Method and system for predicting mechanical performance of casting pump body based on machining parameters

Abstract

The invention relates to a method and system for predicting the mechanical performance of a casting pump body based on machining parameters. Machining parameters of pump body casting currently are collected, and component fluctuation parameters of casting within the recent preset time range are collected; obtaining machining error parameters according to the machining parameters and indexes; fluid characteristic parameters in a pump body operation scene are collected, and a fluid complex coefficient and an operation quality demand coefficient are obtained through analysis; according to the component fluctuation parameters, the machining error parameters, the fluid complex coefficients and the operation quality demand coefficients, stepped prediction resources are configured, the mechanical performance of the casting pump body is predicted, and the mechanical performance parameters of the pump body are obtained through prediction. The technical problem that the mechanical performance of the casting pump body cannot be scientifically and accurately predicted according to the machining parameters in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for predicting the mechanical properties of a cast pump body based on processing parameters. Background Art

[0002] In the field of industrial production, the pump body is a key component in various mechanical equipment, and its performance directly affects the operating efficiency and stability of the entire equipment. The casting process is a common method for producing pump bodies. However, the casting process is affected by numerous processing parameters, making it difficult to accurately control the mechanical properties of the pump body. Currently, for the prediction of the mechanical properties of cast pump bodies, there is a lack of a comprehensive and accurate method and system. It is often judged intuitively by experienced technicians, and this judgment method has the problems of being unscientific and inaccurate. Summary of the Invention

[0003] In view of the technical problems of unscientific and inaccurate prediction of the mechanical properties of pump bodies in the prior art, the present invention provides a method and system for predicting the mechanical properties of cast pump bodies based on processing parameters to solve the problems.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a method for predicting the mechanical properties of a cast pump body based on processing parameters, including: collecting the processing parameters of the current pump body casting, and collecting the composition fluctuation parameters of the casting in the recent preset time range; indexing and obtaining the processing error parameters according to the processing parameters; collecting the fluid characteristic parameters in the pump body operation scenario, and analyzing to obtain the fluid complexity coefficient and the operation quality requirement coefficient; configuring step-by-step prediction resources according to the composition fluctuation parameters, processing error parameters, fluid complexity coefficient and operation quality requirement coefficient, and performing prediction on the mechanical properties of the cast pump body to obtain the mechanical property parameters of the pump body.

[0005] Optionally, collecting the processing parameters of the current pump body casting, and collecting the composition fluctuation parameters of the casting in the recent preset time range includes: collecting the processing parameters of the current pump body casting, where the processing parameters include the pouring temperature, cooling rate, and mold preheating temperature; collecting the composition parameters of the pump body casting in the recent preset time range to obtain a historical composition parameter sequence; and processing the historical composition parameter sequence to obtain the composition fluctuation parameters.

[0006] Among them, processing the historical composition parameter sequence to obtain the composition fluctuation parameters includes: calculating the mean value of the historical composition parameter sequence to obtain the average historical composition parameter; randomly selecting several historical composition parameters in the historical composition parameter sequence, and respectively calculating the absolute error amplitude from the average historical composition parameter to obtain a plurality of composition absolute error amplitudes; and calculating the mean value of the plurality of composition absolute error amplitudes to obtain the composition fluctuation parameters.

[0007] Optionally, according to the processing parameters, indexing is performed to obtain processing error parameters, including: calling a processing parameter library, where the processing parameter library is constructed based on historical processing parameters and includes a set of sample processing parameters and a set of sample processing error parameters, and each processing error parameter is the average error magnitude when different sample processing parameters are controlled; inputting the processing parameters into the processing parameter library, and indexing to obtain the processing error parameters.

[0008] Optionally, fluid characteristic parameters in the pump body operation scenario are collected, and a fluid complexity coefficient and an operation quality requirement coefficient are analyzed and obtained, including: collecting fluid parameters in the pump body operation scenario to obtain a fluid parameter sequence; calculating the mean value of the fluid parameter sequence to obtain an average fluid parameter; randomly selecting several fluid parameters within the fluid parameter sequence, respectively calculating the absolute error magnitudes from the average fluid parameter, and calculating the mean value to obtain the fluid complexity coefficient; using the average fluid parameter as the operation quality requirement coefficient.

[0009] Optionally, according to the composition fluctuation parameter, the processing error parameter, the fluid complexity coefficient, and the operation quality requirement coefficient, stepped prediction resources are configured to perform mechanical property prediction of the cast pump body, and the mechanical property parameters of the pump body are predicted and obtained, including: training an array of stepped pump body performance predictors, where the array of stepped pump body performance predictors includes multiple pump body performance predictors corresponding to multiple performance prediction coefficient intervals, and the training data volume of each pump body performance predictor is different; calculating a performance prediction coefficient according to the composition fluctuation parameter, the processing error parameter, the fluid complexity coefficient, and the operation quality requirement coefficient; selecting a corresponding pump body performance predictor according to the performance prediction coefficient interval into which the performance prediction coefficient falls, inputting the processing parameters, and predicting and outputting to obtain the mechanical property parameters of the pump body.

[0010] Among them, training the array of stepped pump body performance predictors includes: collecting a set of sample processing parameters and a set of sample mechanical property parameters according to the pump body casting data within a historical time; using machine learning to construct multiple pump body performance predictors; dividing the set of sample processing parameters and the set of sample mechanical property parameters to obtain multiple portions of pump body performance prediction training data with different data volumes, and the data volumes of the multiple portions of pump body performance prediction training data are distributed from large to small; respectively using the multiple portions of pump body performance prediction training data to perform supervised training and testing on the multiple pump body performance predictors until convergence; obtaining multiple performance prediction coefficient intervals, where the performance prediction coefficient is greater than or equal to 0 and less than or equal to 1; constructing a mapping relationship with the multiple performance prediction coefficient intervals according to the training data volumes of the multiple pump body performance predictors, where the size of the performance prediction coefficient interval is positively correlated with the training data volume of the pump body performance predictor.

[0011] Among them, a performance prediction coefficient is calculated based on the component fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient, including: collecting the maximum component fluctuation parameter, maximum processing error parameter, maximum fluid complexity coefficient, and maximum operation quality requirement coefficient within the historical time according to the historical data of pump body casting; respectively calculating the ratios of the component fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient to the maximum component fluctuation parameter, maximum processing error parameter, maximum fluid complexity coefficient, and maximum operation quality requirement coefficient, and calculating the performance prediction coefficient through weighted calculation.

[0012] In a second aspect, the present invention provides a system for predicting the mechanical properties of a cast pump body based on processing parameters, including: A processing parameter acquisition module, configured to acquire the processing parameters for current pump body casting and the component fluctuation parameters for casting within a recently preset time range; A processing error parameter indexing module, configured to index and obtain the processing error parameter according to the processing parameter; A fluid characteristic parameter analysis module, configured to acquire the fluid characteristic parameters in the pump body operation scenario and analyze to obtain the fluid complexity coefficient and the operation quality requirement coefficient; A mechanical property prediction module, configured to configure stepped prediction resources according to the component fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient, perform prediction on the mechanical properties of the cast pump body, and predict to obtain the mechanical property parameters of the pump body.

[0013] In the present invention, by acquiring the processing parameters for current pump body casting and the component fluctuation parameters for casting within a recently preset time range, and evaluating the credibility of the prediction result according to the component fluctuation parameter, different training amounts of pump body mechanical property predictors are configured, achieving precise allocation of computing resources; in the present invention, according to the processing parameter, the processing error parameter is indexed and obtained, and the error factor in the processing process is introduced into the prediction process as a weight factor, improving the scientificity of the prediction result; in the present invention, the fluid characteristic parameters in the pump body operation scenario are acquired, and the fluid complexity coefficient and the operation quality requirement coefficient are analyzed, realizing the configuration of different training amounts of pump body mechanical property predictors according to different usage scenarios of the pump body, increasing the credibility of the prediction result; in the present invention, according to the component fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient, stepped prediction resources are configured, the mechanical properties of the cast pump body are predicted, and the mechanical property parameters of the pump body are predicted. The allocation of computing resources is calculated by integrating the weights of various parameters, and the computing resources are precisely configured according to different prediction tasks, so as to achieve the effect of saving computing power and improving prediction efficiency.

[0014] In summary, by implementing the present application, the technical effect of scientifically and accurately predicting the mechanical properties of a casting pump body according to processing parameters can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flowchart of a method for predicting the mechanical properties of a casting pump body based on processing parameters provided by the present invention; Figure 2 It is a schematic structural diagram of a system for predicting the mechanical properties of a casting pump body based on processing parameters provided by the present invention.

[0016] In the drawings, the components represented by the respective reference numerals are as follows: A processing parameter acquisition module 11, a processing error parameter indexing module 12, a fluid characteristic parameter analysis module 13, and a mechanical property prediction module 14. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] 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 skilled in the art without creative efforts fall within the protection scope of the present invention.

[0018] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0019] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0020] Embodiment 1, as Figure 1As shown in the figure, an embodiment of the present invention provides a method for predicting the mechanical properties of a casting pump body based on processing parameters, including: S100: Collect the processing parameters of the current pump body casting, and collect the composition fluctuation parameters of the castings within the recent preset time range; S200: Index and obtain the processing error parameters according to the processing parameters; S300: Collect the fluid characteristic parameters in the pump body operation scenario, and analyze to obtain the fluid complexity coefficient and the operation quality requirement coefficient; S400: Configure the stepped prediction resources according to the composition fluctuation parameters, processing error parameters, fluid complexity coefficient and operation quality requirement coefficient, and perform the prediction of the mechanical properties of the casting pump body to obtain the mechanical property parameters of the pump body.

[0021] In step S100 of the embodiment of the present application, collecting the processing parameters of the current pump body casting and collecting the composition fluctuation parameters of the castings within the recent preset time range includes: Collect the processing parameters of the current pump body casting, where the processing parameters include the pouring temperature, cooling rate, and mold preheating temperature; Collect the composition parameters of the pump body casting within the recent preset time range to obtain a historical composition parameter sequence; Process and obtain the composition fluctuation parameters according to the historical composition parameter sequence.

[0022] During the casting process, the processing parameters of the pump body have a great influence on the performance of the finally cast pump body. In the embodiment of the present application, collecting the processing parameters of the current pump body casting is to obtain the basic situation of the current cast pump body as the basis for predicting the performance of the cast pump body. The parameters that need to be collected include the pouring temperature, cooling rate, and mold preheating temperature. The pouring temperature refers to the temperature when the molten metal enters the mold from the pressure chamber for filling. Improper pouring temperature will cause defects such as shrinkage cavities, air holes, deformation, and cracks in the casting, thus affecting the casting performance; the cooling rate refers to the speed at which the molten metal cools down during the solidification process. The cooling rate will determine parameters such as the grain size, microstructure, and residual stress of the casting, thus affecting the casting performance; the mold temperature generally refers to the surface temperature of the mold. The mold temperature greatly affects the mechanical properties, dimensional accuracy, and service life of the casting. The cooling rate can be measured by the amplitude of temperature drop per unit time. For example, a temperature drop of 35 degrees Celsius in 1 minute can be expressed as 35℃ / min.

[0023] In the actual production process, in addition to the above-mentioned processing parameters, the material composition of the casting also has a great influence on the performance of the casting. The properties of materials with different compositions vary widely. Even for a material in which a certain component content dominates absolutely (for example, a single material in an alloy accounts for 99%), the performance of this material may fluctuate violently due to the fluctuation of the amount of a certain rare earth element with a very small addition amount. Therefore, in the embodiment of the present application, the material composition of the pump body is also a factor that must be considered. Therefore, it is necessary to collect the composition parameters of the pump body casting within a recently preset time range. The preset time can be determined according to the actual output of the factory. For example, if the output is large, sufficient data can be obtained within a relatively short preset time (such as 1 month). If the output is small, it takes a long time (such as 1 year) to collect sufficient sample data. The method for obtaining the composition parameters of the pump body casting can be to take samples of the pump body after each casting is completed, and then use methods such as atomic emission spectrometry or atomic absorption spectrometry to measure the proportion of element content (such as iron, carbon, silicon, manganese, etc.) in the pump body material. By the above method, the composition parameters of the pump body casting within a recently preset time range are collected, and a historical composition parameter sequence can be obtained.

[0024] Among them, according to the historical composition parameter sequence, processing to obtain a composition fluctuation parameter includes: Calculating the mean value of the historical composition parameter sequence to obtain an average historical composition parameter; Randomly select several historical composition parameters within the historical composition parameter sequence, and calculate the absolute error amplitude with the average historical composition parameter respectively to obtain a plurality of composition absolute error amplitudes; Calculating the mean value of the plurality of composition absolute error amplitudes to obtain a composition fluctuation parameter.

[0025] In the embodiment of the present application, calculating the mean value of the historical composition parameter sequence is to calculate the possible amplitude of the fluctuation of the pump body composition during the actual production process based on this. The size of this possible fluctuation amplitude is measured by the composition fluctuation parameter. Specifically, the mean value of the historical composition parameter sequence is the mean value of each element in the historical composition parameter sequence. For example, there are three sets of composition parameters within the preset time, and the iron content is 99%, 98%, and 97% respectively. Then the mean value of the iron element in this composition parameter sequence is (99% + 98% + 97%) / 3 = 98%. By this method, the mean value of the historical composition parameter sequence of all elements can be calculated as the average historical composition parameter.

[0026] Further, after obtaining the mean value of the historical component parameter sequence, it is necessary to calculate the possible amplitude of the fluctuation of the pump body component based on this. The calculation method is to randomly select several historical component parameters within the historical component parameter sequence, calculate the absolute error amplitude from the average historical component parameter respectively, and obtain multiple component absolute error amplitudes. For example, the average historical component parameter of iron element is 98%, and several randomly selected historical component parameters are 99%, 98%, 97%. It is easy to see that the absolute error amplitudes of these historical component parameters from the average historical component parameter (taking the absolute value after subtraction) are 1%, 0%, 1%, and the mean value of the multiple component absolute error amplitudes is (1% + 0% + 1%) / 3 ≈ 0.667%. According to the above calculation method, the component fluctuation parameters of the contents of multiple elements in the cast pump body can be calculated, and the mean value can be calculated as the final component fluctuation parameter.

[0027] In step S200 of the embodiment of the present application, according to the processing parameters, the processing error parameters are indexed and obtained, including: Call the processing parameter library, where the processing parameter library is constructed based on historical processing parameters and includes a sample processing parameter set and a sample processing error parameter set. Each processing error parameter is the average error amplitude when different sample processing parameters are controlled. Input the processing parameters into the processing parameter library, and index to obtain the processing error parameters.

[0028] In the actual production process of a casting pump body, due to reasons such as environmental temperature changes, equipment aging, and non-standard operation of personnel, there will be certain deviations between the preset actual pouring temperature, cooling rate, and mold preheating temperature and the preset parameters. In the embodiments of the present application, the introduction of processing error parameters is to estimate the difference between the actual performance and the predicted performance of the casting pump body produced under this processing parameter according to the error range between the actual value and the preset value of the processing parameter in the previous actual production process, so as to judge the credibility of the prediction result. Specifically, the processing error range under the historical parameter identical to the processing parameter to be predicted can be indexed through a pre-established processing parameter library. The database contains two types of data. One is the sample processing parameter, that is, the preset processing parameter. For example, a pouring temperature of 600 °C, a cooling rate of 30 °C / min, and a mold preheating temperature of 200 °C can form a set of sample processing parameters. The other type of data is the sample processing error parameter, which is the average error range when different sample processing parameters are controlled. For example, within a certain period of time, 3 casting pump bodies were produced with preset processing parameters of a pouring temperature of 600 °C, a cooling rate of 30 °C / h, and a mold preheating temperature of 200 °C. The actual pouring temperatures during the 3 productions were 603 °C, 597 °C, and 600 °C respectively. Then the error ranges are the absolute values of the differences from 600 °C, which are 3 °C, 3 °C, and 0 °C respectively, and the average error range is (3 °C + 3 °C + 0 °C) / 3 = 2 °C, and the error range is 0.33%, which is used as the processing error parameter. By using this method, the average processing error parameters of multiple processing parameters can be calculated.

[0029] Among them, since the temperature measurement element of the equipment itself is no longer reliable when measuring the processing error parameter, a specially calibrated temperature detection device needs to be used for measurement when measuring the sample processing error parameter.

[0030] Collect multiple sample processing parameters and the corresponding sample processing error parameters of the sample processing parameters to obtain a sample processing parameter set and a sample processing error parameter set, constituting a processing parameter library. Among them, the processing parameter library should contain all common processing parameters and the corresponding processing error parameters. As long as the processing parameter is input, the corresponding processing error parameter can be output. The construction method of this processing parameter library is prior art and will not be elaborated here.

[0031] In step S300 of the embodiments of the present application, the fluid characteristic parameters in the pump body operation scenario are collected, and the fluid complexity coefficient and the operation quality requirement coefficient are analyzed, including: Collect the fluid parameters in the pump body operation scenario to obtain a fluid parameter sequence; Calculate the mean value of the fluid parameter sequence to obtain the average fluid parameter; Randomly select several fluid parameters within the fluid parameter sequence, calculate the absolute error magnitudes with respect to the average fluid parameter respectively, and calculate the mean value to obtain the fluid complexity coefficient; Use the average fluid parameter as the operation quality requirement coefficient.

[0032] In the embodiments of the present application, the introduction of the fluid complexity coefficient and the operation quality requirement coefficient is to evaluate the usage environment of the pump body under normal operation, to evaluate the degree of its requirement for casting quality, and to determine whether the accuracy of the physical performance prediction of the pump body can meet the requirements. Optionally, the fluid parameters in the pump body operation scenario can be common fluid parameters such as pressure and head. Taking pressure as an example, a pressure sensor can be built into the inner wall of the pump body or the pipeline connected to the pump body to collect the fluid pressure borne by the pump body during normal operation. The unit of pressure is usually MPa. The fluid parameter acquisition frequency can be determined according to the specific usage scenario of the pump body. For example, for a civil water pump, the fluid parameter acquisition frequency can be set to once per hour, and a total of 24 data are collected to obtain a fluid parameter sequence. Then, it is necessary to calculate the mean value of the fluid parameter sequence to obtain the average fluid parameter; then randomly select several fluid parameters within the fluid parameter sequence, calculate the absolute error magnitudes with respect to the average fluid parameter respectively, and calculate the mean value to obtain the fluid complexity coefficient. For example, if the mean value of the pump body fluid parameter (such as pressure) sequence collected over a period of time is 1 MPa, and several randomly selected fluid parameters within the fluid parameter sequence are 1.3 MPa, 1.0 MPa, and 0.7 MPa, then the absolute errors of these three fluid parameters with respect to the average fluid parameter are 0.3 MPa, 0 MPa, and 0.3 MPa, and the mean value of the absolute errors of these three fluid parameters with respect to the average fluid parameter is 0.2 MPa. The mean value of the absolute error magnitudes of the fluid parameters with respect to the average fluid parameter is the fluid complexity coefficient, that is, 0.2 MPa / 1 MPa = 20%. And the mean value (such as 1 MPa) of the pump body fluid parameter (such as pressure) sequence collected over the aforementioned period of time is the operation quality requirement coefficient.

[0033] In step S400 of the embodiments of the present application, according to the composition fluctuation parameter, the processing error parameter, the fluid complexity coefficient, and the operation quality requirement coefficient, configure stepped prediction resources to perform the mechanical property prediction of the cast pump body, and the mechanical property parameters of the pump body are obtained through prediction, including: Train an array of stepped pump body performance predictors, where the array of stepped pump body performance predictors includes multiple pump body performance predictors corresponding to multiple performance prediction coefficient intervals, and the training data volume of each pump body performance predictor is different; Calculate the performance prediction coefficient according to the composition fluctuation parameter, the processing error parameter, the fluid complexity coefficient, and the operation quality requirement coefficient; According to the performance prediction coefficient interval into which the performance prediction coefficient falls, select the corresponding pump body performance predictor, input the processing parameters, and predict and output to obtain the mechanical property parameters of the pump body.

[0034] Among them, training the stepped pump body performance predictor array includes: According to the pump body casting data within the historical time, collect the sample processing parameter set and collect the sample mechanical property parameter set; Use machine learning to construct multiple pump body performance predictors; Divide the sample processing parameter set and the sample mechanical property parameter set to obtain multiple pump body performance prediction training data with different data volumes, and the data volumes of the multiple pump body performance prediction training data are distributed from large to small; Respectively use the multiple pump body performance prediction training data to conduct supervised training and testing on the multiple pump body performance predictors until convergence; Obtain multiple performance prediction coefficient intervals, where the performance prediction coefficient is greater than or equal to 0 and less than or equal to 1; Construct the mapping relationship with the multiple performance prediction coefficient intervals according to the training data volumes of the multiple pump body performance predictors, where the size of the performance prediction coefficient interval is positively correlated with the training data volume of the pump body performance predictor.

[0035] In the embodiments of the present application, the sample processing parameter set is the pouring temperature, cooling rate, and mold preheating temperature of the cast pump body described in S100, and the sample mechanical property parameters are a series of parameters that can reflect the quality of the pump body, including yield strength (MPa), Vickers hardness (HV), etc. By collecting the processing parameters of multiple cast pump bodies and the mechanical property parameters of the corresponding finished cast pump bodies under this parameter, the sample processing parameter set and the sample mechanical property parameter set can be obtained. The sample processing parameter set and the sample mechanical property parameter set are divided into a training set and a validation set in a ratio of 8:2 for training the stepped pump body performance predictor array.

[0036] The input of this pump body performance predictor is the processing parameters (pouring temperature, cooling rate, mold preheating temperature), and the output is the predicted mechanical properties (yield strength, Vickers hardness) under this processing parameter. Therefore, when building the pump body performance predictor, a deep neural network (DNN) can be used for building. The DNN can effectively capture the non-linear relationship between the casting process parameters and the mechanical properties and perform stably in multi-output prediction tasks.

[0037] Optionally, the input layer of the pump body performance predictor has 3 nodes (pouring temperature, cooling rate, mold preheating temperature). The hidden layer is divided into three layers. The first layer has 128 nodes, and the activation function is ReLU (to alleviate gradient vanishing); the second layer has 64 nodes, and the activation function is ReLU; the third layer has 32 nodes, and the activation function is Swish (to enhance the non-linear expression ability). The output layer has 2 nodes (yield strength, Vickers hardness), and the activation function is linear. In terms of setting the model hyperparameters, the Adam optimizer is used (learning rate = 0.001, β1 = 0.9, β2 = 0.999); the mean squared error (MSE) is selected as the loss function; L2 regularization is selected for regularization (coefficient = 0.001) to prevent overfitting; the batch size for each training is 32 (to balance the training speed and gradient stability); the number of training epochs is initially set to 200 epochs, and an early stopping mechanism is added (patience = 10, that is, the training is terminated if the validation loss does not decrease for 10 consecutive epochs). When the decrease in the validation set loss (MSE) is < 0.1% for 5 consecutive epochs, it is determined that the model converges, and the pump body performance predictor is obtained.

[0038] In the embodiments of the present application, by controlling the size of the training sample volume, multiple pump body performance predictors can be obtained through the above-mentioned pump body performance predictor training method. For example, the aforementioned sample processing parameter set and sample mechanical property parameter set can be divided into three parts, each containing 3000 sets of data, 2000 sets of data, and 1000 sets of data, and the first pump body performance predictor, the second pump body performance predictor, and the third pump body performance predictor are respectively trained. They are used to handle the pump body performance prediction tasks under different performance prediction coefficient intervals. For example, the first pump body performance predictor is used to handle the performance prediction task with a performance prediction coefficient interval of 0 - 0.4, the second pump body performance predictor is used to handle the performance prediction task with a performance prediction coefficient interval of 0.4 - 0.7, and the third pump body performance predictor is used to handle the performance prediction task with a performance prediction coefficient interval of 0.7 - 1. Since the performance prediction coefficient represents the difficulty and importance of performance prediction, the larger the performance prediction coefficient interval, the larger the amount of training data allocated to the pump body performance predictor. In this way, it is possible to accurately allocate computing resources according to different prediction tasks, so as to save computing power and improve the prediction efficiency and accuracy.

[0039] Among them, calculating the performance prediction coefficient according to the composition fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient includes: According to the historical data of pump body casting, collect the maximum composition fluctuation parameter, maximum processing error parameter, maximum fluid complexity coefficient, and maximum operation quality requirement coefficient within the historical time; Calculate the ratios of the component fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient to the maximum component fluctuation parameter, maximum processing error parameter, maximum fluid complexity coefficient, and maximum operation quality requirement coefficient respectively, and calculate the performance prediction coefficient through weighted calculation; In the embodiment of the present application, the performance prediction coefficient is an index reflecting the difficulty and importance of performance prediction. By the magnitude of the performance prediction coefficient, the pump body performance predictors trained with different amounts of training data can be reasonably configured for pump body performance prediction.

[0040] Through the method in the foregoing steps, the average component fluctuation parameter (such as 2%) in the historical time, average processing error parameter (such as pouring temperature error parameter 2°C, cooling rate error parameter 3°C / h, mold preheating temperature error parameter 3°C), average fluid complexity coefficient (such as 0.2MP), and average operation quality requirement coefficient (such as 1MPa) can be obtained; Calculate the ratios of the above-mentioned component fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient to the maximum component fluctuation parameter (such as 4%), maximum processing error parameter (such as pouring temperature error parameter 4°C, cooling rate error parameter 6°C / h, mold preheating temperature error parameter 6°C), maximum fluid complexity coefficient (such as 0.4MP), and maximum operation quality requirement coefficient (such as 2MPa) respectively, and the component fluctuation parameter ratio can be obtained as 2% / 4% = 0.5, pouring temperature error parameter ratio as 2°C / 4°C = 0.5, cooling rate error parameter ratio as (3°C / h) / (6°C / h) = 0.5, mold preheating temperature error parameter ratio as 3°C / 6°C = 0.5, fluid complexity coefficient ratio as 0.2MP / 0.4MP = 0.5, operation quality requirement coefficient ratio as 1MPa / 2MPa = 0.5. Take the average of all the above ratios, and the performance prediction coefficient can be obtained as (0.5 + 0.5 + 0.5 + 0.5 + 0.5 + 0.5) / 5 = 0.5. The historical time can be determined as appropriate according to the specific production situation (such as 6 months), as long as the data obtained within the historical time is sufficiently representative.

[0041] Finally, according to the performance prediction coefficient interval into which the performance prediction coefficient falls, select the corresponding pump body performance predictor, input the processing parameters (pouring temperature, cooling rate, mold preheating temperature), and the mechanical property parameters (such as yield strength, Vickers hardness) of the pump body can be predicted and output.

[0042] Embodiment 2, as Figure 2 shown, based on the same inventive concept as the method for predicting the mechanical properties of a cast pump body based on processing parameters provided in Embodiment 1, the embodiment of the present invention further provides a system for predicting the mechanical properties of a cast pump body based on processing parameters, including: The processing parameter acquisition module 11 is used to acquire the processing parameters for the current pump body casting and the component fluctuation parameters for the casting in the most recent preset time range; The processing error parameter indexing module 12 is used to index and obtain the processing error parameters according to the processing parameters; The fluid characteristic parameter analysis module 13 is used to acquire the fluid characteristic parameters in the pump body operation scenario and analyze to obtain the fluid complexity coefficient and the operation quality requirement coefficient; The mechanical property prediction module 14 is used to configure stepped prediction resources according to the component fluctuation parameters, processing error parameters, fluid complexity coefficient and operation quality requirement coefficient, and perform the mechanical property prediction of the cast pump body, and predict to obtain the mechanical property parameters of the pump body.

[0043] Furthermore, the processing parameter acquisition module 11 is also used for: Acquiring the processing parameters for the current pump body casting, where the processing parameters include the pouring temperature, cooling rate, and mold preheating temperature; Acquiring the component parameters for the pump body casting in the most recent preset time range to obtain a historical component parameter sequence; Processing and obtaining the component fluctuation parameters according to the historical component parameter sequence.

[0044] Among them, processing and obtaining the component fluctuation parameters according to the historical component parameter sequence includes: Calculating the mean value of the historical component parameter sequence to obtain the average historical component parameter; Randomly selecting several historical component parameters from the historical component parameter sequence, and respectively calculating the absolute error amplitude from the average historical component parameter to obtain multiple component absolute error amplitudes; Calculating the mean value of the multiple component absolute error amplitudes to obtain the component fluctuation parameter.

[0045] Furthermore, the processing error parameter indexing module 12 is also used for: Invoking the processing parameter library, where the processing parameter library is constructed based on historical processing parameters and includes a sample processing parameter set and a sample processing error parameter set, and each processing error parameter is the average error amplitude when different sample processing parameters are controlled; Inputting the processing parameters into the processing parameter library and indexing to obtain the processing error parameters.

[0046] Furthermore, the fluid characteristic parameter analysis module 13 is also used for: Acquiring the fluid parameters in the pump body operation scenario to obtain a fluid parameter sequence; Calculating the mean value of the fluid parameter sequence to obtain the average fluid parameter; Randomly select several fluid parameters within the sequence of fluid parameters, calculate the absolute error magnitudes with respect to the average fluid parameters respectively, and calculate the mean value to obtain the fluid complexity coefficient; Use the average fluid parameter as the operation quality requirement coefficient.

[0047] Furthermore, the mechanical property prediction module 14 is further configured to: Train a stepped pump body performance predictor array, where the stepped pump body performance predictor array includes multiple pump body performance predictors corresponding to multiple performance prediction coefficient intervals, and the training data volume of each pump body performance predictor is different; Calculate and obtain the performance prediction coefficient according to the composition fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient; According to the performance prediction coefficient interval into which the performance prediction coefficient falls, select the corresponding pump body performance predictor, input the processing parameters, and predict and output to obtain the mechanical property parameters of the pump body.

[0048] Among them, training the stepped pump body performance predictor array includes: According to the pump body casting data within the historical time, collect the sample processing parameter set and the sample mechanical property parameter set; Adopt machine learning to construct multiple pump body performance predictors; Divide the sample processing parameter set and the sample mechanical property parameter set to obtain multiple portions of pump body performance prediction training data with different data volumes, and the data volumes of the multiple portions of pump body performance prediction training data are distributed from large to small; Respectively use the multiple portions of pump body performance prediction training data to perform supervised training and testing on the multiple pump body performance predictors until convergence; Obtain multiple performance prediction coefficient intervals, where the performance prediction coefficient is greater than or equal to 0 and less than or equal to 1; Construct the mapping relationship with multiple performance prediction coefficient intervals according to the training data volumes of the multiple pump body performance predictors, where the size of the performance prediction coefficient interval is positively correlated with the training data volume of the pump body performance predictor.

[0049] Among them, calculating and obtaining the performance prediction coefficient according to the composition fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient includes: According to the historical data of pump body casting, collect the maximum composition fluctuation parameter, maximum processing error parameter, maximum fluid complexity coefficient, and maximum operation quality requirement coefficient within the historical time; Calculate the ratios of the component fluctuation parameter, processing error parameter, fluid complexity coefficient, and operation quality requirement coefficient to the maximum component fluctuation parameter, maximum processing error parameter, maximum fluid complexity coefficient, and maximum operation quality requirement coefficient respectively, and calculate the performance prediction coefficient through weighted calculation.

[0050] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0051] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0053] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means realizes the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0055] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept.

[0056] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for predicting the mechanical properties of a casting pump body based on processing parameters, characterized in that: The method comprises: Collect the processing parameters of the current pump body casting, and collect the composition fluctuation parameters of the casting within the recent preset time range; According to the processing parameters, obtain the processing error parameters by indexing; Collect the fluid characteristic parameters in the pump operation scenario, and analyze and obtain the fluid complexity coefficient and operation quality requirement coefficient; According to the composition fluctuation parameters, processing error parameters, fluid complexity coefficient and operation quality requirement coefficient, a step-by-step prediction resource is configured to predict the mechanical properties of the casting pump body, and the mechanical performance parameters of the pump body are obtained by prediction.

2. The method for predicting mechanical properties of a casting pump body based on processing parameters according to claim 1, characterized in that: Collect the processing parameters of the current pump body casting, and collect the composition fluctuation parameters of the casting within the recent preset time range, including: Collecting the processing parameters of the current pump body casting, wherein the processing parameters include pouring temperature, cooling rate, and mold preheating temperature; Collect the composition parameters of the pump body casting within the recent preset time range to obtain a historical composition parameter sequence; According to the historical component parameter sequence, component fluctuation parameters are obtained through processing.

3. The method for predicting mechanical properties of a casting pump body based on processing parameters according to claim 2, characterized in that: According to the historical component parameter sequence, processing and obtaining component fluctuation parameters includes: Calculating the mean of the historical component parameter sequence to obtain an average historical component parameter; Randomly selecting a number of historical component parameters in the historical component parameter sequence, and calculating the absolute error margins with the average historical component parameter respectively, to obtain a plurality of component absolute error margins; The average of the absolute error amplitudes of the multiple components is calculated to obtain the component fluctuation parameter.

4. The method for predicting mechanical properties of a casting pump body based on processing parameters according to claim 1, characterized in that: According to the processing parameters, the processing error parameters are obtained by indexing, including: Calling a processing parameter library, wherein the processing parameter library is constructed based on historical processing parameters, including a sample processing parameter set and a sample processing error parameter set, wherein each processing error parameter is an average error amplitude when different sample processing parameters are controlled; The processing parameters are input into the processing parameter library, and the processing error parameters are obtained by indexing.

5. The method for predicting mechanical properties of a casting pump body based on processing parameters according to claim 1, characterized in that: Collect the fluid characteristic parameters in the pump operation scenario, and analyze and obtain the fluid complexity coefficient and operation quality requirement coefficient, including: Collect fluid parameters in the pump operation scenario and obtain the fluid parameter sequence; Calculating the mean of the fluid parameter sequence to obtain an average fluid parameter; Randomly selecting a number of fluid parameters in the fluid parameter sequence, respectively calculating the absolute error amplitudes with the average fluid parameter, and calculating the average to obtain the fluid complexity coefficient; The average fluid parameter is used as the operation quality requirement coefficient.

6. The method for predicting mechanical properties of a casting pump body based on processing parameters according to claim 1, characterized in that: According to the composition fluctuation parameters, processing error parameters, fluid complexity coefficient and operation quality requirement coefficient, a step-by-step prediction resource is configured to predict the mechanical properties of the casting pump body, and the mechanical performance parameters of the pump body are obtained by prediction, including: Training a stepped pump performance predictor array, wherein the stepped pump performance predictor array includes a plurality of pump performance predictors corresponding to a plurality of performance prediction coefficient intervals, and the amount of training data for each pump performance predictor is different; Calculating and obtaining a performance prediction coefficient according to the composition fluctuation parameter, the processing error parameter, the fluid complexity coefficient and the operation quality requirement coefficient; According to the performance prediction coefficient interval into which the performance prediction coefficient falls, a corresponding pump body performance predictor is selected, the processing parameters are input, and the mechanical performance parameters of the pump body are obtained through prediction output.

7. The method for predicting mechanical properties of a casting pump body based on processing parameters according to claim 6 is characterized in that: Train a stepped array of pump performance predictors, including: According to the pump casting data in the historical period, a sample processing parameter set and a sample mechanical property parameter set are collected; Using machine learning to build multiple pump performance predictors; The sample processing parameter set and the sample mechanical property parameter set are divided to obtain a plurality of pump body performance prediction training data with different data volumes, wherein the data volumes of the plurality of pump body performance prediction training data are distributed from large to small; Using the plurality of pump performance prediction training data respectively, supervised training and testing the plurality of pump performance predictors are performed until convergence; Obtain multiple performance prediction coefficient intervals, where the performance prediction coefficient is greater than or equal to 0 and less than or equal to 1; According to the training data volume of the plurality of pump performance predictors, a mapping relationship with a plurality of performance prediction coefficient intervals is constructed, wherein the size of the performance prediction coefficient interval is positively correlated with the training data volume of the pump performance predictor.

8. The method for predicting mechanical properties of a casting pump body based on processing parameters according to claim 6, characterized in that: According to the composition fluctuation parameter, processing error parameter, fluid complexity coefficient and operation quality requirement coefficient, the performance prediction coefficient is calculated, including: Based on the historical data of pump casting, the maximum composition fluctuation parameters, maximum processing error parameters, maximum fluid complexity coefficient and maximum operation quality requirement coefficient within the historical period are collected; The ratios of the composition fluctuation parameter, processing error parameter, fluid complexity coefficient and operation quality requirement coefficient to the maximum composition fluctuation parameter, maximum processing error parameter, maximum fluid complexity coefficient and maximum operation quality requirement coefficient are calculated respectively, and the performance prediction coefficient is obtained by weighted calculation.

9. A system for predicting mechanical properties of a casting pump body based on processing parameters, characterized in that: include: The processing parameter acquisition module is used to acquire the processing parameters of the current pump body casting and the composition fluctuation parameters of the casting within the recent preset time range; A machining error parameter indexing module, used to obtain machining error parameters by indexing according to the machining parameters; The fluid characteristic parameter analysis module is used to collect the fluid characteristic parameters in the pump operation scenario and analyze and obtain the fluid complexity coefficient and the operation quality requirement coefficient; The mechanical property prediction module is used to configure the step-by-step prediction resources according to the composition fluctuation parameters, processing error parameters, fluid complexity coefficient and operation quality requirement coefficient, to predict the mechanical properties of the casting pump body and obtain the mechanical property parameters of the pump body.

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