CN114529105A - Engine crankshaft high-temperature performance analysis processing method based on BP neural network - Google Patents
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Abstract
The invention discloses an engine crankshaft high-temperature performance analysis processing method based on a BP neural network, belonging to the technical field of engine crankshaft high-temperature performance analysis and comprising the following steps: acquiring relevant sample data of an engine crankshaft; according to the method, the performance of the engine crankshaft can be detected at low cost, the performance data of the crankshaft under high-temperature conditions which can be obtained at high cost can be obtained, the crankshaft product can be randomly sampled, and the mechanical performance of the crankshaft can be detected under the high-temperature conditions of normal temperature and different temperatures respectively: meanwhile, the performance curves of various mechanical properties such as the hardness, the tensile strength, the yield strength, the spheroidization grade, the pearlite content and the like of the engine crankshaft under the high-temperature condition can be quickly obtained by utilizing the BP neural network estimation; meanwhile, the crankshaft high-temperature performance estimation system is constructed by utilizing the SpringMVC architecture, and the import and export of performance data and the visualization of statistical analysis of product performance data can be realized.
Description
Technical Field
The invention belongs to the technical field of engine crankshaft high-temperature performance analysis, and particularly relates to an engine crankshaft high-temperature performance analysis processing method based on a BP neural network.
Background
With the rapid development of the social progress and industrialization, automobiles gradually enter common families, the distance between people is greatly shortened due to the appearance of the automobiles, the space-time distance is shortened, the components in the automobiles are numerous, and different accessories have different functions, wherein an engine is one of the most core and the most basic components in the automobiles, the engine is the heart of the automobiles, a power source of the automobiles is not only the automobiles, but also a plurality of mechanical structures capable of doing work need to be driven by the engine, and a large number of components are arranged in the engines and support the work of the engines together, wherein an engine crankshaft is one of the most important structures in the engines, and the crankshaft bears the force transmitted by connecting rods and converts the force into torque to be output through the crankshaft and drive other accessories on the engines to work. The crankshaft is subjected to the combined action of centrifugal force of rotating mass, gas inertia force with periodic change and reciprocating inertia force, so that the crankshaft is subjected to the action of bending and twisting load.
Disclosure of Invention
The invention aims to: in order to solve the problems, the engine crankshaft high-temperature performance analysis processing method based on the BP neural network is provided.
In order to achieve the purpose, the invention adopts the following technical scheme: the engine crankshaft high-temperature performance analysis processing method based on the BP neural network comprises the following steps:
s1, obtaining relevant sample data of the engine crankshaft;
s2, fitting a crankshaft high-temperature performance curve;
s3, estimating high-temperature performance data of the crankshaft;
s4, obtaining engine crankshaft test data;
s5, inputting the obtained engine crankshaft test data into a database;
and S6, importing the input data into a SpringMVP framework for processing.
As a further description of the above technical solution:
and in the step S1, acquiring sample data related to the crankshaft of the engine, training the sample data, and changing the connection weight of the network nodes to enable the network nodes to have the functions of estimation and classification, wherein the acquisition channel of the sample data is one or more of internet acquisition, USB data import acquisition and Bluetooth acquisition.
As a further description of the above technical solution:
and in the step S2, the crankshaft high-temperature performance curve is fitted within a certain time, and the fitted crankshaft high-temperature performance curves are respectively numbered for 75-95 min.
As a further description of the above technical solution:
in S3, the crankshaft high temperature performance data is estimated, a range is drawn for the crankshaft high temperature performance data, and the partition is performed according to the drawn range.
As a further description of the above technical solution:
in the step S4, engine crankshaft test data are obtained, and the specific steps are as follows:
a1, obtaining normal-temperature and high-temperature performance test data of the crankshaft;
a2, acquiring data to be tested of the normal temperature performance of the crankshaft;
and A3, acquiring high-temperature performance recovery test data of the crankshaft.
As a further description of the above technical solution:
in S5, the acquired engine crankshaft test data is input into the database, and backup processing is performed on the acquired data.
As a further description of the above technical solution:
in S6, the input data is imported into a SpringMVP framework for processing, the SpringMVP framework is composed of a data access layer, an analysis layer, a control layer, and a view layer, and the processing specifically includes:
b1, the imported data will enter the data access layer first;
b2, the data enters an analysis layer for analysis;
b3, the control layer performs user interaction;
b4, the view layer acquires data from the model layer.
As a further description of the above technical solution:
in B1, the imported data will first enter the data access layer, and perform operations including adding, deleting and modifying on the test data, the estimation data and the feedback data.
As a further description of the above technical solution:
in B2, data enter an analysis layer for analysis, a data access layer and the analysis layer form a model layer, the model layer is coordinated according to self rules to complete statistical analysis operation, and data required by a control layer are returned.
As a further description of the above technical solution:
in B3, the control layer performs user interaction, and requests the required data from the model layer according to the user command acquired from the view layer, and in B4, the view layer acquires the data from the model layer to visually display the data for the user, and transmits the user request to the control layer.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
according to the method, the performance of the engine crankshaft can be detected at low cost, the performance data of the crankshaft under high-temperature conditions which can be obtained at high cost can be obtained, the crankshaft product can be randomly sampled, and the mechanical performance of the crankshaft can be detected under the high-temperature conditions of normal temperature and different temperatures respectively: meanwhile, the performance curves of various mechanical properties such as the hardness, the tensile strength, the yield strength, the spheroidization grade, the pearlite content and the like of the engine crankshaft under the high-temperature condition can be quickly obtained by utilizing the BP neural network estimation; meanwhile, the method utilizes a SpringMVC framework to construct a crankshaft high-temperature performance estimation system, can realize the import and export of performance data and the visualization of statistical analysis of product performance data, can realize the detection and inference of mechanical properties of the engine crankshaft such as hardness, tensile strength, yield strength, spheroidization grade, pearlite content and the like under a high-temperature condition in a normal-temperature environment, and has the accuracy rate of more than 95%.
Drawings
FIG. 1 is a flow chart of an engine crankshaft high-temperature performance analysis processing method based on a BP neural network.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, the present invention provides a technical solution: the engine crankshaft high-temperature performance analysis processing method based on the BP neural network comprises the following steps:
s1, acquiring sample data related to the engine crankshaft, training the sample data, and changing the connection weight of the network nodes to enable the network nodes to have the functions of estimation and classification, wherein the acquisition channel of the sample data is acquired by the Internet;
s2, fitting the crankshaft high-temperature performance curve within a certain time, and numbering the fitted crankshaft high-temperature performance curves respectively for 75 min;
s3, estimating the high-temperature performance data of the crankshaft, drawing up a range for the high-temperature performance data of the crankshaft, and partitioning according to the drawn-up range;
s4, obtaining engine crankshaft test data, specifically comprising the following steps:
a1, obtaining normal-temperature and high-temperature performance test data of the crankshaft;
a2, acquiring data to be tested of the normal temperature performance of the crankshaft;
a3, obtaining high-temperature performance recovery test data of the crankshaft;
s5, inputting the obtained engine crankshaft test data into a database, and performing backup processing on the obtained data;
s6, importing the input data into a springMVP framework for processing, wherein the springMVP framework is composed of a data access layer, an analysis layer, a control layer and a view layer, and the processing specifically comprises the following steps:
b1, the imported data enters a data access layer firstly, and the operation is carried out on the detection data, the estimation data and the feedback data, including the addition, deletion and modification;
b2, the data enters an analysis layer for analysis, a data access layer and the analysis layer form a model layer, the model layer is coordinated according to self rules to complete statistical analysis operation, and data required by a control layer is returned;
b3, the control layer carries out user interaction, and asks for needed data from the model layer according to the user command obtained from the view layer;
b4, the view layer acquires data from the model layer, presents the data to a user for visualization, and transmits the user request to the control layer.
In the embodiment, the method can realize that the performance surface of the engine crankshaft can be detected at low cost in a normal temperature environment, the performance data of the crankshaft under a high-temperature condition which can be obtained at high cost can be obtained, the crankshaft product can be randomly sampled, the mechanical performance of the crankshaft can be detected under the high-temperature conditions of normal temperature and different temperatures, and meanwhile, the performance data can be obtained and the sample database can be established.
Example 2
Referring to fig. 1, the present invention provides a technical solution: the engine crankshaft high-temperature performance analysis processing method based on the BP neural network comprises the following steps:
s1, obtaining sample data related to the engine crankshaft, training the sample data, changing the connection weight of the network nodes to enable the network nodes to have the functions of estimation and classification, wherein the sample data obtaining channel is used for obtaining Internet and importing USB data;
s2, fitting the high-temperature performance curve of the crankshaft within a certain time, and numbering the fitted high-temperature performance curve of the crankshaft for 85 min;
s3, estimating the high-temperature performance data of the crankshaft, drawing up a range for the high-temperature performance data of the crankshaft, and partitioning according to the drawn-up range;
s4, obtaining engine crankshaft test data, specifically comprising the following steps:
a1, obtaining normal-temperature and high-temperature performance test data of the crankshaft;
a2, acquiring data to be tested of the normal temperature performance of the crankshaft;
a3, obtaining high-temperature performance recovery test data of the crankshaft;
s5, inputting the obtained engine crankshaft test data into a database, and performing backup processing on the obtained data;
s6, importing the input data into a spring MVP framework for processing, wherein the spring MVP framework is composed of a data access layer, an analysis layer, a control layer and a view layer, and the processing specifically comprises the following steps:
b1, the imported data enters a data access layer firstly, and the operation is carried out on the detection data, the estimation data and the feedback data, including the addition, deletion and modification;
b2, the data enters an analysis layer for analysis, a data access layer and the analysis layer form a model layer, the model layer is coordinated according to self rules to complete statistical analysis operation, and data required by a control layer is returned;
b3, the control layer carries out user interaction, and asks for needed data from the model layer according to the user command obtained from the view layer;
b4, the view layer acquires data from the model layer, presents the data to a user for visualization, and transmits the user request to the control layer.
In the embodiment, the performance curves of various mechanical properties such as hardness, tensile strength, yield strength, spheroidization grade, pearlite content and the like of the crankshaft of the engine under a high-temperature condition can be rapidly obtained by estimating based on the BP neural network, and basic statistical analysis is carried out on various properties of crankshaft products.
Example 3
Referring to fig. 1, the present invention provides a technical solution: the engine crankshaft high-temperature performance analysis processing method based on the BP neural network comprises the following steps:
s1, acquiring sample data related to the engine crankshaft, training the sample data, changing the connection weight of the network node to enable the network node to have the functions of estimation and classification, wherein the acquisition channel of the sample data is Internet acquisition, USB data import acquisition and Bluetooth acquisition;
s2, fitting the crankshaft high-temperature performance curve within a certain time, and numbering the fitted crankshaft high-temperature performance curve respectively for 75-95 min;
s3, estimating the high-temperature performance data of the crankshaft, drawing up a range for the high-temperature performance data of the crankshaft, and partitioning according to the drawn-up range;
s4, obtaining engine crankshaft test data, specifically comprising the following steps:
a1, obtaining normal-temperature and high-temperature performance test data of the crankshaft;
a2, acquiring data to be tested of the normal temperature performance of the crankshaft;
a3, obtaining high-temperature performance recovery test data of the crankshaft;
s5, inputting the obtained engine crankshaft test data into a database, and performing backup processing on the obtained data;
s6, importing the input data into a springMVP framework for processing, wherein the springMVP framework is composed of a data access layer, an analysis layer, a control layer and a view layer, and the processing specifically comprises the following steps:
b1, the imported data enters a data access layer firstly, and the operation is carried out on the detection data, the estimation data and the feedback data, including the addition, deletion and modification;
b2, the data enters an analysis layer for analysis, a data access layer and the analysis layer form a model layer, the model layer is coordinated according to self rules to complete statistical analysis operation, and data required by a control layer is returned;
b3, the control layer carries out user interaction, and asks for needed data from the model layer according to the user command obtained from the view layer;
b4, the view layer acquires data from the model layer, presents the data to a user for visualization, and transmits the user request to the control layer.
In the embodiment, the method utilizes a SpringMVC framework to construct a crankshaft high-temperature performance estimation system, the import and export of performance data and the visualization of statistical analysis of product performance data are realized, the detection and inference of mechanical properties of the engine crankshaft under high-temperature conditions such as hardness, tensile strength, yield strength, spheroidization grade, pearlite content and the like can be realized in a normal-temperature environment by adopting the method, and the accuracy rate reaches over 95%.
In examples 1-3, in order to test the accuracy of the method, a genetic algorithm is used to predict and analyze the errors of random test points, and the results are as follows:
the above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. The engine crankshaft high-temperature performance analysis processing method based on the BP neural network is characterized by comprising the following steps: the method comprises the following steps:
s1, obtaining relevant sample data of the engine crankshaft;
s2, fitting a crankshaft high-temperature performance curve;
s3, estimating high-temperature performance data of the crankshaft;
s4, obtaining engine crankshaft test data;
s5, inputting the obtained engine crankshaft test data into a database;
and S6, importing the input data into a SpringMVP framework for processing.
2. The method for analyzing and processing the high temperature performance of the engine crankshaft based on the BP neural network of claim 1, wherein in S1, sample data related to the engine crankshaft is obtained, sample data training is performed, and a connection weight of a network node is changed, wherein the obtaining channel of the sample data is one or more of internet obtaining, USB data import obtaining, or bluetooth obtaining.
3. The method for analyzing and processing the high temperature performance of the engine crankshaft based on the BP neural network as claimed in claim 1, wherein in the step S2, the crankshaft high temperature performance curve is fitted within a certain time, and the fitted crankshaft high temperature performance curves are respectively numbered for 75-95 min.
4. The method as claimed in claim 1, wherein in S3, the crankshaft high temperature performance data is estimated, a range is drawn for the crankshaft high temperature performance data, and the range is partitioned according to the drawn range.
5. The method for analyzing and processing the high-temperature performance of the engine crankshaft based on the BP neural network as claimed in claim 1, wherein in the step S4, the engine crankshaft test data is obtained by the specific steps of:
a1, obtaining normal-temperature and high-temperature performance test data of the crankshaft;
a2, obtaining data to be tested of the normal temperature performance of the crankshaft;
and A3, acquiring high-temperature performance recovery test data of the crankshaft.
6. The BP neural network-based engine crankshaft high temperature performance analysis processing method according to claim 1, wherein in S5, acquired engine crankshaft test data is input into a database, and backup processing is performed on the acquired data.
7. The method for analyzing and processing the high temperature performance of the engine crankshaft based on the BP neural network of claim 1, wherein in S6, the input data is imported into a SpringMVP framework for processing, the SpringMVP framework is composed of a data access layer, an analysis layer, a control layer and a view layer, and the processing specifically comprises the following steps:
b1, the imported data will enter the data access layer first;
b2, the data enters an analysis layer for analysis;
b3, carrying out user interaction by the control layer;
b4, the view layer acquires data from the model layer.
8. The method as claimed in claim 7, wherein the imported data is first entered into a data access layer in B1, and the operation is performed on the detected data, the estimated data and the measured data, including addition, deletion and modification.
9. The method for analyzing and processing the high temperature performance of the engine crankshaft based on the BP neural network as claimed in claim 7, wherein in B2, the data enters an analysis layer for analysis, the data access layer and the analysis layer form a model layer, the model layer is coordinated according to its own rules to complete statistical analysis operation, and then the data required by the control layer is returned.
10. The method as claimed in claim 7, wherein in B3, the control layer performs user interaction, and requests the model layer for the required data according to the user command obtained from the view layer, and in B4, the view layer obtains the data from the model layer to give the user a visual presentation, and transmits the user request to the control layer.
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