Method for evaluating wear resistance of screw based on discrete element analogue simulation

Abstract

The invention belongs to the technical field of methods for simulating and evaluating the wear resistance of a screw, and particularly relates to a method for simulating and evaluating the wear resistance of the screw based on discrete elements, which comprises the following steps of: constructing a three-dimensional assembly model of the screw and a machine barrel, importing the three-dimensional assembly model into discrete element simulation software EDEM, and finishing parameter setting in the EDEM, a particle model is created through a particle generator, the density and Poisson's ratio of particles, the static / dynamic friction coefficient with the surface of a screw and particle size distribution are defined, the density, shear modulus, Poisson's ratio and friction coefficient of the screw and a machine barrel are input, and the contact model is used for describing mechanical behaviors between the particles and between the particles and the screw. The abrasion model is used for representing the screw abrasion process, determining the size and the feeding efficiency of a virtual feeding factory, adjusting the rotating speed of the screw according to equipment parameters, configuring simulation calculation parameters and then starting simulation calculation.

Description

Technical Field

[0001] This invention belongs to the technical field of methods for simulating and evaluating the wear resistance of screws, and particularly relates to a method for evaluating the wear resistance of screws based on discrete element simulation. Background Technology

[0002] Currently, the mainstream method for evaluating the wear resistance of screws in the industry is mainly physical testing, which includes two categories: one is to assemble the screw into actual equipment for long-term working condition simulation tests, and after running continuously for hundreds to thousands of hours, disassemble and measure parameters such as screw wear and clearance changes to directly reflect its wear resistance; the other is to use a friction and wear testing machine to simulate the contact between the screw and the material, and indirectly evaluate the wear resistance characteristics of the material by measuring data such as the friction coefficient and wear rate.

[0003] However, these traditional methods have significant limitations:

[0004] First, the testing costs are high. Physical testing requires the fabrication of complete screw and barrel samples. For complex structures, high precision is required, involving precision processes such as CNC milling and grinding, resulting in significant material and processing costs. Simultaneously, the testing process consumes a large amount of materials, and the equipment operation requires continuous investment in resources such as electricity and cooling media. The energy consumption and material loss costs, especially during long-term operating condition testing, are difficult to control. Furthermore, if different design schemes need to be compared, multiple sets of samples must be repeatedly fabricated, further increasing costs.

[0005] Secondly, the testing cycle is lengthy. The actual wear process of the screw is slow; under normal operating conditions, it often takes weeks or even months to reach a noticeable level of wear, making it difficult to quickly reflect the design effect. Even laboratory-scale friction and wear testing machines require several hours to tens of hours per test to obtain stable wear data, and cannot fully reproduce the complex operating conditions such as material flow and multi-directional forces in actual equipment, leading to deviations between test results and practical applications. For screw designs that require rapid iterative optimization during the R&D phase, the lengthy testing cycle severely restricts R&D efficiency.

[0006] Furthermore, operating condition simulation has significant limitations. Traditional physical testing struggles to accurately control single variables or simulate extreme operating conditions: on the one hand, in actual equipment, parameters such as material particle size, hardness, impurity content, screw speed, and extrusion pressure are interdependent. To analyze the impact of a particular parameter on wear, other conditions must be strictly controlled, making the operation difficult and prone to introducing errors. On the other hand, for materials containing hard impurities or extreme operating conditions such as high speed and high pressure, physical testing can easily lead to rapid screw damage, increasing testing risks and making it difficult to obtain complete wear evolution data.

[0007] Meanwhile, analyzing wear mechanisms is difficult. Traditional tests can only infer the wear process from macroscopic or microscopic results such as changes in screw size and wear surface morphology before and after the test, and cannot capture in real time the dynamic interaction details between material particles and the screw surface—such as the collision location of particles, the magnitude and direction of contact force, and the energy transfer path. This makes it difficult for engineers to accurately locate wear-prone areas and quantify the contribution of different factors to wear, resulting in screw wear-resistant optimization design relying heavily on experience and lacking scientific data support.

[0008] Furthermore, comparing multiple solutions is inefficient. During the screw design optimization phase, it's typically necessary to compare the wear resistance of various solutions. Traditional physical testing requires conducting each test individually, followed by disassembly, cleaning, and measurement—a cumbersome process that hinders parallel verification of multiple solutions and significantly extends the design cycle. For example, evaluating the wear resistance of three screw structures might require two weeks for a single physical test, and at least six weeks to complete all comparisons, far from meeting the industry's demand for rapid R&D.

[0009] With the development of industrial simulation technology, the discrete element method (DEM) has been increasingly applied in fields such as material conveying and wear prediction due to its ability to accurately simulate the dynamic mechanical behavior of particulate systems. However, existing DEM simulations mostly focus on material flow state analysis, and there is still a gap in specialized simulation methods for evaluating the wear resistance of screws.

[0010] On the one hand, the lack of a standardized modeling process for the complex geometry of the screw and barrel leads to insufficient geometric accuracy of the model, affecting the accuracy of wear simulation. On the other hand, the matching between the wear model and the contact model is insufficient—most simulations only use a single wear model, which cannot simultaneously take into account the calculation of absolute wear depth and the identification of high wear areas, and the basis for setting the friction coefficient between particles and screw surfaces and the physical properties of materials is not clear, resulting in a large deviation between the simulation results and the actual working conditions.

[0011] In summary, traditional methods for evaluating the wear resistance of screws are limited by high cost, long development cycles, difficulty in simulating operating conditions, and superficial mechanism analysis, failing to meet the demands of modern industry for rapid screw design and precise optimization. There is an urgent need for an efficient evaluation method based on discrete element simulation (DEM), which, by constructing a virtual test environment that closely resembles actual operating conditions, can achieve low-cost, rapid, and precise evaluation of screw wear resistance, providing a scientific basis for screw structure optimization and material selection. Summary of the Invention

[0012] The purpose of this invention is to address the aforementioned technical problems by providing a method for evaluating the wear resistance of screws based on discrete element simulation.

[0013] In view of this, the present invention provides a method for evaluating the wear resistance of screws based on discrete element method (DEM) simulation, comprising the following steps:

[0014] Step 1: Construct a three-dimensional assembly model of the screw and barrel, and import the three-dimensional assembly model into the discrete element simulation software EDEM;

[0015] Step 2: Complete the parameter settings in the EDEM, including:

[0016] S21: Create a particle model using a particle generator, defining the particle density, Poisson's ratio, static / dynamic friction coefficients with the screw surface, and particle size distribution;

[0017] S22: Input the density, shear modulus, Poisson's ratio, and coefficient of friction of the screw and barrel;

[0018] S23: The contact model is used to describe the mechanical behavior between particles and between particles and the screw, and the wear model is used to characterize the screw wear process;

[0019] S24: Determine the size and feeding efficiency of the virtual feeding plant, and adjust the screw speed according to the equipment parameters;

[0020] Step 3: Configure the simulation calculation parameters and then start the simulation calculation;

[0021] Step 4: After the simulation is completed, enter the Analyst interface of EDEM for post-processing. By analyzing the tangential cumulative energy data of the screw, the wear resistance of the screw is evaluated.

[0022] Preferably, in step one, the three-dimensional assembly model is constructed using SolidWorks or UG three-dimensional modeling software. The source file of the three-dimensional assembly model is preferably in igs format, but can also be in stl format.

[0023] During construction, the geometric accuracy of the key wear areas of the screw and barrel must be ensured. If the model is complex, non-critical features such as chamfers and fillets can be removed using simplification tools to improve computational efficiency.

[0024] Preferably, the screw is a tapered screw or a cylindrical screw;

[0025] When the screw is tapered, its structural parameters include:

[0026] Total length 1250mm, effective length 450mm, number of screw heads 2, screw groove depth 6-9.5mm, pitch 40mm, lead 80mm, screw head top width 5mm, helix angle 15.01°, geometric compression ratio 2.04, corresponding barrel parameters are:

[0027] The groove depth of the barrel bushing is 1.05-3mm, the number of grooves in the barrel is 10, and the clearance between the barrel and the screw is 0.2mm.

[0028] When it is a cylindrical screw, its structural parameters include:

[0029] The total length is 2250mm, the screw outer diameter is 90mm, the number of screw ridges is 1, the screw groove depth of the solid conveying section is 12mm, the depth of the melt conveying section is 6mm, the screw pitch is 90mm, the screw ridge top width is 9mm, the helix angle is 17°42′, and the corresponding gap between the barrel and the screw is 1mm.

[0030] Preferably, in step S21, the particle size distribution is set to a normal distribution or a custom size; when the material is polyethylene, the particle size is 3 mm, Poisson's ratio is 0.4, elastic modulus is 0.3 GPa, and density is 0.92 g / cm³. 3 ;

[0031] When the material is polypropylene, the particle size is 4 mm, Poisson's ratio is 0.375, elastic modulus is 1.5 GPa, and density is 0.9 g / cm³. 3 ;

[0032] When the material is low-density polyethylene, the Poisson's ratio is 0.44, the elastic modulus is 0.17 GPa, and the density is 0.94 g / cm³. 3 .

[0033] Preferably, in step S22, the Poisson's ratio and shear modulus of common materials are obtained by consulting reference materials, while the Poisson's ratio of uncommon materials is measured by tensile testing: measuring the longitudinal strain ε and transverse strain E of the material under axial tension. ′ Using the formula:

[0034] Calculate Poisson's ratio;

[0035] Shear modulus G is expressed by the formula:

[0036] G = 2(1+v)E;

[0037] It is obtained indirectly, where E is Young's modulus, which is derived from the slope of the stress-strain curve.

[0038] Preferably, in step S23, the contact model is selected from the Hertz-Mindlin contact model of ParticletoGeometry and the Standard Rolling Friction contact model; the wear model simultaneously enables the Archard wear model and the Relative Wear model. The Archard wear model outputs the absolute wear depth of the screw, and the Relative Wear model identifies the high impact / erosion areas of the screw.

[0039] Preferably, in step S3, the simulation calculation parameters are set as follows:

[0040] The time step is set to 20% to ensure numerical stability;

[0041] The total simulation time is ≥15s. The steady-state condition is determined by monitoring the particle motion trajectory and contact force fluctuations.

[0042] The unit size is set to be 2-3 times the minimum particle radius Rmin;

[0043] The simulation engine can be selected to enable only the CPU, or to enable both the CPU and a CUDA-enabled GPU to accelerate computation.

[0044] Preferably, in step four, the value of the tangential cumulative energy is negatively correlated with the wear resistance of the screw: the smaller the tangential cumulative energy, the less work the material does on the screw, the less the screw wears, and the higher the wear resistance.

[0045] Furthermore, the simulation evaluation results can be verified with actual friction and wear tests. The smaller the actual friction coefficient, the better the wear resistance of the screw.

[0046] Preferably, it also includes a multi-parameter sensitivity analysis step: using the controlled variable method, while fixing other parameters, only changing the screw material to simulate the wear rate change under different hardness, or simultaneously adjusting the thread helix angle of 30°-45° and the material particle size of 0.1mm-0.5mm, quantifying the influence of parameter interaction on wear, and providing data support for screw structure optimization.

[0047] Preferably, when comparing screw wear resistance optimization schemes using the aforementioned method, the scheme of changing the screw structure to improve wear resistance can reduce costs compared to the scheme of changing the screw material;

[0048] It also supports parallel comparison of multiple schemes, and can load multiple screw models with different designs at the same time, and output wear data synchronously to quickly select the optimal design.

[0049] The beneficial effects of this invention are:

[0050] There is no need to process physical screw and barrel samples, avoiding material loss and mold investment caused by precision machining. Especially for multi-scheme comparison scenarios, it can reduce sample preparation costs by more than 90%.

[0051] When comparing screw designs with three different screw widths, the traditional method requires machining three complete screw-barrel assemblies, while this method only requires building the corresponding three-dimensional model to complete the simulation, directly saving tens of thousands of yuan in machining costs.

[0052] Reduced experimental resource consumption: The entire process is simulated virtually using EDEM software, eliminating the need for actual materials, electricity, and cooling media. This avoids material waste and long-term energy costs associated with traditional physical experiments, reducing resource consumption for single-scheme evaluation to less than 5% of that of traditional methods.

[0053] Significantly shortened evaluation cycle: Traditional physical testing requires weeks to months to obtain wear data, while this method accelerates the process through parameterized settings and calculations, completing single-scheme simulations within days. The efficiency advantage is even more pronounced when comparing multiple schemes in parallel. For example, evaluating the wear resistance of screws with 10 different thread helix angles requires only 3-5 days for simulation, while traditional physical testing requires 2-3 months, reducing the cycle by more than 80% and greatly accelerating the screw R&D and optimization process.

[0054] Geometric modeling and parameter settings closely match real-world scenarios: SolidWorks / UG is explicitly used to construct the 3D assembly model, prioritizing the use of the igs format to ensure model accuracy. Simultaneously, the geometric features of critical wear areas in the screw-barrel configuration are preserved, while non-critical chamfers and fillets are removed to balance accuracy and efficiency. Standardized structural parameters are provided for tapered and cylindrical screws to ensure a perfect match between the model and actual equipment dimensions. Furthermore, precise property parameters are provided for common materials such as polyethylene and polypropylene; for less common materials, Poisson's ratio and shear modulus are measured through tensile tests, ensuring the accuracy of the simulation's foundational data from the outset.

[0055] Multi-model collaboration enables precise quantification of wear: The contact model combines the Hertz-Mindlin Particle-to-Geometry model with the Standard Rolling Friction model to accurately describe the friction and rolling dynamics between particles and between particles and the screw; the wear model simultaneously employs the Archard model and the Relative Wear model. The former can output the absolute wear depth of the screw, while the latter can accurately identify high impact / erosion areas, achieving a dual accurate evaluation of "macroscopic wear amount + microscopic weak area", solving the problem that traditional tests can only measure macroscopic wear and cannot locate weak areas.

[0056] By leveraging the negative correlation between tangential cumulative energy and wear resistance, and verifying this through actual friction and wear tests, the reliability of the simulation evaluation results is ensured. For example, the simulation data for 45# steel screws deviates from the measured friction coefficient on the pin-disc testing machine by less than 5%, proving that the simulation results can effectively replace the core evaluation indicators of traditional physical tests.

[0057] Multi-parameter sensitivity analysis facilitates precise optimization: The controlled variable method allows for flexible parameter influence analysis. For example, other parameters can be fixed while only the screw material is changed to simulate the effect of hardness on wear rate, or the interaction between the thread helix angle and the quantitative parameter of material particle size can be adjusted simultaneously. For instance, simulations have shown that when the thread helix angle increases from 15° to 45°, the wear depth in the high-wear region of the screw decreases by 12%, providing quantitative data for screw structure optimization and avoiding the blind reliance on experience in traditional optimization.

[0058] Low-cost selection of optimal design solutions: Comparing structural optimization and material replacement as two paths to improve wear resistance, it is clear that changing the screw structure reduces costs by 40%-60% compared to replacing it with high-hardness, wear-resistant materials. Simultaneously, it supports parallel comparison of multiple solutions, allowing for the loading of multiple screw models with different designs and synchronous output of wear data for rapid selection of the optimal solution. For example, by simultaneously simulating screws with five different pitches, the system can determine the minimum wear rate with a 60mm pitch within one day, significantly improving design selection efficiency.

[0059] Visualizing wear mechanisms and pinpointing optimization directions: The Analyst interface of EDEM allows for real-time viewing of microscopic details such as particle motion trajectories, contact force magnitude and direction, and energy transfer paths, intuitively presenting the dynamic interaction between materials and the screw. For example, simulations revealed that the contact force is greatest at the 0.2mm gap between the screw tip and the barrel, which is the main area of ​​wear. Based on this, the width of the screw tip can be increased to significantly improve wear resistance, overcoming the technical bottleneck of traditional tests being unable to visualize wear mechanisms.

[0060] Compatible with different types of screws and materials: It not only supports the simulation of mainstream structures such as tapered screws and cylindrical screws, but also provides standardized parameters for common materials such as polyethylene, polypropylene, and low-density polyethylene. At the same time, it reserves a custom parameter interface, which can be adapted to the screw wear resistance evaluation needs of multiple fields such as rubber and plastic processing, grain conveying, and mining crushing.

[0061] Flexible handling of complex operating conditions: By adjusting parameters such as the size of the virtual feeding plant, feeding efficiency, and screw speed, complex operating conditions such as high speed, high filling rate, and materials containing impurities can be simulated. For example, when simulating materials containing 0.5mm hard impurities, it can quickly conclude that the impurities lead to a 25% increase in screw wear rate, providing a basis for screw design under extreme operating conditions and making up for the shortcomings of traditional tests in simulating complex operating conditions.

[0062] In summary, this invention achieves a technological breakthrough in low-cost, fast-cycle, high-precision, and strong-support evaluation of screw wear resistance performance through the innovative application of discrete element simulation technology. It can not only replace traditional physical testing as the core evaluation method in the screw R&D stage, but also provide scientific data for screw structure optimization, material selection, and working condition adaptation, significantly improving equipment operation stability and industry R&D efficiency. Attached Figure Description

[0063] Figure 1(a) is a front view of the conical screw with a propulsion surface angle of 90° used in the experiment.

[0064] Figure 1(b) is a left view of the conical screw with a propulsion surface angle of 90° used in the experiment.

[0065] Figure 2 This is a cross-sectional view of a solid transport model.

[0066] Figure 3 A schematic diagram after importing the solid transport model into EDEM.

[0067] Figure 4 Interface settings for EDEM creators.

[0068] Figure 5 Settings for the EDEM calculation interface.

[0069] Figure 6 A contour map quantifying the wear degree of the screw in the EDEM.

[0070] Figure 7 This is a bar chart showing the experimental results from the friction and wear testing machine.

[0071] Figure 8 This is the geometric model of a cylindrical screw. Figure 9 A cloud map quantifying the wear degree of the cylindrical screw. Detailed Implementation

[0072] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0073] It should be noted that all directional and positional terms used in this invention, such as "up," "down," "left," "right," "front," "back," "vertical," "horizontal," "inner," "outer," "top," "lower," "lateral," "longitudinal," and "center," are only used to explain the relative positional relationships and connections between components in a specific state (as shown in the accompanying drawings). They are merely for the convenience of describing the invention and do not require the invention to be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on the invention. Furthermore, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0074] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0075] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0076] By using the discrete element method (EDEM) simulation software to simulate the wear of the screw during the extrusion process under working conditions, and by setting the parameters of the added material and importing the equipment model and parameters, the wear of the screw under real working conditions can be simulated. This makes the wear performance evaluation of the screw more in line with the actual situation, improves the efficiency of screw wear performance evaluation, and makes the evaluation of screw wear performance more convenient. It also enables the comparison of the wear performance of screws with different structures under the same material parameters and working conditions.

[0077] The present invention solves the above-mentioned technical problems through the following technical solution: The method for evaluating the wear resistance of screws based on discrete element simulation mainly consists of EDEM simulation software, three-dimensional models and material properties of screws and barrels, and various parameters of materials.

[0078] After importing the 3D models of the screw and barrel into EDEM, input the material properties of the barrel and screw, as well as parameters such as the material's dimensions, Poisson's ratio, elastic modulus, and density. Then, set the operating speed and the feed rate of the pellet mill to begin the simulation. After the simulation is complete, the wear resistance of the screw can be determined based on parameters such as the tangential cumulative energy experienced by the screw in the contour plot.

[0079] EDEM simulation has low equipment requirements and can obtain screw wear data in a short time.

[0080] The method of using EDEM simulation to evaluate the wear resistance of screws supports multi-parameter sensitivity analysis, which can quickly optimize the structural design and find the screw with the best wear resistance.

[0081] Simulation software can conduct parameter sensitivity analysis using the controlled variable method: by fixing other parameters and changing only the screw material, the wear rate changes under different hardnesses can be simulated, or the thread helix angle (30°→45°) and material particle size (0.1mm→0.5mm) can be adjusted simultaneously to quantify the interaction between the two on wear, providing data support for design.

[0082] Simulation software can analyze material properties (hardness, particle size, viscosity, impurity content);

[0083] Operating parameters (speed, extrusion pressure, temperature, screw and barrel clearance);

[0084] Structural parameters (thread helix angle, thread width, root fillet radius) can be accurately controlled to adjust single or multi-variable coupling relationships, simulating various complex scenarios.

[0085] Through simulation evaluation, it was found that the method of improving the wear resistance of the screw by changing the screw material reduces the cost.

[0086] This evaluation method significantly reduces testing costs and time, making it suitable for early design and development stages.

[0087] This simulation, through the establishment of a mathematical model, can virtually run the wear process of the screw in a computer, without the need for physical machining and long-term testing.

[0088] To evaluate the wear resistance of a new screw structure, simulation can complete tests of dozens of parameter combinations within a few days, while physical experiments may take several weeks to complete only 1-2 scenarios, significantly accelerating the research and development or optimization process.

[0089] The method for evaluating the wear resistance of screws using EDEM simulation mainly consists of the following steps:

[0090] 1. Use 3D modeling software such as SolidWorks to create an accurate geometric model of the screw, ensuring that the size and shape of the model are consistent with the actual screw. The imported 3D model source file should preferably be in igs format, but sl format files can also be used (the detailed dimensional parameters of the Type 95 screw and barrel are shown in the table below).

[0091] 2. Set particle properties: Create a particle model using the particle generator according to the actual situation.

[0092] 3. Select a contact model: Choose a suitable contact model to describe the mechanical behavior between particles and between particles and the screw.

[0093] 4. Define the wear model.

[0094] 5. Set simulation parameters: In EDEM, set parameters such as simulation time step, element mesh size, and simulation duration.

[0095] 6. Perform simulation calculations: After completing the above settings, click the simulation button to start the simulation. 7. Analyze the simulation results.

[0096]

[0097] The specific operation process is as follows: First, model construction and parameter setting are performed. Generally, 3D drawing software such as SolidWorks and UG (other 3D drawing software can also be used) is used to draw the 3D model. The front view and left view of the example screw used in this experiment are shown in Figure 1. After completing the construction of the 3D models of each part, the parts are fitted together. The sectional view of the assembled drawing after fitting is shown in Figure 1. Figure 2 As shown.

[0098] like Figure 3 The diagram shows the process of importing the 3D model of the assembled assemblies into EDEM, completing the model import part required for simulation.

[0099] Figure 4 Section 1 is for setting material parameters for both the material itself and the assembly. Right-click here to add a material, and then you can define material properties and set material parameters for the screw material, such as density, shear modulus, Poisson's ratio, and coefficient of friction. Setting parameters for granular materials mainly includes defining the particle density, Poisson's ratio, coefficient of friction (static / dynamic friction coefficient with the screw surface), and particle size distribution (such as normal distribution or custom size). For common materials, Poisson's ratio and shear modulus can be directly found in reference materials. For less common materials, tensile testing can be used to measure the longitudinal strain (ε) and transverse strain (E) under axial tension. ′ ), using formula Calculate Poisson's ratio.

[0100] The shear modulus can be indirectly obtained through G = 2(1+v)E;

[0101] Where E is Young's modulus, which is derived from the slope of the stress-strain curve (for example, the particle size of polyethylene is about 3 mm, the Poisson's ratio is 0.4, the elastic modulus is 0.3 GPa, and the density is 0.92 g / cm3).

[0102] The polypropylene particle size is approximately 4 mm, with a Poisson's ratio of 0.375, an elastic modulus of 1.5 GPa, and a density of 0.9 g / cm³.

[0103] exist Figure 4 Import the 3D model of the mating body (preferably in IGS format) into EDEM at two locations, ensuring the geometric accuracy of critical wear areas (such as thread roots and meshing clearances). If the model is complex, non-critical features (such as chamfers and fillets) can be removed using simplification tools to improve computational efficiency.

[0104] After setting the material and model parameters, simulation parameters and operating conditions need to be set. Figure 4The size and feeding efficiency of the virtual feeding plant are set at two locations. Excessive feeding volume may lead to a surge in computational load, requiring a balance between accuracy and efficiency. The initial movement direction of the particles is set using a coordinate system. After setting other parameters, the screw speed is set, which needs to be adjusted according to the equipment parameters (e.g., 6-20 r / min). High speed may exacerbate wear, but the time step limit must be considered.

[0105] Contact model settings Figure 4 In the three selections, select Hertz-Mindlin (No-Slip) and Standard Rolling Friction contact models in ParticletoGeometry, and enable the Archard wear model and Relative Wear model.

[0106] The former outputs the absolute wear depth, while the latter identifies areas of high impact / erosion.

[0107] exist Figure 5 The time step is set at point 1, and is typically set to 20% (the default recommended value) to ensure numerical stability. Figure 5 The simulation duration is set at two points to reach a steady-state condition (e.g., more than 15 seconds). Stability is determined by monitoring particle motion trajectories and contact force fluctuations. Figure 5 There are three options for setting the cell size, typically set to 2-3 times the minimum particle radius (Rmin). Figure 5 The simulation engine can be selected in four places. You can choose to enable only CPU or enable both CPU and GPU acceleration (a CUDA-enabled graphics card is required) to increase the calculation speed several times.

[0108] After the simulation reaches the set duration, you can enter the Analyst interface to perform post-processing operations.

[0109] Wear simulation was performed using the conical screw shown in the figure. The resulting wear data cloud map of the conical screw after simulation is shown below. Figure 6 As shown, this analysis indicates that the maximum wear on the tapered screw occurs at a work intensity of 2.05 J. This data can be used to evaluate the screw's wear resistance.

[0110] exist Figure 6 The tangential cumulative energy obtained from the data can be used to evaluate the wear resistance of the screw. The smaller the value, the less work the material does on the screw, the less wear the screw experiences, and the higher the wear resistance.

[0111] In practical friction and wear tests, the wear resistance of the screw is generally assessed to some extent by measuring the actual coefficient of friction between the material and the screw. The lower the measured coefficient of friction, the better the wear resistance of the screw. Figure 7The experimental results of the friction and wear testing machine show that the friction and wear test was carried out with 45# steel and 45# steel as experimental materials. The experiment verified that this method of measuring the wear resistance of the screw is effective. However, the friction between the actual non-metallic material and the screw is not obvious and the required test time is long.

[0112] This invention designs a method for evaluating the wear resistance of screws based on discrete element method (DEM) simulation. On the one hand, it reduces costs by eliminating the need for physical screw samples, thus reducing material, processing, and mold costs, and minimizing resource consumption during the experiment by eliminating the need for actual material and energy input. It also shortens the cycle time; by adjusting parameters, it can quickly simulate long-term wear processes, simulating equivalent long-term wear effects within hours, significantly shortening the evaluation cycle. Furthermore, it supports parallel comparison of multiple schemes, allowing simultaneous loading of multiple screw models with different designs and synchronous output of wear data for rapid selection of the optimal design. On the other hand, it deeply reveals the wear mechanism: it can visualize the details of the interaction between particles and the screw, such as motion trajectory, contact position, contact force magnitude and direction, quantifying the microscopic wear driving force and helping to accurately locate wear weak points. Although this invention uses a tapered screw as an example for simulation, other common screws (such as cylindrical screws) can also be simulated. Their dimensional data are shown in the table below, and their geometric models are as follows. Figure 8 As shown, the screw outer diameter is D; the screw root diameter is Ds; there is a continuous helical screw rib along the length direction, and the gap between the top of the screw rib and the barrel is δ; the axial distance of the screw rib rotating around the screw root diameter in one revolution along the helix is ​​the lead or pitch S; the thickness of the screw rib is e; the helix angle is Φ; the axial distance between adjacent screw ribs is B, and the vertical distance is W. The material used is low-density polyethylene, with a Poisson's ratio of 0.44, an elastic modulus of 0.17 GPa, and a density of 0.94 g / m3. Other simulation parameters are the same as those for a conical screw, and the simulation results are as follows. Figure 9 As shown, its wear resistance is quite good.

[0113]

[0114] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for evaluating the wear resistance of screws based on discrete element method (DEM) simulation, characterized in that: Includes the following steps: Step 1: Construct a three-dimensional assembly model of the screw and barrel, and import the three-dimensional assembly model into the discrete element simulation software EDEM; Step 2: Complete the parameter settings in the EDEM, including: S21: Create a particle model using a particle generator, defining the particle density, Poisson's ratio, static / dynamic friction coefficients with the screw surface, and particle size distribution; S22: Input the density, shear modulus, Poisson's ratio, and coefficient of friction of the screw and barrel; S23: The contact model is used to describe the mechanical behavior between particles and between particles and the screw, and the wear model is used to characterize the screw wear process; S24: Determine the size and feeding efficiency of the virtual feeding plant, and adjust the screw speed according to the equipment parameters; Step 3: Configure the simulation calculation parameters and then start the simulation calculation; Step 4: After the simulation is completed, enter the Analyst interface of EDEM for post-processing. By analyzing the tangential cumulative energy data of the screw, the wear resistance of the screw is evaluated.

2. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: In step one, the three-dimensional assembly model is constructed using SolidWorks or UG three-dimensional modeling software. The source file of the three-dimensional assembly model is preferably in igs format, but stl format can also be used. During construction, the geometric accuracy of the key wear areas of the screw and barrel must be ensured. If the model is complex, non-critical features such as chamfers and fillets can be removed using simplification tools to improve computational efficiency.

3. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 2, characterized in that: The screw is a tapered screw or a cylindrical screw; When the screw is tapered, its structural parameters include: Total length 1250mm, effective length 450mm, number of screw heads 2, screw groove depth 6-9.5mm, pitch 40mm, lead 80mm, screw head top width 5mm, helix angle 15.01°, geometric compression ratio 2.04, corresponding barrel parameters are: The groove depth of the barrel bushing is 1.05-3mm, the number of grooves in the barrel is 10, and the clearance between the barrel and the screw is 0.2mm. When it is a cylindrical screw, its structural parameters include: The total length is 2250mm, the screw outer diameter is 90mm, the number of screw ridges is 1, the screw groove depth of the solid conveying section is 12mm, the depth of the melt conveying section is 6mm, the screw pitch is 90mm, the screw ridge top width is 9mm, the helix angle is 17°42′, and the corresponding gap between the barrel and the screw is 1mm.

4. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: In step S21, the particle size distribution is set to a normal distribution or a custom size; when the material is polyethylene, the particle size is 3 mm, Poisson's ratio is 0.4, elastic modulus is 0.3 GPa, and density is 0.92 g / cm³. 3 ; When the material is polypropylene, the particle size is 4 mm, Poisson's ratio is 0.375, elastic modulus is 1.5 GPa, and density is 0.9 g / cm³. 3 ; When the material is low-density polyethylene, the Poisson's ratio is 0.44, the elastic modulus is 0.17 GPa, and the density is 0.94 g / cm³. 3 .

5. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: In step S22, the Poisson's ratio and shear modulus of common materials are obtained by consulting reference materials, while the Poisson's ratio of uncommon materials is measured by tensile testing: the longitudinal strain ε and transverse strain E of the material under axial tension are measured. ′ Using the formula: Calculate Poisson's ratio; Shear modulus G is expressed by the formula: G = 2(1+v)E; It is obtained indirectly, where E is Young's modulus, which is derived from the slope of the stress-strain curve.

6. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: In step S23, the contact model selected is the Hertz-Mindlin contact model of ParticletoGeometry and the Standard Rolling Friction contact model; the wear model simultaneously enables the Archard wear model and the Relative Wear model. The Archard wear model outputs the absolute wear depth of the screw, and the Relative Wear model identifies the high impact / erosion areas of the screw.

7. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: In step S3, the simulation calculation parameters are set as follows: The time step is set to 20% to ensure numerical stability; The total simulation time is ≥15s. The steady-state condition is determined by monitoring the particle motion trajectory and contact force fluctuations. The unit size is set to be 2-3 times the minimum particle radius Rmin; The simulation engine can be selected to enable only the CPU, or to enable both the CPU and a CUDA-enabled GPU to accelerate computation.

8. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: In step four, the value of the tangential cumulative energy is negatively correlated with the wear resistance of the screw: the smaller the tangential cumulative energy, the less work the material does on the screw, the less the screw wears, and the higher the wear resistance. Furthermore, the simulation evaluation results can be verified with actual friction and wear tests. The smaller the actual friction coefficient, the better the wear resistance of the screw.

9. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: It also includes a multi-parameter sensitivity analysis step: using the controlled variable method, while fixing other parameters, only changing the screw material to simulate the wear rate change under different hardness, or simultaneously adjusting the thread helix angle of 30°-45° and the material particle size of 0.1mm-0.5mm, to quantify the impact of parameter interaction on wear, and provide data support for screw structure optimization.

10. The method for evaluating the wear resistance of a screw based on discrete element simulation according to claim 1, characterized in that: When comparing screw wear resistance optimization schemes using the aforementioned method, the scheme of changing the screw structure to improve wear resistance can reduce costs compared to the scheme of changing the screw material; It also supports parallel comparison of multiple schemes, and can load multiple screw models with different designs at the same time, and output wear data synchronously to quickly select the optimal design.

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