Injection molding gear mold structural strength calculation method based on datamation design

Abstract

The invention relates to the technical field of mold design, in particular to an injection molding gear mold structural strength calculation method based on datamation design. The method comprises the following steps: firstly, acquiring fixed parameter data, injection molding real-time data and historical data; meanwhile, performing finite element analysis on the fixed parameter data to obtain first stress distribution data; then, fixed parameter data and injection molding real-time data are input into the trained multi-task local stress distribution prediction model, and local area masks and local stress distribution prediction data are obtained; then, obtaining a stress distribution influence coefficient according to a real-time stress distribution influence weight output by the stress distribution influence prediction model; obtaining second stress distribution data according to the stress distribution influence coefficient and the first stress distribution data; combining the local stress distribution prediction data, the local area mask and the second stress distribution data to obtain final stress distribution data; and obtaining the structural strength of the injection gear mold according to the final stress distribution data.

Description

Technical Field

[0001] The invention relates to the technical field of mold design, and in particular to a method for calculating the structural strength of an injection gear mold based on digital design. Background Art

[0002] As modern manufacturing develops towards high precision, high efficiency and low cost, the demand for gears is increasing and the requirements for quality and performance are also constantly improving. Injection gears, with their advantages of high production efficiency, low cost and strong designability, are gradually replacing traditional metal gears in many fields, thus promoting the widespread application of injection gear molds. In order to ensure product production quality, guarantee the performance of the mold and meet the safety requirements of production, it is essential to calculate the structural strength of the injection gear mold.

[0003] At present, the existing technology still has shortcomings in the calculation of the structural strength of injection molding gear molds; on the one hand, the three-dimensional model of the gear mold modeled by the existing technology is different from the actual mold, and it is impossible to accurately analyze the stress conditions of local areas such as chamfers, fillets and process holes; on the other hand, the existing technology manually inputs preset process parameters and combines the finite element analysis method to obtain the stress information of the gear mold during the injection molding process, but this cannot cope with the dynamic changes of parameters during the injection molding process, thereby reducing the accuracy and reliability of the calculation of the structural strength of the injection molding gear mold.

[0004] Therefore, a method for calculating the structural strength of injection gear molds based on data-based design was proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a method for calculating the structural strength of an injection gear mold based on data-based design. The method first obtains fixed parameter data, injection real-time data and historical data; at the same time, finite element analysis is used for the fixed parameter data to obtain first stress distribution data; then, the fixed parameter data and injection real-time data are input into the trained multi-task local stress distribution prediction model to obtain a local area mask and local stress distribution prediction data; then, according to the real-time stress distribution influence weight output by the stress distribution influence prediction model, the stress distribution influence coefficient is obtained; according to the stress distribution influence coefficient and the first stress distribution data, the second stress distribution data is obtained; the local stress distribution prediction data, the local area mask and the second stress distribution data are combined to obtain the final stress distribution data; according to the final stress distribution data, the structural strength of the injection gear mold is obtained.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for calculating the structural strength of an injection gear mold based on data-based design, comprising: Obtain fixed parameter data, injection molding real-time data and historical data; Using finite element analysis on the fixed parameter data to obtain first stress distribution data; Constructing a multi-task local stress distribution prediction model, training the multi-task local stress distribution prediction model using the historical data, inputting the fixed parameter data and the injection molding real-time data into the trained multi-task local stress distribution prediction model, and obtaining a local area mask and local stress distribution prediction data; Constructing a stress distribution influence prediction model, using the historical data to train the stress distribution influence prediction model, and obtaining the historical stress distribution influence weight; inputting the injection molding real-time data and the historical stress distribution influence weight into the trained stress distribution influence prediction model, and obtaining the real-time stress distribution influence weight; and obtaining the stress distribution influence coefficient according to the real-time stress distribution influence weight; According to the stress distribution influence coefficient and the first stress distribution data, second stress distribution data is obtained; and the local stress distribution prediction data, the local area mask and the second stress distribution data are combined to obtain final stress distribution data; According to the final stress distribution data, the structural strength of the injection gear mold is obtained.

[0007] Furthermore, the fixed parameter data includes mold structure parameters and injection molding process parameters; the injection molding real-time data includes injection molding sensor data and injection molding stress data; and the historical data includes historical parameter data, historical injection molding data and historical stress distribution data.

[0008] Furthermore, the multi-task local stress distribution prediction model is trained using the historical data, and the fixed parameter data, the injection real-time data and the first stress distribution data are input into the trained multi-task local stress distribution prediction model to obtain the local area mask and the local stress distribution prediction data. The process includes: Acquiring historical parameter data, historical injection molding data and historical stress distribution data from the historical data; Inputting the historical parameter data, the historical injection molding data and the historical stress distribution data into the multi-task local stress distribution prediction model for training to obtain a pre-trained local stress distribution prediction model; Inputting the fixed parameter data and the injection molding real-time data into the pre-trained local stress distribution prediction model to obtain the local area mask and the stress distribution prediction data; The local stress distribution prediction data is obtained according to the local area mask and the stress distribution prediction data.

[0009] Further, the multi-task local stress distribution prediction model includes: an input layer, a feature extraction layer, a local feature recognition layer, a stress distribution prediction layer based on a local area mask, and an output layer; The input layer is used to perform feature transformation on the labeled input data to obtain data features; The feature extraction layer is used to extract the data features to obtain deep features; The local feature recognition layer is used to recognize the deep features and obtain local area mask features; The stress distribution prediction layer based on the local area mask is used to obtain stress distribution prediction features according to the local area mask features and the data features; The output layer is used to receive the local area mask features and the stress distribution prediction features to obtain the local area mask and the stress distribution prediction data.

[0010] Further, the stress distribution influence prediction model is trained using the historical data to obtain the historical stress distribution influence weight; the injection real-time data and the historical stress distribution influence weight are input into the trained stress distribution influence prediction model to obtain the real-time stress distribution influence weight; and the process of obtaining the stress distribution influence coefficient according to the real-time stress distribution influence weight includes: Acquiring the injection molding real-time data; Acquire historical injection molding data and historical stress distribution data from the historical data; Inputting the historical injection molding data and the historical stress distribution data into the stress distribution influence prediction model for training to obtain the historical stress distribution influence weight; Inputting the injection molding real-time data and the historical stress distribution influence weight into the trained stress distribution influence prediction model for processing to obtain the real-time stress distribution influence weight; The real-time stress distribution influence weights of different injection molding data are weighted summed to obtain the stress distribution influence coefficient.

[0011] Furthermore, the calculation formula of the stress distribution influence coefficient is: ; in, represents the stress distribution influence coefficient; Indicates the number of types of injection molding data; represents the weighting factor of the i-th injection molding data; The real-time stress distribution influence weight of the i-th injection molding data is represented.

[0012] Further, the stress distribution impact prediction model includes: an input layer, a coding layer, a stress distribution impact prediction layer, a decoding layer and an output layer; The input layer is used to perform feature transformation on the model input data to obtain input features; The encoding layer is used to encode the input features to obtain encoded features; The stress distribution impact prediction layer is used to process the encoding features by using a ConvLSTM layer to obtain prediction features; The decoding layer is used to decode the prediction feature to obtain a decoded prediction feature; The output layer is used to receive the decoded prediction features and process them using a fully connected layer to obtain a stress distribution impact prediction result.

[0013] Further, the second stress distribution data is obtained according to the stress distribution influence coefficient and the first stress distribution data; and the implementation process of obtaining the final stress distribution data by combining the local stress distribution prediction data, the local area mask and the second stress distribution data includes: Acquire the stress distribution influence coefficient, the first stress distribution data, the local stress distribution prediction data, the local area mask and the second stress distribution data; Multiplying the stress distribution influence coefficient and the first stress distribution data to obtain the second stress distribution data; Obtaining the second stress distribution data except for the local area according to the local area mask and the second stress distribution data; The final stress distribution data is obtained by combining the second stress distribution data except for the local area with the local stress distribution prediction data.

[0014] Furthermore, the calculation formula for obtaining the final stress distribution data is: ; in, represents the final stress distribution data; represents the local stress distribution prediction data; represents the local area mask; represents the stress distribution influence coefficient; Represents an element-wise product operation; represents the first stress distribution data.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention proposes a multi-task local stress distribution prediction model for predicting stress distribution data of local areas; the model uses a local feature recognition layer and a stress distribution prediction layer to respectively realize two tasks, namely, regional feature recognition detection and stress distribution prediction; the model uses the local area mask obtained by the output of the local feature recognition layer to allow the stress distribution prediction layer to focus on the stress distribution prediction of a specific area, thereby effectively analyzing and processing the stress distribution of local areas such as chamfers, fillets and process holes, and further improving the accuracy and reliability of the structural strength calculation of injection gear molds.

[0016] 2. The present invention proposes a stress distribution influence coefficient prediction method for dynamically adjusting stress distribution data; the method obtains the stress distribution influence coefficient according to the real-time stress distribution influence weight output by the stress distribution influence prediction model; the stress distribution influence coefficient can be used to adjust the stress distribution according to the dynamic parameters in the injection molding process, thereby improving the accuracy and reliability of the injection molding gear mold structure strength calculation.

[0017] 3. The present invention proposes a stress distribution data calculation method for obtaining final mold stress distribution data; the method first applies the stress distribution influence coefficient to the first stress distribution data to obtain adjusted second stress distribution data; then combines the local stress distribution prediction data and the local area mask to obtain the final stress distribution data; the dynamically adjusted stress distribution data and the local stress distribution prediction data are merged through the calculation formula of the final stress distribution data, thereby improving the accuracy and reliability of the injection gear mold structure strength calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for calculating the structural strength of an injection gear mold based on digital design according to the present invention; Figure 2 It is a structural schematic diagram of the multi-task local stress distribution prediction model of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The present invention proposes a method for calculating the structural strength of an injection gear mold based on digital design. The method proposed by the present invention will be specifically described below in conjunction with Example 1 and Example 2, as follows:

[0021] Embodiment 1 In order to improve the production quality and production strength of injection molded gears, a certain injection molded gear manufacturer used a method for calculating the structural strength of injection molded gears based on data-based design proposed by the present invention. The process diagram of the method is as follows: Figure 1 As shown, the specific process includes: Obtain fixed parameter data, injection molding real-time data and historical data; Furthermore, the fixed parameter data includes mold structure parameters and injection molding process parameters; the injection molding real-time data includes injection molding sensor data and injection molding stress data; the historical data includes historical parameter data, historical injection molding data and historical stress distribution data; Furthermore, the mold structure parameters include: parting surface design parameters, cavity parameters, pouring system parameters, cooling system parameters, etc.; the injection molding process parameters include: temperature parameters, pressure parameters, time parameters, etc.; Furthermore, the injection molding sensing data includes injection molding temperature sensing data, injection molding pressure sensing data, injection molding flow rate sensing data, etc. The injection molding stress data is discrete data collected by using traditional methods such as strain gauge measurement method and ultrasonic detection method; Furthermore, some injection molding sensor data are selected for numerical illustration, and the details can be referred to Table 1; Table 1 Numerical representation of injection molding sensor data

[0022] Furthermore, historical data is used to train the model and can be directly called from the historical database; historical parameter data includes historical mold structure parameters and historical injection molding process parameters; historical injection molding data includes historical injection molding sensor data and historical injection molding stress data.

[0023] By introducing fixed parameter data, real-time injection molding data and historical data, a reliable input data basis can be provided for the subsequent multi-task local stress distribution prediction model and stress distribution influence prediction model, thereby effectively improving the accuracy and reliability of the injection gear mold structure strength calculation.

[0024] Using finite element analysis on fixed parameter data to obtain first stress distribution data; Furthermore, the process of obtaining the first stress distribution data is as follows: Establishing a three-dimensional model according to the mold structure parameters in the fixed parameter data; Import the constructed model and material properties into the finite element analysis software; Load the injection molding process parameters in the fixed parameter data and set the boundary conditions; A suitable solver is selected according to the model, and the solving program of the finite element analysis software is started to obtain the first stress distribution data.

[0025] The first stress distribution data obtained by finite element analysis in this embodiment is limited by the accuracy of model establishment and cannot accurately represent the stress distribution of local areas such as chamfers, fillets and process holes.

[0026] Construct a multi-task local stress distribution prediction model, use historical data to train the multi-task local stress distribution prediction model, input fixed parameter data and injection real-time data into the trained multi-task local stress distribution prediction model, and obtain local area mask and local stress distribution prediction data; Furthermore, the multi-task local stress distribution prediction model is trained using historical data, and the fixed parameter data, injection real-time data and first stress distribution data are input into the trained multi-task local stress distribution prediction model to obtain the local area mask and the local stress distribution prediction data. The process includes: Obtain historical parameter data, historical injection molding data and historical stress distribution data from historical data; Inputting historical parameter data, historical injection molding data, and historical stress distribution data into a multi-task local stress distribution prediction model for training to obtain a pre-trained local stress distribution prediction model; Inputting fixed parameter data and injection molding real-time data into the pre-trained local stress distribution prediction model to obtain local area mask and stress distribution prediction data; According to the local area mask and the stress distribution prediction data, the local stress distribution prediction data is obtained.

[0027] In this embodiment, the local feature recognition layer and the stress distribution prediction layer in the multi-task local stress distribution prediction model are used to respectively realize the two tasks of regional feature recognition detection and stress distribution prediction; the model uses the local area mask obtained by the output of the local feature recognition layer to allow the stress distribution prediction layer to focus on the stress distribution prediction of a specific area, thereby effectively analyzing and processing the stress distribution of local areas such as chamfers, fillets and process holes, and further improving the accuracy and reliability of the structural strength calculation of the injection gear mold.

[0028] Furthermore, the structure of the multi-task local stress distribution prediction model can be referred to Figure 2 ,The model includes : an input layer, a feature extraction layer, a local feature recognition layer, a stress distribution prediction layer based on ,local region mask and an output layer; The input layer is used to perform feature transformation on the labeled input data to obtain data features; Furthermore, the input data includes historical parameter data and historical injection molding data; the historical stress distribution data is used for comparison of model output.

[0029] The feature extraction layer is used to extract data features and obtain deep features; The local feature recognition layer is used to identify deep features and obtain local area mask features; Furthermore, the local feature recognition layer adopts a residual convolutional network, which includes multiple residual blocks and a fully connected layer; the residual block learns the mapping relationship between input features and output features through jump connections, which is used to extract the feature relationship between different regions; the fully connected layer is used to output local region mask features.

[0030] The stress distribution prediction layer based on the local area mask is used to obtain stress distribution prediction features according to the local area mask features and data features; Furthermore, the stress distribution prediction layer based on local area mask includes a mask modulation branch and an LSTM branch. The mask modulation branch uses four serial mask modulation residual blocks to learn the spatial characteristics of stress distribution in the local area; the LSTM branch uses four serial LSTM layers to learn the temporal variation characteristics of stress distribution; finally, the stress distribution prediction layer based on local area mask fuses the output features of different branches through channel merging operations to obtain stress distribution prediction features; Furthermore, the expression of the masked modulated residual block is as follows: ; in, represents the output features of the masked modulated residual block; Represents the Relu activation function; Represents a 3×3 convolution kernel with independent parameters; represents the local mask modulation layer; represents the input features of the mask modulated residual block; Represents the local area mask feature.

[0031] Furthermore, the expression of the local mask modulation layer is as follows: ; in, represents the output features of the local mask modulation layer; Represents a convolution kernel of size 1×1 and its parameters are independent; Represents the local area mask feature; Represents an element-wise product operation; Represents the input features of the local mask modulation layer.

[0032] The output layer is used to receive local area mask features and stress distribution prediction features to obtain local area mask and stress distribution prediction data.

[0033] The multi-task local stress distribution prediction model in this embodiment provides accurate local mask information for the stress distribution prediction layer based on the local area mask by inputting the extracted features into the local feature recognition layer, and then utilizes different branches in the stress distribution prediction layer and combines the mask feature modulation to enable the model to focus on stress distribution prediction in a specific area, thereby improving the accuracy of the final stress distribution data calculation.

[0034] Construct a stress distribution influence prediction model, use historical data to train the stress distribution influence prediction model, and obtain the historical stress distribution influence weight; input the injection molding real-time data and the historical stress distribution influence weight into the trained stress distribution influence prediction model to obtain the real-time stress distribution influence weight; obtain the stress distribution influence coefficient according to the real-time stress distribution influence weight; Furthermore, the stress distribution influence prediction model is trained using historical data to obtain historical stress distribution influence weights; the injection molding real-time data and the historical stress distribution influence weights are input into the trained stress distribution influence prediction model to obtain real-time stress distribution influence weights; and the process of obtaining the stress distribution influence coefficient according to the real-time stress distribution influence weights includes: Get real-time data of injection molding; Obtain historical injection molding data and historical stress distribution data from historical data; Inputting historical injection molding data and historical stress distribution data into the stress distribution influence prediction model for training, and obtaining the historical stress distribution influence weight; The real-time injection molding data and the historical stress distribution influence weights are input into the trained stress distribution influence prediction model for processing to obtain the real-time stress distribution influence weights; The real-time stress distribution influence weights of different injection molding data are weighted summed to obtain the stress distribution influence coefficient.

[0035] The stress distribution influence coefficient is obtained by utilizing the real-time stress distribution influence weight output by the stress distribution influence prediction model. The stress distribution influence coefficient can be used to adjust the stress distribution by dynamic parameters in the injection molding process, thereby improving the accuracy and reliability of the injection gear mold structure strength calculation.

[0036] Furthermore, the calculation formula of the stress distribution influence coefficient is: ; in, represents the stress distribution influence coefficient; Indicates the number of types of injection molding data; represents the weighting factor of the i-th injection molding data; Represents the real-time stress distribution influence weight of the i-th injection molding data.

[0037] Furthermore, the real-time stress distribution influence weight, stress distribution influence coefficient and stress distribution data are spatially distributed, and the weight, coefficient and data corresponding to each regional position in the mold are one-to-one corresponding; each regional position may be affected by different injection molding data and the stress distribution influence weight is different.

[0038] Furthermore, the number of types of injection molding data and the weighting factors of the corresponding injection molding data can be flexibly set by those skilled in the art.

[0039] By using the weighting factor to weight the impact weight of the injection molding data that may affect the stress distribution, the comprehensive real-time stress distribution impact weight, that is, the stress distribution impact coefficient, is obtained; since the data collected during the injection molding process is dynamically changing, the degree of impact on the stress distribution is also dynamically changing, so the calculated stress distribution impact coefficient is also changing; the stress distribution data simulated by the software can be adjusted and corrected using the stress distribution impact coefficient, thereby improving the accuracy and reliability of the injection molding gear mold structure strength calculation.

[0040] Further, the stress distribution impact prediction model includes: an input layer, a coding layer, a stress distribution impact prediction layer, a decoding layer and an output layer; The input layer is used to transform the model input data to obtain input features; The encoding layer is used to encode the input features to obtain encoded features; The stress distribution impact prediction layer is used to process the encoding features by using the ConvLSTM layer to obtain the prediction features; The decoding layer is used to decode the prediction features to obtain decoded prediction features; The output layer is used to receive the decoded prediction features and process them using the fully connected layer to obtain the stress distribution impact prediction results.

[0041] By utilizing the ConvLSTM layer in the stress distribution impact prediction model to extract time series features, the trend of stress distribution changes can be predicted. Combined with the advantages of the deep learning model, the stress distribution influence weights under different injection molding processes can be accurately obtained. The weights can be used to effectively adjust the stress distribution data in real time.

[0042] According to the stress distribution influence coefficient and the first stress distribution data, the second stress distribution data is obtained; and the final stress distribution data is obtained by combining the local stress distribution prediction data, the local area mask and the second stress distribution data; Further, the second stress distribution data is obtained according to the stress distribution influence coefficient and the first stress distribution data; and the implementation process of obtaining the final stress distribution data by combining the local stress distribution prediction data, the local area mask and the second stress distribution data includes: Obtaining a stress distribution influence coefficient, first stress distribution data, local stress distribution prediction data, a local area mask, and second stress distribution data; Multiplying the stress distribution influence coefficient and the first stress distribution data to obtain second stress distribution data; According to the local area mask and the second stress distribution data, second stress distribution data other than the local area is obtained; The final stress distribution data is obtained by combining the second stress distribution data except for the local area with the local stress distribution prediction data.

[0043] By combining the dynamically adjusted stress distribution data, local area mask and local stress distribution prediction data, comprehensive and accurate mold stress distribution data can be obtained, thereby improving the accuracy and reliability of injection gear mold structure strength calculation.

[0044] Furthermore, the calculation formula for obtaining the final stress distribution data is: ; in, Represents the final stress distribution data; Represents the local stress distribution prediction data; Represents a local area mask; represents the stress distribution influence coefficient; Represents an element-wise product operation; Represents the first stress distribution data.

[0045] Furthermore, the value range of the local region mask is [0,1]; Represents a non-local region mask.

[0046] This embodiment uses the calculation formula of the final stress distribution data to organically combine the output data of the multi-task local stress distribution prediction model with the finite element analysis data after the influence coefficient is adjusted through the local area mask, thereby effectively improving the accuracy and reliability of the injection gear mold structure strength calculation.

[0047] According to the final stress distribution data, the structural strength of the injection gear mold is obtained.

[0048] This embodiment provides a method for calculating the structural strength of an injection gear mold based on data-based design. The method first obtains fixed parameter data, injection real-time data, and historical data; then, the fixed parameter data and injection real-time data are input into the trained multi-task local stress distribution prediction model to obtain a local area mask and local stress distribution prediction data; then, according to the real-time stress distribution influence weight output by the stress distribution influence prediction model, the stress distribution influence coefficient is obtained; according to the stress distribution influence coefficient and the first stress distribution data obtained by finite element analysis, the second stress distribution data is obtained; the local stress distribution prediction data, the local area mask, and the second stress distribution data are combined to obtain the final stress distribution data; according to the final stress distribution data, the structural strength of the injection gear mold is obtained. This method can effectively improve the accuracy and reliability of the calculation of the structural strength of the injection gear mold.

[0049] Embodiment 2 The present invention proposes a method for calculating the structural strength of an injection gear mold based on data-based design. In order to further verify the effectiveness of the model and method flow proposed by the present invention, the present invention conducts model accuracy tests and method flow accuracy tests for different model selections and different method flows. The present invention selects two injection gear manufacturers, A and B, to conduct the above two groups of accuracy tests respectively.

[0050] The present invention selects the historical data of injection molding gear manufacturer A in the past three years as the data set of the model, among which the data from the first to second years are used as the training set of the model, and the data from the third year are used as the verification set; the sampling of the data set of the model refers to the following rules: taking weeks as units, 5 days of data per week are randomly selected; among which, 2 groups of data are selected every day in the morning, noon and evening; all data come from the same type of gear mold.

[0051] Furthermore, the historical data includes: historical parameter data, historical injection molding data and historical stress distribution data.

[0052] The present invention inputs the training sets collected from injection molding gear manufacturer A into different models for training, and obtains respective pre-training models; then inputs the validation sets into each pre-training model to obtain model prediction data; then, the model prediction data is tested and compared with the real data to obtain the model accuracy test results; the model accuracy tests are respectively recorded as test one, test two, test three and test four. The models corresponding to each test are: the multi-task local stress distribution prediction model proposed by the present invention, recorded as model one; the local stress distribution prediction model without a local feature recognition layer and the local area mask is obtained by manual adjustment, recorded as model two; the local stress distribution prediction model without a local feature recognition layer and a local area mask, recorded as model three; the local stress distribution prediction model using traditional CNN, recorded as model four.

[0053] The present invention uses a preset threshold to compare the model prediction data with the real data, and obtains the accuracy test results under different models according to the proportion within a reasonable range; the model accuracy test results are shown in Table 2.

[0054] Table 2 Model accuracy test results

[0055] From the results in Table 2, it can be seen that the accuracy test results of the model proposed in the present invention are better than those of other models; thus, it can be shown that the multi-task local stress distribution prediction model proposed in the present invention can predict accurate local stress distribution data, further improving the accuracy of the structural strength of the injection gear mold; In order to further test the accuracy of the method flow, this embodiment collects historical data of injection molding gear manufacturer B in the past two years, and then divides the sampled data set into 4 groups of data for testing according to the same data sampling rules, which are respectively recorded as: test sample one, test sample two, test sample three and test sample four; each group of test data is processed according to method flow one and method flow two, respectively, to obtain final stress distribution data one and final stress distribution data two; wherein, method flow one is the method for calculating the final stress distribution data proposed by the present invention; method flow two removes the process of adjusting the influence coefficient of the first stress distribution data in the method flow of the present invention; finally, combined with the proofreading of the historical real stress distribution data, the test accuracy under different processes is obtained; the method flow accuracy test results are shown in Table 3; Table 3 Method flow accuracy test results

[0056] It can be seen from the results in Table 3 that the accuracy test results obtained by using method flow 1, that is, the scheme for calculating the final stress distribution data proposed by the present invention, are better than those obtained by using method flow 2. This shows that it is necessary to dynamically adjust the first stress distribution data using the stress distribution influence coefficient, which can improve the real-time accuracy of the structural strength of the injection gear mold.

[0057] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating the structural strength of an injection gear mold based on data-based design, characterized in that: include: Obtain fixed parameter data, injection molding real-time data and historical data; Using finite element analysis on the fixed parameter data to obtain first stress distribution data; Constructing a multi-task local stress distribution prediction model, training the multi-task local stress distribution prediction model using the historical data, inputting the fixed parameter data and the injection molding real-time data into the trained multi-task local stress distribution prediction model, and obtaining a local area mask and local stress distribution prediction data; Constructing a stress distribution influence prediction model, using the historical data to train the stress distribution influence prediction model, and obtaining the historical stress distribution influence weight; inputting the injection molding real-time data and the historical stress distribution influence weight into the trained stress distribution influence prediction model, and obtaining the real-time stress distribution influence weight; and obtaining the stress distribution influence coefficient according to the real-time stress distribution influence weight; According to the stress distribution influence coefficient and the first stress distribution data, second stress distribution data is obtained; and the local stress distribution prediction data, the local area mask and the second stress distribution data are combined to obtain final stress distribution data; According to the final stress distribution data, the structural strength of the injection gear mold is obtained.

2. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 1 is characterized in that: The fixed parameter data includes mold structure parameters and injection molding process parameters; the real-time injection molding data includes injection molding sensor data and injection molding stress data; and the historical data includes historical parameter data, historical injection molding data and historical stress distribution data.

3. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 1 is characterized in that: The process of training the multi-task local stress distribution prediction model by using the historical data, inputting the fixed parameter data, the injection real-time data and the first stress distribution data into the trained multi-task local stress distribution prediction model, and obtaining the local area mask and the local stress distribution prediction data includes: Acquiring historical parameter data, historical injection molding data and historical stress distribution data from the historical data; Inputting the historical parameter data, the historical injection molding data and the historical stress distribution data into the multi-task local stress distribution prediction model for training to obtain a pre-trained local stress distribution prediction model; Inputting the fixed parameter data and the injection molding real-time data into the pre-trained local stress distribution prediction model to obtain the local area mask and the stress distribution prediction data; The local stress distribution prediction data is obtained according to the local area mask and the stress distribution prediction data.

4. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 3 is characterized in that: The multi-task local stress distribution prediction model includes: an input layer, a feature extraction layer, a local feature recognition layer, a stress distribution prediction layer based on a local area mask, and an output layer; The input layer is used to perform feature transformation on the labeled input data to obtain data features; The feature extraction layer is used to extract the data features to obtain deep features; The local feature recognition layer is used to recognize the deep features and obtain local area mask features; The stress distribution prediction layer based on the local area mask is used to obtain stress distribution prediction features according to the local area mask features and the data features; The output layer is used to receive the local area mask features and the stress distribution prediction features to obtain the local area mask and the stress distribution prediction data.

5. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 1 is characterized in that: The stress distribution influence prediction model is trained using the historical data to obtain the historical stress distribution influence weight; the injection molding real-time data and the historical stress distribution influence weight are input into the trained stress distribution influence prediction model to obtain the real-time stress distribution influence weight; The process of obtaining the stress distribution influence coefficient according to the real-time stress distribution influence weight includes: Acquiring the injection molding real-time data; Acquire historical injection molding data and historical stress distribution data from the historical data; Inputting the historical injection molding data and the historical stress distribution data into the stress distribution influence prediction model for training to obtain the historical stress distribution influence weight; Inputting the injection molding real-time data and the historical stress distribution influence weight into the trained stress distribution influence prediction model for processing to obtain the real-time stress distribution influence weight; The real-time stress distribution influence weights of different injection molding data are weighted summed to obtain the stress distribution influence coefficient.

6. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 5 is characterized in that: The calculation formula of the stress distribution influence coefficient is: ； in, represents the stress distribution influence coefficient; Indicates the number of types of injection molding data; represents the weighting factor of the i-th injection molding data; The real-time stress distribution influence weight of the i-th injection molding data is represented.

7. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 1 is characterized in that: The stress distribution impact prediction model comprises: an input layer, a coding layer, a stress distribution impact prediction layer, a decoding layer and an output layer; The input layer is used to perform feature transformation on the model input data to obtain input features; The encoding layer is used to encode the input features to obtain encoded features; The stress distribution impact prediction layer is used to process the encoding features by using a ConvLSTM layer to obtain prediction features; The decoding layer is used to decode the prediction feature to obtain a decoded prediction feature; The output layer is used to receive the decoded prediction features and process them using a fully connected layer to obtain a stress distribution impact prediction result.

8. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 1 is characterized in that: obtaining the second stress distribution data according to the stress distribution influence coefficient and the first stress distribution data; The implementation process of obtaining the final stress distribution data by combining the local stress distribution prediction data, the local area mask and the second stress distribution data includes: Acquire the stress distribution influence coefficient, the first stress distribution data, the local stress distribution prediction data, the local area mask and the second stress distribution data; Multiplying the stress distribution influence coefficient and the first stress distribution data to obtain the second stress distribution data; Obtaining the second stress distribution data except for the local area according to the local area mask and the second stress distribution data; The final stress distribution data is obtained by combining the second stress distribution data except for the local area with the local stress distribution prediction data.

9. The method for calculating the structural strength of an injection gear mold based on digital design according to claim 8 is characterized in that: The calculation formula for obtaining the final stress distribution data is: ； in, represents the final stress distribution data; represents the local stress distribution prediction data; represents the local area mask; represents the stress distribution influence coefficient; Represents an element-wise product operation; represents the first stress distribution data.

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