Intelligent temperature control system and method for casting system

Abstract

The invention belongs to but is not limited to the technical field of low-pressure casting processes, and discloses an intelligent temperature control system and method for a casting system, and integrated sensing modules are distributed according to feature areas of a casting mold and are used for monitoring temperature changes of different positions in the mold in real time; the data acquisition and processing module receives the data from the integrated sensing module, and preprocesses and stores the data; the central control module receives data provided by the data acquisition and processing module, and dynamically adjusts casting equipment and a temperature intelligent control system execution module according to preset casting process parameters and real-time data; and the execution module comprises a heating device and a cooling device and is used for adjusting process parameters of a heating channel and process parameters of a cooling channel according to an instruction of the central control system and intelligently regulating and controlling the temperature of the mold. According to the method, the temperature control accuracy can be improved, the temperature can be adjusted in time, and the purposes of optimizing the casting process, reducing defects and ensuring high-quality manufacturing of the aluminum alloy hub are achieved.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of low-pressure casting process, and in particular relates to an intelligent temperature control system and method for a casting system. Background Art

[0002] Lightweighting is one of the important strategies for energy conservation and emission reduction of fuel vehicles and efficiency improvement of new energy vehicles. Among them, the use of aluminum alloy materials to replace traditional steel materials is a key means to achieve lightweighting of automobiles. The low-pressure casting process has become the mainstream process for producing automotive aluminum alloy castings due to its stable filling, good shrinkage compensation effect and high degree of automation. In the low-pressure casting process, the influence of mold temperature on the quality of castings is particularly important. The alloy melt is in direct contact with the mold during the filling and solidification process, and is cooled and solidified through heat exchange. Therefore, the temperature distribution of the mold directly determines the solidification sequence and cooling rate of the alloy melt, which has a key influence on the solidification structure and performance of the casting. By controlling the mold temperature within a reasonable range through a suitable cooling process, it can ensure that the alloy solidifies rapidly in the expected order, thereby significantly reducing casting defects such as shrinkage cavities and shrinkage, and shortening the production cycle, achieving a dual improvement in casting quality and production efficiency.

[0003] At present, in the low-pressure casting production of aluminum alloy castings, the temperature control of the mold mainly adopts an open-loop control method. Usually, the mold temperature is not detected and monitored in real time, but relies on on-site technicians to directly adjust the cooling process parameters according to the casting defects. Although this method is easy to operate, it has obvious defects: the adjustment of the cooling process is greatly affected by the experience and subjective judgment of the technicians, lacks scientific basis, resulting in unstable process improvement effects and difficulty in achieving automatic and intelligent control of mold temperature. With the advancement of the lightweight process of automobiles and the increasing complexity of casting structures, it is more challenging to achieve reasonable control of mold temperature. Traditional temperature control methods can no longer meet modern production needs.

[0004] Temperature measurement and control of low-pressure die-casting aluminum alloy wheel hub molds is a key task in the manufacturing field. It involves obtaining and analyzing temperature data from the mold operation status to achieve precise temperature control. Effective temperature measurement and control methods can help manufacturing companies reduce production costs, improve product quality and increase production efficiency. In the low-pressure casting process, the temperature of the mold has a direct impact on the molding quality and production stability of the aluminum alloy wheel hub. Therefore, accurate temperature measurement and timely temperature adjustment are particularly important. Existing temperature control methods have the problems of poor accuracy and untimely temperature adjustment. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a casting system temperature intelligent control system and method.

[0006] The present invention is implemented as follows: a casting system temperature intelligent control system, comprising:

[0007] Integrated sensing module: It consists of multiple temperature sensors, which are set according to the characteristic area distribution of the casting mold and are used to monitor the temperature changes at different positions in the mold in real time; the sensors are connected to the central control system wirelessly or wiredly;

[0008] Data acquisition and processing module: used to receive data from the integrated sensor module, and perform preprocessing and storage; at the same time, this module also has data analysis functions, which can predict potential problems in the casting process based on historical data and current data, and issue an early warning when it exceeds the set range;

[0009] Central control module: This module is the core of the system. It is used to receive data provided by the data acquisition and processing module, and dynamically adjust the casting equipment and temperature intelligent control system execution module according to the preset casting process parameters and real-time data. The central control system also has remote monitoring and fault diagnosis functions, which can display various parameters of the casting process in real time and automatically diagnose and handle faults when they occur.

[0010] Execution module: includes a heating device and a cooling device, which are used to adjust the process parameters of the heating channel and the cooling channel according to the instructions of the central control system, and intelligently control the mold temperature.

[0011] Furthermore, the system uniquely identifies and stores all data of each casting process (including temperature, time, process parameters, etc.) in a database through a data storage and tracing method; when necessary, the casting process data of the product can be quickly traced and queried by entering the batch number or unique identification of the cast product, providing strong support for product quality control and problem analysis.

[0012] Furthermore, the temperature sensor of the integrated sensing module is arranged in the characteristic area of ​​the mold based on the structural characteristics of the casting; a random forest model is established according to the temperature measurement results of the temperature sensor and the casting quality inspection results, and recursive feature elimination is performed to determine the correlation between the temperature measurement data of each temperature sensor and the casting quality, thereby optimizing the number of temperature sensors, obtaining the temperature measurement positions of the temperature sensors corresponding to various defects of the casting, and screening the temperature sensors;

[0013] The data acquisition and processing module analyzes the screened temperature data of the temperature sensor measuring points, the cooling process parameters and the corresponding casting quality, constructs the relationship between the cooling process parameters, the starting temperature of each mold temperature sensor and the casting quality through the gradient boosting decision tree model, and controls the casting system temperature through the central control module based on the constructed relationship.

[0014] Further, the acquisition of the mold feature area includes:

[0015] Collect historical process data of castings and casting molds, and determine the characteristic areas of casting molds based on the shrinkage volume results of simulated castings based on X-ray detection and historical process data;

[0016] The historical process data of the casting and the casting mold are collected, including: cooling process plan data and casting quality data;

[0017] The cooling process plan data includes cooling channel configuration, switching time and / or coolant flow rate at each production stage;

[0018] The casting quality data includes casting quality indicators, and the casting quality indicators include defect type, defect location, characteristic area yield strength and / or tensile strength.

[0019] Furthermore, the random forest model is established based on the temperature measurement results of the temperature sensors and the casting quality inspection results, and recursive feature elimination is performed to determine the correlation between the temperature measurement data of each temperature sensor and the casting quality, thereby optimizing the number of temperature sensors, obtaining the temperature measurement positions of the temperature sensors corresponding to various defects of the casting, and screening the temperature sensors, including:

[0020] A sensitivity test was conducted on the mold to select the temperature data that meets the mold closing time as the independent variable and the casting quality as the dependent variable for calculation. A random forest model was established and the importance of each feature was calculated. According to the feature importance, temperature sensors that reflect the casting quality were selected as standard temperature sensors in the top mold, bottom mold and side mold of the mold.

[0021] Furthermore, the random forest model is established based on the temperature measurement results of the temperature sensor and the casting quality inspection results, and recursive feature elimination is performed, including:

[0022] Step 1: Use the acquired features to train the random forest model and calculate the weight or coefficient of each feature of the random forest model;

[0023] Step 2: Sort the features according to their weights or coefficients;

[0024] Step 3: Delete one or more features with the smallest weight or coefficient in the ranking, and retrain the random forest model with the remaining features;

[0025] Step 4: Repeat steps 2 and 3 until the desired number of features is reached or no further features can be removed.

[0026] Furthermore, the temperature sensor temperature feature importance calculation includes:

[0027] Calculate the node Gini impurity;

[0028] Calculate the contribution of each feature;

[0029] Cumulative feature contribution;

[0030] Calculate the feature importance of all trees in the random forest and average the feature importance values ​​across all trees to get the final importance of the feature.

[0031] Furthermore, the temperature data of the selected temperature sensor measuring points, cooling process parameters and corresponding casting quality are analyzed, and the relationship between cooling process parameters, the starting temperature of each mold temperature sensor and casting quality is constructed through the gradient boosting decision tree model. The casting system temperature is controlled based on the constructed relationship, including:

[0032] Obtain the selected temperature data of the temperature sensor measuring points, cooling process parameters and parameter data sets corresponding to the casting quality;

[0033] Under the condition of ensuring the quality of castings, the relationship model between the initial temperature of the mold feature area and the cooling process is output based on the parameter data set to predict the performance strength of the wheel hub;

[0034] The relationship model is optimized and iterated according to the difference between the casting quality and performance strength and the actual value;

[0035] The relationship model is evaluated based on the mean square error and the coefficient of determination. When the prediction success rate is greater than the set value, the optimization iteration is stopped.

[0036] Furthermore, the cooling process scheme data and casting quality data are used to train a gradient boosting decision tree model and dynamically update the mold cooling process scheme and control rules;

[0037] Use the gradient boosting decision tree model to predict cooling process solutions that meet the inspection standards;

[0038] The learning rate of the gradient boosting decision tree model is 0.01-0.3, and the tree depth is 3-10.

[0039] Another object of the present invention is to provide a casting system temperature intelligent control method of a casting system temperature intelligent control system, comprising the following steps:

[0040] Step 1: Determine the mold feature area and arrange the temperature sensor;

[0041] Step 2: Collect historical process data of target products to establish a database;

[0042] Step 3: Establish a temperature sensor weight coefficient model and optimize the quantity;

[0043] Step 4: Establish the cooling process, mold initial temperature and casting quality model.

[0044] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0045] First, the intelligent temperature control method of the casting system provided by the present invention improves the accuracy of temperature control and adjusts the temperature in time by setting a temperature sensor based on a random forest model and a gradient boosting decision tree model, so as to achieve the purpose of optimizing the casting process, reducing defects and ensuring high-quality manufacturing of aluminum alloy wheels.

[0046] Due to the adoption of the above technical solution, the present invention has the following advantages compared with the existing method:

[0047] 1. The on-site production data is combined with simulation to determine the mold feature area according to the X-ray detection of wheel hub defects. Thermocouples are set in the mold feature area for temperature measurement according to the structural characteristics of the wheel hub. A random forest model is established based on the temperature measurement results and casting quality inspection, and recursive feature elimination is performed to determine the correlation between each thermocouple temperature measurement data and the wheel hub quality, optimize the number of thermocouples, and find the thermocouple temperature measurement positions corresponding to various casting defects.

[0048] 2. A gradient boosting decision tree model is established based on cooling process parameters, mold temperature, casting quality and characteristic area performance, and the casting quality is judged based on the cooling process and mold temperature. Compared with manual control, it is possible to find out whether the casting has defects during the production process and then adjust it, change the cooling parameters, control the mold temperature, and achieve automation and intelligence.

[0049] 3. Predict casting quality based on mold temperature range, abandon the time delay of traditional cooling process. When defects occur in castings, on-site workers change cooling parameters based on experience, which cannot improve mold temperature in a short time. Control according to mold temperature range. In each casting production process, the opening and closing time of cooling process can be determined according to thermocouple temperature, and controlled within a reasonable temperature range to ensure casting quality.

[0050] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0051] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0052] The technical solution of the present invention can significantly reduce production costs and enhance market competitiveness by optimizing the casting process and improving product quality. Specifically, by accurately controlling the mold temperature and cooling parameters, the generation of casting defects can be reduced, thereby reducing the scrap rate and rework rate. This not only directly saves material costs and labor costs, but also improves production efficiency and product delivery speed. In addition, high-quality products can win the trust and orders of more customers and further expand market share. Therefore, the technical solution of the present invention has huge commercial value and broad market prospects.

[0053] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0054] The technical solution of the present invention introduces advanced machine learning algorithms (random forest model and gradient boosting decision tree model) in the field of casting, and realizes intelligent control of the casting process. This innovation not only improves the precision and efficiency of the casting process, but also provides a new solution to solve many problems existing in the traditional casting process. At present, the casting industry at home and abroad generally faces problems such as unstable product quality and low production efficiency, and the technical solution of the present invention is designed to address these problems. Therefore, its appearance undoubtedly fills the technical gap in the industry at home and abroad, and promotes the technological progress and development of the entire industry.

[0055] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0056] The technical solution of the present invention successfully solves long-standing technical problems such as difficult mold temperature control and untimely adjustment of cooling process during casting. In traditional casting processes, due to the lack of effective real-time monitoring and control means, it is often difficult to accurately control the mold temperature and cooling rate, resulting in unstable casting quality. The present invention realizes real-time control of mold temperature and cooling process by setting up thermocouples for precise temperature measurement, and combining random forest model and gradient boosting decision tree model for data analysis and prediction. This intelligent control method not only improves the quality stability of castings, but also shortens the production cycle and reduces production costs.

[0057] (4) The technical solution of the present invention overcomes technical prejudice:

[0058] The technical solution of the present invention breaks some technical prejudices and limitations in traditional casting processes. For example, the traditional concept is that the adjustment of the cooling process needs to rely on the workers' experience and judgment, while the present invention realizes automatic and intelligent control by introducing machine learning algorithms; for another example, the traditional concept is that it is difficult to accurately control the mold temperature to each characteristic area, while the present invention realizes precise control of the mold temperature by setting thermocouples and establishing models. These innovations not only overcome the limitations of traditional technical prejudices, but also open up new paths for the development of casting processes.

[0059] Third, the lightweight anti-occlusion UAV target tracking method of the present invention has mainly solved the problems of computational burden, occlusion influence and lack of real-time performance in the actual application of UAVs in the traditional target tracking technology in industrial applications, and has achieved significant technical progress. The specific performance is as follows:

[0060] 1. Existing technical problems solved

[0061] Excessive computing resource requirements: Existing target tracking algorithms usually rely on complex deep neural network models (such as ResNet, etc.), which require powerful computing resources and equipment support, making it difficult for devices with limited resources such as drones to run in real time. This invention significantly reduces the number of model parameters and the amount of calculation by replacing the backbone network of SiamRPN++ with the AlexNet network, allowing drones to track targets efficiently and in real time with limited computing resources, thereby solving the problem of excessive computing burden in practical applications.

[0062] Tracking failure under occlusion: Traditional target tracking methods are prone to tracking failure or mistracking when the target is occluded, and cannot meet the application requirements in complex environments. The present invention introduces weighted neighborhood indicators and extended Kalman filter algorithms. When occlusion or tracking failure occurs, it can automatically switch to Kalman filtering for prediction compensation, effectively solving the problem of tracking failure under occlusion and enhancing the robustness of the method in complex environments.

[0063] Poor real-time performance: The existing algorithms have a slow processing speed on embedded devices and are difficult to meet the real-time requirements of drone target tracking. This invention significantly improves the tracking speed and real-time performance through a lightweight network structure and simplified indicator calculation, ensuring the real-time response capability of drones in dynamic scenes and solving the problem of insufficient real-time performance.

[0064] 2. Significant technological progress

[0065] Lightweight design improves applicability: By adopting a lightweight AlexNet network, the computing and storage burden is reduced, enabling resource-constrained devices such as drones to efficiently perform target tracking tasks. This design significantly improves the applicability of the algorithm in environments with limited computing power and meets the industry's demand for small, lightweight drones.

[0066] Enhanced anti-occlusion capability: The proposed solution combining weighted neighborhood index and extended Kalman filter can automatically switch to Kalman filter prediction when the target is partially occluded or tracking fails, making the tracking process more robust and enhancing the tracking continuity of the drone in complex scenes. This improvement in anti-occlusion capability enables drones to perform better when used in rescue and monitoring scenarios, reducing tracking failures caused by occlusion.

[0067] Double improvement in real-time performance and accuracy: The present invention achieves a balance between computational efficiency and tracking accuracy. The average weighted neighborhood index and threshold determination mechanism effectively reduce unnecessary computational work, while the extended Kalman filter prediction improves tracking accuracy. This double improvement ensures the stability of the drone in highly dynamic scenes, improves the real-time performance and accuracy of tracking, and is suitable for various real-time application scenarios.

[0068] Adapt to complex and changing application scenarios: Due to its anti-occlusion and lightweight design, the present invention is not only suitable for drone applications, but can also be expanded to other mobile devices or small robots for monitoring, environmental surveys, logistics distribution and other scenarios. In these fields, the target tracking solution of the present invention can achieve robust tracking in complex dynamic environments, meeting the industry's needs for diversified scenarios.

[0069] In summary, the lightweight anti-occlusion UAV target tracking method of the present invention has made significant progress in solving the high computing resource requirements, the large impact of occlusion problems and the lack of real-time performance, etc. It provides reliable technical guarantees and broader application prospects for UAV target tracking in complex and dynamic environments, and has important industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a structural diagram of a casting system temperature intelligent control system provided by an embodiment of the present invention;

[0071] Figure 2 A schematic diagram of the specific positions of the mold thermocouple temperature measurement position, cooling channel and heating channel on the mold provided in the embodiment of the present disclosure;

[0072] Figure 3 A schematic diagram of thermocouple weight coefficients and feature importance ranking based on random forest provided in an embodiment of the present disclosure;

[0073] Figure 4A schematic diagram of a method for constructing a gradient boosting decision tree model provided in an embodiment of the present disclosure;

[0074] Figure 5 A schematic diagram of the mold temperature measurement results of the actual production of castings after optimizing the process parameters provided in the embodiment of the present disclosure;

[0075] Figure 6a and Figure 6b An X-ray inspection image of the wheel core of a casting actually produced after optimizing the process parameters provided in the embodiment of the present disclosure;

[0076] Figure 7 It is a flow chart of the casting system temperature intelligent control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] like Figure 1 As shown, an embodiment of the present invention provides a casting system temperature intelligent control system, comprising:

[0079] Integrated sensing module: It consists of multiple temperature sensors, which are set according to the characteristic area distribution of the casting mold and are used to monitor the temperature changes at different positions in the mold in real time; the sensors are connected to the central control system wirelessly or wiredly;

[0080] Data acquisition and processing module: used to receive data from the integrated sensor module, and perform preprocessing and storage; at the same time, this module also has data analysis functions, which can predict potential problems in the casting process based on historical data and current data, and issue an early warning when it exceeds the set range;

[0081] Central control module: This module is the core of the system. It is used to receive data provided by the data acquisition and processing module, and dynamically adjust the casting equipment and temperature intelligent control system execution module according to the preset casting process parameters and real-time data. The central control system also has remote monitoring and fault diagnosis functions, which can display various parameters of the casting process in real time and automatically diagnose and handle faults when they occur.

[0082] Execution module: includes a heating device and a cooling device, which are used to adjust the process parameters of the heating channel and the cooling channel according to the instructions of the central control system, and intelligently control the mold temperature.

[0083] The working principle of the casting system temperature intelligent control system of the present invention is as follows:

[0084] First, the integrated sensing module uses multiple temperature sensors distributed in different characteristic areas of the mold to monitor the temperature of each position in the mold in real time. These temperature sensors are reasonably arranged according to the regional characteristics of the mold to ensure that the temperature changes of each part of the mold can be fully and accurately obtained. The monitored temperature data is transmitted to the central control system via wireless or wired methods to ensure the real-time and accuracy of the data.

[0085] Next, the data acquisition and processing module is responsible for receiving the temperature data from the integrated sensor module and performing preprocessing and storage operations. This module can not only perform basic processing on the temperature data, but also has the ability to analyze data. By analyzing historical data and current data, it can predict temperature anomalies or other potential problems that may occur during the casting process. Once the temperature exceeds the preset safety range, the system will automatically issue a warning signal for early intervention.

[0086] Then, as the core of the system, the central control module receives the temperature information from the data acquisition and processing module, and compares and analyzes it with the preset casting process parameters. On this basis, the central control module will automatically adjust the process parameters and temperature control scheme of the casting equipment according to the real-time monitoring data to keep the temperature within the ideal range. In addition, the central control system has remote monitoring and fault diagnosis functions, which can display the key parameters of the casting process in real time. When the system detects a fault, it can automatically diagnose and process it to ensure the smooth progress of the production process.

[0087] Finally, the execution module includes a heating device and a cooling device. According to the instructions issued by the central control system, the working parameters of the heating and cooling channels are adjusted to accurately control the mold temperature. This intelligent regulation can automatically increase or decrease the temperature according to the different position requirements of the mold, ensure the quality of the casting, and reduce the defects caused by temperature fluctuations during the casting process, ultimately realizing intelligent and precise control of the casting process.

[0088] Furthermore, the system uniquely identifies and stores all data of each casting process (including temperature, time, process parameters, etc.) in a database through a data storage and tracing method; when necessary, the casting process data of the product can be quickly traced and queried by entering the batch number or unique identification of the cast product, providing strong support for product quality control and problem analysis.

[0089] Furthermore, the temperature sensor of the integrated sensing module is arranged in the characteristic area of ​​the mold based on the structural characteristics of the casting; a random forest model is established according to the temperature measurement results of the temperature sensor and the casting quality inspection results, and recursive feature elimination is performed to determine the correlation between the temperature measurement data of each temperature sensor and the casting quality, thereby optimizing the number of temperature sensors, obtaining the temperature measurement positions of the temperature sensors corresponding to various defects of the casting, and screening the temperature sensors;

[0090] The data acquisition and processing module analyzes the screened temperature data of the temperature sensor measuring points, the cooling process parameters and the corresponding casting quality, constructs the relationship between the cooling process parameters, the starting temperature of each mold temperature sensor and the casting quality through the gradient boosting decision tree model, and controls the casting system temperature through the central control module based on the constructed relationship.

[0091] Further, the acquisition of the mold feature area includes:

[0092] Collect historical process data of castings and casting molds, and determine the characteristic areas of casting molds based on the shrinkage volume results of simulated castings based on X-ray detection and historical process data;

[0093] The historical process data of the casting and the casting mold are collected, including: cooling process plan data and casting quality data;

[0094] The cooling process plan data includes cooling channel configuration, switching time and / or coolant flow rate at each production stage;

[0095] The casting quality data includes casting quality indicators, and the casting quality indicators include defect type, defect location, characteristic area yield strength and / or tensile strength.

[0096] Furthermore, the random forest model is established based on the temperature measurement results of the temperature sensors and the casting quality inspection results, and recursive feature elimination is performed to determine the correlation between the temperature measurement data of each temperature sensor and the casting quality, thereby optimizing the number of temperature sensors, obtaining the temperature measurement positions of the temperature sensors corresponding to various defects of the casting, and screening the temperature sensors, including:

[0097] A sensitivity test was conducted on the mold to select the temperature data that meets the mold closing time as the independent variable and the casting quality as the dependent variable for calculation. A random forest model was established and the importance of each feature was calculated. According to the feature importance, temperature sensors that reflect the casting quality were selected as standard temperature sensors in the top mold, bottom mold and side mold of the mold.

[0098] Furthermore, the random forest model is established based on the temperature measurement results of the temperature sensor and the casting quality inspection results, and recursive feature elimination is performed, including:

[0099] Step 1: Use the acquired features to train the random forest model and calculate the weight or coefficient of each feature of the random forest model;

[0100] Step 2: Sort the features according to their weights or coefficients;

[0101] Step 3: Delete one or more features with the smallest weight or coefficient in the ranking and retrain the random forest model with the remaining features;

[0102] Step 4: Repeat steps 2 and 3 until the desired number of features is reached or no further features can be removed.

[0103] Furthermore, the temperature sensor temperature feature importance calculation includes:

[0104] Calculate the node Gini impurity;

[0105] Calculate the contribution of each feature;

[0106] Cumulative feature contribution;

[0107] Calculate the feature importance of all trees in the random forest and average the feature importance values ​​across all trees to get the final importance of the feature.

[0108] Furthermore, the temperature data of the selected temperature sensor measuring points, cooling process parameters and corresponding casting quality are analyzed, and the relationship between cooling process parameters, the starting temperature of each mold temperature sensor and casting quality is constructed through the gradient boosting decision tree model. The casting system temperature is controlled based on the constructed relationship, including:

[0109] Obtain the selected temperature data of the temperature sensor measuring points, cooling process parameters and parameter data sets corresponding to the casting quality;

[0110] Under the condition of ensuring the quality of castings, the relationship model between the initial temperature of the mold feature area and the cooling process is output based on the parameter data set to predict the performance strength of the wheel hub;

[0111] The relationship model is optimized and iterated according to the difference between the casting quality and performance strength and the actual value;

[0112] The relationship model is evaluated based on the mean square error and the coefficient of determination. When the prediction success rate is greater than the set value, the optimization iteration is stopped.

[0113] Furthermore, the cooling process scheme data and casting quality data are used to train a gradient boosting decision tree model and dynamically update the mold cooling process scheme and control rules;

[0114] Use the gradient boosting decision tree model to predict cooling process solutions that meet the inspection standards;

[0115] The learning rate of the gradient boosting decision tree model is 0.01-0.3, and the tree depth is 3-10.

[0116] Another object of the present invention is to provide a casting system temperature intelligent control method of a casting system temperature intelligent control system, comprising the following steps:

[0117] Step 1: Determine the mold feature area and arrange the thermocouples;

[0118] Step 2: Collect historical process data of target products to establish a database;

[0119] Step 3, establishing a thermocouple weight coefficient model and optimizing the quantity;

[0120] Step 4: Establish the cooling process, mold initial temperature and casting quality model.

[0121] In a specific application scenario, take the wheel hub as an example:

[0122] Step 1: Determine the mold feature area and arrange the thermocouples.

[0123] First, it is necessary to collect the historical process data of the target product and mold. These data include numerical simulation results and trial production results. The characteristic area of ​​the casting mold is determined based on the X-ray detection and simulated wheel shrinkage volume results.

[0124] Next, a detailed database is established based on the collected process data. The database should include the entire production process of each product, covering the following key contents:

[0125] Cooling process plan data: record detailed information such as cooling channel configuration, switching time, coolant flow rate, etc. at each production stage.

[0126] Casting quality data: record the corresponding casting quality indicators, such as defect type, defect location, characteristic area yield strength, tensile strength, etc.

[0127] Each database record corresponds to the production process of a specific product. By systematically organizing and storing this data, it can provide a reliable foundation for subsequent process optimization and model training.

[0128] Step 2: Establish the thermocouple weight coefficient model and optimize the quantity.

[0129] Perform sensitivity test on mold: select a set of standard cooling process as reference. In the continuous production process, the first three hubs use standard cooling process to ensure that the mold temperature is at normal temperature and the casting quality is good. The last five hubs choose to close one cooling channel for testing in the standard cooling process. Repeat this process to complete the test of the closure of each cooling channel. Process the data collected by thermocouples, select the temperature data of the 1st second and the 100th second of the mold closing time as independent variables, and the wheel quality as the dependent variable for calculation. Establish a random forest model in python and calculate the importance of each feature. According to the importance of the feature, select a thermocouple that reflects the casting quality in each of the three major mold structures of the mold top mold, bottom mold and side mold as the standard thermocouple for subsequent calculation and analysis. The process is as follows. .

[0130] Step 1. Initialization: First, a model is trained using all available features and the weights or coefficients for each feature are calculated.

[0131] Step 2: Feature Sorting: Then, the features are sorted according to their weights or coefficients, and usually the features with the smallest weights or coefficients are considered the least important.

[0132] Step 3: Feature elimination: Next, remove one or more features with the smallest weight or coefficient in the ranking and retrain the model with the remaining features.

[0133] Step 4: Iterative process: Repeat steps 2 and 3 until the required number of features is reached or no further features can be removed.

[0134] The importance of each thermocouple temperature characteristic is calculated as follows:

[0135] 1. Training a Random Forest Model

[0136] Constructing random forest: Random forest is an integrated model composed of multiple decision trees. Each decision tree randomly selects a subset (sample and feature subset) from the original data during training, and then trains on these subsets.

[0137] 2. Calculate Gini Importance

[0138] Gini importance measures the contribution of each feature in reducing the impurity of the model (Gini impurity).

[0139] Calculate node Gini impurity: For each node j, calculate the Gini impurity Gj:

[0140]

[0141] in. p jk is the probability of the kth class in node j, and k is the number of classes.

[0142] Calculate the contribution of each feature: The contribution of feature i on node j is:

[0143]

[0144] Among them, G parent is the Gini impurity of the parent node, G left and G right are the Gini impurities of the left and right child nodes, N parent is the number of samples of the parent node, N left and N right are the number of samples of the left child node and the right child node respectively.

[0145] Cumulative feature contribution: For each tree t, the importance of feature i Ii(t) is the sum of the contributions of all nodes:

[0146]

[0147] Calculate the feature importance of all trees in the random forest: average the feature importance values ​​in all trees to get the final importance of feature i:

[0148]

[0149] Where T is the number of trees.

[0150] Step 3: Establish cooling process, mold initial temperature and casting quality model.

[0151] After eliminating the thermocouple positions, the temperature data of the selected thermocouple temperature measurement points, the cooling process parameters and the corresponding casting quality are analyzed, and the relationship between the cooling process parameters and the starting temperature of each mold thermocouple and the casting quality is constructed through the gradient boosting decision tree model. The specific operations are as follows:

[0152] The temperature data of each thermocouple temperature measuring point is extracted and the 1st second data of mold closing is T starti , where i represents different positions, arranged in order according to the importance of thermocouple characteristics. The air flow rate of the cooling channel is 80m 3 / h, water cooling channel is 5L / min. The cooling channel opening time is t openj , the closing time is t closej , where j represents different cooling channels. The parameter encoding for each casting is:

[0153] x=(T start1 , T peak1 ,…,T startn , T peakn , t open1 , t close1 ,…,t openm , t closem ,)

[0154] y=(q1,q2,q3,…,q n , Rm1,…,Rm n , Q1,…,Q n ,)

[0155] y is the casting quality inspection, q iIt represents whether the i-th type 3 defect occurs, represented by 0 and 1, 0 represents no defect, and 1 represents defect. Rm1 represents the test result of the tensile strength performance of the hub in the first characteristic area, and Q1 represents the test result of the yield strength performance of the hub in the first characteristic area. The characteristic area includes the outer rim, inner rim, spokes, wheel core and rim of the hub and the finite element simulation results.

[0156] Under the condition of ensuring the quality of castings, the relationship model between the initial temperature of the mold feature area and the cooling process is output, and the performance strength of the wheel hub is predicted.

[0157] The model is optimized and iterated according to the difference between the casting quality and performance strength and the actual value: First, the maximum deviation Emax and the average deviation Eavg between the target value of the casting quality inspection and the actual inspection value under the current cooling process conditions are calculated. Then, based on these deviations, the residual value is determined, and the opening time adjustment value t of the cooling channel is calculated by the residual. openm and closing time adjustment value t closem The cooling process plan is modified according to these adjustment values ​​to obtain new cooling process settings. This optimization process is repeated until the actual quality detection value of the casting falls completely within the target range and the deviation from the target value is reduced to the preset range, and then the optimization is stopped.

[0158] The model is iterated based on the residuals and evaluated based on the mean square error and the coefficient of determination. When the prediction success rate is greater than 90%, the model can be used.

[0159] In step one, the database used for model training contains at least one set of trial production data; at least 10 sets of cooling process solutions are output through the prediction of the gradient boosting decision tree model for on-site production verification.

[0160] In step 1, the GBDT model is used to predict the cooling process plan that meets the inspection standards and is verified on site. For example, according to the ASTM E155 standard, the cooling process plan corresponding to the casting with a defect level not exceeding level 2 is selected for on-site production testing.

[0161] The newly acquired field verification data (including cooling process plan and casting quality) in step 1 are added to the database for training the GBDT model and dynamically updating the mold cooling process plan and control rules.

[0162] In step 1, the upper die, lower die and side die in the mold must all be equipped with thermocouples for measurement and arranged at different positions.

[0163] In step one, at least 90% of the data in the established database is used as a training set for model training, and the remaining data is used as a test set for evaluating the model prediction performance.

[0164] In step 3, the learning rate (learning_rate) of the gradient boosted decision tree (GBDT) model is between 0.01 and 0.3. The tree depth (max_depth) is between 3 and 10.

[0165] In step three, after the casting quality defect prediction model is established, when the accuracy of the model on the test set reaches or exceeds the set value, the model is used to predict the cooling process plan and conduct on-site production verification.

[0166] The feature importance ranking in step 2 is based on the criteria of different areas of the mold, and the thermocouples arranged at different positions of the upper mold, lower mold and side mold are ranked respectively.

[0167] Specific as Figure 7 As shown, the process is as follows:

[0168] Step 101: Determine the mold feature area.

[0169] Taking the low-pressure casting mold of a certain wheel hub as an example, among the 100 trial-produced wheel hubs, the production wheel hubs were subjected to X-ray inspection. Shrinkage defects were found in the outer rim, the junction between the spoke and the outer rim, and the wheel center. The corresponding mold location according to the wheel hub defect location is the mold feature area.

[0170] like Figure 2 As shown in Table 1, the thermocouples of the mold are arranged according to the above mold characteristic areas: ten thermocouples are arranged on the top mold, respectively at the mold ejector port, beside the T4 cooling channel, near the outer side of the inner rim of the top mold, inside the inner rim of the top mold, the edge of the top mold, on the inner side of the top mold rim, on the inner side of the top mold rim, on the outer side of the top mold rim, on the outer side of the top mold rim and the inner rim ejector port. The side mold consists of four side molds, marked with 1, 2, 3, and 4. Thermocouples are arranged on each side mold close to the inner rim, close to the outer rim and on the rim, for a total of 12 thermocouples, and one thermocouple is arranged on the inner and outer sides of the 1# side mold. Six thermocouples are arranged on the bottom mold, respectively beside the bottom mold B1, between B2 and B3, between B2 and B3, beside the bottom mold B3, between the bottom mold B4 and B5, and between the bottom mold B4 and B5. There are 16 cooling channels and two heating channels on the mold, among which B1, B2, B3, B4, and B5 cooling channels are arranged at the bottom mold. The cooling medium is air cooling, and the flow rate is set to 80m 3 / h, according to the number size, from B1 closest to the wheel center to B5 on the outside of the bottom mold; the top mold is arranged with T1, T2, T3, T4, T5, T6, T7 cooling channels, the cooling medium is air cooling, and the flow rate is set to 80m 3 / h, according to the number size, from T1 closest to the ejector port to T7 close to the outside of the top mold. Each of the four side molds is equipped with a cooling channel, the cooling medium is water cooling, the flow rate is set to 5L / mi, the numbers S1, S2, S3, S4 correspond to the side mold numbers, and the side mold cooling channel is arranged near the outer rim of the side mold. Two heating channels are arranged near the wheel core of the top mold, numbered H1 and H2, the heating resistance wire is nickel-chromium alloy, and the diameter of the electric heating wire resistance wire is 0.5mm.

[0171] Table 1 Thermocouple arrangement position

[0172] Mold cooling pipeline code Cooling channel location Die casting machine temperature collection location code Mold temperature collection location B5 (Bottom mold air cooling) TC1 Ejector hole position B4 (Bottom mold air cooling) TC2 T4 lateral B3 (Bottom mold air cooling) TC3 Top mold (inside of wheel rim) B2 (Bottom mold air cooling) TC4 Top mold (outside of wheel rim) B1 (Bottom mold air cooling) TC5 Top mold edge T1 (Top mold air cooling) TC6 On the inner side of the top mold rim T2 (Top mold air cooling) TC7 Lower inner side of top mold rim T3 (Top mold air cooling) TC8 On the inner side of the top mold rim T4 (Top mold air cooling) TC9 Lower inner side of top mold rim T5 (Top mold air cooling) TC10 Inner wheel rim top rod opening T6 (Top mold air cooling) TC11 Next to bottom mold B1 T7 (Top mold air cooling) TC12 Between bottom molds B2 and B3 S1 (Side mold water cooling) TC13 Between bottom molds B2 and B3 S2 (Side mold water cooling) TC14 Next to bottom mold B3 S3 (Side mold water cooling) TC15 Between bottom mold B4 and B5 S4 (Side mold water cooling) TC16 Between bottom mold B4 and B5 H1 (Top mold heating) TC17 1# side mold inner rim H2 (Top mold heating) TC18 1# side mold rim TC19 1# side mold outer rim TC20 2# side mold inner rim TC21 2# side mold rim TC22 2# side mold outer rim TC23 3# side mold inner rim TC24 3# side mold rim TC25 3# side mold outer rim TC26 4# side mold inner rim TC27 4# side mold rim TC28 4# side mold outer rim TC29 1# side mold outer side TC30 1# side mold inner side

[0173] Step 102: Establish a thermocouple weight coefficient model and optimize the quantity.

[0174] To conduct a sensitivity test on the casting, a standard cooling process was first selected as a benchmark, and the standard process was used for the first three hubs to ensure normal mold temperature and good casting quality. Subsequently, the last five hubs were tested, each time closing one cooling channel. The temperature data at the 1st and 100th seconds of the mold closing time were recorded, and these data were used as independent variables, and the hub quality was used as the dependent variable. The importance of each feature was analyzed through the random forest model in Python. Based on the analysis results, one key thermocouple was selected in each of the top mold, bottom mold, and side mold to reflect the casting quality and conduct subsequent analysis. Thermocouple temperature measurement points with an importance greater than 90% were selected for subsequent data analysis.

[0175] Figure 2 Figure 3 As shown in Table 3,

[0176] Table 2 Specific parameters of random forest model

[0177] Random Forest Model Conditions parameter Number of trees 100 to 1000 The number of features considered by each tree 'sqrt' Minimum number of samples required for internal node repartitioning 2 to 10 The number of features to remove in each recursion 1 Split Quality 'gini' Minimum number of samples required for a leaf node 1-10 Metrics for evaluating model performance 'precision'

[0178] Table 3 Codes and specific locations of thermocouple temperature measurement positions after optimization

[0179]

[0180] Step 103: Establish cooling process, initial mold temperature and casting quality model

[0181] like Figure 4 As shown, the construction method of the gradient boosting decision tree model is demonstrated.

[0182] Collect process simulation and actual production trial data to establish a database, including cooling process data of 16 cooling channels, temperature data of 10 mold temperature measurement points, and mechanical properties of characteristic areas of corresponding castings. Each data record covers the entire production process of a product. The cooling process and mold temperature data are collected in steps of 1 second, and the state of the cooling channel is represented by 1 (on) or 0 (off). The database contains at least 100 data.

[0183] Based on the database established in step one, the gradient boosting decision tree (GBDT) method is used to establish a prediction model for the cooling process scheme. First, 90% of the data in the database is used for model training, and the remaining 10% of the data is used for performance evaluation. A gradient boosting decision tree model is established based on 10 thermocouple temperature measurement data, cooling process and casting quality. The initial model training settings include a learning rate of 0.1, a number of trees of 100, a maximum depth of 3, and GBDT is used for training. During the training process, we continuously adjust the learning rate, the number of trees, and the maximum depth until the average prediction accuracy of the model on the test set reaches or exceeds 90%. In this embodiment, the average prediction accuracy of the final model on the test set is 90.3%. Output the cooling process parameters at different mold temperatures according to the model. The process optimization is shown in Table 4: This process was used for trial production, and no scrap was generated during the 2-hour uninterrupted trial production. The temperature data of some wheel hub molds are as follows Figure 5 As shown, the temperature remains stable, and the difference between the maximum temperature and the minimum temperature at the same time does not exceed 5°C. Figure 6a and Figure 6b As shown in Table 5, the X-ray detection of the wheel core before and after the process optimization shows that there are certain shrinkage and shrinkage holes before the optimization. The mechanical properties of the characteristic area of ​​the hub before and after the process optimization are compared, and the performance of each position of the hub is improved as shown in Table 5. As shown in Table 6, the defects in the characteristic area of ​​the actual production castings are compared before and after the optimized process parameters are used. After the optimized process parameters are used, the defects in the characteristic area of ​​the hub are significantly improved.

[0184] Table 4 Process optimization table

[0185]

[0186]

[0187] Table 5 Comparison of mechanical properties of the characteristic area of ​​the wheel hub before and after process optimization, performance of each position of the wheel hub

[0188]

[0189] Table 6 Comparison of characteristic regional defects in actual production castings before and after using optimized process parameters

[0190] Characteristic area defects Initial process example Optimization process example Spoke shrinkage many Very rare Rim shrinkage many Very rare Wheel core air hole More none Pinholes Exceeding Standard few none Insufficient watering few none

[0191] The control method disclosed in this embodiment has the following advantages:

[0192] 1. The on-site production data is combined with simulation to determine the mold feature area according to the X-ray detection of wheel hub defects. Thermocouples are set in the mold feature area for temperature measurement according to the structural characteristics of the wheel hub. A random forest model is established based on the temperature measurement results and casting quality inspection, and recursive feature elimination is performed to determine the correlation between each thermocouple temperature measurement data and the wheel hub quality, optimize the number of thermocouples, and find the thermocouple temperature measurement positions corresponding to various casting defects.

[0193] 2. A gradient boosting decision tree model is established based on cooling process parameters, mold temperature, casting quality and characteristic area performance, and the casting quality is judged based on the cooling process and mold temperature. Compared with manual control, it is possible to find out whether the casting has defects during the production process and then adjust it, change the cooling parameters, control the mold temperature, and achieve automation and intelligence.

[0194] 3. Predict casting quality based on mold temperature range, abandon the time delay of traditional cooling process. When defects occur in castings, on-site workers change cooling parameters based on experience, which cannot improve mold temperature in a short time. Control according to mold temperature range. In each casting production process, the opening and closing time of cooling process can be determined according to thermocouple temperature, and controlled within a reasonable temperature range to ensure casting quality.

[0195] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0196] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A casting system temperature intelligent control system, characterized in that: include: Integrated sensing module: It consists of multiple temperature sensors, which are set according to the characteristic area distribution of the casting mold and are used to monitor the temperature changes at different positions in the mold in real time; the sensors are connected to the central control system wirelessly or wiredly; Data acquisition and processing module: used to receive data from the integrated sensor module, and perform preprocessing and storage; at the same time, this module also has data analysis functions, which can predict potential problems in the casting process based on historical data and current data, and issue an early warning when it exceeds the set range; Central control module: used to receive data provided by the data acquisition and processing module, and dynamically adjust the casting equipment and temperature intelligent control system execution module according to the preset casting process parameters and real-time data; the central control system also has remote monitoring and fault diagnosis functions, which can display various parameters of the casting process in real time, and automatically diagnose and handle faults when they occur; Execution module: includes a heating device and a cooling device, which are used to adjust the process parameters of the heating channel and the cooling channel according to the instructions of the central control system, and intelligently control the mold temperature.

2. The casting system temperature intelligent control system according to claim 1, characterized in that: The system uses a data storage and tracing method to uniquely identify the temperature, time, and process parameters of each casting process and store them in a database; when necessary, by inputting the batch number or unique identification of the cast product, the casting process data of the product can be quickly traced and queried, providing strong support for product quality control and problem analysis.

3. The casting system temperature intelligent control system according to claim 1, characterized in that: The temperature sensor of the integrated sensing module is arranged in the characteristic area of ​​the mold based on the structural characteristics of the casting; a random forest model is established according to the temperature measurement results of the temperature sensor and the casting quality inspection results, and recursive feature elimination is performed to determine the correlation between the temperature measurement data of each temperature sensor and the casting quality, thereby optimizing the number of temperature sensors, obtaining the temperature measurement positions of the temperature sensors corresponding to various defects of the casting, and thus screening the temperature sensors; The data acquisition and processing module analyzes the screened temperature data of the temperature sensor measuring points, the cooling process parameters and the corresponding casting quality, constructs the relationship between the cooling process parameters, the starting temperature of each mold temperature sensor and the casting quality through the gradient boosting decision tree model, and controls the casting system temperature through the central control module based on the constructed relationship.

4. The casting system temperature intelligent control system according to claim 3, characterized in that: The acquisition of the mold feature area includes: Collect historical process data of castings and casting molds, and determine the characteristic areas of casting molds based on the shrinkage volume results of simulated castings based on X-ray detection and historical process data; The historical process data of the casting and the casting mold are collected, including: cooling process plan data and casting quality data; The cooling process plan data includes cooling channel configuration, switching time and / or coolant flow rate at each production stage; The casting quality data includes casting quality indicators, and the casting quality indicators include defect type, defect location, characteristic area yield strength and / or tensile strength.

5. The casting system temperature intelligent control system according to claim 3, characterized in that: The random forest model is established based on the temperature measurement results of the temperature sensors and the casting quality inspection results, and recursive feature elimination is performed to determine the correlation between the temperature measurement data of each temperature sensor and the casting quality, thereby optimizing the number of temperature sensors, obtaining the temperature measurement positions of the temperature sensors corresponding to various defects of the castings, and screening the temperature sensors, including: A sensitivity test was conducted on the mold to select the temperature data that meets the mold closing time as the independent variable and the casting quality as the dependent variable for calculation. A random forest model was established and the importance of each feature was calculated. According to the feature importance, temperature sensors that reflect the casting quality were selected as standard temperature sensors in the top mold, bottom mold and side mold of the mold.

6. The casting system temperature intelligent control system according to claim 3, characterized in that: The method of establishing a random forest model based on the temperature measurement results of the temperature sensor and the casting quality inspection results and performing recursive feature elimination includes: Step 1: Use the acquired features to train the random forest model and calculate the weight or coefficient of each feature of the random forest model; Step 2: Sort the features according to their weights or coefficients; Step 3: Delete one or more features with the smallest weight or coefficient in the ranking and retrain the random forest model with the remaining features; Step 4: Repeat steps 2 and 3 until the desired number of features is reached or no further features can be removed.

7. The casting system temperature intelligent control system according to claim 3, characterized in that: Temperature sensor temperature feature importance calculation, including: Calculate the node Gini impurity; Calculate the contribution of each feature; Cumulative feature contribution; Calculate the feature importance of all trees in the random forest and average the feature importance values ​​across all trees to get the final importance of the feature.

8. The casting system temperature intelligent control system according to claim 3, characterized in that: The temperature data of the selected temperature sensor measuring points, cooling process parameters and corresponding casting quality are analyzed, and the relationship between cooling process parameters, the starting temperature of each mold temperature sensor and casting quality is constructed through the gradient boosting decision tree model. The casting system temperature is controlled based on the constructed relationship, including: Obtain the selected temperature data of the temperature sensor measuring points, cooling process parameters and parameter data sets corresponding to the casting quality; Under the condition of ensuring the quality of castings, the relationship model between the initial temperature of the mold feature area and the cooling process is output based on the parameter data set to predict the performance strength of the wheel hub; The relationship model is optimized and iterated according to the difference between the casting quality and performance strength and the actual value; The relationship model is evaluated based on the mean square error and the coefficient of determination. When the prediction success rate is greater than the set value, the optimization iteration is stopped.

9. The casting system temperature intelligent control system according to claim 3, characterized in that: The cooling process scheme data and casting quality data are used to train the gradient boosting decision tree model and dynamically update the mold cooling process scheme and control rules; Use the gradient boosting decision tree model to predict cooling process solutions that meet the inspection standards; The learning rate of the gradient boosting decision tree model is 0.01-0.3, and the tree depth is 3-10.

10. A casting system temperature intelligent control method of a casting system temperature intelligent control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Determine the mold feature area and arrange the temperature sensor; Step 2: Collect historical process data of target products to establish a database; Step 3: Establish a temperature sensor weight coefficient model and optimize the quantity; Step 4: Establish the cooling process, mold initial temperature and casting quality model.

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