CN119283322A - Control method, device, equipment and medium for injection mold of automobile parts - Google Patents
Control method, device, equipment and medium for injection mold of automobile parts Download PDFInfo
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/77—Measuring, controlling or regulating of velocity or pressure of moulding material
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
- B29C45/17—Component parts, details or accessories; Auxiliary operations
- B29C45/76—Measuring, controlling or regulating
- B29C45/78—Measuring, controlling or regulating of temperature
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76494—Controlled parameter
- B29C2945/76498—Pressure
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76494—Controlled parameter
- B29C2945/76531—Temperature
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C2945/00—Indexing scheme relating to injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould
- B29C2945/76—Measuring, controlling or regulating
- B29C2945/76655—Location of control
- B29C2945/76732—Mould
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Mechanical Engineering (AREA)
- Injection Moulding Of Plastics Or The Like (AREA)
- Moulds For Moulding Plastics Or The Like (AREA)
Abstract
本发明涉及汽车配件注塑模具的控制方法、系统、装置及存储介质，方法包括：获取汽车仪表盘注塑模具的三维数据和注塑工艺参数，基于所述注塑工艺参数对所述三维数据进行结构构建，得到仪表盘模具结构；获取所述汽车仪表盘注塑模具的实时运行数据，将所述实时运行数据与所述仪表盘模具结构进行关联，得到对应的实时模具状态信息；获取汽车仪表盘注塑模具的注塑数据，依据注塑数据对所述仪表盘模具结构进行性能预测，得到对应的模具性能信息；对实时模具状态信息和所述模具性能信息进行方案分析，得到初始模具控制方案；依据调整工艺参数对所述初始模具控制方案进行方案优化，得到对应的全局调整策略。本发明能够能够灵活应对不同的生产需求。
The present invention relates to a control method, system, device and storage medium for an injection mold for automobile parts. The method comprises: obtaining three-dimensional data and injection molding process parameters of an automobile instrument panel injection mold, constructing a structure of the three-dimensional data based on the injection molding process parameters, and obtaining an instrument panel mold structure; obtaining real-time operation data of the automobile instrument panel injection mold, associating the real-time operation data with the instrument panel mold structure, and obtaining corresponding real-time mold status information; obtaining injection molding data of the automobile instrument panel injection mold, and predicting the performance of the instrument panel mold structure according to the injection molding data, and obtaining corresponding mold performance information; performing a scheme analysis on the real-time mold status information and the mold performance information, and obtaining an initial mold control scheme; optimizing the initial mold control scheme according to adjusting the process parameters, and obtaining a corresponding global adjustment strategy. The present invention can flexibly respond to different production needs.
Description
Technical Field
The invention relates to the technical field of auto parts injection molds, in particular to a control method, a device, equipment and a medium of an auto parts injection mold.
Background
The automobile instrument panel is used as an important information display and control center in the automobile, and the manufacturing quality of the automobile instrument panel directly influences the driving experience and the safety of the automobile. With the rapid development of the automobile industry and the continuous increase of the requirements of consumers on the quality of vehicles, the design and manufacturing technology of the injection mold of the automobile instrument panel face an increasing challenge. How to improve the precision, stability and production efficiency of the injection mold of the instrument panel becomes one of the key points of industry research. However, the current control method of the injection mold of the automobile instrument panel still has some problems. Traditional control methods often rely on experience and fixed process parameters, and are difficult to adapt to the production requirements of instrument panels of different materials and different shapes. Meanwhile, the existing control method lacks of comprehensive monitoring and analysis of the real-time state of the die, and cannot discover and solve the problems in the production process in time.
Disclosure of Invention
The invention mainly aims to provide a control method, a device, equipment and a medium for an injection mold of an automobile part, which can flexibly cope with different production requirements and improve the adaptability and flexibility of mold control.
In order to achieve the above object, the present invention provides a control method of an injection mold for an auto part, comprising:
Acquiring three-dimensional data and injection molding process parameters of an injection mold of the automobile instrument panel, and performing structural construction on the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure;
Acquiring real-time operation data of the automobile instrument panel injection mold, and associating the real-time operation data with the instrument panel mold structure to obtain corresponding real-time mold state information;
Acquiring injection molding data of the automobile instrument panel injection mold, and predicting the performance of the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information;
Carrying out scheme analysis on the real-time die state information and the die performance information to obtain an initial die control scheme;
Performing parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and performing self-adaptive adjustment on the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters;
And carrying out scheme optimization on the initial mold control scheme according to the adjustment process parameters to obtain a corresponding global adjustment strategy.
Further, the structure construction is performed on the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure, which comprises:
performing grid division on the three-dimensional data to obtain an initial grid model;
extracting the characteristics of the injection molding process parameters to obtain melt flow characteristic data, molding temperature data and pressure parameters;
Constructing a mold cavity and a gate structure of the initial grid model according to the melt flow characteristic data to obtain a mold foundation structure;
Performing thermodynamic analysis on the die basic structure according to the molding temperature data and the pressure parameters to obtain thermal stress distribution data;
And carrying out structural optimization on the die base structure based on the thermal stress distribution data to obtain the instrument panel die structure.
Further, the obtaining real-time operation data of the injection mold of the automobile instrument panel, associating the real-time operation data with the instrument panel mold structure to obtain corresponding real-time mold state information, includes:
acquiring data of the injection mold of the automobile instrument panel to obtain corresponding mold temperature data, mold pressure data, mold displacement data and mold vibration data;
Mapping the mold temperature data onto the instrument panel mold structure and performing heat flow analysis to obtain temperature gradient distribution of each part of the mold;
Performing association analysis on the mold pressure data and a stress part in the instrument panel mold structure to obtain pressure spectrum characteristics;
performing geometric deviation analysis on the instrument panel die structure according to the die displacement data to obtain a die deformation;
Performing association calculation on the vibration data and connection structure points in the instrument panel die structure to obtain a vibration characteristic vector;
based on an instrument panel mold structure, carrying out multi-source data fusion on the temperature gradient distribution, the pressure spectrum characteristic, the mold deformation quantity and the vibration characteristic vector to obtain a comprehensive mold state matrix;
Carrying out state identification on the comprehensive mold state matrix to obtain corresponding mold state information;
carrying out real-time evaluation on the die state information according to a preset state evaluation index system to obtain the real-time die state information;
wherein, the performing multi-source data fusion to obtain the comprehensive mold state matrix includes:
Dividing the instrument panel die structure into a plurality of structure subareas, wherein each structure subarea corresponds to a matrix row;
Extracting a temperature gradient value of the temperature gradient distribution, a main frequency component of the pressure spectrum, a three-dimensional direction component of the mold deformation quantity and a main vibration component of the vibration characteristic vector as a matrix column of each structural subarea;
Arranging the temperature gradient value, the main frequency component, the three-dimensional direction component and the main vibration component according to a predefined sequence to form a region characteristic vector of each structural subarea;
And combining the regional feature vectors of all the structural subareas to form the comprehensive mould state matrix, wherein each row represents one structural subarea, and each column represents each regional feature vector.
Further, the obtaining the injection molding data of the injection mold of the automobile instrument panel, and performing performance prediction on the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information, includes:
performing shear rate calculation on the injection molding data to obtain the shear rates of different parts in the injection molding process;
Carrying out melt viscosity distribution calculation on the instrument panel die structure based on the shear rate to obtain viscosity distribution data;
Performing pressure field simulation based on the viscosity distribution data and the shear rate to obtain melt rheological characteristic data in the die;
Performing temperature field analysis on the instrument panel die structure based on the in-die melt rheological characteristic data to obtain die temperature distribution data;
Performing buckling deformation analysis on the instrument panel die structure according to the die temperature distribution data to obtain die buckling deformation data;
performing stress-strain analysis on the instrument panel mold structure based on the mold warp deformation data to obtain mold stress-strain distribution data;
performing durability evaluation on the instrument panel die structure according to the die stress strain distribution data to obtain die durability data;
and carrying out comprehensive performance evaluation on the die durability data and the die temperature distribution data to obtain die comprehensive performance information.
Further, the performing solution analysis on the real-time mold state information and the mold performance information to obtain an initial mold control solution includes:
performing association processing on the real-time mold state information and the mold performance information to obtain association information;
Comprehensively evaluating the associated information to obtain a comprehensive mold state evaluation result;
Performing anomaly analysis on the associated information based on the comprehensive mold state evaluation result to obtain abnormal points of the mold performance;
performing optimization adjustment design on the abnormal points of the performance of the die to obtain a corresponding abnormal improvement strategy;
carrying out parameter identification on the associated information according to the abnormal improvement strategy to obtain a specific die parameter adjustment scheme;
and integrating the die parameter adjustment scheme and the abnormal improvement strategy to obtain the initial die control scheme.
Further, the performing parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and performing adaptive adjustment on the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters, including:
Performing multidimensional decomposition on the injection molding data to obtain a parameter sequence comprising four dimensions of temperature, pressure, speed and time;
Carrying out nonlinear time sequence analysis on the parameter sequence and extracting dynamic characteristics to obtain a dynamic characteristic set;
constructing an injection molding process parameter space according to the dynamic feature set, and partitioning the injection molding process parameter space according to a spatial clustering algorithm to obtain initial injection molding process parameters;
Optimizing and adjusting the initial injection molding process parameters based on the mold performance information to obtain the adjustment process parameters;
wherein the optimization adjustment process comprises:
Setting constraint conditions of technological parameters according to the mold performance information, and defining an optimization objective function comprising product quality and production efficiency;
performing iterative optimization on the initial injection molding process parameters under the constraint conditions according to the optimization objective function;
In each iteration, evaluating the objective function value of the current parameter combination to obtain an evaluation result;
Selecting the corresponding parameter combination according to the evaluation result to carry out cross mutation, and generating a new parameter combination;
and when a preset iteration target is reached, combining the parameters obtained by final iteration to serve as the adjustment process parameters.
Further, the performing scheme optimization on the initial mold control scheme according to the adjustment process parameters to obtain a corresponding global adjustment strategy includes:
performing parameter level analysis on the initial mold control scheme according to the adjustment process parameters to obtain parameter importance ranking;
performing orthogonal test design on the initial mold control scheme based on the parameter importance degree sequencing to obtain a test scheme;
Performing simulation operation on the test scheme to obtain a simulation result;
Carrying out decision tree analysis on the simulation result according to a preset decision rule set to obtain decision modification information;
Performing self-adaptive adjustment on the initial mold control scheme according to the decision modification information to obtain a self-adaptive adjustment control scheme;
performing pattern matching on the self-adaptive adjustment control scheme and a preset control pattern library to obtain a control pattern;
And fusing the self-adaptive adjustment control scheme with the control mode to obtain the global adjustment strategy.
The invention also provides a control device of the injection mold of the automobile part, which is applied to the control method of the injection mold of the automobile part of any one of the above steps, and comprises the following steps:
The acquisition module is used for acquiring three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold, and carrying out structural construction on the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure;
The analysis module is used for acquiring real-time operation data of the automobile instrument panel injection mold, and correlating the real-time operation data with the instrument panel mold structure to obtain corresponding real-time mold state information;
The correlation module is used for acquiring injection molding data of the automobile instrument panel injection mold, and predicting the performance of the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information;
the processing module is used for carrying out scheme analysis on the real-time die state information and the die performance information to obtain an initial die control scheme;
The control module is used for carrying out parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and carrying out self-adaptive adjustment on the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters;
and the execution module is used for carrying out scheme optimization on the initial die control scheme according to the adjustment process parameters to obtain a corresponding global adjustment strategy.
The invention also provides control equipment of the injection mold of the automobile part, which comprises the following components:
A memory for storing a program;
and the processor is used for executing the program to realize the steps of the control method of the automobile part injection mold.
The invention also provides a medium storing computer instructions for causing a computer to perform the method of any one of the preceding claims.
The control method, the device, the equipment and the medium for the injection mold of the automobile part have the following beneficial effects:
By acquiring three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold and constructing an instrument panel mold structure based on the parameters, the structural characteristics and process requirements of the mold can be more accurately evaluated, and reliable basic data can be provided for subsequent control and optimization. By acquiring the running data of the die in real time and correlating with the die structure, the real-time monitoring and analysis of the state of the die are realized, the problems in the production process can be found and solved in time, and the stability and reliability of production are improved. The performance of the mold structure is predicted according to the injection molding data, potential performance problems can be identified in advance, and guidance is provided for maintenance and optimization of the mold, so that the service life and the production efficiency of the mold are improved. By comprehensively analyzing the real-time mold state information and the mold performance information, an initial mold control scheme is formulated, so that the operation of the mold is finely managed, and the product quality and the production efficiency are improved. The parameter identification is carried out on the injection molding data, and the self-adaptive adjustment is carried out on the basis of the mold performance information, so that the control method can flexibly cope with the production requirements of instrument panels with different materials and different shapes, and the adaptability and the flexibility of mold control are improved.
Drawings
FIG. 1 is a flow chart of a control method of an injection mold for an automobile part provided by the invention;
FIG. 2 is a block diagram of a control device of an injection mold for an automobile part, which is provided by the invention;
Fig. 3 is a structural diagram of a control device of an injection mold for an automobile part, which is provided by the invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention will be further described with reference to the drawings and detailed description.
Referring to fig. 1, the invention provides a control method of an injection mold of an automobile part, comprising the following steps:
Step S1, three-dimensional data and injection molding process parameters of an injection mold of an automobile instrument panel are obtained, and structural construction is carried out on the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure;
Step S2, acquiring real-time operation data of the injection mold of the automobile instrument panel, and associating the real-time operation data with the instrument panel mold structure to obtain corresponding real-time mold state information;
S3, acquiring injection molding data of the automobile instrument panel injection mold, and performing performance prediction on the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information;
S4, carrying out scheme analysis on the real-time die state information and the die performance information to obtain an initial die control scheme;
s5, carrying out parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and carrying out self-adaptive adjustment on the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters;
and S6, carrying out scheme optimization on the initial mold control scheme according to the adjustment process parameters to obtain a corresponding global adjustment strategy.
Based on the above steps, the detailed procedure is as follows:
And S1, acquiring three-dimensional data and injection molding process parameters of an injection mold of the automobile instrument panel. The three-dimensional data details the geometry and dimensions of the mold. The injection molding process parameters include various specific data required in the injection molding process, such as temperature, pressure, speed, etc.
And carrying out structural construction on the obtained three-dimensional data based on the injection molding process parameters. And simulating the three-dimensional model and the technological parameters. By simulating the injection molding process, the feasibility and potential problems of the mold, such as stress concentration, deformation, etc., are effectively evaluated. A complete and detailed instrument panel mould structure is obtained, which comprises geometrical information and also relevant process parameters.
And S2, acquiring real-time operation data of the injection mold of the automobile instrument panel in the actual production process. Such data is typically collected by various sensors and monitoring devices that are capable of reflecting the operational state of the mold, including temperature, pressure, speed, position, etc., in real time.
These real-time operational data are correlated with the constructed mold structure. This process is typically implemented by data fusion techniques that map real-time data to different parts of the mold structure, generating a dynamic, real-time updated mold state information.
And S3, acquiring injection molding data of an injection mold of the automobile instrument panel, wherein the injection molding data comprise injection molding cycle time, injection molding pressure curve, temperature curve and the like. The injection molding data reflects the mold behavior and product quality during each injection molding cycle.
And carrying out performance prediction on the instrument panel mold structure based on the injection molding data, and predicting the performance change trend of the mold by analyzing the historical data and the real-time data. For example, the life of the mold, the possible failure points, the time required for maintenance, etc. are predicted.
And S4, integrating the real-time mold state information and the mold performance information, and analyzing the association and the difference between the two types of information by using a data mining technology.
Problems and optimization spaces existing in the current mold control are identified, an initial mold control scheme is generated, and feasibility and expected effects of the initial control scheme are evaluated.
And S5, carrying out data preprocessing and feature extraction on the injection molding data, and identifying key injection molding process parameters by using a parameter identification algorithm such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA).
And establishing relation information between the performance of the mold and injection molding process parameters, adaptively adjusting the injection molding process parameters, and verifying the adjusted process parameters.
And S6, guiding the adjustment process parameters into an initial control scheme, and setting weights of 30% of production efficiency, 40% of product quality, 20% of die life and 10% of energy consumption by using a pareto optimization algorithm and considering the production efficiency, the product quality, the die life and the energy consumption.
Optimizing the initial control scheme to obtain a corresponding optimization scheme:
Scheme A, the quality of the product is emphasized, and the production efficiency is slightly reduced
Scheme B, balancing various indexes
Scheme C, emphasis on production efficiency, and proper reduction of die life
And (3) selecting an equalized scheme B as a final global adjustment strategy by comprehensively evaluating each scheme.
For example, option B specifically includes a mold temperature of 176.5 ℃, an injection pressure of 73MPa, a dwell time of 5.5 seconds, a takt time of 28 seconds per piece, and a mold maintenance period of 8000 injection cycles per cycle.
According to the control method of the automobile part injection mold, provided by the invention, the three-dimensional data and the injection molding process parameters of the automobile instrument panel injection mold are obtained, and the instrument panel mold structure is constructed based on the parameters, so that the structural characteristics and the process requirements of the mold can be more accurately evaluated, and reliable basic data is provided for subsequent control and optimization. By acquiring the running data of the die in real time and correlating with the die structure, the real-time monitoring and analysis of the state of the die are realized, the problems in the production process can be found and solved in time, and the stability and reliability of production are improved. The performance of the mold structure is predicted according to the injection molding data, potential performance problems can be identified in advance, and guidance is provided for maintenance and optimization of the mold, so that the service life and the production efficiency of the mold are improved. By comprehensively analyzing the real-time mold state information and the mold performance information, an initial mold control scheme is formulated, so that the operation of the mold is finely managed, and the product quality and the production efficiency are improved. The parameter identification is carried out on the injection molding data, and the self-adaptive adjustment is carried out on the basis of the mold performance information, so that the control method can flexibly cope with the production requirements of instrument panels with different materials and different shapes, and the adaptability and the flexibility of mold control are improved.
In one embodiment, the structural construction of the three-dimensional data based on the injection molding process parameters results in an instrument panel mold structure comprising:
And performing grid division on the three-dimensional data of the instrument panel to generate a grid model consisting of triangle or quadrilateral units. The density and quality of the grid directly affect the accuracy of subsequent analysis, so that grid parameters need to be reasonably set according to the structural characteristics of the instrument panel.
And extracting the characteristics of the injection molding process parameters to obtain melt flow characteristic data, molding temperature data and pressure parameters. The injection molding process parameters include material properties, injection speed, dwell time, etc. And carrying out simulation calculation on the injection molding process parameters to obtain flow characteristic data such as flow paths, speed distribution and the like of the melt in the mold cavity, and temperature change data and pressure distribution data of each part in the molding process.
And constructing a mold cavity and a gate structure of the initial grid model according to the melt flow characteristic data to obtain a mold foundation structure. And determining the position, the size and the number of the pouring gates according to the melt flow characteristic data, and designing the layout of the pouring gate system. And optimizing the cavity structure according to the flow path, such as adding diversion trenches and the like, so as to ensure that the melt can uniformly fill the cavity. This results in a mold base structure comprising a cavity, gate and runner system.
And carrying out thermodynamic analysis on the die foundation structure according to the molding temperature data and the pressure parameters to obtain thermal stress distribution data. And (5) importing the molding temperature data and the pressure parameters into finite element analysis software, and performing transient thermal analysis and structural stress analysis on the mold base structure. And calculating to obtain the temperature field distribution and the stress field distribution of each part of the mold in the injection molding process, thereby determining the hot spot area and the stress concentration area of the mold.
And carrying out structural optimization on the die basic structure based on the thermal stress distribution data to obtain the instrument panel die structure. And for the stress concentration area, local reinforcement is performed by adding rib plates, adjusting structural transition and the like. Meanwhile, the functions of opening and closing, ejection and the like of the die are considered, and structures such as a parting surface, an ejection system and the like are designed. And finally obtaining the instrument panel die structure meeting various performance requirements through multiple rounds of iterative optimization.
According to the embodiment, through grid division and characteristic extraction of injection molding process parameters on three-dimensional data, an accurate data base is provided for mold structure design, and the accuracy and reliability of mold design are effectively improved. And the mold cavity and the gate structure are constructed based on the melt flow characteristic data, so that the high matching of the mold structure and the injection molding process is ensured, and the product molding quality is improved. Thermodynamic analysis is carried out according to the molding temperature data and the pressure parameters, so that a hot spot area and a stress concentration area of the mold can be accurately identified, and a clear direction is provided for subsequent structural optimization. Through carrying out multi-round iterative optimization on the die basic structure, the finally obtained instrument panel die structure is more perfect in performance and function, and the possible problems in the actual die test process are greatly reduced.
In one embodiment, acquiring real-time operation data of an injection mold of an automobile instrument panel, associating the real-time operation data with an instrument panel mold structure to obtain corresponding real-time mold state information, and the method comprises the following steps:
And acquiring data of the injection mold of the automobile instrument panel, wherein the acquired data comprise mold temperature data, mold pressure data, mold displacement data and mold vibration data. Temperature data is collected through a thermocouple or an infrared sensor, pressure data is collected through a pressure sensor, displacement data is collected through a displacement sensor, and vibration data is collected through an acceleration sensor. These data reflect various physical state changes of the mold during the injection molding process.
And mapping the acquired mold temperature data onto a meter panel mold structure, and performing heat flow analysis to obtain the temperature gradient distribution of each part of the mold. The heat flow analysis adopts a finite element method, the die structure is discretized into grid units, and the temperature change rate of each unit is calculated, so that the temperature gradient distribution of the whole die is obtained. This step helps identify hot spot areas and temperature non-uniformities in the mold.
And carrying out association analysis on the mold pressure data and the stress part in the instrument panel mold structure to obtain pressure spectrum characteristics. Correlation analysis identifies critical sites that are subjected to greater pressures by combining pressure data with geometric information of the mold structure. And carrying out Fourier transform on the pressure data of the parts to obtain a pressure spectrum, and extracting main frequency components as pressure spectrum features. This step helps to find stress concentration areas and periodic pressure variations in the mold.
And carrying out geometric deviation analysis on the instrument panel die structure according to the die displacement data to obtain the die deformation. Geometric deviation analysis calculates the deformation in three-dimensional space by comparing the difference between the actual position of the mold and the ideal position. The deformation quantity comprises displacement components in the x direction, the y direction and the z direction, namely three-dimensional directions of length, width and height, and reflects the deformation degree of the mold in the injection molding process.
And carrying out association calculation on the vibration data and the connection structure points in the instrument panel die structure to obtain a vibration characteristic vector. And the correlation calculation is used for identifying key connection points with larger vibration by combining the vibration data with the connection point position information of the die structure. The vibration data of these connection points are subjected to principal component analysis, and principal vibration components are extracted as vibration feature vectors. This step helps to find loose or unstable connections in the mold.
Based on the instrument panel die structure, multi-source data fusion is carried out on temperature gradient distribution, pressure spectrum characteristics, die deformation and vibration characteristic vectors, and a comprehensive die state matrix is obtained. The multi-source data fusion process is that firstly, the instrument panel mould structure is divided into a plurality of structure subareas, and each structure subarea corresponds to one row of the comprehensive mould state matrix. The division of each structural subarea is based on the geometric characteristics and the functional subareas of the mould, so that the states of various parts of the mould can be comprehensively reflected.
For each structural subregion, the temperature gradient value of the temperature gradient distribution, the main frequency component of the pressure spectrum, the three-dimensional directional component of the mold deformation quantity and the main vibration component of the vibration characteristic vector are extracted. These extracted eigenvalues constitute the region eigenvectors of the structural subregion. In the region characteristic vector, the temperature gradient value reflects the intensity of temperature change of the region, the main frequency component of the pressure spectrum represents the main periodical pressure born by the region, the three-dimensional direction component of the deformation describes the deformation condition of the region, and the main vibration component reflects the vibration characteristic of the region.
The temperature gradient value, the main frequency component, the three-dimensional direction component and the main vibration component are arranged according to a predefined sequence to form a region characteristic vector of each structural subarea. The predefined order is typically a temperature gradient value, a pressure frequency component, an x-direction deformation, a y-direction deformation, a z-direction deformation, a principal vibration component. This ordering ensures an orderly organization of the different types of data.
And combining the regional feature vectors of all the structural subregions to form a comprehensive mold state matrix. In this matrix, each row represents a structural sub-region and each column represents each region feature vector. Such a matrix structure allows the overall state information of the mold to be systematically and structurally represented.
And carrying out state identification on the comprehensive mold state matrix to obtain corresponding mold state information. The state recognition adopts a machine learning algorithm, such as a support vector machine or a neural network, takes the comprehensive mold state matrix as input, and outputs various state types of the mold, such as normal, slight abnormal, serious abnormal and the like.
And carrying out real-time evaluation on the die state information according to a preset state evaluation index system to obtain real-time die state information. The state evaluation index system comprises evaluation standards of multiple dimensions such as temperature uniformity, pressure stability, deformation degree, vibration intensity and the like. And comparing the die state information with the standards to give a quantitative evaluation result of the current state of the die, thereby forming real-time die state information.
According to the embodiment, the multi-source data acquisition and fusion analysis are carried out on the automobile instrument panel injection mold, so that the comprehensive and real-time monitoring of the mold state is realized. The method not only can capture the changes of the mold in the aspects of temperature, pressure, displacement, vibration and the like, but also can correlate the data with the mold structure, so that more accurate and meaningful state information can be obtained. By dividing the mold structure into subregions and constructing the comprehensive mold state matrix, the complex mold state information is systematically and structurally represented, and subsequent state identification and evaluation are facilitated. The multi-dimensional and multi-scale state monitoring method greatly improves the accuracy and the comprehensiveness of the state evaluation of the die, and provides a reliable basis for the precise control and the preventive maintenance of the die.
In one embodiment, obtaining injection molding data of an injection mold of an automobile instrument panel, and performing performance prediction on an instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information, the method comprises the following steps:
And (3) performing shear rate calculation on the injection molding data to obtain the shear rates of different parts in the injection molding process. Shear rate is a physical quantity that describes the melt flow rate gradient, with a significant impact on melt flow behavior. And in the calculation process, the shape of the die cavity, injection pressure, injection speed and other factors are considered, the die cavity is scattered into grid cells by adopting a finite element analysis method, and the local shear rate is calculated in each cell.
And based on the calculated shear rate, carrying out melt viscosity distribution calculation on the instrument panel die structure to obtain viscosity distribution data. Melt viscosity is an important parameter that characterizes melt flow resistance.
And performing pressure field simulation by using the viscosity distribution data and the shear rate to obtain the rheological property data of the melt in the die. The pressure field simulation is numerically solved by a finite volume method. The simulation results include rheological parameters such as pressure, speed, etc. at various points in the mold, reflecting the flow characteristics of the melt in the mold.
And carrying out temperature field analysis on the instrument panel mold structure based on the melt rheological characteristic data in the mold to obtain mold temperature distribution data. The temperature field analysis adopts a heat conduction equation, and the temperature distribution of each part of the die is calculated by considering the convection heat transfer caused by melt flow and the heat dissipation of a die cooling system. The temperature distribution has an important influence on the molding quality of the product and the service life of the mold.
And carrying out buckling deformation analysis on the instrument panel die structure according to the die temperature distribution data to obtain die buckling deformation data. And (3) based on a thermal-structural coupling model, the deformation amount of each part of the die is calculated by taking thermal stress and shrinkage stress caused by uneven temperature distribution into consideration in the buckling deformation analysis. Warp deformation affects the dimensional accuracy and appearance quality of the product.
And carrying out stress-strain analysis on the instrument panel mold structure based on the mold buckling deformation data to obtain mold stress-strain distribution data. And the stress-strain analysis adopts a linear elastic mechanical model, and the stress and strain states of all parts of the die are calculated by considering the elastic modulus and poisson ratio of the die material. The stress strain distribution reflects the stress condition of the die and is an important basis for evaluating the strength and rigidity of the die.
And carrying out durability evaluation on the instrument panel die structure according to the die stress strain distribution data to obtain die durability data. The durability evaluation is based on fatigue life theory, and the expected service life of the die under the action of cyclic load is calculated by combining the S-N curve of the die material. The durability data reflects the long-term service performance of the mold.
And carrying out comprehensive performance evaluation on the die durability data and the die temperature distribution data to obtain die comprehensive performance information. The comprehensive performance evaluation adopts a multi-objective optimization method, takes durability and temperature uniformity as evaluation indexes, comprehensively considers the long-term service performance of the die and the molding quality of the product, and obtains the comprehensive performance score of the die.
According to the embodiment, the accurate prediction of the performance of the instrument panel die is realized by comprehensively analyzing the injection molding data and simulating multiple steps. From shear rate calculation to comprehensive performance evaluation, key physical parameters and process factors are considered in each step, and accuracy and reliability of a predicted result are ensured. By adopting an advanced numerical simulation method, a finite element analysis and a thermal-structural coupling model can deeply disclose a complex physical process in the die, and a scientific basis is provided for die design and optimization. The long-term service performance of the die is predicted through durability evaluation, and the service life and the production efficiency of the die are improved. The comprehensive performance evaluation adopts a multi-objective optimization method, balances a plurality of indexes such as the service life of the die, the quality of products and the like, and provides comprehensive reference basis for die design and production decision.
In one embodiment, the solution analysis is performed on the real-time mold state information and the mold performance information to obtain an initial mold control solution, including:
And correlating the acquired real-time die state information with pre-stored die performance information. The real-time mold state information comprises real-time monitoring data such as mold temperature, pressure, injection molding speed and the like. The mold performance information includes mold design parameters, material characteristics, historical production data, and the like. And the association processing establishes a corresponding relation between the real-time state and the performance index through a data matching algorithm to obtain association information reflecting the current working state of the die.
And comprehensively evaluating the associated information to obtain a comprehensive mold state evaluation result. The evaluation process adopts a multidimensional scoring model, quantitatively scores the state of the die from the aspects of service life, product quality, production efficiency and the like of the die, and calculates the comprehensive score according to the weight of each dimension to be used as the evaluation result of the state of the die.
And carrying out anomaly analysis on the associated information based on the comprehensive mold state evaluation result, and identifying abnormal points of the mold performance. The abnormal analysis adopts a statistical process control method, the evaluation result is compared with a preset normal range, and the data points exceeding the range are marked as abnormal points. These outliers reflect that the mold has performance problems in some respects.
And carrying out optimization adjustment design on the detected abnormal points of the die performance, and formulating a corresponding abnormal improvement strategy. According to the type and degree of the abnormal points, combining the mold structure and the process knowledge, designing targeted improvement measures, such as adjusting a cooling system, optimizing a pouring system, modifying the mold structure and the like, and forming a specific abnormal improvement strategy.
And carrying out parameter identification on the associated information according to the abnormal improvement strategy to obtain a specific die parameter adjustment scheme. And determining specific parameters and numerical ranges thereof, such as adjustment schemes of key process parameters of mold temperature, injection molding pressure, dwell time and the like, which are required to be adjusted for realizing the improvement strategy through simulation and data analysis.
And integrating the die parameter adjustment scheme and the abnormal improvement strategy to form a complete initial die control scheme.
According to the embodiment, the real-time die state information and the die performance information are subjected to association processing, so that the current working state of the die is comprehensively grasped, and a rich data basis is provided for subsequent analysis. And the comprehensive evaluation is performed by adopting a multidimensional scoring model, so that the quantification of the die state is more accurate and objective, and the potential problem can be found in time. Abnormal analysis based on the evaluation result can rapidly locate abnormal points of the die performance, and efficiency and accuracy of problem diagnosis are improved. And an optimization adjustment strategy formulated for the abnormal points ensures the pertinence and the effectiveness of the improvement measures. The specific adjustment scheme obtained through parameter identification provides operable guidance for mold control, and improves the control precision and reliability. And finally, the formed initial mould control scheme is integrated, the structural optimization and parameter adjustment are comprehensively considered, and a systematic solution is provided for improving the mould performance and the product quality.
In one embodiment, performing parameter identification on injection molding data to obtain corresponding injection molding process parameters, performing adaptive adjustment on the injection molding process parameters based on mold performance information to obtain corresponding adjustment process parameters, including:
The injection molding data is multi-dimensionally decomposed into a sequence of parameters in four dimensions, temperature, pressure, speed and time. The parameter sequences are then processed by a nonlinear time sequence analysis method to extract dynamic characteristics and form a dynamic characteristic set. Dynamic characteristics include, but are not limited to, trend of change of parameters, periodicity, mutation points, and the like.
And constructing an injection molding process parameter space based on the obtained dynamic characteristic set. The space is a multi-dimensional parametric representation, each dimension corresponding to a process parameter. This parameter space is partitioned using spatial clustering algorithms (e.g., K-means or DBSCAN) to obtain the initial injection molding process parameters. The spatial clustering algorithm aggregates similar parameters together by calculating the distance between the parameters to form different parameter combinations.
And optimizing and adjusting the initial injection molding process parameters based on the mold performance information. The mold performance information includes mold material characteristics, structural design, cooling system efficiency, and the like. The optimization and adjustment process firstly sets constraint conditions of technological parameters, such as upper and lower temperature limits, pressure ranges and the like, according to the die performance information. An optimized objective function is defined that includes product quality and production efficiency, which may involve a weighted sum of factors such as product yield, production cycle time, energy consumption, etc.
Under constraint conditions, an iterative optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) is used for optimizing the initial injection molding process parameters. And in each iteration, evaluating the objective function value of the current parameter combination to obtain an evaluation result. The evaluation result is used for judging the quality of the current parameter combination. And selecting a parameter combination with better performance to perform cross mutation according to the evaluation result, and generating a new parameter combination. Cross-mutation operation simulates the biological evolution process, exploring a better solution by combining and fine-tuning existing parameters.
The iterative process continues until a preset iteration target is reached. The iteration objective can be to reach a certain objective function value or to improve the magnitude of the objective function value by less than a predetermined threshold after a number of successive iterations. The final iteration obtained parameter combination is the adjusted process parameter, and the set of parameters can achieve better balance between the product quality and the production efficiency while meeting the performance constraint of the die.
According to the embodiment, through multi-dimensional decomposition and nonlinear time sequence analysis of injection molding data, accurate parameter identification of a complex injection molding process is realized, and the accuracy of process parameter setting is improved. By constructing an injection molding process parameter space and performing spatial clustering, a large number of parameter combinations are effectively simplified into representative initial parameters, and the efficiency of parameter optimization is greatly improved. The self-adaptive adjustment is carried out based on the mold performance information, so that the optimized process parameters can be better adapted to the characteristics of different molds, and the flexibility and the universality of the use of the molds are improved. By adopting an iterative optimization algorithm, an optimal balance point is found between the product quality and the production efficiency, so that the product quality is ensured, and the production efficiency is improved. By setting constraint conditions and optimizing an objective function, the practicability and reliability of an optimization result are ensured. The whole process realizes intelligent adjustment of injection molding process parameters, reduces human intervention, and improves the stability and consistency of the production process.
In one embodiment, the method for optimizing the initial mold control scheme according to the adjustment process parameters to obtain the corresponding global adjustment strategy includes:
And carrying out parameter hierarchical analysis on the initial mould control scheme to obtain parameter importance ranking. The parameter analytic hierarchy process is a multi-criterion decision method, and the relative importance of each parameter is finally obtained by establishing a hierarchical structure model, decomposing the complex problem into each component factor and comparing the factors in pairs. In this embodiment, the hierarchical analysis is performed on each technological parameter of the injection mold, such as temperature, pressure, time, etc., and the weight of each parameter is calculated, so as to obtain the sequencing result of the parameter importance.
And carrying out orthogonal test design on the initial mold control scheme based on the parameter importance degree sequencing to obtain a test scheme. Orthogonal test design is an efficient method of experimental design that reduces the number of tests by selecting representative combinations of test points. And selecting key parameters according to the parameter importance degree sequence, designing an orthogonal test table, and determining the level of each parameter so as to obtain a group of optimized test schemes.
And performing simulation operation on the test scheme to obtain a simulation result. The simulation operation is to carry out virtual experiments on the test scheme through computer simulation software so as to predict the possible occurrence of actual production. By using professional injection simulation software, various parameters in a test scheme can be input, and a virtual injection process can be performed, so that simulation results including indexes of product quality, production efficiency and the like are obtained.
And carrying out decision tree analysis on the simulation result according to a preset decision rule set to obtain decision modification information. The decision rule set is a series of judgment criteria established according to expert experience and historical data and is used for evaluating the advantages and disadvantages of the simulation result. Decision tree analysis is an intuitive data mining method that represents the decision process by building a tree structure. Comparing the simulation result with the decision rule set, constructing a decision tree, and analyzing the influence of each parameter on the result so as to obtain the parameters and the modification direction which need to be modified.
And carrying out self-adaptive adjustment on the initial mold control scheme according to the decision modification information to obtain a self-adaptive adjustment control scheme. The self-adaptive adjustment refers to a process of automatically adjusting control parameters according to feedback information. And according to the decision modification information, relevant parameters in the initial mold control scheme, such as injection temperature, pressure or time, are adjusted, so that an optimized self-adaptive adjustment control scheme is obtained.
And performing pattern matching on the self-adaptive adjustment control scheme and a preset control pattern library to obtain a control pattern. The control pattern library is a database containing a plurality of typical injection molding process patterns, each pattern corresponding to a particular product type and production requirements. And comparing the parameters of the self-adaptive adjustment control scheme with each mode in the control mode library, and selecting the most matched control mode as an optimization direction.
And fusing the self-adaptive adjustment control scheme with the control mode to obtain a global adjustment strategy.
According to the embodiment, through parameter hierarchical analysis and orthogonal test design, the scientific optimization of the control parameters of the die is realized, and the efficiency and accuracy of parameter adjustment are greatly improved. The combination of simulation operation and decision tree analysis enables the effect of various parameter combinations to be predicted and estimated before actual production, and effectively reduces trial-and-error cost and resource waste. The self-adaptive adjustment mechanism can flexibly adjust the control scheme according to real-time feedback, so that the flexibility and adaptability of die control are improved. The self-adaptive adjustment scheme is fused with a preset control mode, so that the reliability of the control scheme is guaranteed, the personalized requirement is considered, and the balance of standardization and customization of die control is realized. The whole optimization process forms a closed-loop control strategy, so that the mold control effect can be continuously improved, and the product quality and the production efficiency are improved.
Referring to fig. 2, the invention further provides a control device of an auto part injection mold, which is applied to the control method of the auto part injection mold of any one of the above, and includes:
The acquisition module is used for acquiring three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold, and carrying out structure construction on the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure;
The analysis module is used for acquiring real-time operation data of the injection mold of the automobile instrument panel, and correlating the real-time operation data with the structure of the instrument panel mold to obtain corresponding real-time mold state information;
the association module is used for acquiring injection molding data of the automobile instrument panel injection mold, and predicting the performance of the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information;
The processing module is used for carrying out scheme analysis on the real-time die state information and the die performance information to obtain an initial die control scheme;
the control module is used for carrying out parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and carrying out self-adaptive adjustment on the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters;
and the execution module is used for carrying out scheme optimization on the initial mould control scheme according to the adjustment process parameters to obtain a corresponding global adjustment strategy.
According to the control device for the automobile part injection mold, provided by the invention, the three-dimensional data and the injection molding process parameters of the automobile instrument panel injection mold are obtained, and the instrument panel mold structure is constructed based on the parameters, so that the structural characteristics and the process requirements of the mold can be more accurately evaluated, and reliable basic data is provided for subsequent control and optimization. By acquiring the running data of the die in real time and correlating with the die structure, the real-time monitoring and analysis of the state of the die are realized, the problems in the production process can be found and solved in time, and the stability and reliability of production are improved. The performance of the mold structure is predicted according to the injection molding data, potential performance problems can be identified in advance, and guidance is provided for maintenance and optimization of the mold, so that the service life and the production efficiency of the mold are improved. By comprehensively analyzing the real-time mold state information and the mold performance information, an initial mold control scheme is formulated, so that the operation of the mold is finely managed, and the product quality and the production efficiency are improved. The parameter identification is carried out on the injection molding data, and the self-adaptive adjustment is carried out on the basis of the mold performance information, so that the control method can flexibly cope with the production requirements of instrument panels with different materials and different shapes, and the adaptability and the flexibility of mold control are improved.
Referring to fig. 3, the present invention also provides a control apparatus for an injection mold of an auto part, comprising:
A memory for storing a program;
And the processor is used for executing a program and realizing the steps of the control method of the automobile accessory injection mold.
In this embodiment, the processor and the memory may be connected by a bus or other means. The memory may include volatile memory, such as random access memory, or nonvolatile memory, such as read only memory, flash memory, hard disk, or solid state disk. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present invention.
The invention also provides a medium storing computer instructions for causing a computer to perform a method according to any one of the above.
It should be noted that, for convenience and brevity of description, the specific working process of the above-described system and each module may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes using the descriptions and drawings of the present invention or direct or indirect application in other related technical fields are included in the scope of the present invention.
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