue a deformation warning signal when the predicted deformation exceeds a preset threshold value according to the prediction result output by the prediction result output module; An optimization suggestion generation module, used to generate optimization suggestions for processing conditions or material properties according to the deformation warning signal, so as to reduce the deformation of the plastic parts; The optimization formula used by the optimization suggestion generation module is: Among them, T opt and P opt are the optimized temperature and pressure, T base and P base are the reference temperature and pressure, ΔP and ΔT are the differences between the predicted deformation and the reference deformation, Δt is the change in processing time, and E new and E base are the elastic modulus of the new material and the reference material, V new and V base are the Poisson's ratios of the new material and the benchmark material, respectively; WB is a composite index that combines humidity, temperature and light; β1, β2, β3, β4, β5 and μ1, μ2, μ3, μ4, μ5 are adjustment coefficients of each factor, which need to be determined through experiments or data analysis.

2. A deformation prediction system for plastic parts according to claim 1, characterized in that: The deformation prediction model building module further includes: A model training submodule, for training an initial prediction model using historical deformation data, wherein the historical deformation data includes actual deformation amounts of plastic parts under different conditions; The model validation submodule is used to validate the trained prediction model using the validation data set and evaluate the prediction accuracy and generalization ability of the model; The model optimization submodule adjusts the parameters or optimizes the structure of the prediction model according to the verification results to improve the prediction accuracy.

3. A deformation prediction system for plastic parts according to claim 2, characterized in that: The prediction formula of the support vector machine algorithm used by the model training submodule is: Where x is the input feature vector, sign is a sign function used to determine the positive or negative of the expression in the brackets, n is the number of support vectors, i is an index variable used to traverse all support vectors, and y is a i is the label of the training sample, α i is the weight corresponding to the support vector, K(x i ,x) is the kernel function and b is the bias term.

4. The deformation prediction system for plastic parts according to claim 2, characterized in that: The comparison formula used by the model verification submodule is: Among them, R 2 is the coefficient of determination, n is the number of samples, i is the index of the sample point, which is used to traverse all sample points, y i is the actual deformation, To predict the deformation, is the average value of the actual deformation.

5. The deformation prediction system for plastic parts according to claim 2, characterized in that: The optimization formula used by the model optimization submodule is: Among them, J(θ) is the optimization objective function, h θ (x (i) ) is the output of the prediction model, y (i) is the actual deformation, θ is the model parameter, γ is the regularization coefficient, m is the number of training samples, n is the number of features, j is an index variable used to traverse all parameters in the parameter vector θ, and i is an index variable used to traverse all training samples.

6. A method for deformation prediction using the deformation prediction system for plastic parts according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1: Use the data acquisition module to collect the original data of plastic parts; S2: preprocessing the raw data using a data preprocessing module; S3: Extract key factors affecting the deformation of plastic parts using feature extraction module; S4: construct a prediction model for deformation of plastic parts using a deformation prediction model building module; S5: Output the prediction result using the prediction result output module; S6: Based on the prediction results, deformation warning module and optimization suggestion generation module are used to generate deformation warning and optimization suggestions.

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