Intelligent Design Optimization Method and System for Human-Computer Interaction of Injection Molds

Abstract

The present invention discloses a human-computer interaction intelligent design optimization method and system for injection molds, which relates to the technical field of injection molds. The system provides accurate processing data through efficient data acquisition and preprocessing technologies, and regression analysis and machine learning models significantly improve the prediction accuracy of the finished product quality. The key factors in the injection process are deeply analyzed by the processing analysis algorithm module by calculating the temperature distribution T(x, y, z), the plastic material change rate Bh, and the crystallization rate Jj, and these data are summarized into comprehensive processing data JG, and the operation process is simplified through a visual human-computer interaction interface, enabling users to perform optimization adjustments more conveniently. The intelligent analysis and evaluation module realizes automatic production anomaly detection and optimization adjustment through setting thresholds and comprehensive optimization formulas. This intelligent evaluation and optimization method is more efficient than manual inspection, significantly improving the production efficiency and quality control level.

Description

Technical Field

[0001] The present invention relates to the technical field of injection molds, and specifically to an intelligent design optimization method and system for human-computer interaction of injection molds. Background Technique

[0002] The human-computer interaction technology of injection molds is an important part of realizing intelligent manufacturing and Industry 4.0. By designing a friendly and intuitive interface, it improves the user experience and operation efficiency. In the injection molding process, the HMI system can monitor and control key parameters in real time, provide intelligent alarm and fault diagnosis functions, optimize process parameters, and support remote monitoring and maintenance. In the future, with the development of technologies such as artificial intelligence, virtual reality, and big data, the human-computer interaction technology of injection molds will develop towards a more intelligent, automated, and collaborative direction, further improving production efficiency and product quality.

[0003] At the present stage, the traditional injection mold processing technology has obvious defects in parameter optimization and quality control, mainly manifested as problems such as inaccurate parameter adjustment and lagging quality monitoring. These disadvantages stem from the lack of comprehensive and real-time data support for the processing process, resulting in abnormal situations such as uneven mold temperature, improper control of plastic material expansion, and unstable crystallization rate in actual production. These abnormalities will not only reduce the quality of the finished product, but also increase the scrap rate, affecting production efficiency and cost control. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an intelligent design optimization method and system for human-computer interaction of injection molds, which solves the problems mentioned in the background technique.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: including an injection parameter acquisition module, a finished product quality relationship module, a processing analysis algorithm module, a human-computer interaction module, and an intelligent analysis and evaluation module;

[0006] The injection parameter acquisition module uses sensors and monitoring devices to collect the mold processing data during the injection process of the mold in real time, and preprocesses the collected mold processing data to obtain a mold processing data set;

[0007] The finished product quality relationship module is used to utilize the collected mold processing data set, historical data, and experimental data, and divide them into a training set, a validation set, and a test set, and establish a regression analysis model and a machine learning model between the mold processing data and the finished product quality;

[0008] The processing analysis algorithm module is used to construct the mold temperature distribution analysis formula, the plastic material change analysis formula, and the crystallization relationship analysis formula, load the collected mold processing data set, calculate respectively to obtain the temperature distribution T(x, y, z), the plastic material change rate Bh, and the crystallization rate Jj, and conduct associated summarization to produce comprehensive processing data JG;

[0009] The human-computer interaction module is used to use development tools to construct a visual interface, view the optimization results, and input parameters for optimization adjustment;

[0010] The intelligent analysis and evaluation module is used to preset the first processing quality threshold Y1 and the second processing quality threshold Y2 and the obtained comprehensive processing data JG, conduct comparative evaluation and analysis of the quality of the current injection mold. When an abnormality is identified, a comprehensive optimization formula is further constructed, and the comprehensive optimization coefficient Yh is calculated to optimize the injection molding machine.

[0011] Preferably, the injection parameter acquisition module includes a parameter acquisition module and a data preprocessing module;

[0012] The parameter acquisition module is used to install a sensor group around the injection molding machine. The sensor group includes a roughness sensor, a thermocouple sensor, an infrared thermometer, a pressure sensor, and a flowmeter, and to collect the mold processing data during the injection process of the mold in real time. The mold processing data includes the mold surface roughness Ra, the thermal conductivity Km, the plastic material expansion coefficient Ap, and the shear rate Jq;

[0013] The data preprocessing module is used to use a Kalman filter to smooth the data of the mold processing data collected in real time, filter the collected mold processing data, and at the same time use wavelet transform to remove the high-frequency noise of the mold processing data collected by the sensor, and normalize the collected mold processing data.

[0014] Preferably, the finished product quality relationship module includes a regression analysis model unit, a machine learning model unit, and a model optimization and verification unit;

[0015] The regression analysis model unit is used to construct a multiple linear regression model, identify the linear relationship between the mold processing data and the finished product quality, analyze the prediction performance and regression coefficients of the model, and transmit the preprocessed mold data to the regression analysis model to evaluate the influence of each parameter on the finished product quality;

[0016] The machine learning model unit is used to select the Support Vector Machine (SVM) and the Multi-Layer Perceptron, and construct an SVM model and a Neural Network (NN) model respectively. The SVM model is used to capture the non-linear relationship between the mold processing data and the finished product quality. The NN model is used to capture complex non-linear relationships, and the constructed SVM model and NN model are integrated and summarized to obtain a machine learning model. Then, after preprocessing the collected mold processing data set, historical data, and experimental data, they are transmitted to the machine learning model to evaluate the prediction performance of the SVM model and the NN model respectively, and analyze the linear and non-linear relationships between the data;

[0017] The model optimization and verification unit uses Recursive Feature Elimination (RFE) to identify variables that affect the model prediction performance, and eliminates irrelevant and redundant variables, thereby simplifying the model, improving the interpretability and prediction accuracy of the model. Then, the mold processing data is divided into K subsets using K-fold cross-validation. Each time, one of the subsets is used for verification, and the remaining K - 1 subsets are used for training. This is repeated K times, and the result is the average of the K validations. At the same time, grid search is used to traverse all hyperparameter combinations to select the combination that optimizes the model performance. After optimizing the machine learning model, it is necessary to verify the machine learning model to evaluate the performance of the machine learning model and ensure its applicability to practical applications.

[0018] Preferably, the processing analysis algorithm module includes a mold temperature analysis algorithm unit, a material change algorithm unit, a crystallization algorithm unit, and a comprehensive analysis algorithm unit;

[0019] The temperature analysis algorithm unit is used to construct a mold temperature distribution analysis formula, and based on the thermal conductivity Km and the mold surface roughness Ra in the preprocessed mold processing data set, analyze and calculate to obtain the temperature distribution T(x, y, z);

[0020] The temperature distribution T(x, y, z) is calculated and obtained through the following mold temperature distribution analysis formula;

[0021]

[0022] In the formula, T0 represents the initial temperature, which is the temperature of the mold without any heat source and is directly measured by an infrared thermometer. Q represents the heat source intensity, which is the heat intensity provided by the heater or cooling system and is determined by calculating the heat power provided by the injection molding machine per unit time. x, y, and z represent the horizontal axis, vertical axis, and vertical axis respectively, which are the coordinates of the mold in three-dimensional space. These coordinates are usually related to the geometric shape and specific spatial position of the mold. The surface roughness Ra of the mold affects the heat conduction efficiency of the mold surface, thereby affecting the temperature distribution of the mold. The thermal conductivity Km determines the heat conduction ability of the mold material and affects the internal temperature gradient of the mold. exp represents the exponential function, which is used to describe the attenuation characteristics of the temperature distribution.

[0023] Preferably, the material change algorithm unit is used to construct an analysis formula for plastic material change, and based on the plastic material expansion coefficient Ap in the preprocessed mold processing dataset, analyze and calculate to obtain the plastic material change rate Bh.

[0024] The plastic material change rate Bh is calculated and obtained through the following plastic material change analysis formula;

[0025]

[0026] In the formula, V0 represents the initial volume of the plastic. The plastic material expansion coefficient Ap represents the degree of volume expansion of the material when the temperature changes. △T represents the temperature change, which is the change value of the material temperature relative to the initial temperature T0. βm represents the volume change rate coefficient, which represents the change of the material volume with the change rate of temperature. d represents the integration variable, and dt represents the variable within the unit time t. T represents the temperature, which is measured by a thermocouple and an infrared thermometer. represents the change rate, which is the change of the temperature T within the unit time t.

[0027] Preferably, the crystallization algorithm unit is used to construct an analysis formula for crystallization relationship, and based on the shear rate Jq in the preprocessed mold processing dataset, analyze and calculate to obtain the crystallization rate Jj.

[0028] The crystallization rate Jj is calculated and obtained through the following crystallization relationship analysis formula;

[0029]

[0030] In the formula, k1 represents the rate constant, which is the crystallization rate without the action of shear rate. Ea represents the activation energy, which is the energy barrier that needs to be overcome during the crystallization process. R represents the gas constant. Jq0 represents the reference shear rate. n represents the shear rate exponent, which represents the sensitivity of the material to the shear rate. exp represents the exponential function.

[0031] Preferably, the comprehensive analysis algorithm unit is used to correlate and summarize the obtained temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj to obtain comprehensive processing data JG, and analyze the current injection molding machine processing quality;

[0032] The comprehensive processing data JG is obtained through the following algorithm formula;

[0033]

[0034] In the formula, V is a volume integral covering the entire three-dimensional space, and S represents the overall performance of the system.

[0035] Preferably, the human-computer interaction module is used to design a preliminary interface using the QtDesigner interface design tool, define the interface layout and the positions and functions of each control, implement the data input, optimization calculation, and result display functions using the PyQt and PyGObject libraries, and then embed the graph into the GUI through Matplotlib and D3.js to display the data graphically and achieve dynamic interaction.

[0036] Preferably, the energy analysis and evaluation module includes a quality evaluation unit and an optimization algorithm unit;

[0037] The quality evaluation unit is used to set a preset first processing quality threshold Y1 and a second processing quality threshold Y2 based on the mean value of the injection mold processing quality, and then compare and evaluate with the obtained comprehensive processing data JG to analyze the current mold processing quality situation. The specific evaluation scheme is as follows;

[0038] When the comprehensive processing data JG > the first processing quality threshold Y1, it indicates that the processing quality of the current injection molding machine is abnormal. At this time, the optimization algorithm is further executed to optimize the parameters;

[0039] When the second processing quality threshold Y2 ≤ the comprehensive processing data JG ≤ the first processing quality threshold Y1, it indicates that the injection molding machine is processing normally;

[0040] When the comprehensive processing data JG < the second processing quality threshold Y2, it indicates that the injection molding machine is processing abnormally. At this time, the optimization algorithm is further executed to optimize the parameters;

[0041] The optimization algorithm unit is used to construct a comprehensive optimization formula, substitute the obtained temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj into the algorithm formula, calculate to obtain the comprehensive optimization coefficient Yh to optimize the injection molding machine, and through iterative calculation, find the best combination of process parameters that minimizes the optimization target, and according to the calculation results, adjust and optimize by inputting parameters through the interaction interface;

[0042] The comprehensive optimization coefficient Yh is obtained through the following algorithm formula;

[0043]

[0044] In the formula, α, β, and γ respectively represent the weight coefficients of temperature optimization, volume change control, and crystallization rate control, T target represents the target temperature set during design, Bh target represents the target volume change, Jj target represents the target crystallization rate Jj set during design;

[0045] α·∫ V T(x,y,z)-T target dV is used to optimize the temperature distribution inside the mold, making the processing temperature close to the target temperature T target , and by minimizing the error of the temperature distribution, the heating uniformity of the mold is ensured, thereby improving the quality and performance of the injection molded product;

[0046] β·Bh - Bh target | is used to control the volume change of the plastic during the injection molding process, making it close to the designed target volume V target , and by minimizing the volume change error, the dimensions and quality of the finished product are ensured to meet the design specifications, thereby improving the consistency and precision of production;

[0047] γ·|Jj - Jj target | is used to adjust the crystallization rate Jj of the plastic, making it close to the target crystallization rate Jj target , and by minimizing the crystallization rate error, the crystallization process of the plastic is controlled, thereby optimizing the properties of the material and the structural stability of the finished product.

[0048] The human-machine interaction intelligent design optimization method for injection molds includes the following steps:

[0049] S1. First, install a sensor group around the injection molding machine to collect the mold processing data during the injection molding process in real time, and preprocess the collected mold processing data to obtain the mold processing data set;

[0050] S2. Utilize the collected mold processing data set, historical data, and experimental data, and divide them into a training set, a validation set, and a test set to establish a regression analysis model and a machine learning model between the mold processing data and the finished product quality;

[0051] S3. Construct an analysis formula for the mold temperature distribution, an analysis formula for the plastic material change, and an analysis formula for the crystallization relationship, and load the collected mold processing data set, calculate respectively to obtain the temperature distribution T(x, y, z), the plastic material change rate Bh, and the crystallization rate Jj, and perform correlation and summary to generate the comprehensive processing data JG;

[0052] S4. Use a development tool to build a visual interface, define the interface layout and the positions and functions of each control, implement data input, optimization calculation, and result display functions, graphically display the data to achieve dynamic interaction, view the optimization results, and input parameters for optimization adjustment;

[0053] S5. Finally, preset the first processing quality threshold Y1 and the second processing quality threshold Y2, compare them with the obtained comprehensive processing data JG, conduct a comparative evaluation and analysis of the quality of the current injection mold. When an abnormality is identified, further construct a comprehensive optimization formula, calculate to obtain the comprehensive optimization coefficient Yh, and optimize the injection molding machine.

[0054] The present invention provides a human-computer interaction intelligent design optimization method and system for injection molds. It has the following beneficial effects:

[0055] (1) By installing a sensor group around the injection molding machine, the system can collect mold processing data in real time, including the mold surface roughness Ra, thermal conductivity Km, plastic material expansion coefficient Ap, and shear rate Jq. These sensor data are processed by a Kalman filter and smoothing, combined with wavelet transform to remove high-frequency noise, and normalized to ensure the high precision and reliability of the data. Through these precise data collection and processing, the system provides a solid foundation for subsequent processing quality analysis and optimization. Through high-precision data collection and processing technology, the system can provide an accurate mold processing data set, ensuring the data quality in the subsequent analysis and modeling process, thus laying a foundation for the improvement of the finished product quality and production efficiency.

[0056] (2) By using regression analysis models and machine learning models, and combining training sets, validation sets, and test sets, the system deeply analyzes the relationship between mold processing data and finished product quality. By establishing a multiple linear regression model to identify the linear relationship between mold processing data and finished product quality, and using support vector machine SVM and neural network NN models to capture complex non-linear relationships, the system can comprehensively evaluate the impact of mold processing data on finished product quality. By introducing multi-level analysis models, including regression analysis and machine learning models, the system can predict and optimize processing quality from multiple perspectives, providing a more accurate and reliable analysis tool, and improving the prediction ability and optimization effect of the model.

[0057] (3) The system preset a first processing quality threshold Y1 and a second processing quality threshold Y2 through the intelligent analysis and evaluation module, and compared and evaluated them with the obtained comprehensive processing data JG. The system can analyze the processing quality of the current injection mold in real time. Once an anomaly is identified, it triggers the calculation of the comprehensive optimization formula to obtain the comprehensive optimization coefficient Yh, so as to guide the injection molding machine to adjust and optimize parameters. The comprehensive optimization formula considers factors such as temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj, and finds the optimal combination of process parameters through iterative calculation, maximizing the efficiency and quality of the mold processing process. Description of the Drawings

[0058] Figure 1 It is a schematic flow chart of the human-computer interaction intelligent design optimization system for the injection mold of the present invention;

[0059] Figure 2 It is a schematic diagram of the steps of the human-computer interaction intelligent design optimization method for the injection mold of the present invention. Detailed Embodiments

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0061] Embodiment 1

[0062] Please refer to Figure 1 , the present invention provides a human-computer interaction intelligent design optimization system for injection molds. To achieve the above objectives, the present invention is realized through the following technical solutions: including an injection parameter acquisition module, a finished product quality relationship module, a processing analysis algorithm module, a human-computer interaction module, and an intelligent analysis and evaluation module;

[0063] The injection parameter acquisition module uses sensors and monitoring devices to collect the mold processing data during the injection process of the mold in real time, and preprocesses the collected mold processing data to obtain a mold processing data set;

[0064] The finished product quality relationship module is used to utilize the collected mold processing data set, historical data, and experimental data, and divide them into a training set, a validation set, and a test set, and establish a regression analysis model and a machine learning model between the mold processing data and the finished product quality;

[0065] The processing analysis algorithm module is used to construct the analysis formulas for mold temperature distribution, plastic material change, and crystallization relationship, load the collected mold processing data set, and perform separate calculations to obtain the temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj, and conduct associated summarization to generate comprehensive processing data JG;

[0066] The human-computer interaction module is used to use development tools to construct a visual interface, view the optimization results, and input parameters for optimization adjustment;

[0067] The intelligent analysis and evaluation module is used to preset the first processing quality threshold Y1 and the second processing quality threshold Y2 and the obtained comprehensive processing data JG, conduct comparative evaluation and analysis of the quality of the current injection mold. When an anomaly is identified, a comprehensive optimization formula is further constructed to calculate and obtain the comprehensive optimization coefficient Yh to optimize the injection molding machine.

[0068] In this embodiment, the system realizes the comprehensive optimization of the injection molding process by integrating the injection molding parameter acquisition module, finished product quality relationship module, processing analysis algorithm module, human-computer interaction module, and intelligent analysis and evaluation module. Data acquisition and data preprocessing technologies provide high-quality processing data, while regression analysis and machine learning models improve the prediction accuracy of the finished product quality. Compared with traditional empirical adjustment methods, this system provides a more scientific and accurate production process optimization scheme, thereby significantly improving production efficiency and product quality. The processing analysis algorithm module calculates and obtains the temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj, and conducts associated calculations to obtain comprehensive processing data JG, deeply understanding the key factors in the injection molding process. The human-computer interaction module simplifies the operation process through a visual interface, facilitating users to perform optimization adjustments. Compared with traditional single-factor analysis technologies, the multi-dimensional analysis method and intuitive user interface of this system greatly improve the control ability of the production process and the convenience of user operation. The intelligent analysis and evaluation module automatically identifies production anomalies and performs optimization adjustments by setting the first processing quality threshold Y1, the second processing quality threshold Y2, and the comprehensive optimization formula. This intelligent evaluation and optimization method is more efficient than manual inspection, and the ability of real-time monitoring and automatic adjustment significantly improves the efficiency and quality control level of the production process. Compared with the prior art, this system demonstrates significant advantages in optimization strategies and automatic control, providing a more advanced solution for the injection molding industry.

[0069] Embodiment 2

[0070] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The injection molding parameter acquisition module includes a parameter acquisition module and a data preprocessing module;

[0071] The parameter acquisition module is used to install a sensor group around the re-injection molding machine. The sensor group includes a roughness sensor, a thermocouple sensor, an infrared thermometer, a pressure sensor, and a flowmeter, which are used to collect the mold processing data during the re-injection molding process of the mold in real time. The mold processing data includes the mold surface roughness Ra, the thermal conductivity Km, the plastic material expansion coefficient Ap, and the shear rate Jq.

[0072] The data preprocessing module is used to filter the mold processing data collected in real time by using a Kalman filter to smooth the data, and at the same time use wavelet transform to remove the high-frequency noise of the mold processing data collected by the sensors, and perform normalization processing on the mold processing data collected at home.

[0073] In this embodiment, the system significantly improves the accuracy of data acquisition and the efficiency of data processing by integrating a variety of sensors and advanced data processing technologies. The parameter acquisition module obtains key mold processing data in real time and comprehensively monitors the state of the injection molding process. The data preprocessing module uses a Kalman filter and wavelet transform to remove data noise and perform data normalization processing, providing a high-quality data basis for subsequent analysis. This design not only improves the reliability of data acquisition and processing, but also lays a solid foundation for quality control and optimization analysis of the production process.

[0074] Embodiment 3

[0075] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The finished product quality relationship module includes a regression analysis model unit, a machine learning model unit, and a model optimization and verification unit;

[0076] The regression analysis model unit is used to construct a multiple linear regression model, identify the linear relationship between the mold processing data and the finished product quality, analyze the prediction performance and regression coefficients of the model, and transmit the preprocessed mold data to the regression analysis model to evaluate the impact of each parameter on the finished product quality;

[0077] The machine learning model unit is used to select the support vector machine SVM and the multi-layer perceptron, and construct a support vector machine SVM model and a neural network NN model respectively. The support vector machine SVM model is used to capture the non-linear relationship between the mold processing data and the finished product quality, and the neural network NN model is used to capture complex non-linear relationships. Then, the constructed support vector machine SVM model and the neural network NN model are integrated and summarized to obtain a machine learning model. After the collected mold processing data set, historical data, and experimental data are preprocessed, they are transmitted to the machine learning model to evaluate the prediction performance of the support vector machine SVM model and the neural network NN model respectively, and analyze the linear and non-linear relationships between the data;

[0078] The model optimization and validation unit simplifies the model, improves the interpretability and prediction accuracy of the model by using Recursive Feature Elimination (RFE) to identify variables that affect the model's prediction performance and removing irrelevant and redundant variables. Then, it uses K-fold cross-validation to divide the mold processing data into K subsets. Each time, one subset is used for validation, and the remaining K - 1 subsets are used for training. This is repeated K times, and the result is the average of the K validations. At the same time, grid search is used to traverse all hyperparameter combinations to select the combination that optimizes the model performance. After optimizing the machine learning model, it is necessary to validate the machine learning model, evaluate the performance of the machine learning model, and ensure its applicability to actual applications.

[0079] In this embodiment, the system realizes in-depth analysis and optimization of the complex relationship between mold processing data and finished product quality by integrating a regression analysis model unit, a machine learning model unit, and a model optimization and validation unit. The regression analysis model unit uses a multiple linear regression model to conduct a detailed analysis of the linear relationship between mold processing data and finished product quality, clarifies the direct impact of each parameter on the finished product quality, and provides basic data for optimization. The machine learning model unit captures the non-linear relationship between mold processing data and finished product quality through two advanced machine learning techniques, namely Support Vector Machine (SVM) and Neural Network (NN) models, constructs a comprehensive prediction model, and improves the prediction accuracy and robustness through model integration. Finally, the model optimization and validation unit combines Recursive Feature Elimination (RFE) and K-fold cross-validation techniques to optimize the feature selection and hyperparameter adjustment of the model, improving the prediction accuracy and applicability of the model. This multi-level modeling and optimization process not only enhances the prediction ability of the finished product quality but also greatly improves the reliability and practicality of the model, providing strong support for quality control and process improvement in injection molding production.

[0080] Embodiment 4

[0081] This embodiment is an explanatory note based on Embodiment 2. Please refer to Figure 1 , specifically: The processing analysis algorithm module includes a mold temperature analysis algorithm unit, a material change algorithm unit, a crystallization algorithm unit, and a comprehensive analysis algorithm unit;

[0082] The temperature analysis algorithm unit is used to construct a mold temperature distribution analysis formula, and based on the thermal conductivity Km and the mold surface roughness Ra in the preprocessed mold processing dataset, it performs analysis and calculation to obtain the temperature distribution T(x, y, z);

[0083] The temperature distribution T(x, y, z) is calculated and obtained through the following mold temperature distribution analysis formula;

[0084]

[0085] In the formula, T0 represents the initial temperature, which is the temperature of the mold when there is no any heat source and is obtained by direct measurement with an infrared thermometer. Q represents the heat source intensity, which is the heat intensity provided by the heater or cooling system and is determined by calculating the heat power provided by the injection molding machine per unit time. x, y, and z are the horizontal axis, vertical axis, and vertical axis respectively, representing the coordinates of the mold in three-dimensional space. These coordinates are usually related to the geometric shape and specific spatial position of the mold. The surface roughness Ra of the mold affects the heat conduction efficiency of the mold surface, thereby affecting the temperature distribution of the mold. The thermal conductivity Km determines the heat conduction ability of the mold material and affects the internal temperature gradient of the mold. exp represents the exponential function, which is used to describe the decay characteristics of the temperature distribution.

[0086] The material change algorithm unit is used to construct an analysis formula for plastic material change, and based on the plastic material expansion coefficient Ap in the preprocessed mold processing dataset, analyze and calculate to obtain the plastic material change rate Bh;

[0087] The plastic material change rate Bh is obtained by calculating through the following plastic material change analysis formula;

[0088]

[0089] In the formula, V0 represents the initial volume of the plastic. The plastic material expansion coefficient Ap represents the degree of volume expansion of the material when the temperature changes. △T represents the temperature change, which is the change value of the material temperature relative to the initial temperature T0. βm represents the volume change rate coefficient, which represents the change of the material volume with the change rate of temperature. d represents the integration variable, dt represents the variable within the unit time t, and T represents the temperature, which is obtained by measurement with a thermocouple and an infrared thermometer. represents the change rate, which is the change of the temperature T within the unit time t.

[0090] The crystallization algorithm unit is used to construct an analysis formula for crystallization relationship, and based on the shear rate Jq in the preprocessed mold processing dataset, analyze and calculate to obtain the crystallization rate Jj;

[0091] The crystallization rate Jj is obtained by calculating through the following crystallization relationship analysis formula;

[0092]

[0093] In the formula, k1 represents the rate constant, which is the crystallization rate without the action of shear rate. Ea represents the activation energy, which is the energy barrier that needs to be overcome during the crystallization process. R represents the gas constant. Jq0 represents the reference shear rate. n represents the shear rate exponent, which represents the sensitivity of the material to the shear rate. exp represents the exponential function.

[0094] The comprehensive analysis algorithm unit is used to correlate and summarize the obtained temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj to obtain comprehensive processing data JG, and analyze the current processing quality of the injection molding machine.

[0095] The comprehensive processing data JG is obtained through the following algorithm formula;

[0096]

[0097] In the formula, V is the volume integral, covering the entire three-dimensional space, and S represents the overall performance of the system.

[0098] In this embodiment, the system realizes the comprehensive analysis and optimization of the key factors in the injection molding process by integrating the mold temperature analysis algorithm unit, material change algorithm unit, crystallization algorithm unit, and comprehensive analysis algorithm unit. The temperature analysis algorithm unit uses the mold temperature distribution analysis formula to accurately calculate the temperature distribution T(x, y, z) inside the mold through the thermal conductivity Km and the mold surface roughness Ra, providing a quantitative basis for temperature control. The material change algorithm unit constructs a plastic material change analysis formula based on the plastic material expansion coefficient Ap, calculates the material expansion rate Bh of the plastic under temperature change, and reveals the influence of temperature change on the plastic performance. The crystallization algorithm unit calculates the crystallization rate Jj through the crystallization relationship analysis formula in combination with the shear rate Jq, helping to optimize the crystallization process of the plastic. Finally, the comprehensive analysis algorithm unit correlates and calculates the temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj to obtain comprehensive processing data JG, and conducts a comprehensive processing quality assessment. This module not only provides accurate physical process modeling and quantitative analysis for the injection molding process, but also improves the prediction ability of production quality through comprehensive calculation, providing scientific data support for the optimization of the injection molding process.

[0099] Embodiment 5

[0100] This embodiment is an explanatory description based on Embodiment 1, please refer to Figure 1 , specifically: The human-computer interaction module is used to design a preliminary interface using the QtDesigner interface design tool, define the interface layout and the positions and functions of each control, use the PyQt and PyGObject libraries to implement data input, optimization calculation, and result display functions, and then embed the graphics into the GUI through Matplotlib and D3.js to display the data graphically and achieve dynamic interaction.

[0101] In this embodiment, the system designs a preliminary interface using Qt Designer and combines the PyQt and PyGObject libraries to implement data input, optimization calculation, and result display. The human-computer interaction module provides a feature-rich and intuitive user interface. This design enables users to easily input data, adjust optimization parameters, and view calculation results in real-time. Through the application of Matplotlib and D3.js, the graphical display of data is embedded in the GUI, realizing dynamic data visualization and interactive operation functions, significantly enhancing users' ability to understand and analyze data. The human-computer interaction module provides intelligent production optimization and adjustment support through integrated data input, optimization calculation, and result display functions, making the optimization of the production process more efficient and scientific.

[0102] Embodiment 6

[0103] This embodiment is an explanatory description based on Embodiment 4. Please refer to Figure 1 , specifically: The intelligent analysis and evaluation module includes a quality evaluation unit and an optimization algorithm unit;

[0104] The quality evaluation unit is used to preset a first processing quality threshold Y1 and a second processing quality threshold Y2 based on the mean value of the injection mold processing quality, and then compare and evaluate with the obtained comprehensive processing data JG to analyze the current mold processing quality situation. The specific evaluation scheme is as follows;

[0105] When the comprehensive processing data JG > the first processing quality threshold Y1, it indicates that the processing quality of the current injection molding machine is abnormal. At this time, the optimization algorithm is further executed for parameter optimization;

[0106] When the second processing quality threshold Y2 ≤ the comprehensive processing data JG ≤ the first processing quality threshold Y1, it indicates that the processing quality of the injection molding machine is normal at this time;

[0107] When the comprehensive processing data JG < the second processing quality threshold Y2, it indicates that the processing quality of the injection molding machine is abnormal at this time. At this time, the optimization algorithm is further executed for parameter optimization;

[0108] The optimization algorithm unit is used to construct a comprehensive optimization formula, substitute the obtained temperature distribution T(x, y, z), plastic material change rate Bh, and crystallization rate Jj into the algorithm formula for calculation to obtain a comprehensive optimization coefficient Yh to optimize the injection molding machine. Through iterative calculation, the best combination of process parameters that minimizes the optimization target is found, and according to the calculation results, the parameters are adjusted and optimized through the interactive interface;

[0109] The comprehensive optimization coefficient Yh is obtained through the following algorithm formula;

[0110] Yh = min[α·∫ VT(x,y,z) - T target dV + β·Bh - Bh target | + γ·Jj - Jj target ;

[0111] Wherein, α, β, and γ respectively represent the weight coefficients for temperature optimization, volume change control, and crystallization rate control, T target represents the target temperature set during design, Bh target represents the target volume change, Jj target represents the target crystallization rate Jj set during design;

[0112] α·∫ V T(x,y,z) - T target dV is used to optimize the temperature distribution inside the mold, making the processing temperature close to the target temperature T target , and ensuring the heating uniformity of the mold by minimizing the error of the temperature distribution, thereby improving the quality and performance of the injection-molded product;

[0113] β·|Bh - Bh target | is used to control the volume change of the plastic during the injection process, making it close to the designed target volume V target , and ensuring that the size and quality of the finished product meet the design specifications by minimizing the volume change error, thereby improving the production consistency and precision;

[0114] γ·|Jj - Jj target | is used to adjust the crystallization rate Jj of the plastic to make it close to the target crystallization rate Jj target , and controlling the crystallization process of the plastic by minimizing the crystallization rate error, thereby optimizing the properties of the material and the structural stability of the finished product.

[0115] In this embodiment, the system provides scientific quality monitoring and optimization means for the injection molding production process through the efficient combination of a quality assessment unit and an optimization algorithm unit. By setting a first processing quality threshold Y1 and a second processing quality threshold Y2, the quality assessment unit can accurately identify abnormal situations in mold processing quality, and judge the state of the production process based on the comprehensive processing data JG, ensuring that problems are discovered and adjusted in a timely manner during the production process. This data-based automated quality assessment mechanism is more efficient and reliable than traditional manual inspections. The optimization algorithm unit systematically analyzes the temperature distribution T(x, y, z), the plastic material change rate Bh, and the crystallization rate Jj by constructing a comprehensive optimization formula, calculates the comprehensive optimization coefficient Yh, and finds the optimal combination of process parameters through iterative calculations. This algorithm-driven optimization method can not only manage production parameters in a refined manner but also optimize and adjust specific quality problems, thereby effectively improving the processing quality of the mold and enhancing the final performance of the product and the consistency of production. Compared with traditional single-factor adjustment methods, the multi-dimensional analysis and automated optimization means of this module significantly improve the quality control level and production efficiency of the production process, reflecting the advanced technical means of modern manufacturing in production optimization.

[0116] Embodiment 7

[0117] Please refer to Figure 1 and Figure 2 , an intelligent design optimization method for human-computer interaction of injection molds, comprising the following steps:

[0118] S1. First, install a sensor group around the injection molding machine to collect mold processing data during the injection molding process in real time, and preprocess the collected mold processing data to obtain a mold processing data set;

[0119] S2. Use the collected mold processing data set, historical data, and experimental data, and divide them into a training set, a validation set, and a test set to establish a regression analysis model and a machine learning model between the mold processing data and the finished product quality;

[0120] S3. Construct a mold temperature distribution analysis formula, a plastic material change analysis formula, and a crystallization relationship analysis formula, load the collected mold processing data set, calculate respectively to obtain the temperature distribution T(x, y, z), the plastic material change rate Bh, and the crystallization rate Jj, and perform relevant association and summary to generate comprehensive processing data JG;

[0121] S4. Use a development tool to construct a visualization interface, define the interface layout and the positions and functions of each control, implement data input, optimization calculation, and result display functions, graphically display the data to achieve dynamic interaction, view the optimization results, and input parameters for optimization and adjustment;

[0122] S5. Finally, preset the first processing quality threshold Y1 and the second processing quality threshold Y2, and compare and evaluate them with the obtained comprehensive processing data JG to analyze the quality of the current injection mold. When an abnormality is identified, further construct a comprehensive optimization formula, calculate to obtain the comprehensive optimization coefficient Yh, and optimize the injection molding machine.

[0123] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. Injection mold human-computer interaction intelligent design optimization system, characterized by: It includes injection molding parameter acquisition module, finished product quality relationship module, processing analysis algorithm module, human-computer interaction module and intelligent analysis and evaluation module; The injection molding parameter acquisition module uses a sensor group to collect mold processing data in the mold re-injection process in real time, and pre-processes the collected mold processing data to obtain a mold processing data set; The finished product quality relationship module is used to utilize the collected mold processing data set, historical data and experimental data, and divide them into a training set, a validation set and a test set to establish a regression analysis model and a machine learning model between the mold processing data and the finished product quality; The processing analysis algorithm module is used to construct a mold temperature distribution analysis formula, a plastic material change analysis formula and a crystallization relationship analysis formula, and load the collected mold processing data set to calculate and obtain the temperature distribution T (x, y, z), the plastic material change rate Bh and the crystallization rate Jj, and then correlate and summarize them to generate comprehensive processing data JG; The processing analysis algorithm module includes a mold temperature analysis algorithm unit, a material change algorithm unit, a crystallization algorithm unit and a comprehensive analysis algorithm unit; The temperature analysis algorithm unit is used to construct a mold temperature distribution analysis formula, and to analyze and calculate the temperature distribution T (x, y, z) according to the thermal conductivity Km and the mold surface roughness Ra in the mold processing data set after preprocessing; The temperature distribution T(x, y, z) is calculated and obtained by the following mold temperature distribution analysis formula; Wherein, T0 represents the initial temperature, which is directly measured by an infrared thermometer, Q represents the intensity of the heat source, x, y and z are the horizontal axis, vertical axis and vertical axis respectively, representing the coordinates of the mold in three-dimensional space, and exp represents the exponential function; The material change algorithm unit is used to construct a plastic material change analysis formula, and to analyze and calculate the plastic material change rate Bh based on the plastic material expansion coefficient Ap in the preprocessed mold processing data set; The plastic material change rate Bh is calculated and obtained by the following plastic material change analysis formula: In the formula, V0 represents the initial volume of the plastic, △T represents the temperature change, which represents the change value of the material temperature relative to the initial temperature T0, βm represents the volume change rate coefficient, d represents the integral variable, dt represents the variable per unit time t, and T represents the temperature; The crystallization algorithm unit is used to construct a crystallization relationship analysis formula, and analyze and calculate the crystallization rate Jj according to the shear rate Jq in the mold processing data set after preprocessing; The crystallization rate Jj is calculated by the following crystallization relationship analysis formula: In the formula, k1 represents the rate constant, Ea represents the activation energy, R represents the gas constant, Jq0 represents the base shear rate, n represents the shear rate exponent, and exp represents the exponential function; The comprehensive analysis algorithm unit is used to correlate and summarize the obtained temperature distribution T (x, y, z), plastic material change rate Bh and crystallization rate Jj to obtain comprehensive processing data JG and analyze the current injection molding machine processing quality; The comprehensive processing data JG is obtained by calculation through the following algorithm formula; In the formula, V is the volume integral, covering the entire three-dimensional space, and S represents the overall performance of the system; The human-computer interaction module is used to use development tools to build a visual interface, view optimization results and input parameters for optimization adjustment; The intelligent analysis and evaluation module is used to preset the first processing quality threshold value Y1 and the second processing quality threshold value Y2 and the obtained comprehensive processing data JG, and compare and evaluate the quality of the current injection mold. When an abnormality is identified, a comprehensive optimization formula is further constructed to calculate and obtain the comprehensive optimization coefficient Yh to optimize the injection molding machine; The intelligent analysis and evaluation module includes a quality evaluation unit and an optimization algorithm unit; The quality assessment unit is used to preset a first processing quality threshold value Y1 and a second processing quality threshold value Y2 based on the mean value of the injection mold processing quality, and then compare and evaluate with the acquired comprehensive processing data JG to analyze the current mold processing quality. The specific assessment scheme is as follows; When the comprehensive processing data JG> the first processing quality threshold Y1, it means that the processing quality of the current injection molding machine is abnormal, and the optimization algorithm is further executed to optimize the parameters; When the second processing quality threshold Y2≤comprehensive processing data JG≤first processing quality threshold Y1, it indicates that the processing quality of the injection molding machine is normal; When the comprehensive processing data JG is less than the second processing quality threshold value Y2, it indicates that the processing quality of the injection molding machine is abnormal. Then, the optimization algorithm is further executed to optimize the parameters. The optimization algorithm unit is used to construct a comprehensive optimization formula, substitute the obtained temperature distribution T(x, y, z), plastic material change rate Bh and crystallization rate Jj into the algorithm formula, calculate and obtain the comprehensive optimization coefficient Yh to optimize the injection molding machine, and adjust and optimize the parameters through the interactive interface according to the calculation results; The comprehensive optimization coefficient Yh is obtained by the following algorithm formula; Where α, β and γ represent the weight coefficients of temperature optimization, volume change control and crystallization rate control, respectively. target Indicates the target temperature set during design, Bh target represents the target volume change, Jj target It represents the target crystallization rate Jj set during design; α·∫ V T(x,y,z)-T target dV is used to optimize the temperature distribution inside the mold so that the processing temperature is close to the target temperature T target ; β·|Bh-Bh target |Used to control the volume change of plastic during the injection molding process, so that it is close to the designed target volume V after control target ; γ·Jj-Jj target |Used to adjust the crystallization rate Jj of the plastic to make it close to the target crystallization rate Jj target .

2. The injection mold human-computer interaction intelligent design optimization system according to claim 1, characterized in that: The injection molding parameter acquisition module includes a parameter acquisition module and a data preprocessing module; The parameter acquisition module is used to install a sensor group around the re-injection molding machine, and the sensor group includes a roughness sensor, a thermocouple sensor, an infrared thermometer, a pressure sensor and a flow meter to collect mold processing data of the mold re-injection molding process in real time. The mold processing data includes mold surface roughness Ra, thermal conductivity Km, plastic material expansion coefficient Ap and shear rate Jq; The data preprocessing module is used to filter the mold processing data collected in real time by using a Kalman filter to smooth the data, and at the same time use a wavelet transform to remove the high-frequency noise of the mold processing data collected by the sensor, and then normalize the collected mold processing data.

3. The injection mold human-computer interaction intelligent design optimization system according to claim 2, characterized in that: The finished product quality relationship module includes a regression analysis model unit, a machine learning model unit and a model optimization and verification unit; The regression analysis model unit is used to construct a multivariate linear regression model, identify the linear relationship between mold processing data and finished product quality, analyze the prediction performance and regression coefficient of the model, and transmit the preprocessed mold data to the regression analysis model to evaluate the impact of each parameter on the quality of the finished product; The machine learning model unit is used to select a support vector machine SVM and a multi-layer perceptron, and respectively construct a support vector machine SVM model and a neural network NN model. The support vector machine SVM model is used to capture the nonlinear relationship between mold processing data and finished product quality, and the neural network NN model is used to capture complex nonlinear relationships. The constructed support vector machine SVM model and the neural network NN model are integrated and summarized to obtain a machine learning model. The collected mold processing data set, historical data and experimental data are pre-processed and then transmitted to the machine learning model. The prediction performance of the support vector machine SVM model and the neural network NN model are respectively evaluated, and the linear and nonlinear relationship between the data is analyzed; The model optimization and verification unit uses recursive feature elimination (RFE) to identify variables that affect the model prediction performance and eliminates irrelevant and redundant variables. K-fold cross validation is then used to divide the mold processing data into K subsets, one of the subsets is used for verification each time, and the remaining K-1 subsets are used for training. This is repeated K times, and the result is the average of the K verifications. At the same time, grid search is used to traverse all hyperparameter combinations to select a combination that optimizes the model performance. After the machine learning model is optimized, it is necessary to verify the machine learning model, evaluate the performance of the machine learning model, and ensure that it is suitable for practical applications.

4. The injection mold human-computer interactive intelligent design optimization system according to claim 1, characterized in that: The human-computer interaction module is used to design a preliminary interface using the QtDesigner interface design tool, define the interface layout and the position and function of each control, use the PyQt and PyGObject libraries to implement data input, optimization calculation and result display functions, and then embed graphics into the GUI through Matplotlib and D3.js to display data graphically and realize dynamic interaction.

5. The method for intelligent design optimization of injection molds by human-computer interaction is applied to the system for intelligent design optimization of injection molds by human-computer interaction according to any one of claims 1 to 4, characterized in that: The following steps are involved: S1. First, a sensor group is installed around the injection molding machine to collect mold processing data of the mold during the injection molding process in real time, and the collected mold processing data is preprocessed to obtain a mold processing data set; S2. Using the collected mold processing data set, historical data and experimental data, and dividing them into training set, validation set and test set, a regression analysis model and a machine learning model are established between the mold processing data and the finished product quality; S3, construct mold temperature distribution analysis formula, plastic material change analysis formula and crystallization relationship analysis formula, and load the collected mold processing data set to calculate and obtain temperature distribution T (x, y, z), plastic material change rate Bh and crystallization rate Jj, and correlate and summarize the production comprehensive processing data JG; S4. Use development tools to build a visual interface, define the interface layout and the location and function of each control, implement data input, optimization calculation and result display functions, display data graphically to achieve dynamic interaction, view optimization results and input parameters for optimization adjustment; S5. Finally, the first processing quality threshold value Y1 and the second processing quality threshold value Y2 are preset and compared with the obtained comprehensive processing data JG to evaluate and analyze the quality of the current injection mold. When an abnormality is identified, a comprehensive optimization formula is further constructed to calculate and obtain the comprehensive optimization coefficient Yh to optimize the injection molding machine.

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