CN115091283B - Control and adjustment method and system for efficient grinding of crankshaft - Google Patents
Control and adjustment method and system for efficient grinding of crankshaft Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B5/00—Machines or devices designed for grinding surfaces of revolution on work, including those which also grind adjacent plane surfaces; Accessories therefor
- B24B5/36—Single-purpose machines or devices
- B24B5/42—Single-purpose machines or devices for grinding crankshafts or crankpins
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
- B24B49/02—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation according to the instantaneous size and required size of the workpiece acted upon, the measuring or gauging being continuous or intermittent
- B24B49/04—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation according to the instantaneous size and required size of the workpiece acted upon, the measuring or gauging being continuous or intermittent involving measurement of the workpiece at the place of grinding during grinding operation
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Abstract
The application discloses a control and adjustment method and a system for efficient grinding of a crankshaft, and relates to the technical field of artificial intelligence, wherein the method comprises the following steps: classifying crankshaft multidimensional data information through a crankshaft characteristic decision tree to obtain crankshaft characteristic information; finite element segmentation is carried out on the crankshaft image information to obtain crankshaft image segmentation information; performing integrated learning based on the crankshaft characteristic information and the crankshaft image segmentation information to construct a crankshaft grinding parameter integrated analysis model; inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information; and acquiring error parameters of the grinding device, and controlling and adjusting the crankshaft grinding based on the error parameters of the grinding device and the crankshaft grinding parameter information. The technical effects of determining crankshaft grinding parameters by constructing a crankshaft grinding parameter integrated analysis model, improving the crankshaft grinding precision and the grinding efficiency and further ensuring the crankshaft grinding quality are achieved.
Description
Technical Field
The application relates to the field of artificial intelligence, in particular to a control and adjustment method and system for efficient grinding of a crankshaft.
Background
The crankshaft is a core component for ensuring the normal operation of the engine, is a key component of the piston engine, bears the force transmitted by the connecting rod, converts the force into torque, outputs the torque through the crankshaft and drives other accessories on the engine to work, and plays an important role in bearing impact load and transmitting power in the engines of cars, trucks, motorcycles, ships, aeromodels and lawnmowers. For this purpose, each part of the crankshaft, such as the flange end, the shaft end, the main journal and the connecting rod journal, must be precisely ground.
However, the prior art has the technical problems of low crankshaft grinding precision and long time consumption, and the quality of crankshaft grinding is affected.
Disclosure of Invention
The application solves the technical problems of influence on crankshaft grinding quality caused by low crankshaft grinding precision and long time consumption in the prior art by providing the control and adjustment method and the system for efficiently grinding the crankshaft, and achieves the technical effects of determining crankshaft grinding parameters by constructing a crankshaft grinding parameter integrated analysis model, improving the crankshaft grinding precision and the grinding efficiency and further ensuring the crankshaft grinding quality.
In view of the above problems, the present application provides a method and a system for controlling and adjusting a high-efficiency grinding crankshaft.
In a first aspect, the present application provides a control adjustment method for efficient grinding of a crankshaft, the method comprising: acquiring crankshaft acquisition data information, wherein the crankshaft acquisition data information comprises crankshaft multidimensional data information and crankshaft image information; constructing a crankshaft characteristic decision tree, and classifying the crankshaft multidimensional data information through the crankshaft characteristic decision tree to obtain crankshaft characteristic information; finite element segmentation is carried out on the crankshaft image information to obtain crankshaft image segmentation information; based on the crankshaft characteristic information and the crankshaft image segmentation information, uploading the crankshaft characteristic information and the crankshaft image segmentation information to a data integration training platform for learning, and constructing a crankshaft grinding parameter integration analysis model; acquiring data information of a crankshaft to be ground and image information of the crankshaft to be ground; inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information; and acquiring error parameters of a grinding device, and controlling and adjusting the crankshaft grinding based on the error parameters of the grinding device and the crankshaft grinding parameter information.
In another aspect, the present application also provides a control adjustment system for efficient grinding of a crankshaft, the system comprising: the data acquisition module is used for acquiring crankshaft acquisition data information, wherein the crankshaft acquisition data information comprises crankshaft multidimensional data information and crankshaft image information; the characteristic classification module is used for constructing a crankshaft characteristic decision tree, classifying the crankshaft multidimensional data information through the crankshaft characteristic decision tree and obtaining crankshaft characteristic information; the image segmentation module is used for carrying out finite element segmentation on the crankshaft image information to obtain crankshaft image segmentation information; the model construction module is used for uploading the crankshaft characteristic information and the crankshaft image segmentation information to a data integration training platform for learning and constructing a crankshaft grinding parameter integration analysis model; the data acquisition module is used for acquiring data information of the crankshafts to be ground and image information of the crankshafts to be ground; the model output module is used for inputting the data information of the crankshaft to be ground and the image information of the crankshaft to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information; and the control and adjustment module is used for acquiring error parameters of the grinding device and controlling and adjusting the crankshaft grinding based on the error parameters of the grinding device and the crankshaft grinding parameter information.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
classifying crankshaft multidimensional data information through a crankshaft characteristic decision tree to obtain crankshaft characteristic information, carrying out finite element segmentation on the crankshaft image information to obtain crankshaft image segmentation information, constructing a crankshaft grinding parameter integrated analysis model based on crankshaft characteristic information and crankshaft image segmentation information integrated training, and inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information; and controlling and adjusting the crank grinding based on the error parameter of the grinding device and the crank grinding parameter information. And further, the crankshaft grinding parameters are determined by constructing an integrated analysis model of the crankshaft grinding parameters, so that the crankshaft grinding precision and the grinding efficiency are improved, and the technical effect of ensuring the crankshaft grinding quality is further achieved.
Drawings
FIG. 1 is a flow chart of a method for controlling and adjusting a high-efficiency grinding crankshaft according to the present application;
FIG. 2 is a schematic flow chart of a crankshaft grinding parameter integrated analysis model constructed in the control and adjustment method of the high-efficiency grinding crankshaft;
FIG. 3 is a schematic flow chart of obtaining information of crankshaft grinding parameters in a method for controlling and adjusting a high-efficiency grinding crankshaft according to the present application;
FIG. 4 is a schematic diagram of a control and adjustment system for efficient grinding of crankshafts in accordance with the present application;
reference numerals illustrate: the system comprises a data acquisition module 11, a feature classification module 12, an image segmentation module 13, a model construction module 14, a data acquisition module 15, a model output module 16 and a control adjustment module 17.
Detailed Description
The application solves the technical problems of influence on crankshaft grinding quality caused by low crankshaft grinding precision and long time consumption in the prior art by providing the control and adjustment method system for efficiently grinding the crankshaft, and achieves the technical effects of determining crankshaft grinding parameters by constructing a crankshaft grinding parameter integrated analysis model, improving the crankshaft grinding precision and the grinding efficiency and further ensuring the crankshaft grinding quality.
Example 1
As shown in FIG. 1, the application provides a control adjustment method for efficiently grinding a crankshaft, comprising the following steps:
step S100: acquiring crankshaft acquisition data information, wherein the crankshaft acquisition data information comprises crankshaft multidimensional data information and crankshaft image information;
specifically, the crankshaft is a core component for ensuring the normal operation of the engine, is a key component of the piston engine, bears the force transmitted by the connecting rod, converts the force into torque, outputs the torque through the crankshaft and drives other accessories on the engine to work, and plays an important role in bearing impact load and transmitting power in the engines of cars, trucks, motorcycles, ships, aeromodels and lawnmowers. For this purpose, each part of the crankshaft, such as the flange end, the shaft end, the main journal and the connecting rod journal, must be precisely ground.
The method comprises the steps of acquiring crankshaft data information, wherein the crankshaft acquired data information comprises crankshaft multidimensional data information and crankshaft image information, the crankshaft multidimensional data information comprises various information such as crankshaft types, materials, structural dimensions, application purposes and the like, the crankshaft image information comprises information such as colors, structures and surface features of a crankshaft, the crankshaft data information is comprehensively acquired, and the follow-up model training result is more accurate.
Step S200: constructing a crankshaft characteristic decision tree, and classifying the crankshaft multidimensional data information through the crankshaft characteristic decision tree to obtain crankshaft characteristic information;
further, the step S200 of the present application further includes:
step S210: obtaining a crankshaft structural form attribute, and taking the crankshaft structural form attribute as a first crankshaft classification characteristic;
step S220: obtaining a crankshaft process material attribute, and taking the crankshaft process material attribute as a second crankshaft classification characteristic;
step S230: obtaining a crankshaft application type attribute, and taking the crankshaft application type attribute as a third crankshaft classification characteristic;
step S240: and constructing the crankshaft characteristic decision tree based on the first crankshaft classification characteristic, the second crankshaft classification characteristic and the third crankshaft classification characteristic.
Specifically, to build a crankshaft feature decision tree specifically, crankshaft classification features are first determined. The crankshaft structural form attribute is used as a first crankshaft classification feature, the crankshaft structural form attribute is classified according to a crankshaft structure, and the main structure of the crankshaft comprises a crankshaft main journal, a connecting rod journal, a crankshaft arm, a front journal, a rear journal and the like, and can be divided into an integral crankshaft, a combined crankshaft, a disc type crankshaft and the like according to structural composition types. And taking the crankshaft process material property as a second crankshaft classification characteristic, wherein the crankshaft process material property is a manufacturing material of a crankshaft, and comprises a forged steel crankshaft, a cast iron crankshaft and the like, and the grinding force is correspondingly different according to different material properties.
And taking the crankshaft application type attribute as a third crankshaft classification characteristic, wherein the crankshaft application type attribute is the application type of the crankshaft in the engine, such as a gasoline engine crankshaft, a diesel engine crankshaft and the like, and the crankshaft requirements are different according to different application attributes. The decision tree is a decision analysis method for solving the probability that the expected value of the net present value is larger than or equal to zero by forming the decision tree on the basis of knowing the occurrence probability of various conditions, and judging the feasibility of the net present value, and is a graphical method for intuitively applying probability analysis.
And taking the first crankshaft classification feature, the second crankshaft classification feature and the third crankshaft classification feature as internal nodes of the crankshaft feature decision tree respectively, and carrying out information entropy calculation on the internal nodes to carry out priority classification on the feature with the minimum entropy value, so that the crankshaft feature decision tree is constructed in a recursion way. And classifying the crankshaft multidimensional data information through the crankshaft characteristic decision tree to obtain corresponding crankshaft characteristic information, namely crankshaft characteristic classification results, wherein the crankshaft characteristic classification results comprise structural form characteristics, process material characteristics, application type characteristics and the like. And classifying the crankshaft multidimensional data through the accuracy construction of the crankshaft characteristic decision tree, thereby improving the accuracy and the specificity of the crankshaft data processing result.
Step S300: finite element segmentation is carried out on the crankshaft image information to obtain crankshaft image segmentation information;
specifically, finite element segmentation is carried out on the crankshaft image information, the crankshaft image information is segmented into a plurality of image grids according to the image structure size attribute of the crankshaft, the more the grids are segmented, the more the detail of the image is displayed, the crankshaft image segmentation information is obtained, and the follow-up analysis of the appearance characteristics of the image is more accurate and efficient.
Step S400: based on the crankshaft characteristic information and the crankshaft image segmentation information, uploading the crankshaft characteristic information and the crankshaft image segmentation information to a data integration training platform for learning, and constructing a crankshaft grinding parameter integration analysis model;
as shown in fig. 2, further, the step S400 of the present application further includes:
step S410: inputting the crankshaft characteristic information and the crankshaft image segmentation information into a deep convolutional neural network for training, and constructing a crankshaft grinding parameter analysis model;
step S420: acquiring multi-party crankshaft acquisition data information, and respectively inputting the multi-party crankshaft acquisition data information into the deep convolutional neural network for distributed training to acquire a multi-party crankshaft grinding parameter analysis model;
step S430: extracting training model parameters of the one-side crankshaft grinding parameter analysis model and the multiparty crankshaft grinding parameter analysis model;
step S440: and the data integration training platform performs joint training on the training model parameters to obtain the crankshaft grinding parameter integration analysis model.
Specifically, based on the crankshaft characteristic information and the crankshaft image segmentation information, the crankshaft characteristic information and the crankshaft image segmentation information are uploaded to a data integration training platform for learning, and the data integration training platform is used for training a plurality of source data models, so that the final model construction is more reasonable and accurate. Firstly, inputting the crankshaft characteristic information and the crankshaft image segmentation information into a deep convolutional neural network for training, wherein the deep convolutional neural network is a feedforward neural network which comprises convolutional calculation and has a deep structure, and the characteristic recognition stability is high. A crankshaft grinding parameter analysis model is built through training and used for processing crankshaft data in the platform or an enterprise so as to analyze crankshaft grinding parameter information. In order to analyze crankshaft grinding parameters more accurately and comprehensively, crankshaft data acquisition is carried out on other multiple platforms or enterprises in the same way, and the multiparty crankshaft acquisition data information is respectively input into the deep convolutional neural network for distributed training, so that a multiparty crankshaft grinding parameter analysis model corresponding to the multiparty crankshaft acquisition data information is obtained.
And extracting the one-side crankshaft grinding parameter analysis model and training model parameters of the multiparty crankshaft grinding parameter analysis model, wherein the training model parameters comprise crankshaft parameters, crankshaft grinding types, grinding speeds, grinding force parameters, model corresponding weights and the like. The data integration training platform performs joint training on the training model parameters, builds the integrated analysis model of the crankshaft grinding parameters after federal integration training, has high safety coefficient in the training process, ensures that the output result of the integrated analysis model of the crankshaft grinding parameters after the federal parameter learning is more reasonable and accurate, has more comprehensive application range, and further ensures the crankshaft grinding quality.
Step S500: acquiring data information of a crankshaft to be ground and image information of the crankshaft to be ground;
specifically, in order to ensure the grinding accuracy of the crankshaft to be ground, acquiring data information of the crankshaft to be ground and image information of the crankshaft to be ground, including various information such as the type, material, structural size, application use, crankshaft structure, surface characteristics and the like of the crankshaft to be ground, so as to ensure the accuracy of the model output grinding parameters.
Step S600: inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information;
as shown in fig. 3, further, the step S600 of inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information further includes:
step S610: the crankshaft grinding parameter integrated analysis model comprises an input layer, an image convolution logic layer, a hidden layer and an output layer;
step S620: inputting the image information of the crankshaft to be ground into the image convolution logic layer through an input layer, and outputting appearance characteristics of the crankshaft to be ground;
step S630: inputting the data information of the crankshaft to be ground and the appearance characteristics of the crankshaft to be ground into the hidden layer, and outputting the crankshaft grinding parameter information;
step S640: and outputting the crank grinding parameter information as a model output result through the output layer.
Further, the step S620 of inputting the image information of the crankshaft to be ground into the image convolution logic layer through the input layer, and outputting the appearance feature of the crankshaft to be ground further includes:
step S621: acquiring a crankshaft application standard, and acquiring a preset convolution feature set according to the crankshaft application standard, wherein the preset convolution feature set comprises a crankshaft curvature feature, a smoothness feature and a burr value feature;
step S622: inputting the image information of the crankshaft to be ground into the image convolution logic layer through an input layer to perform feature extraction;
step S623: and obtaining output information of the image convolution logic layer, wherein the output information comprises appearance characteristics of the crankshaft to be ground, which conform to the preset convolution characteristic set.
Specifically, the crankshaft data information to be ground and the crankshaft image information to be ground are input into the crankshaft grinding parameter integrated analysis model, and the crankshaft grinding parameter integrated analysis model specifically comprises an input layer, an image convolution logic layer, a hidden layer and an output layer. And inputting the image information of the crankshaft to be ground into the image convolution logic layer through an input layer, wherein the image convolution logic layer is used for extracting image features.
The method comprises the steps of firstly obtaining a crankshaft application standard, wherein the crankshaft application standard is an appearance standard of a crankshaft capable of being practically applied, and obtaining a preset convolution feature set according to the crankshaft application standard, wherein the preset convolution feature set comprises crankshaft bending features, surface smoothness features and surface burr value feature requirement standards. And inputting the image information of the crankshaft to be ground into the image convolution logic layer through an input layer to perform feature extraction, namely performing convolution operation on the image features to obtain output information of the image convolution logic layer, wherein the output information comprises the appearance features of the crankshaft to be ground, which accord with the preset convolution feature set, namely the image appearance features which accord with the application standard.
Inputting the crankshaft data information to be ground and the crankshaft appearance characteristics to be ground into the hidden layer, wherein the hidden layer is used for carrying out crankshaft grinding parameter analysis through crankshaft multidimensional data characteristics and image characteristics, training can be carried out through historical data, and the crankshaft grinding parameter information is output. The crankshaft grinding parameter information comprises grinding force, grinding speed, grinding route, grinding angle and the like, and is output through the output layer as a model output result. The crankshaft grinding parameters are determined by constructing the crankshaft grinding parameter integrated analysis model, so that the crankshaft grinding parameters output by the model are more reasonable and accurate, and further the crankshaft grinding precision and the grinding efficiency are improved.
Step S700: and acquiring error parameters of a grinding device, and controlling and adjusting the crankshaft grinding based on the error parameters of the grinding device and the crankshaft grinding parameter information.
Further, the step S700 of obtaining the error parameter of the wearing device and performing control adjustment on the grinding of the crankshaft based on the error parameter of the wearing device and the information of the grinding parameter of the crankshaft further includes:
step S710: obtaining error parameters of the grinding device through an acoustic emission sensor;
step S720: obtaining crankshaft grinding compensation parameters according to the error parameters of the grinding device;
step S730: iteratively updating the crankshaft grinding parameter integrated analysis model based on the crankshaft grinding compensation parameters to obtain a crankshaft grinding parameter integrated optimization analysis model;
step S740: and correcting the error parameter of the abrasion device based on the output parameter information of the crankshaft grinding parameter integrated optimization analysis model.
Specifically, during the grinding of the crankshaft, the abrasion phenomenon of the grinding device, such as a grinding machine, a grinding wheel, etc., inevitably occurs, and in order to ensure the machining accuracy, the diameter change or the abrasion amount of the grinding wheel needs to be detected in time. The error parameters of the grinding device are obtained through an acoustic emission sensor, the acoustic emission phenomenon is an elastic wave generated by the rapid release of strain energy caused by structural change of a solid material, and based on the principle, the acoustic emission sensor is considered to be arranged on a grinding wheel frame and used for measuring acoustic emission signals in the grinding process of the grinding wheel, so that the abrasion error parameters generated in the grinding process of the grinding wheel are obtained.
According to the error parameters of the grinding device, error compensation is required to be carried out on the grinding precision of the crankshaft in the grinding process, so that the crankshaft grinding compensation parameters, such as the parameters of increasing the grinding feeding amount, increasing the grinding force and the like, are obtained. And iteratively updating the crankshaft grinding parameter integrated analysis model based on the crankshaft grinding compensation parameters to obtain an updated crankshaft grinding parameter integrated optimization analysis model, and correcting the error parameters of the abrasion device based on the output parameter information of the crankshaft grinding parameter integrated optimization analysis model. The crankshaft grinding is controlled and adjusted through the corrected grinding parameters, so that the output parameters are more fit with the actual application effect, the accuracy of grinding parameter output and the updating instantaneity are improved, and the crankshaft grinding precision quality is further ensured.
Further, step S740 of the present application further includes:
step S741: analyzing the generation reason of the error parameters of the grinding device to obtain error generation reason information;
step S742: constructing a crankshaft grinding solution list, wherein the crankshaft grinding solution list is arranged according to error generation types;
step S743: matching the error generation reason information with the crankshaft grinding solution list to obtain a crankshaft error solution;
step S744: and if the error generation cause information is a limiting error, eliminating the error parameter of the grinding device based on the crankshaft error solution.
Specifically, the error parameters of the grinding device are analyzed for generation reasons, and error generation reason information such as improper granularity of the grinding wheel, insufficient balance degree, abrasion of the grinding wheel, surface cracks of the grinding wheel, vibration of a machine tool and the like is obtained. A crankshaft grinding solution list is constructed, the crankshaft grinding solution list is arranged according to error generation types, different error generation types correspond to corresponding solutions, and the crankshaft grinding solution list can control the roundness of a grinding wheel, reduce the feeding amount, replace the grinding wheel, repair the working parameters of the grinding wheel and the like.
And matching the error generation cause information with the crankshaft grinding solution list to obtain a crankshaft error solution corresponding to the error generation type, and if the error generation cause information is a limiting error, the limiting error is an error which can be eliminated by adjusting the working parameter or the use type of the grinding device, and eliminating the error parameter of the grinding device based on the crankshaft error solution. The error parameters of the grinding device are eliminated by matching with a proper crankshaft error solution, so that the grinding error is reduced, the crankshaft grinding precision and the grinding efficiency are improved, and the technical effect of crankshaft grinding quality is further ensured.
In summary, the control and adjustment method and system for efficiently grinding the crankshaft provided by the application have the following technical effects:
classifying crankshaft multidimensional data information through a crankshaft characteristic decision tree to obtain crankshaft characteristic information, carrying out finite element segmentation on the crankshaft image information to obtain crankshaft image segmentation information, constructing a crankshaft grinding parameter integrated analysis model based on crankshaft characteristic information and crankshaft image segmentation information integrated training, and inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information; and controlling and adjusting the crank grinding based on the error parameter of the grinding device and the crank grinding parameter information. And further, the crankshaft grinding parameters are determined by constructing an integrated analysis model of the crankshaft grinding parameters, so that the crankshaft grinding precision and the grinding efficiency are improved, and the technical effect of ensuring the crankshaft grinding quality is further achieved.
Example two
Based on the same inventive concept as the control adjustment method for efficiently grinding a crankshaft in the foregoing embodiment, the present application further provides a control adjustment system for efficiently grinding a crankshaft, as shown in fig. 4, the system includes:
the data acquisition module 11 is used for acquiring crankshaft acquisition data information, wherein the crankshaft acquisition data information comprises crankshaft multidimensional data information and crankshaft image information;
the feature classification module 12 is configured to construct a crankshaft feature decision tree, and classify the crankshaft multidimensional data information through the crankshaft feature decision tree to obtain crankshaft feature information;
the image segmentation module 13 is used for carrying out finite element segmentation on the crankshaft image information to obtain crankshaft image segmentation information;
the model construction module 14 is used for uploading the crankshaft characteristic information and the crankshaft image segmentation information to a data integration training platform for learning to construct a crankshaft grinding parameter integration analysis model;
the data acquisition module 15 is used for acquiring data information of the crankshafts to be ground and image information of the crankshafts to be ground;
the model output module 16 is used for inputting the data information of the crankshaft to be ground and the image information of the crankshaft to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information;
and the control and adjustment module 17 is used for acquiring the error parameter of the grinding device and performing control and adjustment on the crankshaft grinding based on the error parameter of the grinding device and the crankshaft grinding parameter information.
Further, the model building module further includes:
the data training unit is used for inputting the crankshaft characteristic information and the crankshaft image segmentation information into a deep convolutional neural network for training, and constructing a crankshaft grinding parameter analysis model;
the model training unit is used for obtaining multi-party crankshaft acquisition data information, and respectively inputting the multi-party crankshaft acquisition data information into the deep convolutional neural network for distributed training to obtain a multi-party crankshaft grinding parameter analysis model;
the parameter extraction unit is used for extracting training model parameters of the one-side crankshaft grinding parameter analysis model and the multiparty crankshaft grinding parameter analysis model;
and the model joint training unit is used for the data integration training platform to perform joint training on the training model parameters so as to obtain the crankshaft grinding parameter integration analysis model.
Further, the model output module further includes:
the model forming unit is used for the crankshaft grinding parameter integrated analysis model and comprises an input layer, an image convolution logic layer, a hidden layer and an output layer;
the model input unit is used for inputting the image information of the crankshaft to be ground into the image convolution logic layer through the input layer and outputting the appearance characteristics of the crankshaft to be ground;
the parameter output unit is used for inputting the data information of the crankshaft to be ground and the appearance characteristics of the crankshaft to be ground into the hidden layer and outputting the crankshaft grinding parameter information;
and the model output unit is used for outputting the crank grinding parameter information as a model output result through the output layer.
Further, the model input unit further includes:
the device comprises a characteristic acquisition unit, a characteristic analysis unit and a characteristic analysis unit, wherein the characteristic acquisition unit is used for acquiring a crankshaft application standard, and acquiring a preset convolution characteristic set according to the crankshaft application standard, wherein the preset convolution characteristic set comprises a crankshaft bending characteristic, a smoothness characteristic and a burr value characteristic;
the feature extraction unit is used for inputting the image information of the crankshaft to be ground into the image convolution logic layer through the input layer to perform feature extraction;
and the image characteristic output unit is used for obtaining the output information of the image convolution logic layer, wherein the output information comprises the appearance characteristics of the crankshaft to be ground, which accord with the preset convolution characteristic set.
Further, the control adjustment module further includes:
an error parameter obtaining unit for obtaining the error parameter of the grinding device through an acoustic emission sensor;
the compensation parameter obtaining unit is used for obtaining crankshaft grinding compensation parameters according to the error parameters of the grinding device;
the model updating unit is used for carrying out iterative updating on the crankshaft grinding parameter integrated analysis model based on the crankshaft grinding compensation parameters to obtain a crankshaft grinding parameter integrated optimization analysis model;
and the parameter correction unit is used for correcting the error parameter of the abrasion device based on the output parameter information of the crankshaft grinding parameter integrated optimization analysis model.
Further, the system further comprises:
the error generation unit is used for carrying out generation reason analysis on the error parameters of the grinding device to obtain error generation reason information;
a solution construction unit for constructing a crankshaft grinding solution list, the crankshaft grinding solution list being arranged according to an error generation type;
the scheme matching unit is used for matching the error generation reason information with the crankshaft grinding solution list to obtain a crankshaft error solution;
and an error elimination unit for eliminating the error parameter of the grinding device based on the crankshaft error solution if the error generation cause information is a limitation error.
Further, the feature classification module further includes:
the structure classification unit is used for obtaining the crankshaft structure form attribute and taking the crankshaft structure form attribute as a first crankshaft classification characteristic;
the material classification unit is used for obtaining the crankshaft process material attribute and taking the crankshaft process material attribute as a second crankshaft classification characteristic;
the application classification unit is used for obtaining crankshaft application type attributes and taking the crankshaft application type attributes as third crankshaft classification characteristics;
and the decision tree construction unit is used for constructing the crankshaft characteristic decision tree based on the first crankshaft classification characteristic, the second crankshaft classification characteristic and the third crankshaft classification characteristic.
The application provides a control and adjustment method for efficiently grinding a crankshaft, which comprises the following steps: acquiring crankshaft acquisition data information, wherein the crankshaft acquisition data information comprises crankshaft multidimensional data information and crankshaft image information; constructing a crankshaft characteristic decision tree, and classifying the crankshaft multidimensional data information through the crankshaft characteristic decision tree to obtain crankshaft characteristic information; finite element segmentation is carried out on the crankshaft image information to obtain crankshaft image segmentation information; based on the crankshaft characteristic information and the crankshaft image segmentation information, uploading the crankshaft characteristic information and the crankshaft image segmentation information to a data integration training platform for learning, and constructing a crankshaft grinding parameter integration analysis model; acquiring data information of a crankshaft to be ground and image information of the crankshaft to be ground; inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information; and acquiring error parameters of a grinding device, and controlling and adjusting the crankshaft grinding based on the error parameters of the grinding device and the crankshaft grinding parameter information. The technical problems of influence on crankshaft grinding quality caused by low crankshaft grinding precision and long time consumption in the prior art are solved. The technical effects of determining crankshaft grinding parameters by constructing a crankshaft grinding parameter integrated analysis model, improving the crankshaft grinding precision and the grinding efficiency and further ensuring the crankshaft grinding quality are achieved.
The specification and drawings are merely exemplary of the present application, and the present application is intended to cover modifications and variations of the present application provided they come within the scope of the application and its equivalents.
Claims (7)
1. A method for controlling and adjusting an efficient grinding crankshaft, the method comprising:
acquiring crankshaft acquisition data information, wherein the crankshaft acquisition data information comprises crankshaft multidimensional data information and crankshaft image information;
constructing a crankshaft characteristic decision tree, and classifying the crankshaft multidimensional data information through the crankshaft characteristic decision tree to obtain crankshaft characteristic information;
finite element segmentation is carried out on the crankshaft image information to obtain crankshaft image segmentation information;
based on the crankshaft characteristic information and the crankshaft image segmentation information, uploading the crankshaft characteristic information and the crankshaft image segmentation information to a data integration training platform for learning, and constructing a crankshaft grinding parameter integration analysis model, wherein the method comprises the following steps of: inputting the crankshaft characteristic information and the crankshaft image segmentation information into a deep convolutional neural network for training, and constructing a crankshaft grinding parameter analysis model; acquiring multi-party crankshaft acquisition data information, and respectively inputting the multi-party crankshaft acquisition data information into the deep convolutional neural network for distributed training to acquire a multi-party crankshaft grinding parameter analysis model; extracting training model parameters of the one-side crankshaft grinding parameter analysis model and the multiparty crankshaft grinding parameter analysis model; the data integration training platform carries out joint training on the training model parameters to obtain the crankshaft grinding parameter integration analysis model;
acquiring data information of a crankshaft to be ground and image information of the crankshaft to be ground;
inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information;
and acquiring error parameters of a grinding device, and controlling and adjusting the crankshaft grinding based on the error parameters of the grinding device and the crankshaft grinding parameter information.
2. The method of claim 1, wherein said inputting the crankshaft data information to be ground and the crankshaft image information to be ground into the crankshaft grinding parameter integration analysis model to obtain crankshaft grinding parameter information comprises:
the crankshaft grinding parameter integrated analysis model comprises an input layer, an image convolution logic layer, a hidden layer and an output layer;
inputting the image information of the crankshaft to be ground into the image convolution logic layer through an input layer, and outputting appearance characteristics of the crankshaft to be ground;
inputting the data information of the crankshaft to be ground and the appearance characteristics of the crankshaft to be ground into the hidden layer, and outputting the crankshaft grinding parameter information;
and outputting the crank grinding parameter information as a model output result through the output layer.
3. The method of claim 2, wherein inputting the crankshaft image information to be ground into the image convolution logic layer through an input layer, outputting crankshaft appearance features to be ground, comprises:
acquiring a crankshaft application standard, and acquiring a preset convolution feature set according to the crankshaft application standard, wherein the preset convolution feature set comprises a crankshaft curvature feature, a smoothness feature and a burr value feature;
inputting the image information of the crankshaft to be ground into the image convolution logic layer through an input layer to perform feature extraction;
and obtaining output information of the image convolution logic layer, wherein the output information comprises appearance characteristics of the crankshaft to be ground, which conform to the preset convolution characteristic set.
4. The method of claim 1, wherein said obtaining a grinding device error parameter, and wherein said controlling adjustments to crankshaft grinding based on said grinding device error parameter and said crankshaft grinding parameter information comprises:
obtaining error parameters of the grinding device through an acoustic emission sensor;
obtaining crankshaft grinding compensation parameters according to the error parameters of the grinding device;
iteratively updating the crankshaft grinding parameter integrated analysis model based on the crankshaft grinding compensation parameters to obtain a crankshaft grinding parameter integrated optimization analysis model;
and correcting the error parameters of the grinding device based on the output parameter information of the crankshaft grinding parameter integrated optimization analysis model.
5. The method of claim 4, wherein the method comprises:
analyzing the generation reason of the error parameters of the grinding device to obtain error generation reason information;
constructing a crankshaft grinding solution list, wherein the crankshaft grinding solution list is arranged according to error generation types;
matching the error generation reason information with the crankshaft grinding solution list to obtain a crankshaft error solution;
and if the error generation cause information is a limiting error, eliminating the error parameter of the grinding device based on the crankshaft error solution.
6. The method of claim 1, wherein said constructing a crankshaft signature decision tree comprises:
obtaining a crankshaft structural form attribute, and taking the crankshaft structural form attribute as a first crankshaft classification characteristic;
obtaining a crankshaft process material attribute, and taking the crankshaft process material attribute as a second crankshaft classification characteristic;
obtaining a crankshaft application type attribute, and taking the crankshaft application type attribute as a third crankshaft classification characteristic;
and constructing the crankshaft characteristic decision tree based on the first crankshaft classification characteristic, the second crankshaft classification characteristic and the third crankshaft classification characteristic.
7. A control and regulation system for efficient grinding of crankshafts, said system comprising:
the data acquisition module is used for acquiring crankshaft acquisition data information, wherein the crankshaft acquisition data information comprises crankshaft multidimensional data information and crankshaft image information;
the characteristic classification module is used for constructing a crankshaft characteristic decision tree, classifying the crankshaft multidimensional data information through the crankshaft characteristic decision tree and obtaining crankshaft characteristic information;
the image segmentation module is used for carrying out finite element segmentation on the crankshaft image information to obtain crankshaft image segmentation information;
the model construction module is used for uploading the crankshaft characteristic information and the crankshaft image segmentation information to a data integration training platform for learning, and constructing a crankshaft grinding parameter integration analysis model, wherein the model construction module comprises the following components: the data training unit is used for inputting the crankshaft characteristic information and the crankshaft image segmentation information into a deep convolutional neural network for training, and constructing a crankshaft grinding parameter analysis model; the model training unit is used for obtaining multi-party crankshaft acquisition data information, and respectively inputting the multi-party crankshaft acquisition data information into the deep convolutional neural network for distributed training to obtain a multi-party crankshaft grinding parameter analysis model; the parameter extraction unit is used for extracting training model parameters of the one-side crankshaft grinding parameter analysis model and the multiparty crankshaft grinding parameter analysis model; the model joint training unit is used for performing joint training on the training model parameters by the data integration training platform to obtain the crankshaft grinding parameter integration analysis model;
the data acquisition module is used for acquiring data information of the crankshafts to be ground and image information of the crankshafts to be ground;
the model output module is used for inputting the data information of the crankshaft to be ground and the image information of the crankshaft to be ground into the crankshaft grinding parameter integrated analysis model to obtain crankshaft grinding parameter information;
and the control and adjustment module is used for acquiring error parameters of the grinding device and controlling and adjusting the crankshaft grinding based on the error parameters of the grinding device and the crankshaft grinding parameter information.
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