CN120439537B

CN120439537B - Plastic spiral bevel gear tooth surface working area deformation control method and system

Info

Publication number: CN120439537B
Application number: CN202510962450.7A
Authority: CN
Inventors: 童爱军; 王得峰; 李旭东
Original assignee: Dongguan Xinghuo Gear Co ltd
Current assignee: Dongguan Xinghuo Gear Co ltd
Priority date: 2025-07-14
Filing date: 2025-07-14
Publication date: 2025-09-19
Anticipated expiration: 2045-07-14
Also published as: CN120439537A

Abstract

The application provides a method and a system for controlling deformation of a tooth surface working area of a plastic spiral bevel gear, wherein the method comprises the steps of establishing a manufacturing parameter model according to gear structure parameters and injection manufacturing environment parameters of an initial plastic spiral bevel gear die; the method comprises the steps of carrying out precision detection on gear teeth formed by primary injection molding, obtaining error detection data, carrying out classification analysis on the error data to generate an error decomposition result, associating the error decomposition result with a manufacturing parameter model to construct an error tracing model, determining target parameter items of error sources and corresponding influence factor weights according to the error tracing model, generating an error factor mapping table, carrying out reverse geometric correction on an initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table, and carrying out numerical interval adjustment on injection molding process parameters to generate a corrected mold structure model and a corrected process parameter set. The scheme of the application can effectively improve the accuracy of the plastic spiral bevel gear in the injection molding process.

Description

Plastic spiral bevel gear tooth surface working area deformation control method and system

Technical Field

The application relates to the field of plastic gear manufacturing, in particular to a method and a system for controlling deformation of a tooth surface working area of a plastic spiral bevel gear.

Background

In modern precision transmission, the plastic spiral bevel gear can realize the angle conversion of torque in a structure with limited space, has the characteristics of light weight, low cost, low noise and the like, and is gradually and widely applied to the fields of intelligent household appliances, service robots, office equipment, medical appliances and the like. Compared with the traditional metal spiral bevel gear, the plastic material can be manufactured in batches through injection molding, and the processing cost is remarkably reduced. However, due to the special three-dimensional structure and the complexity of the manufacturing process, the precision control of the plastic spiral bevel gear becomes an engineering problem which is not thoroughly solved for a long time.

Compared with a plastic cylindrical gear, the tooth surface of the spiral bevel gear is a three-dimensional space curved surface, the stress direction is complex, and the precision requirements on geometric features such as tooth pitch, tooth profile, spiral line and the like are higher. In the injection molding process, the plastic is easily subjected to nonlinear shrinkage in a tooth surface working area under the influence of factors such as uneven cooling, change of the filling speed of a mold cavity, centering error of the mold, demolding interference and the like. Such non-linear shrinkage has a high degree of uncertainty and may take many forms, such as tooth tip collapse, tooth root misalignment, axial warping, or overall gear ring eccentricity, directly affecting gear mesh performance. Moreover, since the working engagement area of the spiral bevel gear is tapered, any minute deformation may be amplified to a wide range of tooth surface errors, which are difficult to eliminate by simple die repair or post-processing means.

Disclosure of Invention

The application provides a method and a system for controlling deformation of a tooth surface working area of a plastic spiral bevel gear, which are used for solving the problem that a plastic spiral bevel gear is easy to generate deformation errors in the injection molding process in the related art.

The first aspect of the application provides a plastic spiral bevel gear tooth surface working area deformation control method, which comprises the following steps:

establishing a manufacturing parameter model of the plastic spiral bevel gear according to the gear structure parameter and the injection manufacturing environment parameter of the initial plastic spiral bevel gear mold;

The method comprises the steps of obtaining error detection data reflecting deformation conditions of a tooth surface working area by detecting precision of gear teeth of the plastic spiral bevel gear after primary injection molding, classifying and analyzing the error detection data according to the manufacturing parameter model, and generating an error decomposition result;

Constructing an error traceability model of a quantization error influence factor by correlating the error decomposition result with the manufacturing parameter model;

determining target parameter items of all error sources and corresponding influence factor weights according to the error tracing model, and generating an error factor mapping table;

and carrying out reverse geometric correction on the initial plastic spiral bevel gear mould according to the correction direction of the error factor mapping table, and carrying out numerical interval adjustment on the injection molding process parameters to generate a corrected mould structure model and a corrected process parameter set.

Optionally, in a first implementation manner of the first aspect of the present application, the step of establishing a manufacturing parameter model of the plastic spiral bevel gear according to the gear structure parameter and the injection manufacturing environment parameter of the initial plastic spiral bevel gear mold includes:

extracting gear structure parameters according to a design drawing of an initial plastic spiral bevel gear mold, and carrying out numerical analysis on the gear structure parameters to generate a structure parameter vector set;

analyzing a historical molding record and a set process file of the injection molding manufacturing system to obtain injection molding manufacturing environment parameters;

constructing a parameter mapping relation table by matrix assembly of the structural parameter vector set and the manufacturing environment parameters, and generating a manufacturing parameter reference matrix according to the parameter mapping relation table;

And storing the manufacturing parameter reference matrix into a callable model structure body to generate a manufacturing parameter model corresponding to the target plastic spiral bevel gear.

Optionally, in a second implementation manner of the first aspect of the present application, the step of obtaining error detection data reflecting a deformation condition of a tooth surface working area by performing precision detection on the gear teeth after the primary injection molding of the plastic spiral bevel gear, and classifying and analyzing the error detection data according to the manufacturing parameter model to generate an error decomposition result includes:

Measuring a plurality of tooth surfaces of the plastic spiral bevel gear along the axial direction and the radial direction to respectively obtain tooth pitch deviation data, tooth profile error data and spiral line error data, and carrying out structural storage on corresponding data based on the measuring position and the tooth number to generate an error original data set;

Generating a classification error sample set by performing association analysis on the error original data set and the manufacturing parameter model, wherein the classification error sample set comprises a shape class error, a tooth surface defect class error and a geometric precision class error;

determining an error distribution curve according to the spatial distribution of the corresponding error types in the classified error sample set, and generating an error deformation function corresponding to the error types;

and determining a candidate parameter set which causes error type generation by carrying out variable correlation calculation on the error deformation function and each manufacturing parameter item of the manufacturing parameter model, and carrying out joint mapping on the candidate parameter set and the classifying error sample set to generate an error decomposition result.

Optionally, in a third implementation manner of the first aspect of the present application, the step of constructing an error tracing model according to the error decomposition result and the manufacturing parameter model includes:

Constructing a sensitivity scoring matrix of the error type to different manufacturing parameter items according to the error decomposition result;

Establishing a corresponding parameter response function for the error type according to the sensitivity scoring matrix, and generating a multidimensional parameter interference projection graph by carrying out control weight configuration on input variables of the parameter response function;

Extracting error response interval characteristics by carrying out distributed density cluster analysis on the parameter interference projection graph, and generating a target parameter positioning vector in a parameter space according to the error response interval characteristics;

and carrying out linkage indexing on the target parameter positioning vector and the error decomposition result, establishing a bidirectional mapping relation between the error type and the manufacturing parameter item, and constructing an error tracing model.

Optionally, in a fourth implementation manner of the first aspect of the present application, the step of determining, according to the error tracing model, a target parameter item and a corresponding impact factor weight of each error source, and generating an error factor mapping table includes:

Determining key manufacturing parameter items forming an error generation mechanism by extracting target parameter positioning vectors corresponding to different error types based on the bidirectional mapping relation, and aggregating the key manufacturing parameter items to form an error master control parameter set;

Performing numerical integration analysis on the function gradient between each key manufacturing parameter item in the error main control parameter set and the error deformation function to obtain the response amplitude of each key manufacturing parameter item in an error influence path, and generating an influence factor original scoring table according to the response amplitude;

carrying out normalization processing and weight distribution reconstruction on the influence factor original scoring table to generate an influence factor weight matrix corresponding to each key manufacturing parameter item;

And generating an error factor mapping table by field mapping integration of the error type, the key manufacturing parameter item and the influence factor weight.

Optionally, in a fifth implementation manner of the first aspect of the present application, the step of performing inverse geometric correction on the initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table, and performing numerical interval adjustment on the injection molding process parameter to generate a corrected mold structure model and a corrected process parameter set includes:

extracting geometric parameter dimensions to be compensated according to correction direction information corresponding to each key manufacturing parameter item in the error factor mapping table;

generating a mould local correction area by performing inverse geometric mapping on the geometric parameter dimension and the initial plastic spiral bevel gear mould;

Performing offset reconstruction on cavity curved surface nodes in the local mould correction area to generate a corrected mould three-dimensional structure model;

Identifying key process variables in the injection molding process according to the parameter sensitivity level of the influence factor weight matrix, and generating an injection molding process parameter adjustment set by carrying out offset iteration on a numerical interval corresponding to the key process variables;

And combining and packaging the corrected three-dimensional structure model of the mold and the injection molding process parameter adjustment set to generate a corrected structure model of the mold and a corrected process parameter set.

Optionally, in a sixth implementation manner of the first aspect of the present application, the method further includes:

Performing injection molding processing according to the corrected mold structure model and the technological parameter set to generate a target plastic spiral bevel gear test piece;

The method comprises the steps of detecting tooth pitch, tooth profile and spiral line of the same dimension as that of the initial plastic spiral bevel gear mold test piece by the target plastic spiral bevel gear test piece to obtain a corrected error data set;

Generating a structured data entry by field binding the corrected error data set, the manufacturing parameter model, the error factor mapping table and the corresponding correction parameter item, and storing the data entry into an error correction sample database;

the error prediction feature space is constructed by carrying out feature extraction on a plurality of groups of data items in the error correction sample database;

generating an error prediction model by carrying out sample training on the feature space;

Inputting the target structure parameters to be processed and the target manufacturing environment parameters into the error prediction model to obtain the expected error type and the corresponding compensation parameters;

And optimizing the cavity parameters and injection molding process parameters of the current plastic spiral bevel gear mold according to the compensation parameters.

The second aspect of the application provides a plastic spiral bevel gear tooth surface working area deformation control device, which comprises:

the construction module is used for constructing a manufacturing parameter model of the plastic spiral bevel gear according to the gear structure parameter and the injection manufacturing environment parameter of the initial plastic spiral bevel gear mould;

the acquisition module is used for acquiring error detection data reflecting the deformation condition of a tooth surface working area by detecting the precision of the gear teeth after the primary injection molding of the plastic spiral bevel gear, and classifying and analyzing the error detection data according to the manufacturing parameter model to generate an error decomposition result;

The determining module is used for constructing an error tracing model according to the error decomposition result and the manufacturing parameter model, determining target parameter items of each error source and corresponding influence factor weights according to the error tracing model, and generating an error factor mapping table;

and the correction module is used for carrying out reverse geometric correction on the initial plastic spiral bevel gear die according to the correction direction of the error factor mapping table, carrying out numerical interval adjustment on the injection molding process parameters, and generating a corrected die structure model and a corrected process parameter set.

A third aspect of the embodiment of the present application provides an electronic device, including a memory and a processor, where the processor is configured to execute a computer program stored on the memory, and when the processor executes the computer program, each step in the method for controlling deformation of a working area of a tooth surface of a plastic spiral bevel gear provided in the first aspect of the embodiment of the present application is implemented.

A fourth aspect of the embodiment of the present application provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for controlling deformation of a working area of a tooth surface of a plastic spiral bevel gear according to the first aspect of the embodiment of the present application.

In summary, according to the method and system for controlling deformation of a tooth surface working area of a plastic spiral bevel gear, which are provided by the scheme of the application, a manufacturing parameter model of the plastic spiral bevel gear is established according to gear structure parameters and injection manufacturing environment parameters of an initial plastic spiral bevel gear die, error detection data reflecting deformation conditions of the tooth surface working area is obtained by detecting precision of the tooth after the initial injection molding of the plastic spiral bevel gear, the error detection data is classified and analyzed according to the manufacturing parameter model to generate an error decomposition result, an error tracing model is constructed according to the error decomposition result and the manufacturing parameter model, target parameter items and corresponding influence factor weights of error sources are determined according to the error tracing model, an error factor mapping table is generated, reverse geometric correction is carried out on the initial plastic spiral bevel gear die according to the correction direction of the error factor mapping table, numerical interval adjustment is carried out on the injection molding process parameters, and a corrected die structure model and a process parameter set are generated. According to the scheme, the manufacturing parameter model is built, the actual errors are obtained, the error decomposition and the traceability modeling are carried out, the error factor mapping relation is finally built, the mould structure and the injection parameters are reversely corrected according to the error factor mapping relation, and the accuracy of the plastic spiral bevel gear in the injection molding process can be effectively improved.

Drawings

FIG. 1 is a schematic flow chart of a method for controlling deformation of a tooth surface working area of a plastic spiral bevel gear according to an embodiment of the present application;

FIG. 2 is a schematic program diagram of a plastic spiral bevel gear tooth surface working area deformation control device according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the objects, features and advantages of the present application more comprehensible, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In order to solve the problem that a plastic spiral bevel gear is easy to generate deformation errors in the injection molding process in the related art, an embodiment of the present application provides a method for controlling deformation of a plastic spiral bevel gear tooth surface working area, as shown in fig. 1, which is a schematic flow chart of the method for controlling deformation of the plastic spiral bevel gear tooth surface working area, and the method for controlling deformation of the plastic spiral bevel gear tooth surface working area comprises the following steps:

And 110, establishing a manufacturing parameter model of the plastic spiral bevel gear according to the gear structure parameters and the injection manufacturing environment parameters of the initial plastic spiral bevel gear mould.

Specifically, in this embodiment, by extracting and analyzing the gear structure parameters and the injection manufacturing environment parameters of the initial plastic spiral bevel gear mold, a manufacturing parameter model including geometric design information and molding process conditions can be established. The model is based on a design drawing, carries out numerical modeling on key geometric quantities such as a center modulus, a tooth width, a tooth tip circle diameter, a maximum wall thickness and the like, combines manufacturing environment parameters including a mold structural scheme, an injection molding equipment model, a temperature pressure condition, a raw material type and the like in an injection molding manufacturing system, and is input into a model structure body in a structuring mode to form a basic input set on which follow-up data processing depends.

In an alternative implementation manner of the embodiment, the step of establishing a manufacturing parameter model of the plastic spiral bevel gear according to the gear structure parameter of the initial plastic spiral bevel gear die and the injection manufacturing environment parameter comprises the steps of extracting the gear structure parameter according to a design drawing of the initial plastic spiral bevel gear die, carrying out numerical analysis on the gear structure parameter to generate a structure parameter vector set, analyzing a historical forming record and a set process file of an injection manufacturing system to obtain the injection manufacturing environment parameter, carrying out matrix assembly on the structure parameter vector set and the manufacturing environment parameter, constructing a parameter mapping relation table, generating a manufacturing parameter reference matrix according to the parameter mapping relation table, and storing the manufacturing parameter reference matrix into a callable model structure to generate a manufacturing parameter model corresponding to the target plastic spiral bevel gear.

Specifically, in the present embodiment, in the initial stage, by analyzing the design drawing of the plastic spiral bevel gear mold, geometric parameters of the gear can be accurately obtained, including the center modulus (standardized dimension for describing the pitch of the gear), the tooth width (width of the tooth face), the tip circle diameter (circumferential diameter formed by the tooth tip), and the maximum wall thickness (maximum material thickness from the tooth root to the tooth tip). In the drawings, these parameters are typically presented in the form of dimension labels, which can be directly converted into digitized data by the numerical extraction function of Computer Aided Design (CAD) software. At this time, each parameter is given a unique identifier and converted into a numerical vector, for example, the center modulus, the tooth width, the tooth tip diameter, and the maximum wall thickness are mapped to four components of the vector, respectively, to thereby obtain a structural parameter vector set. The vector set provides a standardized geometric reference for subsequent error analysis, which not only ensures a high degree of consistency between design intent and manufacturing execution, but also provides accurate input values for the error resolution module. For example, in some model of speed reduction components of a sweeping robot, if the center modulus is 0.5 mm, the tooth width is 12 mm, the tooth tip diameter is 68 mm, and the maximum wall thickness is 2.1 mm, the structural parameter vector may be represented as [0.5, 12, 68, 2.1] for subsequent modules to call directly. Historical molding records and set process files in the injection molding manufacturing system are introduced into the parametric model building process. The history molding record often contains the information such as the glue injection speed, the pressure maintaining pressure, the cooling time, the mold temperature curve, the model number of the injection molding machine and the like, and the set process file describes the process conditions such as the mold temperature control setting, the raw material flow, the screw rotating speed and the like in detail. By parsing the entries in the readability document or database, various environmental variables can be extracted as numerical values or enumerated types using Text Mining and regular expression techniques. After the physical performance parameters of the material brands (such as the thermal expansion coefficient, the viscosity and the crystallization rate of the polyoxymethylene POM) are searched, the physical performance parameters can also be included in the environmental parameter set. By associating the injection molding machine model with its corresponding injection molding machine technical manual, finer functional characteristics, such as screw diameter and maximum injection volume, etc., can be obtained. All environment parameters are encoded according to predefined fields after analysis, so that an injection molding manufacturing environment parameter set is formed, and process background information is provided for subsequent error tracing. And commonly importing the structural parameter vector set and the injection molding environment parameter set into a matrix type assembly algorithm. Wherein each row represents a structural parameter or environmental parameter dimension, each column corresponds to a group of parameter values in the same production batch, and the row-column structure of the matrix can intuitively embody the interaction relationship among different parameters. And performing element level matching on the structural parameters and the environmental parameters in a two-dimensional table through matrix assembly, and constructing a parameter mapping relation table. Based on the parameter mapping relation table, the manufacturing parameter reference matrix can be obtained by utilizing a matrix generation technology in linear algebra, and the matrix encapsulates all geometric and process variables in a numerical form, so that the subsequent comparison and comparison of error detection data can be rapidly completed by means of matrix operation. The manufacturing parameter reference matrix may be stored in a callable model structure by encapsulation. The model structure is an object-oriented software unit comprising a parameter input port, parameter verification logic and a parameter reading interface. After the matrix is used as a core member variable of the structure body, manufacturing parameters can be acquired at any time in other algorithm modules through function call or interface call, so that the parameters are ensured to be consistent and repeated input is avoided. Therefore, the generated manufacturing parameter model can be in one-to-one correspondence with the target plastic spiral bevel gear and can be used as a unified data source of modules such as subsequent error decomposition, error tracing, compensation correction and the like.

And 120, accurately detecting the gear teeth after primary injection molding of the plastic spiral bevel gear to obtain error detection data reflecting the deformation condition of the gear surface working area, and classifying and analyzing the error detection data according to the manufacturing parameter model to generate an error decomposition result.

Specifically, in this embodiment, a high-precision gear measurement system is used to detect spatial features of the gear teeth after primary injection molding, so that actual shape data of the tooth surface working area in the axial and radial directions can be obtained, and thus multidimensional detection results of tooth pitch errors, tooth profile deviations and spiral line errors are obtained. The detection data are archived and stored according to the gear tooth numbers and the space positions, and correlation matching is carried out by combining the manufacturing parameter models, so that the detection data can be effectively classified according to error types. Through pattern analysis and spatial distribution recognition of an error curve, an error deformation function can be further constructed, preliminary decomposition of errors is completed, and an input variable set with clear classification and clear attribute is provided for subsequent modeling.

In an alternative implementation manner of the embodiment, the method comprises the steps of carrying out precision detection on gear teeth after primary injection molding of a plastic spiral bevel gear, obtaining error detection data reflecting deformation conditions of a gear surface working area, carrying out classification analysis on the error detection data according to a manufacturing parameter model, and generating an error decomposition result, wherein the steps comprise respectively obtaining pitch deviation data, tooth profile error data and spiral error data by measuring a plurality of gear surfaces of the plastic spiral bevel gear along axial and radial directions, carrying out structural storage on corresponding data based on measured positions and gear tooth numbers, generating an error original data set, carrying out correlation analysis on the error original data set and the manufacturing parameter model, generating a classification error sample set, wherein the classification error sample set comprises shape type errors, tooth surface defect type errors and geometric precision type errors, determining an error distribution curve according to spatial distribution of corresponding error types in the classification error sample set, generating an error deformation function corresponding to the error types, and carrying out variable correlation calculation on each manufacturing parameter item of the error deformation function and the manufacturing parameter model, determining a candidate parameter set which leads to error type generation, and carrying out joint mapping on the candidate parameter set and the error classification error decomposition result, so as to generate the error sample set.

Specifically, in this embodiment, by performing correlation analysis on the error raw data set and the manufacturing parameter model, the pitch deviation, the tooth profile error and the spiral error data collected by the measuring instrument can be cross-matched with the geometry and the process parameters stored in the model structure body, so as to reveal the performance differences of each error under different process and design conditions. The original data table and the parameter matrix are aligned according to batch numbers by utilizing a database connection technology, and then information such as a center modulus, a tooth width, a pressure maintaining pressure, a mold temperature and the like corresponding to the original data table is carried on each record, so that the parallel expression of an error value and manufacturing parameters is completed in the same data line. On the basis, a clustering analysis algorithm is adopted to classify the data lines according to the similarity of error characteristics, and three sample sets of shape type errors, tooth surface defect type errors and geometric precision type errors are finally formed, wherein the shape type errors comprise integral geometric mismatch phenomena such as big head, small head, eccentric, waisted and the like, the tooth surface defect type errors are focused on local tooth surface gaps and scratch characteristics, and the geometric precision type errors are reflected in fine size deviations such as accumulated tooth pitch deviation, helix angle inclination and the like. Then, by drawing a spatial distribution diagram of the classifying error sample in a three-dimensional coordinate system, each error curve can be subjected to spatial mapping, and an error distribution curve is generated by utilizing a curve fitting technology according to distribution density and trend, for example, a least square method is adopted to fit data points of the tooth pitch deviation changing along with the axial position, so as to obtain a function expression describing the change of the tooth pitch deviation along the width direction, wherein the function expression is an error deformation function, or spline interpolation is used to carry out smoothing treatment on spiral line errors, so that a continuous and micro deformation curve is obtained. The curve not only reveals the concentrated representation position of errors in the tooth surface working area, but also provides a quantitative basis for the subsequent deformation compensation. And performing variable correlation calculation on the error deformation function and each parameter item in the manufacturing parameter model, namely introducing central modulus, injection speed, cooling time and other dimensions into a function input space, and evaluating the influence degree of each parameter on function output by using a pearson correlation coefficient or mutual information quantization method so as to identify candidate parameter sets causing various errors. And carrying out joint mapping on the obtained parameter subset and the original classifying error sample to form an error decomposition result, wherein the error decomposition result is presented in the form of the corresponding relation of error type, deformation function parameters and sensitive manufacturing factors, so that the main influencing factors of tooth profile shrinkage under the specific mold temperature and pressure injection conditions can be indicated, and the subsequent mold correction logic can be guided. For example, after the pressure maintaining deficiency and the tooth surface waisting phenomenon are found to have high correlation, the pressure maintaining time can be adjusted preferentially in the subsequent design. Through the analysis and the mapping, the closed loop tracing and the quantization decomposition from the error observation to the error origin are realized.

And 130, constructing an error tracing model according to the error decomposition result and the manufacturing parameter model, determining target parameter items of each error source and corresponding influence factor weights according to the error tracing model, and generating an error factor mapping table.

Specifically, in this embodiment, a mapping relationship is established between the categorized error data and each parameter in the manufacturing parameter model, and a response mechanism of the key variable to each type of error can be extracted by using a multivariate statistical analysis method. On the basis, an error traceability model for describing the mapping relation between the error type and the manufacturing factors is constructed through methods such as function fitting, variable sensitivity evaluation, cluster recognition and the like. The model can reveal which specific geometric or process factors are closely related to the formation of certain types of errors during complex molding processes, thereby providing a structured understanding of the sources of manufacturing errors. By analyzing key manufacturing parameter items on which each error type depends in the error tracing model and calculating the corresponding influence factor weight of the key manufacturing parameter items in the error forming mechanism, a data table structure containing the mapping relation among the error types, the key parameters and the weights of the key parameters can be established. The data table is an error factor mapping table and has a data supporting function for guiding model correction and process compensation. In the construction process, the normalization method is adopted to perform normalization processing on the influence degree of each parameter, and binding between the error type and the parameter item is performed through the field indexing mechanism, so that the table can express the sensitivity of the parameter and has traceability.

In an optional implementation manner of the embodiment, the step of constructing an error tracing model according to an error decomposition result and a manufacturing parameter model comprises the steps of constructing sensitivity scoring matrixes of error types on different manufacturing parameter items according to the error decomposition result, establishing corresponding parameter response functions on the error types according to the sensitivity scoring matrixes, generating a multidimensional parameter interference projection graph by controlling weight configuration on input variables of the parameter response functions, extracting error response interval characteristics by performing distributed density cluster analysis on the parameter interference projection graph, generating target parameter positioning vectors in a parameter space according to the error response interval characteristics, and establishing a bidirectional mapping relation between the error types and the manufacturing parameter items and constructing the error tracing model by performing linkage indexing on the target parameter positioning vectors and the error decomposition result.

Specifically, in the present embodiment, the error decomposition result includes a joint mapping relationship between each error type (such as pitch deviation, profile shape deviation, spiral distortion deviation) and a candidate set of manufacturing parameters (such as injection pressure, dwell time, mold temperature, cooling rate, etc.), and a corresponding error deformation function expression. By means of the mapping relation, the error deformation function can be partially unfolded in the action area of each manufacturing parameter item, and the partial derivative of the function in the direction of each parameter axis is calculated by adopting a numerical differentiation technology so as to quantitatively evaluate the instantaneous influence rate of the manufacturing parameter on the target error type. Specifically, a single parameter is perturbed at the center point of the candidate parameter set in a tiny increment manner (for example, ±1% or ±0.1 unit), and the output value is sampled on the error deformation function, so that a differential quantity of the error function value before and after each parameter perturbation can be obtained, and the ratio of the differential quantity to the perturbation quantity is regarded as the sensitivity score of the parameter. After repeating this process for all manufacturing parameters, a two-dimensional matrix can be constructed with rows representing the error types and columns representing the manufacturing parameter items, the matrix elements corresponding to the sensitivity scores between each pair of "error types-manufacturing parameters". After the sensitivity scoring matrix is obtained, a corresponding parameter response function can be established for each error type, the parameter response function can adopt methods such as polynomial regression or radial basis function network, and the like, a mathematical model is constructed by taking manufacturing parameters as independent variables and error function values as dependent variables, and then different weight combinations are applied in an input space to simulate parameter disturbance. In the fitting process, sampling the value range of the key parameter and generating a plurality of virtual samples, and correcting the prediction results of the samples according to the error curve obtained by actual detection so as to ensure that the response function can accurately reflect the influence trend of parameter fluctuation on error output. After the fitting is completed, the response function needs to be verified and corrected, and the fitting result is compared and evaluated through cross verification or a leave-one-out method, so that the variable combination with poor performance is removed or the internal parameters of the fitting algorithm are adjusted. After the verification is qualified, the response function corresponding to each error type can be drawn into a response surface or curve in the visual interface, and the change trend of the output error can be observed by changing the input parameter value. The key technological parameters which are most sensitive to the error change are selected from the established parameter response functions, and the relative weights are distributed to the parameters according to different influence degrees, so that in the subsequent simulation process, the change of each parameter can generate different pulling effects on the error output according to a preset proportion. The weight configuration may be scaled based on the magnitude of the values in the sensitivity scoring matrix, e.g., with higher weights being assigned to the most sensitive parameters and lower weights being assigned to the less sensitive parameters. And inputting all weighted parameters into the response function, selecting a plurality of point samples in a gridding mode in a parameter space, and operating the response function for each sample point to calculate a corresponding error value. Mapping the weighted sample points and their response results into a multi-dimensional visual coordinate system forms a so-called multi-dimensional parametric disturbance projection map. By means of parameter interference projection graph, the distribution density of projection points can be subjected to cluster analysis to extract the characteristics of error response intervals. The response interval characteristics can be expressed by counting the central coordinates and boundary ranges of the cluster, and a multi-dimensional interval description comprising parameter axis threshold values and response intensity levels is formed. Based on the interval characteristics, a target parameter positioning vector can be generated in the parameter space, wherein the vector is composed of each key parameter axis coordinate value and indicates a parameter configuration point which is preferentially compensated or optimized. By carrying out linkage indexing on the target parameter positioning vector and the error decomposition result obtained previously, a bidirectional mapping relation between the error type and the manufacturing parameter item can be established, and an error tracing model can be constructed according to the bidirectional mapping relation. The model records the forward mapping (from manufacturing parameters to error response) of the error type and the parameter vector, and also comprises the reverse mapping (from error detection to manufacturing factor positioning), thereby realizing the closed-loop traceability of the error source. In the construction process, the hash mapping or index table is used for realizing quick inquiry, and the mapping relation is stored and retrieved through a graph database or a multidimensional array. The finally generated traceable model can rapidly position main parameter factors when new detection data are given, and output corresponding compensation quantity and matching priority so as to drive the subsequent geometric correction of the die and the adjustment of technological parameters.

In an alternative implementation manner of the embodiment, the steps of determining target parameter items of various error sources and corresponding influence factor weights according to an error tracing model and generating an error factor mapping table comprise the steps of determining key manufacturing parameter items forming an error generating mechanism by extracting target parameter positioning vectors corresponding to different error types based on a bidirectional mapping relation, aggregating the key manufacturing parameter items to form an error main control parameter set, obtaining response amplitude values of the key manufacturing parameter items in an error influence path by carrying out numerical integral analysis on function gradients between the key manufacturing parameter items and an error deformation function in the error main control parameter set, generating an influence factor original scoring table according to the response amplitude values, generating an influence factor weight matrix corresponding to the key manufacturing parameter items by carrying out normalization processing and weight distribution reconstruction on the influence factor original scoring table, and generating the error factor mapping table by carrying out field mapping integration on the error types, the key manufacturing parameter items and the influence factor weights.

Specifically, the relation between key manufacturing parameter items and the error deformation function is subjected to gradient and numerical integral analysis, each key parameter is required to be introduced into the error deformation function as an independent variable, and the key parameter is a mathematical expression which takes a space coordinate or a position number as input, so that curve characteristics of tooth pitch deviation, tooth profile deformation or spiral angle error along with the position change are expressed. By performing a small incremental perturbation on the parameter variables and calculating the error function response, the partial derivative of the error function, i.e., the gradient, with respect to each manufacturing parameter term can be obtained. The partial derivative reflects the increase in error caused by a slight change in the manufacturing parameters at a particular parameter point, and is an intrinsic quantity that characterizes sensitivity quantitatively. the gradient function is subjected to numerical integration in a parameter value interval, so that the cumulative influence of the parameter on the overall error distribution can be comprehensively reflected, and the result is called a response amplitude. The response amplitude takes the amplitude of the integral result as a measurement unit, and the accumulated error change amplitude from the minimum value to the maximum value in a given parameter interval can be measured. After completing the gradient score calculations for all key manufacturing parameter items, the resulting response magnitudes can be summarized as an impact factor raw scoring table. The scoring table is formed by numerical entries with parameter entries as rows and corresponding response magnitudes as columns, each entry in the table describing the cumulative effect intensity of a particular manufacturing parameter on the error-forming path. For example, when the relation between the injection speed and the pitch deviation is examined, if the magnitude calculated by integration is 2.5, the term "injection speed-pitch deviation" in the table is indicated as 2.5, and if the magnitude of the dwell time-to-pitch error is 1.2, the corresponding term "dwell time-to-pitch error" is 1.2. The normalization processing is carried out on the content of the original scoring table, and the purpose is to eliminate the difference of the extreme ranges of different parameter dimensions and error functions, so that the numerical values of all parameter items fall into a unified interval, and the general value range is [0,1]. Normalization can be achieved by linear stretching or maximum-minimum rescaling (Min-Max Scaling), subtracting the minimum amplitude from the amplitude of each parameter term, and dividing the amplitude by the range, while preserving the linear proportional relationship between the amplitude and the range. after the numerical normalization is completed, the parameter items are reconstructed according to a preset weight distribution strategy, so that the importance differences of the parameters under different error types can be distinguished on the basis of normalization. The weight distribution reconstruction can comprehensively consider the dispersion degree and the priority of the parameter items by using an entropy weight method or a hierarchical analysis method. For example, if the entropy weight calculation indicates that the dwell time still exhibits a high dispersion after all magnitudes are normalized, the weight corresponding to the dwell time will be amplified to reflect its strong discrimination in the error formation path. The final result forms an influence factor weight matrix, wherein the rows of the matrix represent key manufacturing parameter items, the columns represent different error types, and the matrix entries are weight values corresponding to the parameters, so that the relative share of the matrix entries in various error contributions is described. By means of the influence factor weight matrix, the error type, the key manufacturing parameter items and the corresponding weight values can be further combined into a structured entry, and an error factor mapping table is generated. The mapping table adopts a field mapping technology to design a database or an electronic table, the juxtaposition of data items is completed in a single row or a single column, for example, the entry is marked in the form of 'tooth pitch deviation-injection speed-0.72', and the mapping relation among error types, target parameters and weight values is recorded through an index column. The field mapping integration process can utilize the external key association of the relational database or the nested index structure of the multidimensional array to realize quick retrieval, so that when the actual error data is detected, the corresponding high-weight parameter item can be immediately positioned according to the error type and the compensation priority can be queried. The visual structure provided by the mapping table not only enables the decision process of error compensation to be transparent, but also enhances the model expandability, and provides clear updating basis for the subsequent model iteration based on new data.

And 140, performing inverse geometric correction on the initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table, and performing numerical interval adjustment on injection molding process parameters to generate a corrected mold structure model and a corrected process parameter set.

Specifically, in this embodiment, the error type and the correction direction provided in the error factor mapping table are combined, the geometric compensation content related to the error type and the correction direction is mapped back to the original mold CAD model, and the inverse geometric modeling operation is performed in the local area of the mold. Meanwhile, based on key technological parameter items given in the mapping table, the interval adjustment is carried out on the original injection molding technological window by combining the influence factor weight information of the key technological parameter items, and a new technological parameter set for injection molding processing is generated. After the die structure correction and the process parameter adjustment are finished cooperatively, a group of corrected manufacturing configuration files can be formed, the files are used for driving the production of a next round of test pieces, the plastic spiral bevel gear produced in the next round is more accurate through the corrected cavity parameters and the injection molding process parameters of the plastic spiral bevel gear die, the time for qualified high-precision plastic spiral bevel gears is shortened, and the manufacturing cost of the high-precision plastic spiral bevel gears is reduced.

In an alternative implementation manner of the embodiment, the steps of performing inverse geometric correction on the initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table, performing numerical interval adjustment on injection molding process parameters to generate a corrected mold structure model and process parameter set comprise extracting geometric parameter dimensions to be compensated according to correction direction information corresponding to key manufacturing parameter items in the error factor mapping table, performing inverse geometric mapping on the geometric parameter dimensions and the initial plastic spiral bevel gear mold to generate a mold local correction area, performing offset reconstruction on cavity curved surface nodes in the mold local correction area to generate a corrected mold three-dimensional structure model, identifying key process variables in the injection molding process according to parameter sensitivity levels of the influence factor weight matrix, performing offset iteration on the numerical intervals corresponding to the key process variables to generate an injection molding process parameter adjustment set, and performing combined encapsulation on the corrected mold three-dimensional structure model and the injection molding process parameter adjustment set to generate a corrected mold structure model and process parameter set.

Specifically, in this embodiment, according to the correction direction information of each key manufacturing parameter item recorded in the error factor mapping table, the geometric parameter dimension to be compensated can be extracted from the geometric design of the mold. The geometrical parameter dimension refers to the curve or curve characteristic of the mold directly related to the tooth surface deformation, such as the tooth top curvature radius, the tooth root bevel angle, the tooth surface transition curve control point position and other data, and the geometrical dimension range to be adjusted can be determined by correlating the positive or negative correction amount given in the mapping table with the corresponding curve parameter. For example, when the mapping table indicates that the profile ending angle is small, the angle dimension may be listed in the set to be compensated when a two degree slope is required at the root transition. Next, a mold local correction region may be generated by inverse geometric mapping the geometric parameter dimensions to the three-dimensional CAD model of the initial plastic spiral bevel gear mold. The inverse geometric mapping refers to projecting the target tooth surface shape compensation quantity onto the curved surface of the die cavity, determining Control Points (Control Points) affected by correction through an inverse algorithm of a parameterized curved surface (such as NURBS curved surface), and identifying the local area of the curved surface. The process needs to perform secondary interpolation calculation on the curved surface parameter space, and decompose the compensation vector into parameter space coordinate increment, so that the node range needing to be reconstructed is positioned on the original curved surface control grid. For example, if the radius of curvature of 0.05 mm is increased on the curved surface of the tooth top arc, the control point of the B-spline in the area can be solved reversely, and the corresponding local correction boundary is drawn. After the local correction area is defined, the offset reconstruction operation can be carried out on the cavity curved surface nodes in the area so as to generate a corrected three-dimensional structure model of the die. The core of bias reconstruction is to apply an offset to the selected control point opposite to the error direction and to maintain the surface continuity and curvature consistency after correction by a smooth interpolation algorithm such as laplace smoothing or weighted moving least squares. The method can avoid sharp bending angles or uneven curved surfaces caused by local adjustment, and the corrected geometric shapes still maintain high smoothness in vision and function. Taking the middle tooth profile section as an example, if the area is found to be excessively contracted, the adjacent control points in the control grid can be biased according to the compensation amount given in the mapping table, a new three-dimensional model is generated through a local subdivision surface or regeneration surface technology, and finally a correction model connected with the original mold structure is formed. After the geometry compensation is completed, key process variables in the injection molding process including, but not limited to, injection speed, dwell time, cooling rate, and mold temperature control accuracy, etc., can be identified in combination with the parameter sensitivity level information in the influence factor weight matrix. The parameter sensitivity level refers to a weight value obtained through early gradient integration and correlation analysis and is used for representing the influence degree of different process variables on error generation. Taking a high weight variable as an example, if the cooling rate is high in weight to the helix inclination error, the cooling rate can be listed as a key process variable. For each key variable, bias iteration can be performed in the original process setting interval according to the weight proportion, namely, a certain amount of adjustment value is added or reduced on the basis value of a given variable, and the simulated response of the adjustment value to the tooth surface error is evaluated. The offset iterative process can be verified through design experiments (DOE) or numerical simulation (such as injection simulation software), and the corresponding values of the optimal offset are recorded, and finally summarized into an injection molding process parameter adjustment set. And combining and packaging the corrected three-dimensional structure model of the mould and the obtained injection molding process parameter adjustment set, namely, defining geometric compensation data and process adjustment data in the same manufacturing configuration file at the same time, thereby forming a unified manufacturing instruction set and providing integrated input for actual production.

In an alternative implementation manner of the embodiment, injection molding is performed according to a corrected mold structure model and a process parameter set to generate a target plastic spiral bevel gear test piece, a corrected error data set is obtained through tooth pitch, tooth profile and spiral line detection of the same dimension as that of an initial plastic spiral bevel gear mold test piece, a structured data item is generated through field binding of the corrected error data set, a manufacturing parameter model, an error factor mapping table and a corresponding correction parameter item, the data item is stored in an error correction sample database, feature extraction is performed on multiple groups of data items in the error correction sample database to construct an error prediction feature space, an error prediction model is generated through sample training of the feature space, a target structure parameter to be processed and a target manufacturing environment parameter are input into the error prediction model to obtain an expected error type and a corresponding compensation parameter, and the cavity parameter and the injection molding process parameter of the current plastic spiral bevel gear mold are optimized according to the compensation parameter.

Specifically, in this embodiment, after the modification of the geometric and technological parameters of the mold is completed, the modified three-dimensional mold structure model and the modified technological parameters are put into an injection molding production process, and a new batch of plastic spiral bevel gear test pieces are processed in an injection molding mode. Injection molding refers to injecting a molten plastic cylinder into a modified mold cavity through an injection molding machine, and opening the mold after pressure maintaining and cooling to obtain a test piece, which aims at verifying the combined effect of geometry and process adjustment. The new test piece is measured according to three dimensions of tooth pitch, tooth profile and spiral line by utilizing the same high-precision gear measuring center as the initial test piece, the tooth pitch measurement is combined with optical scanning by adopting a linear encoder of a gear measuring machine, the tooth profile detection captures a surface curve by a contact profiler, and the spiral line error is realized by adopting a three-coordinate measuring machine to alternately pick up points in the axial direction and the radial direction. And through three groups of check data acquired by the measuring equipment, a corrected error data set can be acquired, and a real case is provided for subsequent model verification. And uniformly weaving the corrected error data set, the manufacturing parameter model, the error factor mapping table and the corresponding correction parameter item into a structured data entry by means of a data integration and field binding technology. The structured data entry refers to a complete record in the relational database, and includes fields such as an error measurement value, an initial parameter vector, a corresponding key parameter identifier in the mapping table, and a compensation amount thereof. For example, when the pitch deviation of a test piece is 0.03 mm, the corresponding information of the center modulus, the injection speed, the related influence factor weight, the geometric correction value of the current mold and the like can be recorded in the same item. After the entry is written into the error correction sample database, new data can coexist with the historical samples for statistics and model training of larger sample sizes. On the basis of the database with enough accumulated entries, the characteristic extraction can be carried out on a plurality of groups of data records, so as to construct an error prediction characteristic space. Feature extraction is to extract the variable combination with the most information quantity for error prediction from records by using algorithms such as Principal Component Analysis (PCA) or Recursive Feature Elimination (RFE), for example, taking injection pressure, cooling time and mold temperature as principal components, and combining tooth surface error indexes to form new composite features. The feature space can greatly reduce the data dimension, reserve key influence factors and lay a foundation for the subsequent training of the machine learning model. After feature space construction is complete, the labeled samples may be trained using a supervised learning algorithm, such as random forest regression (Random Forest Regression) or Support Vector Regression (SVR), to generate an error prediction model. In the training process, the model is input as the extracted composite characteristic, and output as the corresponding error type and the numerical prediction thereof. By cross-validating and grid searching (GRID SEARCH) optimization model superparameters, versions with higher prediction accuracy can be obtained for direct invocation in new projects. Before a new production round, only the target structure parameters and the target manufacturing environment parameters are input into the trained error prediction model, and the expected error type and the corresponding compensation parameter data can be obtained. The expected error type refers to the numerical intervals given by the mold for pitch deviation, profile deviation, or spiral error, and the compensation parameters include the mold cavity geometry trim value and the amount of micro-bias of the injection molding process variables, such as suggesting a 5% increase in dwell time or a 0.2 ℃/s decrease in cooling rate. The parameters of the current mold cavity and the injection molding machine can be optimally set according to the compensation parameters output by the model, and then batch production is put into. By the scheme, a closed-loop control system driven by data is formed, key influencing factors can be locked rapidly at the initial stage of development, the qualification rate of the first part of the new-generation plastic spiral bevel gear is improved remarkably, and powerful guarantee is provided for subsequent batch consistency design.

According to the method, a manufacturing parameter model of a plastic spiral bevel gear is built according to gear structure parameters and injection manufacturing environment parameters of an initial plastic spiral bevel gear die, error detection data reflecting deformation conditions of the tooth surface working area are obtained through accuracy detection of gear teeth after primary injection molding of the plastic spiral bevel gear, classification analysis is conducted on the error detection data according to the manufacturing parameter model to generate an error decomposition result, an error tracing model for quantifying error influence factors is built through association of the error decomposition result and the manufacturing parameter model, target parameter items of each error source and corresponding influence factor weights are determined according to the error tracing model, an error factor mapping table is generated, reverse geometric correction is conducted on the initial plastic spiral bevel gear die according to the correction direction of the error factor mapping table, numerical value interval adjustment is conducted on injection molding process parameters, and a corrected die structure model and a corrected process parameter set are generated. According to the scheme, the manufacturing parameter model is built, the actual errors are obtained, the error decomposition and the traceability modeling are carried out, the error factor mapping relation is finally built, the mould structure and the injection parameters are reversely corrected according to the error factor mapping relation, and the accuracy of the plastic spiral bevel gear in the injection molding process can be effectively improved.

Fig. 2 is a diagram of a plastic spiral bevel gear tooth surface working area deformation control device according to an embodiment of the present application, which can be used to implement the plastic spiral bevel gear tooth surface working area deformation control method in the foregoing embodiment. As shown in fig. 2, the plastic spiral bevel gear tooth surface working area deformation control device mainly comprises:

A construction module 10 for constructing a manufacturing parameter model of the plastic spiral bevel gear according to the gear structure parameter and the injection manufacturing environment parameter of the initial plastic spiral bevel gear mold;

The obtaining module 20 is configured to obtain error detection data reflecting a deformation condition of a tooth surface working area by performing precision detection on the gear teeth after primary injection molding of the plastic spiral bevel gear, and perform classification analysis on the error detection data according to the manufacturing parameter model to generate an error decomposition result;

The determining module 30 is configured to construct an error tracing model according to the error decomposition result and the manufacturing parameter model, determine a target parameter item and a corresponding influence factor weight of each error source according to the error tracing model, and generate an error factor mapping table;

The correction module 40 is configured to perform inverse geometric correction on the initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table, and perform numerical interval adjustment on the injection molding process parameter, so as to generate a corrected mold structure model and a corrected process parameter set.

In an alternative implementation manner of the embodiment, the construction module is specifically configured to extract gear structure parameters according to a design drawing of an initial plastic spiral bevel gear mold, perform numerical analysis on the gear structure parameters to generate a structure parameter vector set, obtain injection molding manufacturing environment parameters by analyzing a history molding record and a set process file of an injection molding manufacturing system, construct a parameter mapping relation table by performing matrix assembly on the structure parameter vector set and the manufacturing environment parameters, generate a manufacturing parameter reference matrix according to the parameter mapping relation table, and generate a manufacturing parameter model corresponding to a target plastic spiral bevel gear by storing the manufacturing parameter reference matrix in a callable model structure.

In an optional implementation manner of the embodiment, the acquisition module is specifically configured to respectively acquire tooth pitch deviation data, tooth profile error data and spiral line error data by measuring a plurality of tooth surfaces of the plastic spiral bevel gear along axial and radial directions, and store the corresponding data in a structured manner based on measurement positions and tooth numbers to generate an error original data set, perform association analysis on the error original data set and a manufacturing parameter model to generate a classification error sample set, wherein the classification error sample set comprises a shape class error, a tooth surface defect class error and a geometric precision class error, determine an error distribution curve according to spatial distribution of corresponding error types in the classification error sample set, generate an error deformation function corresponding to the error types, determine a candidate parameter set causing error types to be generated by performing variable correlation calculation on the error deformation function and manufacturing parameter items of the manufacturing parameter model, and perform joint mapping on the candidate parameter set and the classification error sample set to generate an error decomposition result.

In an alternative implementation manner of the embodiment, the method is specifically used for constructing a sensitivity scoring matrix of error types for different manufacturing parameter items according to error decomposition results, establishing a corresponding parameter response function for the error types according to the sensitivity scoring matrix, generating a multidimensional parameter interference projection graph by controlling weight configuration on input variables of the parameter response function, extracting error response interval characteristics by performing distributed density cluster analysis on the parameter interference projection graph, generating a target parameter positioning vector in a parameter space according to the error response interval characteristics, and establishing a bidirectional mapping relation between the error types and the manufacturing parameter items and constructing an error tracing model by performing linkage indexing on the target parameter positioning vector and the error decomposition results.

In an optional implementation manner of the embodiment, the determining module is specifically configured to determine key manufacturing parameter items forming an error generating mechanism by extracting target parameter positioning vectors corresponding to different error types based on a bi-directional mapping relationship, aggregate the key manufacturing parameter items to form an error master control parameter set, obtain response amplitude values of the key manufacturing parameter items in an error influence path by performing numerical integration analysis on function gradients between the key manufacturing parameter items and an error deformation function in the error master control parameter set, generate an influence factor original scoring table according to the response amplitude values, generate an influence factor weight matrix corresponding to the key manufacturing parameter items by performing normalization processing and weight distribution reconstruction on the influence factor original scoring table, and generate an error factor mapping table by performing field mapping integration on the error types, the key manufacturing parameter items and the influence factor weights.

In an alternative implementation manner of the embodiment, the correction module is specifically configured to extract geometric parameter dimensions to be compensated according to correction direction information corresponding to each key manufacturing parameter item in the error factor mapping table, generate a mold local correction area by performing inverse geometric mapping on the geometric parameter dimensions and an initial plastic spiral bevel gear mold, generate a corrected mold three-dimensional structure model by performing offset reconstruction on cavity curved surface nodes in the mold local correction area, identify key process variables in an injection molding process according to parameter sensitivity levels affecting a factor weight matrix, generate an injection molding process parameter adjustment set by performing offset iteration on numerical intervals corresponding to the key process variables, and generate a corrected mold structure model and a process parameter set by performing combined encapsulation on the corrected mold three-dimensional structure model and the injection molding process parameter adjustment set.

In an alternative implementation manner of the embodiment, the correction module is further used for performing injection molding processing according to the corrected mold structure model and the technological parameter set to generate a target plastic spiral bevel gear test piece, obtaining a corrected error data set through tooth pitch, tooth profile and spiral line detection of the same dimension as that of the initial plastic spiral bevel gear mold test piece, generating a structured data item through field binding of the corrected error data set, the manufacturing parameter model, the error factor mapping table and the corresponding correction parameter item, storing the structured data item into an error correction sample database, performing feature extraction on multiple groups of data items in the error correction sample database to construct an error prediction feature space, performing sample training on the feature space to generate an error prediction model, inputting target structure parameters to be processed and target manufacturing environment parameters into the error prediction model to obtain an expected error type and corresponding compensation parameters, and optimizing cavity parameters and injection molding technological parameters of the current plastic spiral bevel gear mold according to the compensation parameters.

According to the technical scheme, the deformation control device for the tooth surface working area of the plastic spiral bevel gear is provided, a manufacturing parameter model of the plastic spiral bevel gear is built according to gear structure parameters and injection manufacturing environment parameters of an initial plastic spiral bevel gear die, error detection data reflecting deformation conditions of the tooth surface working area are obtained through accuracy detection of gear teeth after primary injection molding of the plastic spiral bevel gear, classification analysis is conducted on the error detection data according to the manufacturing parameter model to generate an error decomposition result, an error tracing model for quantifying error influence factors is built through association of the error decomposition result and the manufacturing parameter model, target parameter items of each error source and corresponding influence factor weights are determined according to the error tracing model, an error factor mapping table is generated, reverse geometric correction is conducted on the initial plastic spiral bevel gear die according to the correction direction of the error factor mapping table, numerical value interval adjustment is conducted on injection molding process parameters, and a corrected die structure model and a process parameter set are generated. According to the scheme, the manufacturing parameter model is built, the actual errors are obtained, the error decomposition and the traceability modeling are carried out, the error factor mapping relation is finally built, the mould structure and the injection parameters are reversely corrected according to the error factor mapping relation, and the accuracy of the plastic spiral bevel gear in the injection molding process can be effectively improved.

Fig. 3 provided according to the scheme of the present application is an electronic device provided by an embodiment of the present application. The electronic equipment can be used for realizing the plastic spiral bevel gear tooth surface working area deformation control method in the previous embodiment, and mainly comprises the following steps:

Memory 301, processor 302, and computer program 303 stored on memory 301 and executable on processor 302, memory 301 and processor 302 being connected by communication. When the processor 302 executes the computer program 303, the plastic spiral bevel gear tooth surface working area deformation control method in the foregoing embodiment is implemented. Wherein the number of processors may be one or more.

The memory 301 may be a high-speed random access memory (RAM, random Access Memory) memory or a non-volatile memory (non-volatile memory), such as a disk memory. The memory 301 is used for storing executable program code, and the processor 302 is coupled to the memory 301.

Further, an embodiment of the present application further provides a computer readable storage medium, which may be provided in the electronic device in each of the foregoing embodiments, and the computer readable storage medium may be a memory in the foregoing embodiment shown in fig. 3.

The computer readable storage medium has stored thereon a computer program which when executed by a processor implements the plastic spiral bevel tooth flank working area deformation control method of the foregoing embodiment. Further, the computer-readable medium may be any medium capable of storing a program code, such as a usb (universal serial bus), a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.

While the application has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modifications or substitutions do not depart from the spirit and scope of the embodiments of the application.

Claims

1. The plastic spiral bevel gear tooth surface working area deformation control method is characterized by comprising the following steps of:

The method comprises the steps of carrying out precision detection on gear teeth after primary injection molding of a plastic spiral bevel gear to obtain error detection data reflecting deformation conditions of a gear surface working area, carrying out classification analysis on the error detection data according to a manufacturing parameter model to generate an error decomposition result, respectively obtaining tooth pitch deviation data, tooth profile error data and spiral line error data by measuring a plurality of gear surfaces of the plastic spiral bevel gear along axial and radial directions, carrying out structural storage on corresponding data based on measurement positions and gear tooth numbers to generate an error original data set, carrying out association analysis on the error original data set and the manufacturing parameter model to generate a classification error sample set, wherein the classification error sample set comprises shape type errors, gear surface defect type errors and geometric precision type errors, determining an error distribution curve according to spatial distribution of corresponding error types in the classification error sample set to generate an error deformation function corresponding to the error types, carrying out variable correlation calculation on the error deformation function and each manufacturing parameter item of the manufacturing parameter model to determine a candidate parameter set which leads to error types, and carrying out joint decomposition result on the candidate parameter set and the error sample set;

Constructing an error tracing model according to the error decomposition result and the manufacturing parameter model, determining target parameter items of each error source and corresponding influence factor weights according to the error tracing model, and generating an error factor mapping table;

Performing reverse geometric correction on the initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table, and performing numerical interval adjustment on injection molding process parameters to generate a corrected mold structure model and a corrected process parameter set;

and detecting the tooth pitch, the tooth profile and the spiral line of the same dimension as the plastic spiral bevel gear after primary injection molding on the target plastic spiral bevel gear test piece to obtain a corrected error data set.

2. The method for controlling deformation of a plastic spiral bevel gear tooth surface working area according to claim 1, wherein the step of establishing a manufacturing parameter model of the plastic spiral bevel gear according to the gear structure parameter and the injection manufacturing environment parameter of the initial plastic spiral bevel gear mold comprises the steps of:

Constructing a parameter mapping relation table by matrix assembly of the structural parameter vector set and the injection molding manufacturing environment parameters, and generating a manufacturing parameter reference matrix according to the parameter mapping relation table;

3. The method for controlling deformation of a working area of a tooth surface of a spiral bevel gear according to claim 2, wherein the step of constructing an error tracing model according to the error decomposition result and the manufacturing parameter model comprises the following steps:

4. The method for controlling deformation of a plastic spiral bevel gear tooth surface working area according to claim 3, wherein the step of determining target parameter items of each error source and corresponding influence factor weights according to the error tracing model and generating an error factor mapping table comprises the following steps:

5. The method for controlling deformation of a plastic spiral bevel gear tooth surface working area according to claim 4, wherein the step of performing inverse geometric correction on the initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table and performing numerical interval adjustment on injection molding process parameters to generate a corrected mold structure model and a corrected process parameter set comprises the following steps:

6. The method of plastic spiral bevel tooth flank working area deformation control of claim 5, further comprising:

7. A plastic spiral bevel gear tooth surface working area deformation control device, characterized in that the plastic spiral bevel gear tooth surface working area deformation control device is used for realizing the plastic spiral bevel gear tooth surface working area deformation control method according to claim 6, and the plastic spiral bevel gear tooth surface working area deformation control device comprises:

The system comprises an acquisition module, a classification error sample set and a classification error sample set, wherein the acquisition module is used for carrying out precision detection on gear teeth after primary injection molding of the plastic spiral bevel gear to acquire error detection data reflecting the deformation condition of a gear surface working area, carrying out classification analysis on the error detection data according to the manufacturing parameter model to generate an error decomposition result, the acquisition module is used for respectively acquiring tooth pitch deviation data, tooth profile error data and spiral line error data by measuring a plurality of gear surfaces of the plastic spiral bevel gear along the axial direction and the radial direction, carrying out structured storage on corresponding data based on measurement positions and gear tooth numbers to generate an error original data set;

The correction module is used for carrying out inverse geometric correction on the initial plastic spiral bevel gear mold according to the correction direction of the error factor mapping table, carrying out numerical interval adjustment on injection molding process parameters to generate a corrected mold structure model and a process parameter set, carrying out injection molding processing according to the corrected mold structure model and the process parameter set to generate a target plastic spiral bevel gear test piece, and obtaining a corrected error data set through carrying out tooth pitch, tooth profile and spiral line detection on the target plastic spiral bevel gear test piece, wherein the tooth pitch, the tooth profile and the spiral line are the same as those of the plastic spiral bevel gear after primary injection molding.

8. An electronic device comprising a memory and a processor, wherein:

The processor is used for executing the computer program stored on the memory;

The processor, when executing the computer program, implements the steps in the plastic spiral bevel tooth surface working area deformation control method as claimed in claim 6.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps in the plastic spiral bevel tooth surface working area deformation control method as claimed in claim 6.