Intelligent plasticizing screw design system and method based on material characteristics

Abstract

The invention relates to a plasticizing screw intelligent design system and method based on material characteristics, and belongs to the field of material processing and forming. The invention discloses a plasticizing screw intelligent design system based on material characteristics. Comprising a material characteristic database, a screw structure parameter database, an experimental design module, a computer-aided design module, a computer-aided numerical simulation module, a screw plasticizing performance evaluation module, a screw structure optimization module, an optimal screw structure machine learning module and an optimal screw structure generation module. According to the system and the method, the optimal plasticizing screw structure geometric parameter data based on the plasticizing performance evaluation target criterion can be output only by inputting the material characteristic parameters, and the screw model can be established according to the output screw structure geometric parameter data; the device is used for the next step of screw processing and manufacturing and is applied to the plasticizing production of corresponding materials.

Description

Technical Field

[0001] The invention relates to a plasticizing screw intelligent design system and method based on material properties, belonging to the field of material processing and molding. Background Art

[0002] In the modern plastics industry, injection molding and extrusion molding are the main batch molding methods for plastic products. The specifications, screw configuration, mold structure, material properties and various settable process parameters of the injection molding machine and extruder will have a great impact on the quality of the final product. The necessary condition for the plastic molding process is plasticization. The plastic is added to the barrel of the injection molding machine or extruder through the hopper. The plasticization of the plastic is mainly achieved through the heat generated and transmitted by the external heater of the barrel and the rotational shear of the plasticizing screw inside the barrel. The plastic is transformed from solid to liquid. The screw used for plasticizing plastics is one of the most critical core components and greatly affects the performance of plastic molding equipment and products.

[0003] The plasticizing capacity of the screw directly affects the plastic processing cycle and production efficiency. According to the rheological properties of the material, the flow model is established under isothermal conditions in the cavity flow channel established by the screw and the barrel, namely the melt conveying theory, which can be used to describe the plasticizing capacity of the screw. The screw melt conveying theory model shows that under certain process parameters (such as barrel temperature and screw speed), the plasticizing capacity of the screw is related to the processability of the material and the structure and geometric dimensions of the screw. According to the different thermophysical properties, crystal morphology and rheological properties of the processed polymer materials and their composite materials, the plasticizing screw should be designed with different structures and geometric dimensions to achieve effective plasticization and provide production efficiency. The plasticizing screw is usually divided into three sections in axial length, namely the conveying section, the compression section and the metering section, to match the three stages of the plasticization process of the material from solid to liquid. The plasticization process of the material in the screw is: 1) Plastic particles are added from the hopper to the barrel of the injection molding machine, and the solid plastic particles are initially transported and compacted in the conveying section of the screw; 2) With the rotation of the screw and the action of the axial force of the screw ribs, the plastic gradually enters the compression section of the screw and is more tightly compacted; at the same time, under the action of the heat transmitted from the external heater of the barrel, part of the plastic above the screw groove of the screw changes from solid to liquid to form a molten film; under the action of the shear force generated by the rotation of the screw, the molten film gradually expands and a molten pool is generated at the screw ribs of the screw. The closer to the front end of the screw push, the larger the molten pool is, and the smaller the solid bed formed by the solid plastic is; 3) Under the action of the axial force of the screw, the plastic enters the metering section of the screw, and the solid plastic particles are completely converted into liquid. Under the precise size control of the metering section, accurate metering and flow control of the liquid plastic can be achieved. The screw groove of the conveying section is the deepest, which mainly plays the role of transporting and initially compacting the plastic particles; the screw groove depth of the compression section usually changes gradually from deep to shallow, and the extrusion effect on the material is more obvious, and as the screw groove depth decreases, the shearing effect of the screw becomes more obvious. Therefore, the compression section is an important transition stage for the polymer material to transform from a glassy state to a highly elastic state; the screw groove depth of the metering section is the shallowest and the shearing effect is the strongest. Under the action of high shear and continuous heating of the barrel temperature, the polymer material has now transformed from a highly elastic state to a viscous flow state.

[0004] In addition to the common three-stage screw, there are many new types of screws, such as gradual screw, sudden screw, non-metering screw, corrugated screw, split screw and separation screw, among which the gradual screw, sudden screw and separation screw are the most common. The compression section of the gradual screw is longer, generally accounting for more than 40% of the total thread length, and the metering section is shorter, generally not exceeding 15% of the total thread length. The longer compression section makes the depth of the screw groove change slowly, and the pressure and temperature changes of the material are gentle, which is suitable for processing materials with poor thermal stability. The characteristic of the sudden screw is that the compression section is relatively short, accounting for about 15% of the effective length of the screw, and the feeding section is relatively long, generally accounting for 70% of the effective length of the screw. Its structural feature is that the depth of the screw groove changes sharply in the shorter compression section, so that the shear rate and temperature of the material when being processed rise rapidly, which is suitable for processing materials with sudden characteristics. The separation screw is characterized by the presence of secondary threads, which can separate the solid and liquid phases of the polymer during the plasticizing process, allowing the gas in the compressed plastic particles to be discharged, which is beneficial to the reduction of bubbles.

[0005] The plasticization state of the material before entering the mold directly affects the quality of the molded product, and this influence cannot usually be balanced by adjusting the machine parameters. Therefore, the precise design of the screw structure and size is particularly important. The influence of the screw structure on the phase change behavior of the material has the characteristics of multivariable nonlinear coupling. However, traditional screw design is usually based on melt conveying theory and plastic machinery design manuals for empirical design. With the continuous development of the plastics industry and the emergence of new materials, how to customize the screw design according to the characteristics of different materials has become an urgent need in the field of plastic processing and molding.

[0006] With the advent of Industry 4.0, smart manufacturing has entered all walks of life, and the plastics industry has also opened a new chapter. The precision control of plastic products is different from the machining of metal products. Due to the characteristics of the materials, it is impossible for a general-purpose injection molding screw to adapt to all types of materials. In the past, the design method of injection molding screws was usually that engineers relied on experience and a lot of trial and error to manufacture a special screw. Now, with the maturity of computer-aided technology, the design of screws has gradually changed from the traditional empirical trial and error method to the use of computer-aided technology. In recent years, many studies have used computer-aided technology to guide the design of screws, while there are few reports on the use of machine learning methods to assist in the design of screws. Machine learning methods have the advantages of safety, reliability, continuity, high efficiency, and high precision. They are auxiliary tools favored by many engineers in engineering. Taking advantage of the digital drive of machine learning methods, complex problems in screw design can be digitized through computer-aided design, so that the design of special plasticizing screws for different material characteristics can be realized.

[0007] Patent CN102194033A discloses a variant design method, which first establishes a product model, and then performs product structure variant design, including a description of the relationship between object characteristics and a variant design process. In the variant design process, it is necessary to determine the basic characteristics, determine or calculate the value of the derived characteristics according to the constraint relationship set between the basic characteristics and the derived characteristics, and drive the three-dimensional model in the CAD system to perform variant design through the basic characteristics and derived characteristics of the object characteristic table, and finally perform process variant design. Patent CN109658499A invents a model establishment method, device and storage medium, which obtains the target development activity requirements of an instance object; obtains the target category of the instance object; obtains the specific attribute items of the instance object from the technology spectrum model according to the target development requirements and the target category; and establishes a full information attribute model of the instance object according to the target development activity requirements, the target category and the specific attribute items. Patent US9959684B2 proposes a method for changing the shape of a geometric entity model by means of deformation using a computer graphics system, characterized in that it is used to generate geometric entities that fit together, because the geometric entities are opposite to each other and have mating surfaces, which need to be manufactured with precise accuracy (up to 10 microns), such as toothed screws (cylindrical, conical) of screw compressor devices, gear transmission systems, gears of gear pumps and similar geometric entity mating pairs. The proposed method includes deforming a semi-finished model of a first matching entity by means of a model of a first tool, the surface of which is automatically calculated by several mathematical laws, each of which is a polynomial, in which at least one coefficient represents one of several features defining the shape of the surface of the second geometric entity. The deformation of the semi-finished model of the second matching entity is carried out by means of a model of a second tool, which represents the deformed target model of the first entity. In addition, the deformation is carried out according to the movement law that imitates the geometric entities and fits together during use. Patent KR20150031652A relates to a design method for a plasticizing screw for an injection molding machine or an extruder, and in particular to a three-dimensional modeling method for a plasticizing screw, characterized in that the diameter and pitch value of the screw can be variably set, including forming a two-dimensional spiral line, then applying sweeping to form a three-dimensional entity, and then forming the screw axis, characterized in that the screw diameter value and the pitch value can be variably set within the interval, thereby providing a three-dimensional modeling method for a plasticizing screw. The dimensional accuracy of the plasticizing screw can be improved in actual operation, the design freedom of the plasticizing screw to be manufactured can be ensured, and various shapes can be realized accordingly. However, although the above-listed invention patents have certain applicability for the design of certain mechanical parts, they lack a digital process for constraint parameters and conditions, especially for the design of plasticizing screw parts that are closely related to material properties, and the relationship between part shape and material properties cannot be established.

[0008] Patent CN110809510A discloses a screw shape inference device, a screw shape inference method, and a screw shape inference program. The acquisition module acquires input information including resin properties, and acquires the required value of physical quantities related to the mixed resin or the mixing device as output information; the storage module is used to store a knowledge file containing input information, physical quantities, and correlations between multiple screw shapes; the search module and the shape making module infer the screw shape that meets the required value based on the input information and the knowledge file. Patent KR20230026290A discloses a screw recommendation function and a screw recommendation method in an artificial intelligence-based injection molding system. The artificial intelligence-based injection molding system with a screw recommendation function includes: a resin information input part, which receives resin information including the type of resin used for injection molding; a screw diameter recommendation part, which determines the candidate screw diameter of the resin and the candidate screw model supporting the candidate screw diameter based on the physical property data of the resin corresponding to the resin and the injection molding data mapped to the resin; and a screw diameter recommendation part, which determines the candidate screw diameter of the resin and the candidate screw model supporting the candidate screw diameter for each candidate screw diameter. A candidate screw model for selecting a screw diameter; a first selection part, in which a target screw diameter among the candidate screw diameters is input from a user, and a target screw model among the candidate screw models is input; and a screw material and design recommendation part, in which the candidate screw material and design are determined according to the physical property data, the injection molding data, the target screw diameter, the target screw model and the screw design data; a second selection part, for receiving the target screw material and design among the input candidate screw materials and designs; and a selection result output part, for outputting the selection result of the target screw diameter, the target screw model and the target screw material and design. The above two patents are the prior art that are closest to the purpose of the present invention. However, although the above two patents and prior art mention the procedure of learning and inferring or recommending the screw shape through the resin physical property information, there is no clear relevant information data, especially what information data is used to infer or recommend the screw shape, how to evaluate the screw shape, what indicators are used to infer and evaluate the screw shape, what algorithms or models are used to obtain the optimal screw shape, whether the inferred screw shape meets the plasticization conditions required by the resin, and many other issues. No supporting measures and methods are given. The physical quantity parameter data established by the relevant prior art is relatively limited. Patent CN110809510A only mentions the inference of the screw shape without the specific design function of the screw shape related dimensions matching the material characteristics. Patent KR20230026290A only mentions the matching and selection of the screw diameter, model and screw material through the information of the resin type and additive type. Therefore, the screw models that can be output by these two patents are relatively rough and do not have the specific design function corresponding to the screw shape and size.

[0009] Patent P2002-214107A discloses a method for configuring screw segments of a twin-screw extruder, which creates a database by performing flow analysis and kneading extrusion tests on a variety of resin materials in the segmented configuration of a twin-screw kneading extruder of various sizes, and provides data corresponding to the conditions required by the user, and proposes the best solution to give the screw segment configuration configuration of the relevant conditions; when the user uses it, there is no need to perform flow analysis and kneading extrusion tests of the twin-screw mixing extruder separately, and only needs to send the required conditions to obtain the corresponding data, thereby obtaining the most suitable screw segment configuration suggestion for the conditions. Patent US2022 / 0080646Al discloses a device, method and computer program for screw configuration reasoning, wherein an acquisition unit is used to obtain input information including resin characteristics, and obtains a specified value of a physical quantity related to the mixing resin or mixing machine as output information; a storage unit is used to store knowledge information containing input information, physical quantities and correlations between multiple screw configurations; a search unit and a configuration generation unit are used to infer a screw configuration that can meet the specified value based on the input information and knowledge information. The above patent technology clarifies the application target of screw configuration based on resin material information, and clarifies the equipment application of twin-screw extruders and resin mixers, but there are still major problems in specific implementation. Because the twin-screw extruder used for mixing involves a particularly complex and diverse material system, the screw configuration of the twin screw is different from that of the conventional single screw and is particularly complex. The conventional screw elements alone include multiple types of conveying elements, meshing elements, mixing elements, etc. of different shapes and sizes. These elements can be combined and configured in different orders to form different overall screw configurations. Patent P2002-214107A clearly states that the creation of the database must be completed through experimental testing. How to complete the flow analysis and kneading extrusion test under different material systems and different screw configurations to create the database has become the main problem in the implementation of this patent. The corresponding cost of collecting data and creating the database, especially the time cost, is too high, and there is a problem of low data reliability caused by experimental errors; while patent US2022 / 0080646Al does not clearly define the specific measures and methods for database creation. The basis for its optimal screw configuration configuration is the effect of resin material mixing. It clarifies the resin temperature, machine power, solid ratio, residence time, maximum pressure and maximum torque as indicators, but does not clarify the way to obtain the relevant parameter indicators. Collecting through experiments also takes up a large time cost and experimental errors affect the reliability of the data. Furthermore, patent P2002-214107A does not give the actual algorithm, indicators or standards and evaluation methods for obtaining the optimal screw configuration configuration. Although patent US2022 / 0080646Al mentions the neural network algorithm model, it does not clarify the applicability of the relevant model. In addition, the above patent only provides for selective combination configuration from the screw configuration database, and does not have the function of designing the screw shape and specific size.

[0010] Patent WO 2011 / 033522 Al discloses a method and system for extruder configuration and visualization, the system includes an extruder information database, including detailed information of the extruder, extruder screw element and extruder barrel; a user interface receives user input, including the extruder, screw element and barrel selected by the user from the extruder information database to be added to the extruder information database, and a component positioning module includes a database of incompatible components; the component positioning module includes an incompatible component database for reviewing user selections and preventing incompatible extruder components from being positioned adjacent to each other; a processor creates an extruder configuration based on user input, and a display module creates an extruder configuration based on user input; the display module creates an extruder configuration based on user input, and creates an extruder configuration based on user input; the processor is used to create an extruder configuration based on user input; and the display module is used to display the extruder configuration. This patent clearly states that it is only applicable to the field of extruders, and only configures the function of manually combining screw element configurations, and does not have the function of designing the structure, shape and size of screw elements.

[0011] Patent US10549453B2 discloses a simulation device, a simulation method and a non-transient computer-readable medium with a simulation program for analyzing the fluid flow of materials in a twin-screw kneading and mixing device. The simulation device includes a one-dimensional analysis unit, which performs low-dimensional fluid flow analysis on the material in the arithmetic object domain of the device according to the setting information including the physical properties of the material and the configuration data and operating conditions of the kneading device used to mix the material, a region selection unit, which is used to receive the object region selected in the arithmetic object region as the object of high-dimensional fluid flow analysis, and a three-dimensional analysis unit, which is used to extract the material physical quantity related to the object region according to the result of the low-dimensional fluid flow analysis, and perform high-dimensional fluid flow analysis on the material in the object region according to the extracted physical quantity and setting information. This patent can output the flow parameter information of the mixing process by inputting the material density, thermal conductivity, specific heat capacity and rheological data information, and the screw element configuration information. This patent does not have the function of designing the structure, shape and size of the screw element.

[0012] It can be seen that according to the specific parameter data of material characteristics, combined with computer-aided design and numerical simulation to obtain plasticizing performance parameter data, establish plasticizing evaluation methods and indicators, and further establish artificial intelligence machine learning models to obtain the optimal plasticizing screw shape, structure and size, a plasticizing screw intelligent design system and method based on material characteristics can be generated, which has many advantages such as matching material properties, no artificial experience interference and conforming to theoretical laws, clear and reliable parameter information data, and intelligent automatic rapid generative design. None of the existing technologies have the above advantages. Summary of the invention

[0013] The purpose of the present invention is to provide an intelligent design system and method for a plasticizing screw based on material properties, which can replace the conventional experience-based manual design method. By combining material characteristic parameters, computer-aided design, computer-aided numerical simulation, and experimental design method, a database of material characteristic parameters and plasticizing screw structure dimensions is established to provide effective data for artificial intelligence machine learning. An artificial intelligence model is obtained through artificial intelligence machine learning, and ultimately the purpose of outputting plasticizing screw structure geometric parameter data by simply inputting material characteristic parameters is achieved. A screw model can be established based on the output screw structure geometric parameter data, which is used for the next step of screw processing and manufacturing and applied to the plasticizing production of corresponding materials.

[0014] The objectives of the present invention are achieved through the following technical solutions.

[0015] A plasticizing screw intelligent design system based on material properties, including a material property database, a screw structure parameter database, an experimental design module, a computer-aided design module, a computer-aided numerical simulation module, a screw plasticizing performance evaluation module, a screw structure optimization module, an optimal screw structure machine learning module, and an optimal screw structure generation module, characterized in that: the material property database stores physical property parameter data of n kinds of materials, and the material physical property parameters mainly include material density, specific heat capacity, thermal conductivity, and rheological parameters to form n groups of material physical property parameter data; the screw structure parameter database includes data and variation ranges of geometric parameters related to the screw structure type, and the screw structure parameters mainly include screw diameter, screw aspect ratio, screw Groove depth, screw ridge width, helix angle, pitch, compression ratio; the experimental design module includes a parameter design part based on the experimental design method, and establishes m types of screw structures according to the screw structure parameters and data range and the experimental design algorithm. The experimental design algorithm mainly includes the orthogonal design method, the Taguchi design method, and the central design method to form m groups of screw structure parameter data; the computer-aided design module establishes corresponding m screw models and barrel flow channel models according to the m groups of screw structure parameter data established by the experimental design module; the computer-aided numerical simulation module carries out numerical simulation of the screw plasticizing process in turn according to the n groups of material property parameter data and the m screw models established by the computer-aided design module, and calculates the flow field parameters in the model flow channel, and the flow field The parameters mainly include temperature and viscosity. Through numerical simulation calculation, n×m groups of flow field parameter data can be obtained, that is, each material corresponds to m groups of flow field parameter data; the screw plasticizing performance evaluation module determines the plasticizing parameters and weight ratios. The plasticizing parameters mainly include the temperature difference, viscosity difference and average viscosity at the front end of the screw, i.e., the outlet end of the flow channel in the barrel, obtained by numerical simulation calculation. The plasticizing performance index value is obtained according to the plasticizing parameters and weight calculation, that is, the sum of the product of each plasticizing parameter and each weight. Based on the numerical simulation calculation results, n×m groups of plasticizing performance index data are calculated, and each material corresponds to m plasticizing performance index values; the screw structure optimization module performs the calculation on the m groups of screw structure parameter data established by the experimental design module and the screw plasticizing performance evaluation module. The m plasticizing performance index value results are processed, and the screw structure parameter data that can make the plasticizing performance index value closest to the expected value is obtained by using the experimental design algorithm and the expected value size analysis of the plasticizing performance index data, that is, the optimal screw structure parameter data is obtained, each material corresponds to a set of optimal screw structure parameter data, and n materials correspond to n sets of optimal screw structure parameter data; the optimal screw structure machine learning module mainly includes an input data interface, an output data interface, a machine learning model, and a hyperparameter optimization module, the input data interface inputs the physical property parameter data of n materials, and the output data interface inputs the n sets of optimal screw structure parameter data corresponding to the n materials, and the machine learning model is used to perform machine learning training to obtain an artificial intelligence model;The optimal screw structure generation module mainly includes a data input module, a data output module and a model input module. The data input module is used to input the physical property parameter data of the new material, the model input module is used to input the artificial intelligence model, and the data output module is used for the artificial intelligence model to calculate the corresponding new optimal screw structure parameter data according to the input physical property parameter data of the new material.

[0016] Furthermore, the rheological parameters include melt flow index.

[0017] Furthermore, the rheological parameters include equation parameters obtained by regressing rheological test experimental data using a conventional rheological equation.

[0018] Furthermore, the plasticizing performance index of the screw plasticizing performance evaluation module determines the weight of each plasticizing parameter through the plasticizing criterion, and the screw structure optimization module takes the plasticizing criterion, i.e., the optimal plasticizing performance, as the optimization target during optimization, and the corresponding plasticizing performance index takes the temperature difference, viscosity difference and average viscosity expected value of the plasticizing parameters as the minimum.

[0019] Furthermore, the flow field parameters calculated by the computer-aided numerical simulation module also include screw torque, and the plasticizing parameters of the screw plasticizing performance evaluation module also include screw torque.

[0020] Furthermore, the screw plasticizing performance evaluation module also includes a plasticizing criterion and a balance criterion and an energy-saving criterion that take into account the energy consumption generated by the screw torque. The plasticizing criterion corresponds to the plasticizing parameter weight, and the expected value of the temperature difference, viscosity difference and average viscosity is assigned to obtain the plasticizing performance index value; the balance criterion corresponds to the plasticizing parameter weight, and the expected value of the temperature difference, viscosity difference, average viscosity and screw torque is assigned to obtain the plasticizing performance index value; under the premise of a total weight of 100%, the balance criterion preferably prefers that the temperature difference, viscosity difference, and average viscosity together account for 50% of the weight, and the screw torque accounts for 50% of the weight; the energy-saving criterion preferably prefers that the temperature difference, viscosity difference, and average viscosity together account for less than 50% of the weight, and the screw torque accounts for the remaining weight.

[0021] Furthermore, the screw plasticizing performance evaluation module performs dimensionless processing on the plasticizing parameter data before determining the plasticizing performance index value.

[0022] Furthermore, the dimensionless processing method is preferably a maximum-minimum normalization method.

[0023] Furthermore, the plasticizing parameter weights of the screw plasticizing performance evaluation module have the function of assigning weights by interventional artificial experience, that is, the plasticizing parameter weight values ​​can be modified artificially based on experience.

[0024] Furthermore, the plasticizing screw intelligent design system based on material properties also includes a screw plasticizing performance machine learning module and a screw plasticizing performance prediction module. The screw plasticizing performance machine learning module mainly includes a material physical property parameter and screw structure parameter input interface, a plasticizing parameter output interface, a machine learning model, and a hyper-parameter optimization module; the screw plasticizing performance prediction module mainly includes a new material physical property parameter and screw structure parameter input interface, a model input interface after training of the screw plasticizing performance machine learning module, and a plasticizing parameter output interface.

[0025] Furthermore, the material property-based plasticizing screw intelligent design system also includes a computer-aided design model generation system, which can directly generate a screw model file in a format readable by computer-aided design software according to the optimal screw structure parameter data output by the optimal screw structure generation module.

[0026] Furthermore, the machine learning model is a supervised learning model, which mainly includes a multivariate linear regression model, a random forest model, an XGB model, a GBDT model and a neural network model. The prediction accuracy of the machine learning model is calculated by comparing the predicted results with the actual results to obtain the mean square error (MSE) and the coefficient of determination (R 2 ) is used to determine the coefficient of determination. The coefficient of determination ranges from 0 to 1. The closer it is to 1, the higher the model accuracy. The closer the mean square error is to 0, the higher the model accuracy.

[0027] Furthermore, the machine learning model is preferably a random forest model.

[0028] Furthermore, the material property-based plasticizing screw intelligent design system is also provided with a transfer learning module.

[0029] Furthermore, the material property database contains at least 50 materials.

[0030] The intelligent design method of plasticizing screw based on material properties is characterized in that: the method uses the above-mentioned intelligent design system of plasticizing screw based on material properties, and the specific steps of the method are as follows:

[0031] Step 1, storing the physical property parameters of n kinds of materials of different types, different manufacturers and different brands through the material property database, and establishing a database of n groups of material physical property parameter data;

[0032] Step 2, storing screw structure parameter data of different screw types in a screw structure parameter database, and establishing a database containing different screw structure parameters and value variation ranges;

[0033] Step 3, through the experimental design module, select the experimental design algorithm, and establish m types of screw structures corresponding to m groups of screw structure parameter data according to the screw structure parameters and data ranges in the screw structure parameter database;

[0034] Step 4, using a computer-aided design module, according to the m groups of screw structure parameter data established by the experimental design module, establish corresponding m screw models and barrel flow channel models;

[0035] Step 5, using a computer-aided numerical simulation module according to n groups of material property parameter data and m screw models established by a computer-aided design module, sequentially carry out numerical simulation of the screw plasticizing process, and calculate and obtain n×m groups of flow field parameter data, that is, each material corresponds to m groups of flow field parameter data;

[0036] Step 6, determining the plasticizing parameters and weight ratios through the screw plasticizing performance evaluation module, and obtaining the plasticizing performance index values ​​according to the plasticizing parameters and weights, that is, the sum of the products of each plasticizing parameter and each weight, and obtaining n×m groups of plasticizing performance index data based on the numerical simulation calculation results, and each material corresponds to m plasticizing performance index values;

[0037] Step 7, performing data processing on the m groups of screw structure parameter data established by the experimental design module and the m plasticizing performance index value results obtained by the screw plasticizing performance evaluation module through the screw structure optimization module, and obtaining the screw structure parameter data that can make the plasticizing performance index value closest to the expected value by analyzing the experimental design algorithm and the expected value of the plasticizing performance index data, that is, obtaining the optimal screw structure parameter data, each material corresponds to one group of optimal screw structure parameter data, and n materials correspond to n groups of optimal screw structure parameter data;

[0038] Step 8, inputting n groups of material property parameter data through the input data interface of the optimal screw structure machine learning module, inputting n groups of optimal screw structure parameter data corresponding to the n materials through the output data interface, and performing machine learning training through the machine learning model to obtain an artificial intelligence model;

[0039] Step 9: Input the physical property parameter data of the new material through the data input module of the optimal screw structure generation module, input the artificial intelligence model trained by the optimal screw structure machine learning module through the model input module, and the artificial intelligence model calculates the corresponding new optimal screw structure parameter data based on the input new material physical property parameter data. The new optimal screw structure parameter data is output through the data output module to complete the intelligent design task of the plasticizing screw based on material properties.

[0040] Beneficial Effects

[0041] 1. The intelligent design system and method of plasticizing screw based on material properties provided by the present invention obtains flow field parameters by using the computer-aided numerical simulation module under the conditions of the plasticizing screw model established by the experimental design module and the computer-aided design module through the material physical property parameters provided by the material database, and then obtains the optimal screw structure parameters of all materials in the material database through the screw plasticizing performance evaluation module and the screw structure optimization module, and further obtains the model of the optimal screw structure generation module through machine learning and training of the optimal screw structure machine learning module. With the support of the model, the physical property parameters of any input new material can be matched with the corresponding optimal screw structure parameters;

[0042] 2. The intelligent design system and method of plasticizing screw based on material properties provided by the present invention can realize the continuous learning function, continuously add the data set of physical property parameters of new materials and optimal screw structure parameters on the basis of the existing material database, and continuously improve the accuracy of intelligent design;

[0043] 3. The intelligent design system and method of plasticizing screw based on material properties provided by the present invention can provide a material parameter selection function, and selectively select the physical parameters that need to be considered according to needs, and the corresponding optimal screw structure is more applicable.

[0044] 4. The plasticizing screw intelligent design system and method based on material properties provided by the present invention can customize the screw plasticizing performance evaluation module, select evaluation parameters according to needs, and manually assign weights of the evaluation parameters; the corresponding optimal screw can be intelligently designed according to the manually selected evaluation indicators;

[0045] 5. The intelligent design system and method of plasticizing screw based on material properties provided by the present invention provide a variety of machine learning models, and the corresponding models can be manually specified for machine learning and training;

[0046] 6. The intelligent design system and method of plasticizing screw based on material properties provided by the present invention also include a screw plasticizing performance machine learning module and a screw plasticizing performance prediction module, which have the function of outputting flow field parameters and plasticizing parameter data by inputting material physical properties and screw structure parameters.

[0047] 7. The intelligent design system and method of plasticizing screw based on material properties provided by the present invention has the following advantages: the more types of materials the material database contains, the higher the machine learning accuracy is, and the more accurate the optimal screw structure obtained by intelligent design is. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a structural relationship and method sequence diagram of a plasticizing screw intelligent design system and method based on material properties in Example 1;

[0049] Figure 2It is a structural relationship and method sequence diagram of a plasticizing screw intelligent design system and method based on material properties in Example 2;

[0050] Figure 3 is an orthogonal table of screw structural parameters established by the experimental design module of Example 2;

[0051] Figure 4 The screw model and barrel flow channel model established by the computer-aided design module of Example 2;

[0052] Figure 5 The temperature and viscosity flow field distribution at the front end of the screw, i.e., the outlet end of the flow channel in the barrel, obtained by numerical simulation calculation of the PBT material with the grade of VALOX 215HPR under a certain screw structure calculated by the computer-aided simulation module of Example 2;

[0053] Figure 6 It is the mean main effect diagram and mean response table of the plasticizing performance index value data of the plasticizing criterion obtained by the orthogonal experimental design algorithm of the PBT material with the grade VALOX 215HPR in Example 2;

[0054] Figure 7 These are the three optimal screw structures of the new PP material with the brand name Braskem PP CP 442XP Bras obtained by the artificial intelligence model in Example 2;

[0055] Figure 8 This is a data table and a correlation diagram comparing the three optimal screw structural parameters of the new PP material with the brand Braskem PP CP 442XP Bras obtained by the artificial intelligence model in Example 2 and the three optimal screw structural parameters obtained by the orthogonal experimental design method.

[0056] In the figure: 1—material property database, 2—screw structure parameter database, 3—experimental design module, 4—computer-aided design module, 5—computer-aided numerical simulation module, 6—screw plasticizing performance evaluation module; 7—screw structure optimization module, 8—optimal screw structure machine learning module, 9—optimal screw structure generation module; A—n groups of material physical property parameter data, B—m groups of screw structure parameter data, C—m screw models, D—n×m groups of flow field parameter data, E—n×m groups of plasticizing performance index data, F—n groups of optimal screw structure parameter data, G—artificial intelligence model; H—new material physical property parameter data, I—new optimal screw structure parameter data. DETAILED DESCRIPTION

[0057] The preferred embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0058] Example 1

[0059] Reference Figure 1As shown, a plasticizing screw intelligent design system based on material properties comprises a material property database (1), a screw structure parameter database (2), an experimental design module (3), a computer-aided design module (4), a computer-aided numerical simulation module (5), a screw plasticizing performance evaluation module (6), a screw structure optimization module (7), an optimal screw structure machine learning module (8), and an optimal screw structure generation module (9), characterized in that: the material property database (1) stores physical property parameter data of n kinds of materials, and the material physical property parameters mainly include material density, specific heat capacity, thermal conductivity, and rheological parameters, forming n groups of material physical property parameter data (A); the screw structure The parameter database (2) includes data and variation ranges of geometric parameters related to the screw structure type, wherein the screw structure parameters mainly include screw diameter, screw aspect ratio, screw groove depth, screw ridge width, helix angle, screw pitch, and compression ratio; the experimental design module (3) includes a parameter design part based on the experimental design method, and establishes m types of screw structures according to the screw structure parameters and data range and the experimental design algorithm, and the experimental design algorithm mainly includes the orthogonal design method, the Taguchi design method, and the central design method to form m groups of screw structure parameter data (B); the computer-aided design module (4) establishes corresponding m screw models ( C) and a barrel flow channel model; the computer-aided numerical simulation module (5) sequentially performs numerical simulation of the screw plasticizing process according to n groups of material property parameter data (A) and m screw models (C) established by the computer-aided design module (4), and calculates and obtains n×m groups of flow field parameter data (D), that is, each material corresponds to m groups of flow field parameter data; the screw plasticizing performance evaluation module (6) determines the plasticizing parameters and weight ratio, the plasticizing parameters mainly including the temperature difference, viscosity difference and average viscosity at the front end of the screw, i.e., the outlet end of the flow channel in the barrel, obtained by numerical simulation calculation, and the plasticizing performance index value is obtained by calculation according to the plasticizing parameters and weight, that is, the sum of the products of each plasticizing parameter and their respective weights, based on Based on the numerical simulation calculation results, n×m groups of plasticizing performance index data (E) are calculated, and each material corresponds to m plasticizing performance index values; the screw structure optimization module (7) performs data processing on the m groups of screw structure parameter data (B) established by the experimental design module (3) and the m plasticizing performance index value results obtained by the screw plasticizing performance evaluation module (6), and obtains the screw structure parameter data that can make the plasticizing performance index value closest to the expected value by using the experimental design algorithm and the expected value size analysis of the plasticizing performance index data, that is, obtains the optimal screw structure parameter data, each material corresponds to one group of optimal screw structure parameter data, and n materials correspond to n groups of optimal screw structure parameter data (F);The optimal screw structure machine learning module (8) mainly includes an input data interface, an output data interface, a machine learning model, and a hyperparameter optimization module. The input data interface inputs n types of material property parameter data (A), and the output data interface inputs n groups of optimal screw structure parameter data (F) corresponding to the n types of materials. The machine learning model is used for machine learning training to obtain an artificial intelligence model (G); the optimal screw structure generation module (9) mainly includes a data input module, a data output module, and a model input module. The data input module inputs new material property parameter data (H), the model input module inputs the artificial intelligence model (G), and the data output module is used for the artificial intelligence model (G) to calculate the corresponding new optimal screw structure parameter data (I) according to the input new material property parameter data (H).

[0060] The steps of using the above-mentioned plasticizing screw intelligent design system based on material properties are:

[0061] Step 1, storing the physical property parameters of n kinds of materials of different types, different manufacturers and different brands through the material property database (1), and establishing a database of n groups of material physical property parameter data (A);

[0062] Step 2, storing screw structure parameter data of different screw types in a screw structure parameter database (2), and establishing a database containing different screw structure parameters and value variation ranges;

[0063] Step 3, through the experimental design module (3), select the experimental design algorithm, and establish m types of screw structures according to the screw structure parameters and data ranges in the screw structure parameter database, corresponding to m groups of screw structure parameter data (B);

[0064] Step 4, using a computer-aided design module (4), according to the m groups of screw structure parameter data (B) established by the experimental design module (3), establish corresponding m screw models and barrel flow channel models;

[0065] Step 5, using a computer-aided numerical simulation module (5) to sequentially perform numerical simulation of the screw plasticizing process according to the n sets of material property parameter data (A) and the m screw models (C) established by the computer-aided design module (4), and calculate and obtain n×m sets of flow field parameter data (D), that is, each material corresponds to m sets of flow field parameter data;

[0066] Step 6, determining the plasticizing parameters and weight ratios through the screw plasticizing performance evaluation module (6), and obtaining the plasticizing performance index values ​​according to the plasticizing parameters and weights, that is, the sum of the products of each plasticizing parameter and each weight, and obtaining n×m groups of plasticizing performance index data (E) based on the numerical simulation calculation results, wherein each material corresponds to m plasticizing performance index values;

[0067] Step 7, performing data processing on the m groups of screw structure parameter data (B) established by the experimental design module (3) and the m plasticizing performance index value results obtained by the screw plasticizing performance evaluation module (6) through the screw structure optimization module (7), and obtaining the screw structure parameter data that can make the plasticizing performance index value closest to the expected value by using the experimental design algorithm and the expected value size analysis of the plasticizing performance index data, that is, obtaining the optimal screw structure parameter data, each material corresponds to one group of optimal screw structure parameter data, and n materials correspond to n groups of optimal screw structure parameter data (F);

[0068] Step 8, inputting n sets of material property parameter data (A) through the input data interface of the optimal screw structure machine learning module (8), inputting n sets of optimal screw structure parameter data (F) corresponding to the n materials through the output data interface, and performing machine learning training through the machine learning model to obtain an artificial intelligence model (G);

[0069] Step 9, input the new material physical property parameter data (H) through the data input module of the optimal screw structure generation module (9), input the artificial intelligence model (G) through the model input module, the artificial intelligence model (G) calculates the corresponding new optimal screw structure parameter data (I) according to the input new material physical property parameter data (H), and outputs the new optimal screw structure parameter data (I) through the data output module, thereby completing the intelligent design task of the plasticizing screw based on material properties.

[0070] Example 2

[0071] Reference Figure 2 As shown, this embodiment is similar to the above embodiment, except that the material property database (1) of this embodiment stores the physical property parameter data of 62 different grades of materials, including density (unit: kg / m 3 ), specific heat capacity (unit J / (kg·K)), thermal conductivity (unit W / (m·K)), zero shear viscosity (unit Pa·s), power law index, time constant (unit s), reference temperature (unit K), activation energy parameter (unit K), forming 62 sets of material property parameter data (A). The zero shear viscosity, power law index, time constant, reference temperature, and activation energy parameter in the above material property parameters belong to rheological parameters, which are obtained by regressing the rheological test experimental data through the conventional rheological equation. The utility model adopts the Cross rheological equation, as follows:

[0072] η=η0 / (1+(λ·γ·) (1-n) )

[0073] H(T) = exp[α(1 / (T-T0)-1 / (T α -T0))]

[0074] Where η is the melt viscosity, η0 is the melt viscosity at zero shear, λ is the time constant (i.e., the inverse of the shear rate at which the fluid changes from Newtonian behavior to power-law behavior), n is the power-law exponent, α is the activation energy parameter, which is the ratio of the activation energy to the thermodynamic constant, T is the melt temperature, and T α is the reference temperature, T0 is the temperature offset, which is taken as 0 here.

[0075] Taking the PBT material VALOX 215HPR produced by SABIC as an example, its corresponding material properties are: density 1098kg / m 3 , specific heat capacity 1935 J / (kg·K), thermal conductivity 0.2 W / (m·K), shear-free viscosity 108.6108 Pa·s, power law exponent 0.38576, time constant 0.007555 s, reference temperature 529 K, activation energy parameter 6435.535 K.

[0076] The screw type of the screw structure parameter database (2) of this embodiment belongs to a conventional three-stage injection molding screw, which mainly includes a conveying section, a compression section and a metering section. The fixed screw diameter of this embodiment is 30 mm, the pitch is 30 mm and the aspect ratio is 18. The corresponding screw structure parameters that need to be optimized include four parameters: the length ratio of the conveying / compression / metering sections, the compression ratio, the groove depth of the metering section, and the screw ridge width.

[0077] The experimental design algorithm of the experimental design module (3) of this embodiment adopts the orthogonal design method. The method of dividing the length ratio of the three-stage screw is to divide the total length of the screw into 20 parts in equal proportion. The ratio of 1 represents 5% of the total length of the screw. For example, the length ratio of the three sections of conveying / compression / metering is conveying section length: compression section length: metering section length = 9:7:4, which means that the length of the conveying section of the screw accounts for 45% of the total length, the length of the compression section accounts for 35% of the total length, and the length of the metering section accounts for 20% of the total length. The selection of the level value of the three-section length ratio is based on the fact that each section length is within the range of the conventional three-section injection molding screw design criteria, and based on this, it is divided into four levels, namely 9:7:4, 8:6:6, 10:5:5, and 10:4:6; the division of the compression ratio takes into account different compression ratios. The reduction ratio is applicable to materials with different viscosities, so the coverage of its four levels includes high-viscosity materials and low-viscosity materials, with values ​​of 2, 2.5, 3, and 3.5; the design range of the groove depth of the metering section of the three-stage injection molding screw is specified as 0.04 to 0.07 times the screw diameter according to conventional screw design criteria, so as to be applicable to materials with different sensitivities to shear rates. This embodiment is evenly divided into four levels, namely 1.2mm, 1.5mm, 1.8mm, and 2.1mm; the design range of the screw fin width of the three-stage injection molding screw is specified as 0.08 to 0.12 times the screw diameter according to conventional screw design criteria. This embodiment is evenly divided into four levels within its range, namely 2.4mm, 2.85mm, 3.15mm, and 3.6mm. Based on the above-mentioned factor and level design of the orthogonal experiment, the L16 (4 factors and 4 levels) orthogonal table is obtained, as shown in the attached figure. Figure 3 As shown, it includes 16 sets of screw structure parameter data (B).

[0078] The computer-aided design module (4) establishes 16 corresponding screw models (C) and barrel flow channel models based on the 16 sets of screw structure parameter data established by the experimental design module (3). Figure 4 A screw model and a barrel flow channel model are established. The computer-aided numerical simulation module (5) carries out numerical simulation of the screw plasticization process in turn according to the 62 sets of material property parameter data (A) and the 16 screw models (C) established by the computer-aided design module (4), and calculates and obtains 62×16=992 sets of flow field parameter data (D), that is, each material corresponds to 16 sets of flow field parameter data. Figure 5 The temperature and viscosity flow field distribution of the screw front end, i.e., the outlet of the flow channel in the barrel, is obtained by numerical simulation calculation for the PBT material of the corresponding grade VALOX 215HPR under a certain screw structure. According to the numerical simulation calculation results, the temperature difference, viscosity difference and average viscosity of the screw front end, i.e., the outlet of the flow channel in the barrel, as well as the screw torque obtained by overall calculation can be extracted as plasticizing parameter data.

[0079] The screw plasticizing performance evaluation module (6) determines the plasticizing parameters and weight ratios. The plasticizing parameters mainly include the temperature difference, viscosity difference, average viscosity and screw torque at the front end of the screw, i.e., the outlet end of the flow channel in the barrel, obtained by numerical simulation calculation. The plasticizing performance index value is calculated based on the plasticizing parameters and weights, which is the sum of the product of each plasticizing parameter and its weight; 992 sets of plasticizing performance index data (E) are calculated based on the numerical simulation calculation results, and each material corresponds to 16 plasticizing performance index values. In addition, considering that there is an order of magnitude difference between the dimensions and values ​​of the rheological parameters, in order to avoid large errors, the plasticizing parameters are dimensionlessly processed before calculating the plasticizing performance index value in this embodiment, and the maximum-minimum normalization method is adopted. The corresponding formula is as follows:

[0080] x'=[x-min(x)] / [max(x)-min(x)]

[0081] Where x is the data value of the corresponding parameter, max(x) is the maximum value of all the data of the corresponding parameter, min(x) is the minimum value of all the data of the corresponding parameter, and x' is the value after normalization of the data. The plasticization performance index value is the sum of the normalized data of each plasticization parameter and the product of the weight of each plasticization parameter. In this embodiment, the plasticizing criterion, the balance criterion and the energy-saving criterion are used to set different plasticizing performance index values ​​respectively. The plasticizing criterion emphasizes the plasticizing performance, and the weights of the four plasticizing parameters of temperature difference, viscosity difference, average viscosity and screw torque are respectively set to 1; the plasticizing performance index value = normalized temperature difference + normalized viscosity difference + normalized average viscosity + normalized screw torque; the weights of the four plasticizing parameters of temperature difference, viscosity difference, average viscosity and screw torque corresponding to the balance criterion are set to 1, 1, 1, 3 respectively, and the plasticizing performance index value = normalized temperature difference + normalized viscosity difference + normalized average viscosity + 3×normalized screw torque; the weights of the four plasticizing parameters of temperature difference, viscosity difference, average viscosity and screw torque corresponding to the energy-saving criterion are set to 1, 1, 1, 9 respectively, and the plasticizing performance index value = normalized temperature difference + normalized viscosity difference + normalized average viscosity + 9×normalized screw torque.

[0082] Taking the plasticizing criterion as an example, the screw structure optimization module (7) processes the 16 sets of screw structure parameter data (B) established by the experimental design module (3) and the 16 plasticizing performance index value results obtained by the screw plasticizing performance evaluation module (6), and obtains the screw structure parameter data that can make the plasticizing performance index value closest to the expected value by using the experimental design algorithm and the expected value size analysis of the plasticizing performance index data, that is, the optimal screw structure parameter data; each material corresponds to one set of optimal screw structure parameter data, and 62 materials correspond to 62 sets of optimal screw structure parameter data (F). Taking the PBT material of grade VALOX 215HPR as an example, the attached Figure 6The plasticizing criterion mean main effect diagram and mean response table of the plasticizing performance index value data obtained by the orthogonal experimental design algorithm are shown. The plasticizing performance index value determined by the plasticizing criterion = temperature difference + viscosity difference + average viscosity + screw torque. According to the small plasticizing performance index value, the corresponding optimal screw structure parameters are: three-section length ratio 8:6:6, compression ratio 3, metering section groove depth 1.2mm, and screw rib width 3.6mm.

[0083] Corresponding to the balance criterion and energy-saving criterion, taking the PBT material of brand VALOX 215HPR as an example, according to the small value of the plasticizing performance index, the optimal screw structure parameters corresponding to the balance criterion are: three-section length ratio 8:6:6, compression ratio 3, metering section groove depth 1.2mm, screw flight width 3.15mm; the optimal screw structure parameters corresponding to the energy-saving criterion are: three-section length ratio = 8:6:6, compression ratio 3.5, metering section groove depth 1.2mm, screw flight width 2.4mm.

[0084] The optimal screw structure machine learning module (8) of this embodiment mainly includes an input data interface, an output data interface, a machine learning model, and a hyperparameter optimization module. The input data interface inputs 62 material property parameter data (A), and the output data interface inputs 62 groups of optimal screw structure parameter data (F) corresponding to the 62 materials. The machine learning model is used to perform machine learning training to obtain an artificial intelligence model (G); the machine learning model of this embodiment selects a random forest model. After grid search and cross-validation through the hyperparameter optimization module, it is determined that the number of trees is 50, the maximum depth of each tree is 10, the minimum number of samples required before node splitting is 5, the minimum number of samples required for leaf nodes is 3, the maximum number of features considered for each split is 1, and the bootstrap method is used to train each tree. The criterion for measuring the quality of the split is "gini", and the random seed is 5. The machine learning sets the training set of each model to be all 62 groups of data, and the test set to be random 25% data to ensure the adequacy of the training data and the accuracy of the test set. The prediction accuracy of the machine learning model is calculated by comparing the predicted results with the actual results to obtain the mean square error (MSE) and the coefficient of determination (R 2 ), the determination coefficient ranges from 0 to 1, the closer to 1, the higher the model accuracy; the closer the mean square error is to 0, the higher the model accuracy. In this embodiment, the 62 sets of optimal screw structure parameters obtained according to the plasticization criteria corresponding to 62 materials are machine-learned by the random forest model, and the screw structure parameter data results predicted by the artificial intelligence model (G) obtained after machine learning training are compared with the actual results to calculate the determination coefficient R 2 =97.98%, indicating that the obtained artificial intelligence model (G) has high accuracy. According to the plasticization criterion, balance criterion and energy-saving criterion, three corresponding artificial intelligence models (G) can be obtained.

[0085] The optimal screw structure generation module (9) of this embodiment mainly includes a data input module, a data output module and a model input module. The data input module is used to input the new material physical property parameter data (H), the model input module is used to input the artificial intelligence model (G), and the data output module is used for the artificial intelligence model (G) to calculate the corresponding new optimal screw structure parameter data (I) according to the input new material physical property parameter data (H). This embodiment uses a PP material with a brand name of Braskem PP CP 442XPBras for applicability verification. This material is a new material that is not included in the 62 materials used for machine learning. The new material physical property parameter data (H) include: density 709.22kg / m 3 , specific heat capacity 2870 J / (kg·K), thermal conductivity 0.16 W / (m·K), shear-free viscosity 572 Pa·s, power law index 0.299, time constant 0.014250125 s, reference temperature 597.6845 K, activation energy parameter 513 K. The new material property parameter data (H) are respectively input into the optimal screw structure generation module (9), and three sets of new optimal screw structure parameter data (I) can be obtained according to the three artificial intelligence models (G) corresponding to the plasticization criterion, balance criterion and energy-saving criterion. The new optimal screw structure parameter data (I) corresponding to the plasticization criterion are: conveying section length 228mm, compression section length 151mm, metering section length 161mm, compression ratio 3.393, metering section groove depth 1.21mm, screw flight width 3.45mm; the new optimal screw structure parameter data (I) corresponding to the balance criterion are: conveying section length 222mm, compression section length 152mm, metering section length 166mm, compression ratio 3.499, metering section groove depth 1.29mm, screw flight width 3.13mm; the new optimal screw structure parameter data (I) corresponding to the energy-saving criterion are: conveying section length 244mm, compression section length 164mm, metering section length 132mm, compression ratio 3.5, metering section groove depth 1.54mm, screw flight width 2.64mm. Attachment Figure 7 The three optimal screw structures of the new material obtained by the artificial intelligence model are shown, which are the screw structures obtained from top to bottom based on the plasticization criterion, balance criterion and energy-saving criterion respectively.

[0086] In order to verify the accuracy of the method, the new material physical property parameter data (H) of the new PP material with the brand name Braskem PP CP 442XP Bras was used; then the computer-aided design module (4) established 16 corresponding screw models (C) and barrel flow channel models according to the 16 sets of screw structure parameter data established by the experimental design module (3); the computer-aided numerical simulation module (5) was used to carry out numerical simulation calculations to obtain the flow field parameters, and then the temperature difference, viscosity difference and average viscosity at the front end of the screw, i.e., the outlet end of the flow channel in the barrel, as well as the overall calculated screw torque were extracted according to the flow field parameters as plasticizing parameter data, and then the plasticizing parameters and weight ratios were determined according to the screw plasticizing performance evaluation module (6), and the corresponding plasticizing performance index values ​​were calculated according to the plasticizing criterion, balance criterion and energy-saving criterion. ; 16 screw models correspond to 16 plasticizing performance index values, each criterion corresponds to 16 plasticizing performance index values, and there are 3 groups of 16 plasticizing performance index values ​​in total; the screw structure optimization module (7) is used again to process the 16 groups of screw structure parameter data (B) established by the experimental design module (3) and the 3 groups of 16 plasticizing performance index value results obtained by the screw plasticizing performance evaluation module (6), and the screw structure parameter data that can make the plasticizing performance index value closest to the expected value is obtained by analyzing the experimental design algorithm and the expected value of the plasticizing performance index data, that is, 3 groups of optimal screw structure parameter data corresponding to the new material are obtained as data for verifying the accuracy of the artificial intelligence model of this embodiment. Attachment Figure 8 The table and correlation diagram show the comparison of the three optimal screw structural parameters of the new material obtained by the artificial intelligence model and the three optimal screw structural parameters obtained by the orthogonal experimental design method. The correlation determination coefficient R of the optimal screw structural parameter data corresponding to the plasticization criterion, balance criterion and energy saving criterion 2 They are 98.59%, 99.3% and 93.33% respectively, indicating that the prediction accuracy of the artificial intelligence model is very high. The orthogonal experimental design method can only select 16 groups of screw parameter combinations in the orthogonal experimental design table, while the results of the artificial intelligence model are not limited to 16 groups. As long as the values ​​within the range of screw parameter variation can be calculated by the artificial intelligence model based on the plasticizing performance index value as the target value, the plasticizing performance of the screw parameters obtained by the artificial intelligence model is better.

[0087] The present invention includes but is not limited to the above embodiments. Any equivalent substitution or partial improvement made under the spirit and principle of the present invention shall be deemed to be within the protection scope of the present invention.

Claims

1. An intelligent design system for plasticizing screws based on material properties, comprising a material property database, a screw structure parameter database, an experimental design module, a computer-aided design module, a computer-aided numerical simulation module, a screw plasticizing performance evaluation module, a screw structure optimization module, an optimal screw structure machine learning module, and an optimal screw structure generation module, characterized in that: The material property database stores the physical property parameter data of n kinds of materials. The computer-aided design module establishes m groups of screw structure parameter data and corresponding m screw models and barrel flow channel models according to the experimental design module. The computer-aided numerical simulation module sequentially carries out numerical simulation of the screw plasticizing process according to the n groups of material physical property parameter data and the m screw models established by the computer-aided design module, calculates and obtains the flow field parameters in the model flow channel, and obtains n×m groups of flow field parameter data through numerical simulation calculation, wherein each material corresponds to m groups of flow field parameter data; the screw plasticizing performance evaluation module determines the plasticizing parameters and weight ratio, obtains the plasticizing performance index value according to the plasticizing parameters and weight calculation, and obtains n×m groups of plasticizing performance index data based on the numerical simulation calculation results, wherein each material corresponds to m plasticizing performance index values; the screw structure optimization module The module processes the m groups of screw structure parameter data established by the experimental design module and the m plasticizing performance index value results obtained by the screw plasticizing performance evaluation module, and obtains the screw structure parameter data that can make the plasticizing performance index value closest to the expected value by using the experimental design algorithm and the expected value size analysis of the plasticizing performance index data. Each material corresponds to one group of optimal screw structure parameter data, and n materials correspond to n groups of optimal screw structure parameter data; the optimal screw structure machine learning module performs machine learning training on the physical property parameter data of n materials and the corresponding n groups of optimal screw structure parameter data through a machine learning model to obtain an artificial intelligence model; the optimal screw structure generation module uses the artificial intelligence model obtained by the optimal screw structure machine learning module to calculate the corresponding new optimal screw structure parameter data according to the input new material physical property parameter data.

2. The intelligent design system for plasticizing screw based on material properties as claimed in claim 1, characterized in that: The material property parameters of the material property database include material density, specific heat capacity, thermal conductivity, and rheological parameters, and correspond to n types of materials with n groups of material property parameter data; the screw structure parameter database includes data and variation ranges of geometric parameters related to the screw structure type, and the screw structure parameters include screw diameter, screw aspect ratio, screw groove depth, screw ridge width, helix angle, pitch, and compression ratio.

3. The intelligent design system for plasticizing screw based on material properties as claimed in claim 1, characterized in that: The experimental design module includes a parameter design part based on the experimental design method, and m types of screw structures are established according to the screw structure parameters and data range and the experimental design algorithm. The experimental design algorithm includes the orthogonal design method, the Taguchi design method, and the central design method to form m groups of screw structure parameter data.

4. The intelligent design system for plasticizing screw based on material properties according to claim 1, characterized in that: The flow field parameters calculated by the computer-aided numerical simulation module include temperature and viscosity; the plasticizing parameters of the screw plasticizing performance evaluation module include the temperature difference, viscosity difference and average viscosity at the front end of the screw calculated by numerical simulation.

5. The intelligent design system for plasticizing screw based on material properties as claimed in claim 1, characterized in that: The optimal screw structure machine learning module includes an input data interface, an output data interface, a machine learning model, and a hyperparameter optimization module. The input data interface inputs n types of material physical property parameter data, and the output data interface inputs n groups of optimal screw structure parameter data corresponding to the n types of materials. Machine learning training is performed through the machine learning model to obtain an artificial intelligence model; the optimal screw structure generation module includes a data input module, a data output module, and a model input module. The data input module is used to input new material physical property parameter data, the model input module is used to input the artificial intelligence model, and the data output module is used for the artificial intelligence model to calculate the corresponding new optimal screw structure parameter data based on the input new material physical property parameter data.

6. The intelligent design system for plasticizing screw based on material properties according to claim 1, characterized in that: The plasticizing performance index of the screw plasticizing performance evaluation module determines the weight of each plasticizing parameter through the plasticizing criterion. The screw structure optimization module takes the optimal plasticizing performance as the optimization target during optimization, and the corresponding plasticizing performance index takes the minimum plasticizing parameter temperature difference, viscosity difference and average viscosity expected value.

7. The intelligent design system for plasticizing screw based on material properties according to any one of claims 1 to 6, characterized in that: The flow field parameters calculated by the computer-aided numerical simulation module also include screw torque, and the plasticizing parameters of the screw plasticizing performance evaluation module also include screw torque; the screw plasticizing performance evaluation module also includes plasticizing criteria and balance criteria and energy-saving criteria that take into account the energy consumption generated by the screw torque, and the plasticizing criteria correspond to the plasticizing parameter weights, and the plasticizing performance index value is obtained by assigning the minimum expected values ​​of temperature difference, viscosity difference and average viscosity; The balance criterion assigns the plasticizing parameter weights corresponding to the minimum expected values ​​of temperature difference, viscosity difference, average viscosity and screw torque to obtain the plasticizing performance index value; under the premise of 100% total weight, the balance criterion preferably selects temperature difference, viscosity difference and average viscosity as 50% of the weight and screw torque as 50% of the weight; the energy-saving criterion preferably selects temperature difference, viscosity difference and average viscosity as less than 50% of the weight and screw torque as the remaining weight.

8. The intelligent design system for plasticizing screw based on material properties according to any one of claims 1 to 7, characterized in that: The screw plasticizing performance evaluation module performs dimensionless processing on the plasticizing parameter data before determining the plasticizing performance index value.

9. The intelligent design system for plasticizing screw based on material properties according to claim 1, characterized in that: The plasticizing parameter weights of the screw plasticizing performance evaluation module have the function of assigning weights by interventional artificial experience.

10. The intelligent design system for plasticizing screw based on material properties according to claim 1, characterized in that: The material property-based plasticizing screw intelligent design system also includes a screw plasticizing performance machine learning module and a screw plasticizing performance prediction module. The screw plasticizing performance machine learning module includes a material property parameter and screw structure parameter input interface, a plasticizing parameter output interface, a machine learning model, and a hyper-parameter optimization module; the screw plasticizing performance prediction module includes a new material property parameter and screw structure parameter input interface, a model input interface after training of the screw plasticizing performance machine learning module, and a plasticizing parameter output interface.

11. The intelligent design system for plasticizing screw based on material properties according to claim 1, characterized in that: The material property-based plasticizing screw intelligent design system also includes a computer-aided design model generation system, which can directly generate a screw model file in a format readable by computer-aided design software based on the optimal screw structure parameter data output by the optimal screw structure generation module.

12. The intelligent design system for plasticizing screw based on material properties according to claim 1, characterized in that: The machine learning model is a supervised learning model, including a multivariate linear regression model, a random forest model, an XGB model, a GBDT model, and a neural network model. The prediction accuracy of the machine learning model is calculated by comparing the predicted results with the actual results to obtain the mean square error (MSE) and the coefficient of determination (R 2 ) is used to determine the coefficient of determination. The coefficient of determination ranges from 0 to 1. The closer it is to 1, the higher the model accuracy. The closer the mean square error is to 0, the higher the model accuracy.

13. The intelligent design system for plasticizing screw based on material properties according to claim 1, characterized in that: The material property-based plasticizing screw intelligent design system is also provided with a transfer learning module.

14. An intelligent design method for plasticizing screw based on material properties, characterized in that: The method uses the intelligent design system for plasticizing screw based on material properties as described in any one of claims 1 to 13, and the specific steps of the method are as follows: Step 1, storing the physical property parameters of n kinds of materials of different types, different manufacturers and different brands through the material property database, and establishing a database of n groups of material physical property parameter data; Step 2, storing screw structure parameter data of different screw types in a screw structure parameter database, and establishing a database containing different screw structure parameters and value variation ranges; Step 3, through the experimental design module, select the experimental design algorithm, and establish m types of screw structures corresponding to m groups of screw structure parameter data according to the screw structure parameters and data ranges in the screw structure parameter database; Step 4, using a computer-aided design module, according to the m groups of screw structure parameter data established by the experimental design module, establish corresponding m screw models and barrel flow channel models; Step 5, using a computer-aided numerical simulation module according to n groups of material property parameter data and m screw models established by a computer-aided design module, sequentially carry out numerical simulation of the screw plasticizing process, and calculate and obtain n×m groups of flow field parameter data, wherein each material corresponds to m groups of flow field parameter data; Step 6, determining the plasticizing parameters and weight ratios through the screw plasticizing performance evaluation module, obtaining the plasticizing performance index values ​​according to the plasticizing parameters and weights, and obtaining n×m groups of plasticizing performance index data based on the numerical simulation calculation results, where each material corresponds to m plasticizing performance index values; Step 7, performing data processing on the m groups of screw structure parameter data established by the experimental design module and the m plasticizing performance index value results obtained by the screw plasticizing performance evaluation module through the screw structure optimization module, and obtaining the screw structure parameter data that can make the plasticizing performance index value closest to the expected value by analyzing the experimental design algorithm and the expected value of the plasticizing performance index data, each material corresponds to one group of optimal screw structure parameter data, and n materials correspond to n groups of optimal screw structure parameter data; Step 8, inputting n groups of material property parameter data through the input data interface of the optimal screw structure machine learning module, inputting n groups of optimal screw structure parameter data corresponding to the n materials through the output data interface, and performing machine learning training through the machine learning model to obtain an artificial intelligence model; Step 9: Input the physical property parameter data of the new material through the data input module of the optimal screw structure generation module, input the artificial intelligence model trained by the optimal screw structure machine learning module through the model input module, and the artificial intelligence model calculates the corresponding new optimal screw structure parameter data based on the input new material physical property parameter data. The new optimal screw structure parameter data is output through the data output module to complete the intelligent design task of the plasticizing screw based on material properties.

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