Edge characterization method, system, and apparatus based on multiphase flow coupling modeling

Abstract

This application provides a method, system, and device for characterizing cutting edges based on multiphase flow coupling modeling. The method includes: geometrically modeling a nozzle and the cutting edge to be tested; meshing the geometric model according to the inlet and outlet boundaries, solid region, and fluid region to form a mesh model file; importing the mesh model file into a first simulation software; importing the mesh model file into a second simulation software; importing a coupling interface file to connect the first and second simulation software; setting the coupling time step and number of time steps between the first and second simulation software to start iterative calculations until the simulation is completed; extracting and exporting wear depth distribution cloud maps of the cutting edge to be tested after sandblasting; forming discrete point set data about the wear profile based on the wear depth distribution cloud maps; calculating the average wear depth based on the discrete point set data; and obtaining the shape factor of the cutting edge of the cutting edge to be tested based on the average wear depth.

Description

Technical Field

[0001] This application relates to the field of cutting edge characterization technology, and in particular to a cutting edge characterization method, system and device based on multiphase flow coupling modeling. Background Technology

[0002] Carbide cutting tools, with their high hardness, strong wear resistance, and excellent red hardness, are widely used in precision machining fields such as aerospace and automotive manufacturing. However, micro-cracks or burrs inevitably occur on the cutting edge during the manufacturing process. These defects can easily lead to chipping and severe wear during use. Therefore, passivation treatments such as sandblasting are necessary to optimize the cutting edge profile, improve the microstructure, and extend the tool life.

[0003] Currently, methods for studying cutting edge geometry mainly fall into two categories: experimental and numerical simulation. However, experimental methods struggle to observe the effects of abrasive particles and fluids on the cutting edge at the microscale in real time, and also suffer from limitations such as high cost and long development cycles. Regarding cutting edge characterization, traditional cutting edge radius parameters can only reflect approximate local arc values ​​and cannot accurately describe asymmetric or irregular nonlinear profile features. While the cutting edge shape factor K can more comprehensively assess cutting edge quality, its acquisition primarily relies on experimental measurements or simple geometric quantification. Due to the complex microstructure of the cutting edge, existing single simulation software or traditional measurement methods cannot fully and accurately reproduce the true effects of liquid-solid two-phase flow during sandblasting passivation. Therefore, a numerical characterization method based on multiphase flow coupled modeling is urgently needed to accurately reproduce the evolution of the cutting edge morphology through DEM-CFD coupled simulation, achieving standardized extraction and quantitative characterization of the cutting edge shape factor K. Summary of the Invention

[0004] In view of this, it is necessary to provide a method, system and device for characterizing the cutting edge based on multiphase flow coupling modeling, which can at least overcome one of the above defects.

[0005] Firstly, this application provides a method for characterizing the cutting edge based on multiphase flow coupling modeling, the method comprising:

[0006] Perform geometric modeling of the nozzle and the blade under test;

[0007] The geometric model is meshed based on the inlet / outlet boundaries, solid regions, and fluid regions to form a mesh model file;

[0008] Import the mesh model file into the first simulation software, set the inlet boundary as a pressure inlet and the outlet boundary as a pressure outlet, and configure the fluid domain parameters and spatial discretization solution settings, including fluid material and turbulence model.

[0009] Import the mesh model file into the second simulation software and set the physical property parameters of the sandblasting particles, nozzle and blade, as well as the contact parameters between each contact pair;

[0010] Import the coupling interface file to connect the first simulation software with the second simulation software. Set the coupling time step and number of time steps between the first simulation software and the second simulation software, and start iterative calculation until the simulation is completed.

[0011] Wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting were extracted and exported respectively.

[0012] The coordinate information of the wear contour pixels is extracted from the wear depth distribution cloud map to form discrete point set data about the wear contour;

[0013] Calculate the average wear depth of the rake face and the average wear depth of the flank face based on the discrete point set data;

[0014] The shape factor of the cutting edge of the insert under test is obtained based on the average wear depth of the rake face and the average wear depth of the flank face.

[0015] In this application, the configuration of fluid domain parameters and spatial discretization solution settings, including fluid materials and turbulence models, includes:

[0016] The renormalization group k-ε model in the turbulence model is selected to correct the pulsation characteristics of the fluid domain, and the standard wall function is used to constrain the surface flow field near the blade under test.

[0017] In the spatial discrete solution setting, the gradient is calculated using the element-based least squares method, a high-precision scheme is used for the pressure term, and the momentum, turbulent kinetic energy and turbulent dissipation rate are all discretely solved using the second-order upwind scheme to ensure the convergence and accuracy of the flow field calculation.

[0018] In this application, the settings include the physical properties of the sandblasting particles, nozzle, and blade, as well as the contact parameters between each contact pair, including:

[0019] When configuring contact parameters, a contact model and a standard rolling friction model are selected for the interaction between the sandblasting particles, the nozzle, and the blade under test.

[0020] When calculating the evolution of the blade surface morphology, the Archard wear model combined with the relative wear model is selected. The amount of material removed from the surface of the blade under test is characterized by calculating the tangential cumulative force generated by particle impact, thereby simulating the morphological changes during the sandblasting passivation process.

[0021] In this application, the method further includes:

[0022] In the discrete phase physical model, fluid dynamics are transmitted to the second simulation software through the coupling interface file. The fluid dynamics include at least Suffman lift, virtual mass force and pressure gradient force to correct the trajectory of particles in the liquid medium.

[0023] A bidirectional coupling time synchronization strategy is set up. Based on the difference between fluid flow velocity and particle collision frequency, the time steps of the first simulation software and the second simulation software are set to non-equidistant time steps. Data exchange between discrete phase and continuous phase is realized through multi-step loop.

[0024] In this application, obtaining the shape factor of the cutting edge of the insert under test based on the average wear depth of the rake face and the average wear depth of the flank face includes:

[0025] The exported wear depth distribution cloud map is subjected to image digitization processing. The bright wear areas in the wear depth distribution cloud map are identified by grayscale and binarization algorithms, and the pixel coordinates of the wear edge are automatically tracked and extracted using a boundary contour scanning program.

[0026] The extracted pixel coordinates are mapped to physical space coordinates, and the feature quantity describing the wear degree is obtained by numerical integration or average calculation method, and it is defined as the equivalent passivation characterization value of the front face and the equivalent passivation characterization value of the back face.

[0027] The numerical characterization of the asymmetric cutting edge profile of the test insert is completed by calculating the ratio of the equivalent passivation characterization value of the rake face to the equivalent passivation characterization value of the flank face.

[0028] In this application, the geometric modeling of the nozzle and the blade under test includes:

[0029] Establish a relative position constraint model between the nozzle outlet and the cutting edge of the blade under test in a three-dimensional spatial domain;

[0030] Based on the actual passivation process requirements, the spray distance and offset angle are set in the relative position constraint model to construct a geometric space field that can simulate the interaction between the abrasive particle motion vector and the cutting edge surface normal vector.

[0031] In this application, the step of separately capturing and exporting the wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting includes:

[0032] In the second simulation software, pseudo-color processing technology is used to transform the force distribution on the surface of the blade geometry into a depth distribution image with color gradient changes;

[0033] The depth distribution image is output using a specific image format, and grayscale histogram equalization is performed on the image using image digitization processing tools to enhance the visual contrast between the edge of the worn area and the unworn substrate.

[0034] The boundary tracing algorithm is used to extract pixel contours from the binarized image and establish a mapping relationship between pixel coordinates and three-dimensional physical coordinates.

[0035] In one embodiment, calculating the average wear depth of the rake face and the average wear depth of the flank face based on the discrete point set data includes:

[0036] A feature sampling window is defined in the discrete point set data to exclude abnormal discrete points generated by the randomness of discrete element collisions.

[0037] Within the feature sampling window, a statistical weighted average operation is performed on all coordinate points of the rake face and the flank face to obtain a continuous wear feature quantity characterizing the evolution of macroscopic morphology;

[0038] Based on the average wear depth values ​​of the rake face and the flank face, the shape factor reflecting the degree of offset of the cutting edge profile to one side is calculated through asymmetry coefficient conversion, thereby realizing the quantitative characterization of the micro-morphology of complex cutting edges.

[0039] Secondly, a cutting edge characterization system based on multiphase flow coupling modeling is provided, applied to the cutting edge characterization method based on multiphase flow coupling modeling as described in the first aspect, the system comprising:

[0040] The modeling module is used to perform geometric modeling of the nozzle and the blade under test; and to mesh the geometric modeling according to the inlet and outlet boundaries, solid region and fluid region to form a mesh model file;

[0041] The model import module is used to import the mesh model file into the first simulation software, set the inlet boundary as a pressure inlet and the outlet boundary as a pressure outlet, and configure the fluid domain parameters and spatial discretization solution settings, including fluid materials and turbulence models; and to import the mesh model file into the second simulation software, setting the physical property parameters of the sandblasting particles, nozzles and blades, as well as the contact parameters between each contact pair.

[0042] The coupling module is used to import the coupling interface file, so that the first simulation software and the second simulation software are connected. By setting the coupling time step and the number of time steps between the first simulation software and the second simulation software, iterative calculation begins until the simulation is completed.

[0043] The depth statistics module is used to capture and export the wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting; extract the coordinate information of the wear contour pixels based on the wear depth distribution cloud maps to form discrete point set data about the wear contour; and calculate the average wear depth of the rake face and the average wear depth of the flank face based on the discrete point set data.

[0044] The cutting edge characterization module is used to obtain the shape factor of the cutting edge of the insert under test based on the average wear depth of the rake face and the average wear depth of the flank face.

[0045] Thirdly, this application provides an electronic device, comprising:

[0046] processor;

[0047] And a memory having computer-readable instructions stored thereon for controlling the processor to execute the edge characterization method based on multiphase flow coupling modeling as described in the first aspect.

[0048] This application provides a cutting edge characterization method, system, and device based on multiphase flow coupling modeling. Through bidirectional coupling simulation of CFD and DEM, it accurately recreates the microscopic effects of multiphase flow on the cutting edge of cemented carbide cutting tools during sandblasting passivation, overcoming the limitations of traditional experimental methods such as high cost, difficult observation, and susceptibility to environmental interference. By integrating a multiphase flow force model and a material wear model, it achieves quantitative simulation of the evolution of the cutting edge's micromorphology. Furthermore, by combining image processing technology to extract precise geometric contour data from wear cloud maps, it realizes standardized numerical characterization of the cutting edge shape factor K. This effectively solves the technical problems of low accuracy and difficulty in quantifying asymmetric cutting edge characterization, providing a scientific, efficient, and high-precision digital analysis method for optimizing tool passivation processes and predicting tool life. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a cutting edge characterization method based on multiphase flow coupling modeling provided in an embodiment of this application.

[0050] Figure 2 This is a schematic diagram illustrating the mesh generation of a straight-hole nozzle using preprocessing software, as provided in an embodiment of this application.

[0051] Figure 3 This is a simulation diagram of the first simulation software in the coupled solution calculation process provided in an embodiment of this application.

[0052] Figure 4 This is a simulation diagram of the second simulation software in the coupled solution calculation process provided in an embodiment of this application.

[0053] Figure 5The wear depth distribution cloud map of the rake face after the simulation of the second simulation software provided in an embodiment of this application is completed.

[0054] Figure 6 The wear depth distribution cloud map of the flank face after the simulation of the second simulation software provided in an embodiment of this application is completed.

[0055] Figure 7 This is a schematic diagram of the wear contour boundary curve of the rake face after image processing software is used in an embodiment of this application.

[0056] Figure 8 This is a schematic diagram of the wear contour boundary curve of the flank face after image processing software is used in an embodiment of this application.

[0057] Figure 9 This is a diagram showing the equivalent passivation value of the rake face after data extraction by the data analysis software provided in an embodiment of this application.

[0058] Figure 10 This is a diagram showing the equivalent passivation value of the back face after data extraction by the data analysis software provided in an embodiment of this application.

[0059] Figure 11 This is a schematic diagram of a cutting edge characterization system based on multiphase flow coupling modeling, provided as an embodiment of this application.

[0060] Figure 12 This is a schematic diagram of the modules of an electronic device provided in an embodiment of this application.

[0061] Explanation of main component symbols

[0062] The system for characterizing the cutting edge based on multiphase flow coupling modeling includes: 10, modeling module 11, model import module 12, coupling module 13, depth statistics module 14, cutting edge characterization module 15, electronic device 20, processor 21, memory 22, and method steps S100-S900. Detailed Implementation

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0064] It should be noted that, in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0065] It should be noted that in the embodiments of this application, the terms "first," "second," etc., are used only for descriptive purposes and should not be construed as indicating or implying relative importance, nor as indicating or implying order. Features specified as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0066] Based on the embodiments described in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] Carbide cutting tools, with their high hardness, strong wear resistance, and excellent red hardness, are widely used in precision machining fields such as aerospace and automotive manufacturing. However, micro-cracks or burrs inevitably occur on the cutting edge during the manufacturing process. These defects can easily lead to chipping and severe wear during use. Therefore, passivation treatments such as sandblasting are necessary to optimize the cutting edge profile, improve the microstructure, and extend the tool life.

[0068] Currently, methods for studying cutting edge geometry mainly fall into two categories: experimental and numerical simulation. However, experimental methods struggle to observe the effects of abrasive particles and fluids on the cutting edge at the microscale in real time, and also suffer from limitations such as high cost and long development cycles. Regarding cutting edge characterization, traditional cutting edge radius parameters can only reflect approximate local arc values ​​and cannot accurately describe asymmetric or irregular nonlinear profile features. While the cutting edge shape factor K can more comprehensively assess cutting edge quality, its acquisition primarily relies on experimental measurements or simple geometric quantification. Due to the complex microstructure of the cutting edge, existing single simulation software or traditional measurement methods cannot fully and accurately reproduce the true effects of liquid-solid two-phase flow during sandblasting passivation. Therefore, a numerical characterization method based on multiphase flow coupled modeling is urgently needed to accurately reproduce the evolution of the cutting edge morphology through DEM-CFD coupled simulation, achieving standardized extraction and quantitative characterization of the cutting edge shape factor K.

[0069] In view of this, the cutting edge characterization method, system, and equipment based on multiphase flow coupling modeling provided in this application can accurately reproduce the microscopic effect of multiphase flow on the cutting edge of cemented carbide cutting tools during sandblasting passivation through bidirectional coupling simulation of CFD and DEM. This overcomes the limitations of traditional experimental methods, such as high cost, difficulty in observation, and large interference from environmental factors. By integrating the multiphase flow force model and the material wear model, quantitative simulation of the evolution of the micromorphology of the cutting edge is realized. In addition, by combining image processing technology to extract accurate geometric contour data from the wear cloud map, the standardized numerical characterization of the cutting edge shape factor K is realized. This effectively solves the technical problems of low accuracy and difficulty in quantitative characterization of asymmetric cutting edges, and provides a scientific, efficient, and high-precision digital analysis method for optimizing tool passivation processes and predicting tool life.

[0070] Figure 1 This is a flowchart illustrating a cutting edge characterization method based on multiphase flow coupling modeling provided in an embodiment of this application, as shown below. Figure 1 The blade characterization method based on multiphase flow coupling modeling, as shown, includes at least the following steps: S100: Perform geometric modeling of the nozzle and the blade under test; S200: Mesh the geometric model according to the inlet and outlet boundaries, solid region, and fluid region to form a mesh model file; S300: Import the mesh model file into the first simulation software, set the inlet boundary as a pressure inlet and the outlet boundary as a pressure outlet, and configure the fluid domain parameters and spatial discretization solution settings, including fluid material and turbulence model; S400: Import the mesh model file into the second simulation software, and set the physical property parameters of the blasting particles, nozzle, and blade, as well as the contact parameters between each contact pair; S500: Import the coupling connection... The interface file connects the first and second simulation software. By setting the coupling time step and number of time steps between the first and second simulation software, iterative calculations begin until the simulation is complete. S600: The wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting are captured and exported respectively. S700: The coordinate information of the wear contour pixels is extracted from the wear depth distribution cloud maps to form discrete point set data about the wear contour. S800: The average wear depth of the rake face and the average wear depth of the flank face are calculated based on the discrete point set data. S900: The shape factor of the cutting edge of the test insert is obtained based on the average wear depth of the rake face and the average wear depth of the flank face.

[0071] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes performing geometric modeling on the nozzle and the blade to be tested in step S100.

[0072] Specifically, a three-dimensional geometric model of the nozzle and the blade to be tested is established, and detailed dimensions such as nozzle orifice diameter, spray angle, relative position of nozzle outlet and cutting edge, cutting edge profile, blade thickness and necessary mounting and clamping structures are defined. The geometric model can be drawn using CAD software or reconstructed from measurement data and exported to a general format (such as STEP or IGES) as needed for simulation.

[0073] It is understood that the geometric modeling should ensure that the minute gap and relative posture between the cutting edge and the nozzle are accurately described, so as to facilitate subsequent local mesh refinement and fluid-particle coupling simulation; for different tool models or working conditions, the geometric parameters can be adjusted as needed or parametrically modeled for batch simulation.

[0074] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes, in step S200, meshing the geometric model according to the inlet and outlet boundaries, solid region and fluid region to form a mesh model file.

[0075] Specifically, structured or unstructured meshes are generated for the fluid domain and the solid domain respectively. Boundary layer refinement or local mesh refinement is applied to the nozzle outlet, jet channel and blade edge region. The inlet and outlet boundaries, fluid-solid interface and contact surface are labeled. Finally, a mesh model file (e.g. .msh) that can be recognized by simulation software is exported.

[0076] Understandably, mesh generation needs to balance computational accuracy with computational resources. Sufficient mesh density is required in near-wall regions and regions with significant particle-wall interactions. To avoid numerical artifacts, mesh quality checks can be performed and coarsening / refinement can be applied as needed.

[0077] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes, in step S300, importing the mesh model file into the first simulation software, setting the inlet boundary as a pressure inlet and the outlet boundary as a pressure outlet, and configuring the fluid domain parameters and spatial discretization solution settings, including the fluid material and the turbulence model.

[0078] Specifically, the first simulation software, namely the fluid simulation module (CFD), is imported. A pressure inlet is set at the inlet, and the inlet pressure, turbulence intensity, or velocity profile is specified. A pressure outlet is set at the outlet, and the reference pressure or external ambient pressure is specified. In the fluid domain, fluid properties (density, viscosity, etc.) are defined, the turbulence model and wall function are selected, the pressure-velocity coupling algorithm, the time progression scheme (transient), and the spatial discretization format and residual convergence criterion of the momentum and turbulence equations are set.

[0079] It is understood that the configuration should be combined with the sandblasting conditions to select appropriate turbulence models (such as k-ω, SST, LES, etc.) and high-order discretization schemes to improve the analytical accuracy of near-wall and jet flow, and adaptive mesh or local time step refinement can be enabled when necessary to improve simulation stability.

[0080] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes, in step S400, importing the mesh model file into the second simulation software and setting the physical property parameters of the blasting particles, nozzle and blade, as well as the contact parameters between each contact pair.

[0081] Specifically, the mesh and geometric information are imported into the second simulation software, namely the Discrete Element Method (DEM) simulation module, to define the physical property parameters such as particle size distribution, density, elastic modulus, Poisson's ratio, coefficient of restitution, and surface roughness of the sandblasting particles; parameters such as friction coefficient, rolling damping, and contact stiffness are set for particle-particle, particle-nozzle, and particle-blade contacts, and wear / erosion models and their parameters are selected to describe material removal.

[0082] It is understood that the physical properties and contact parameters can be set based on material handbooks and literature values, or can be calibrated experimentally to ensure that the simulation results are consistent with the actual sandblasting conditions; for mixed particle size systems or situations where particle breakage occurs, these should be considered in the model or appropriate approximations should be made.

[0083] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes, in step S500, importing a coupling interface file to connect the first simulation software and the second simulation software, setting the coupling time step and number of time steps between the first simulation software and the second simulation software, and starting iterative calculation until the simulation is completed.

[0084] Specifically, the CFD–DEM coupling interface is loaded and data exchange items are configured (e.g., CFD provides local flow velocity, pressure field and turbulence fluctuation information to DEM; DEM feeds back particle volume fraction, momentum source term and particle impact force to CFD), the global time step of coupling, DEM sub-cycling and synchronization frequency are set, a bidirectional coupling or unidirectional coupling strategy is selected, the timing iteration is performed until the preset simulation time or convergence criterion is reached, and the intermediate results are saved for subsequent post-processing.

[0085] Understandably, the time step of the coupled solution needs to take into account both the CFL constraint of CFD and the collision timescale of DEM. Two-way coupling can more realistically reflect the particle feedback to the flow field, but the computational load is larger. The coupling strategy and step size ratio can be adjusted according to the computational resources and accuracy requirements.

[0086] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes step S600, which involves extracting and exporting the wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting.

[0087] Specifically, after the simulation is completed, the wear depth (or material removal amount) field is intercepted on the blade surface, and two-dimensional or three-dimensional depth distribution cloud maps of the rake face and flank face are generated respectively. The data is exported as an image or data format (such as PNG, TIFF, CSV or VTK) according to a unified coordinate and scale, and the interception position, time point and calibration information are recorded for reproduction and comparison.

[0088] Understandably, the cloud map visually reflects the degree and distribution characteristics of local wear, making it easy to identify the area of ​​maximum wear, wear asymmetry, and the impact of jets on different areas of the tool face; for comparability, the export process should ensure consistent scale and coordinate alignment.

[0089] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes, in step S700, extracting the coordinate information of wear contour pixels according to the wear depth distribution cloud map to form discrete point set data about the wear contour.

[0090] Specifically, the exported wear cloud map is read and image and data preprocessing (denoising, correction, scale calibration) is performed. Threshold segmentation, edge detection and connected component analysis are used to extract the wear area contour. Subpixel fitting or interpolation is used to discretize the contour into a set of points with position and corresponding depth values, and then transforms it into the blade body coordinate system to form a structured discrete point set data file.

[0091] It is understandable that the point set density and extraction accuracy directly affect the subsequent fitting and quantization results. Therefore, sufficient sampling density should be ensured in the cutting edge and wear transition area. If necessary, multiple cross-sectional images or three-dimensional data can be combined for fusion extraction to improve robustness.

[0092] In this embodiment of the application, the cutting edge characterization method based on multiphase flow coupling modeling includes, in step S800, calculating the average wear depth of the rake face and the average wear depth of the flank face based on discrete point set data.

[0093] Specifically, coordinate system 1 and outlier removal are performed on the discrete point sets of the rake face and the flank face respectively. The average wear depth is calculated by area-weighted average or equal partition integration. At the same time, supplementary statistics such as standard deviation, maximum wear depth and wear volume (by surface integration after point set interpolation) are calculated to comprehensively characterize the wear features.

[0094] Understandably, the method for calculating the average wear depth can be either an arithmetic mean or an area / volume-weighted average, depending on the evaluation objective. To reduce the impact of measurement errors, repeated measurements, interpolation, and uncertainty assessment should be implemented.

[0095] In this embodiment of the application, the edge characterization method based on multiphase flow coupling modeling includes, in step S900, obtaining the shape factor of the cutting edge of the insert under test based on the average wear depth of the rake face and the average wear depth of the flank face.

[0096] Specifically, the average wear depth, equivalent passivation value, and wear volume of the front and rear cutting faces are used as inputs. The cutting edge shape factor is calculated according to a preset shape factor expression (e.g., a weighted combination of the ratio of the equivalent passivation value of the front and rear cutting faces to the wear volume ratio). The numerical value, judgment level, and necessary confidence interval or sensitivity analysis results are output, while the baseline unworn cutting edge profile data for comparison are saved.

[0097] It is understood that the shape factor is used to quantitatively describe the symmetry and passivation of the cutting edge, and can serve as a key indicator for tool quality assessment, process optimization, or life prediction. The expression and weights can be determined through calibration experiments or empirical regression methods and adjusted according to different application scenarios.

[0098] In this embodiment, the fluid domain parameters and spatial discretization solution settings, including fluid materials and a turbulence model, are configured. These include: selecting the RNG (Renormalization Group) k-ε model from the turbulence model to correct the pulsation characteristics of the fluid domain, and using standard wall functions to constrain the surface flow field near the blade under test. In the spatial discretization solution settings, the gradient is calculated using the element-based least squares method, a high-precision scheme is used for the pressure term, and upwind schemes are used to discretize and solve the momentum, turbulent kinetic energy, and turbulent dissipation rate to ensure the convergence and accuracy of the flow field calculation.

[0099] Specifically, a turbulence model with turbulence fluctuation correction capability is used to solve the flow characteristics within the fluid domain, and the wall function method is used to constrain the boundary layer flow near the surface of the blade under test. The RNGk-ε model improves the prediction of rapid shearing zones and small-scale fluctuations by introducing turbulence viscosity correction terms and additional turbulence scalar terms, which can better characterize the turbulent characteristics at the jet exit zone and the sandblasting particle channel. In the near-wall region, a standard wall function is used to avoid forcibly dividing the mesh into excessively fine grids within the extremely thin viscous sublayer, and the dimensionless wall distance of the near-wall mesh should be guaranteed. Within the applicable range of wall functions (e.g.) In terms of spatial discretization settings, cell-based least-squares is used to calculate velocity and scalar gradients to obtain a smoother gradient field. High-order precision schemes (such as high-order interpolation or PRESTO style processing) are used for pressure terms to reduce pressure-velocity coupling errors, while second-order upwind schemes are used for momentum, turbulent kinetic energy and turbulent dissipation rate to improve the analytical capability of the dominant convection terms. At the same time, appropriate relaxation factors, residual convergence criteria and time advancement parameters (transient selection) are configured, and local time step refinement or mesh refinement can be enabled in key regions to ensure convergence and local accuracy.

[0100] Understandably, using the RNG k-ε model in conjunction with the wall function and the aforementioned high-order discretization scheme can not only improve the physical reproduction of the near-wall and jet regions under the premise of controllable computational resources, but also help stabilize and converge the numerical solution, thereby providing a reliable flow field input for subsequent particle coupling.

[0101] In this embodiment, the physical properties of the blasting particles, nozzle, and blade, as well as the contact parameters between each contact pair, are set. This includes selecting a contact model (such as the Hertz-mindlin (no slip) model) and a standard rolling friction model for the interaction between the blasting particles, nozzle, and blade under test when configuring the contact parameters. When calculating the blade surface evolution, the Archard wear model combined with a relative wear model is used. The amount of material removed from the blade surface is characterized by calculating the tangential cumulative force generated by particle impact, thereby simulating the morphological changes during the blasting passivation process.

[0102] Specifically, when configuring contact parameters, the Hertz-mindlin (no slip) model is selected for the sandblasting particles, nozzle, and test blade to limit the relative tangential displacement conditions at the contact surface. Simultaneously, a standard rolling friction model is used to describe the rolling damping and resistance of the particles during the contact process. Parameters such as static / dynamic friction coefficients, restitution coefficients, contact stiffness, and damping coefficients are set for each contact surface to fully characterize the collision and friction behavior. In the calculation of the blade surface morphology evolution, it is preferable to combine the Archard wear model with a relative wear correction term considering the impact effect, i.e., calculating local volume loss based on the Archard empirical formula.

[0103]

[0104] in This refers to the volume loss per unit time or unit slip distance. The wear coefficient is... For normal impact force, The tangential slip distance, The material hardness is used; to reflect the influence of impact angle and velocity on removal efficiency, an angle correction function can be introduced. Or a velocity power law term, which makes the amount of material removed depend on both the magnitude of the impact velocity and the angle of incidence.

[0105] It is understood that the contact and wear parameters can be obtained from literature or material handbooks as initial values, and also need to be corrected through experimental calibration (such as single-particle impact test or small-sample sandblasting test) to ensure the quantitative reliability of numerical simulation.

[0106] In this embodiment, the method further includes: in the discrete phase physical model, transmitting fluid dynamics to the second simulation software through a coupling interface file; the fluid dynamics include at least Suffman lift, virtual mass force, and pressure gradient force to correct the particle trajectory in the liquid medium. A bidirectional coupling time synchronization strategy is set; based on the difference between fluid flow velocity and particle collision frequency, the time steps of the first and second simulation software are set to non-isochronous steps, and data exchange between the discrete and continuous phases is achieved through multi-step loops.

[0107] Specifically, in the discrete-phase physical model, the fluid-particle force terms are transmitted to the second simulation software via a coupling interface file. These fluid dynamics include at least drag, Saffman lift, added / virtual mass, and pressure gradient force, with additional terms such as Brownian or Magnus forces considered when necessary. The drag term is calculated using an appropriate drag coefficient correlation formula based on the local Reynolds number and particle shape. Lift and virtual mass are used to correct the particle trajectory in strong shear or accelerated flow fields, thereby improving the physical realism of the discrete-phase motion. When setting the time synchronization strategy for bidirectional coupling, an asynchronous timestep mechanism is adopted: the time step of the first simulation software (CFD) is... Time step compared to the second simulation software (DEM / discrete phase) Set as unequal proportions (generally) By performing several sub-cycling operations on the DEM within each CFD step and exchanging data at predetermined synchronization intervals, multi-step cyclic coupling between the continuous and discrete phases is achieved; during data exchange, quantities such as velocity, force, and volume fraction are interpolated or averaged over time to maintain conservation and numerical stability.

[0108] Understandably, using multi-step loops with non-isochronous step sizes can significantly reduce the overall computational cost while ensuring the accuracy of particle collisions and dynamics. However, the step size ratio and synchronization frequency must be reasonably selected based on the particle collision timescale, the CFL limit of CFD, and the required accuracy.

[0109] In this embodiment, the shape factor of the cutting edge of the insert under test is obtained based on the average wear depth of the rake face and the average wear depth of the flank face. This includes: performing image digitization processing on the exported wear depth distribution cloud map; identifying the bright wear areas in the wear depth distribution cloud map using grayscale and binarization algorithms; and automatically tracking and extracting the pixel coordinates of the wear edges using a boundary contour scanning program. The extracted pixel coordinates are mapped to physical space coordinates, and a feature quantity describing the degree of wear is obtained through numerical integration or averaging methods. This feature quantity is defined as the equivalent passivation characterization value of the rake face and the equivalent passivation characterization value of the flank face. The asymmetric cutting edge contour of the insert under test is numerically characterized by calculating the ratio of the equivalent passivation characterization value of the rake face to the equivalent passivation characterization value of the flank face.

[0110] Specifically, when performing image digitization processing on the exported wear depth distribution cloud map, the original image is first converted to grayscale and binarized using adaptive thresholding or the Otsu method to separate the wear region from the substrate; then, noise is removed through morphological operations (opening and closing operations) and wear edges are extracted using edge detection operators such as Canny; the contour pixel coordinates are automatically tracked and extracted using connected component analysis and boundary contour scanning programs (such as chain codes or Freeman chains); the image pixel coordinates are mapped to physical space coordinates through a known scale factor or calibration target (e.g., pixel coordinates are mapped to physical space coordinates). Convert to physical coordinates The extracted contour points are interpolated or fitted according to the depth value to obtain a continuous wear profile; the equivalent passivation characterization values ​​(e.g., equivalent cross-sectional area or equivalent radius) of the rake face and the flank face are calculated by numerical integration (area / volume integration of the fitted curve within a given interval) or weighted averaging method.

[0111] Understandably, this image digitization and mapping process requires scaling and repeated extraction to reduce systematic errors, and the ratio between the extracted equivalent passivation characterization values ​​can be directly used to quantify the asymmetry of the cutting edge and the non-uniformity of passivation, serving as the basic quantity for shape factor calculation.

[0112] In this embodiment, geometric modeling of the nozzle and the blade under test includes: establishing a relative position constraint model between the nozzle outlet and the cutting edge of the blade under test in a three-dimensional spatial domain. Based on the actual passivation process requirements, the spray distance and offset angle are set in the relative position constraint model to construct a geometric spatial field capable of simulating the interaction between the abrasive particle motion vector and the normal vector of the cutting edge surface.

[0113] Specifically, a three-dimensional parametric model is constructed with the blade reference coordinate system as the origin, and the nozzle exit point is clearly defined. Reference point for cutting edge relative position vector Define the spray distance in the model parameters. With offset angle (The angle between the nozzle spindle and the blade normal), and define the abrasive particle motion vector in the model as a vector. With the normal vector of the cutting edge surface Record the angle of incidence This allows for the calculation of impact efficiency and wear functions in simulations. The model also parameterizes the nozzle orifice diameter, spray angle, nozzle orientation, and blade mounting angle to enable batch parameter scanning and operational condition simulation.

[0114] Understandably, by using a parameterized relative position constraint model, the injection distance and offset angle can be quickly adjusted before simulation to simulate different passivation processes (e.g., close-range strong impact or long-range wide dispersion injection); incident angle With velocity vector Accurate recording of data is beneficial for its use as an independent variable in the angle / velocity correction function in subsequent contact and wear models, thereby improving the physical realism and comparability of abrasive-surface interactions.

[0115] In this embodiment, the wear depth distribution cloud maps of the rake and flank faces of the test insert after sandblasting are extracted and exported, including: in the second simulation software, using pseudo-color processing technology to convert the force distribution on the surface of the insert geometry into a depth distribution image with color gradient changes. The depth distribution image is output using a specific image format, and grayscale histogram equalization is performed on the image using image digitization tools to enhance the visual contrast between the edge of the worn area and the unworn substrate. A boundary tracing algorithm is used to extract pixel contours from the binarized image, establishing a mapping relationship between pixel coordinates and three-dimensional spatial physical coordinates.

[0116] Specifically, after simulation, a high-precision depth field is generated on the blade surface and exported in a high-bit-depth format (such as 16-bit TIFF or floating-point VTK) to retain micrometer-level depth information. Pseudo-color processing is used to encode the depth values ​​into color levels, and the corresponding depth value matrix (CSV / VTK) is output simultaneously before export. Subsequently, grayscale conversion and histogram equalization are applied to the color level image to enhance boundary contrast. Closed contours are extracted using Canny edge detection combined with chain code (Freeman chain) or contour tracking algorithms. Known pixel calibration factors are used... and image origin offset pixel coordinates Mapped to physical coordinates If necessary, subpixel fitting is performed on the contour to improve positioning accuracy, and each contour point is associated with its original depth value to form a depth point set for subsequent analysis.

[0117] Understandably, saving the depth cloud map in both lossless numerical matrix and image color scale formats can maintain numerical accuracy while ensuring visual readability; the strict mapping from pixels to physical space and sub-pixel fitting can significantly reduce scale error, making the subsequent area / volume integration and equivalent passivation value calculation have a traceable calibration basis.

[0118] In this embodiment, calculating the average wear depth of the rake face and the average wear depth of the flank face based on discrete point set data includes: defining a feature sampling window in the discrete point set data to exclude abnormal discrete points generated by the randomness of discrete element particle collisions; performing a statistical weighted average operation on all coordinate points of the rake face and flank face within the feature sampling window to obtain a continuous wear characteristic quantity characterizing the evolution of the macroscopic morphology; and calculating a shape factor reflecting the degree of offset of the cutting edge profile to one side based on the average wear depth values ​​of the rake face and flank face through asymmetric coefficient transformation, thereby achieving a quantitative characterization of the microscopic morphology of complex cutting edges.

[0119] Specifically, firstly, several overlapping or non-overlapping feature sampling windows are defined for each cutting edge in the discrete point set. The window size and density are determined by the cutting edge length and the point set resolution. Within each window, an outlier removal strategy is applied (e.g., based on IQR: removing points less than Q1-1.5·IQR or greater than Q3+1.5·IQR, or based on z-score: removing points with an absolute value greater than 3). Then, the remaining points are weighted and averaged according to local area weight or neighborhood kernel function weight (such as Gaussian weight) to obtain the window-level average wear depth. Average wear depth of the entire cutting edge ,in This represents the weight of the i-th window. The asymmetry coefficient is defined. )for:

[0120]

[0121] By using a preset or calibration mapping function Convert to shape factor For example, using linear mapping or weighted combination:

[0122]

[0123] Where the coefficient This can be determined through experimental calibration to ensure... Within the expected order of magnitude. Calculate and output the standard deviation, maximum value, and wear volume simultaneously as confidence level and diagnostic supplements.

[0124] Understandably, by first denoising at the window level and then using a weighted average, the impact of random fluctuations in single particle impacts on the overall evaluation can be effectively suppressed; using normalized asymmetric coefficients facilitates comparisons between blades of different sizes and materials, while the mapping coefficient of the shape factor should be determined through calibration tests or regression analysis to reflect the actual measurement standard of "passivation severity" in engineering.

[0125] The complete workflow of the edge characterization method based on multiphase flow coupling modeling provided in this application embodiment is described below with an exemplary embodiment.

[0126] In this application embodiment, a complete workflow for a cutting edge characterization method based on multiphase flow coupling modeling is proposed. The entire workflow covers geometric modeling, mesh generation, bidirectional coupling solution, wear contour map export, and image / data post-processing (see...). Figures 2-4 )in, Figure 2 This is a schematic diagram illustrating the mesh generation of a straight-hole nozzle using preprocessing software, as provided in an embodiment of this application. Figure 3 This is a simulation diagram of the first simulation software in the coupled solution calculation process provided in an embodiment of this application. Figure 4This is a graph showing the variation of the residuals in the sandblasting fluid-structure interaction solution monitored by the second simulation software during the coupled solution calculation process provided in an embodiment of this application, as a function of the number of iteration steps. The horizontal axis represents the number of iteration steps, and the vertical axis (Residuals) represents the discrete residual values ​​of each governing equation (continuity equation, momentum equation, energy equation, and turbulent transport equation) at the current iteration step. Essentially, it represents the numerical imbalance between the left-hand side (flux term) and the right-hand side (source term) of the algebraic equation system during the numerical solution process. This index directly reflects the degree to which the current numerical solution deviates from the exact solution of the governing equations. To eliminate the differences in dimensions and orders of magnitude between different physical quantities, the second simulation software scales the original residuals, making the global residuals dimensionless relative to the corresponding physical quantity reference flux, thereby achieving a unified comparison of the convergence behavior of each equation. The residuals are typically expressed on a logarithmic scale (ranging from 1e-01 to 1e-09), visually representing the evolution of each governing equation with the number of iterations. Smaller residual values ​​indicate lower imbalance and better convergence of the numerical solution. The continuity curve represents the residuals from solving the continuity equation, reflecting the degree to which fluid mass conservation is satisfied. The x-velocity, y-velocity, and z-velocity curves represent the residuals from solving the momentum equations in three orthogonal directions in Cartesian coordinates, reflecting the degree to which momentum conservation is satisfied in the corresponding directions. The k-curve represents the residuals from the turbulent kinetic energy transport equation, reflecting the balance error between the generation, diffusion, and dissipation of turbulent kinetic energy. The epsilon curve represents the residuals from the turbulent kinetic energy dissipation rate transport equation, reflecting the balance error between turbulent dissipation rate transport and the source term. In the computational fluid dynamics solution process, this residual plot directly reflects the degree of imbalance on both sides of the equations after substituting the iterative approximate solution into the discrete governing equations. Its functions include:

[0127] When the residuals of each physical quantity continuously decrease to the preset threshold and tend to fluctuate steadily, it can be determined that the discrete equation system has iteratively converged to a stable solution. The rationality of calculation settings such as relaxation factor, time step, and mesh quality can be evaluated by whether the residuals diverge or oscillate violently. The convergence level of the continuity residuals is directly related to the numerical accuracy of mass conservation in the computational domain and is the basic criterion for judging the physical authenticity of the calculation results. In the sandblasting fluid-structure interaction simulation that includes particle phase and structural deformation, the small periodic fluctuations of the turbulence variable residuals often correspond to real physical fluctuations caused by structural shedding or particle-fluid momentum exchange rather than numerical divergence, and can be used as an important criterion for the credibility of the coupling calculation results. In the embodiments of this invention, the residuals of the main physical equations of the numerical simulation all entered a stable periodic fluctuation stage in the later stage of iteration. Although the continuity residual did not decrease further to a lower order of magnitude (not reaching 1e-05), the residuals of the other physical equations all decreased to the order of 1e-05 to 1e-07 and remained stable, indicating that the numerical simulation has reached a convergence state. The prediction results of the blade edge shape evolution law under sandblasting obtained based on the flow field solution have engineering-acceptable accuracy.

[0128] First, a parametric geometric model (including the injection distance) of the nozzle outlet and the cutting edge of the blade under test is established in a three-dimensional space domain. Offset angle and angle of incidence (See the definition of the relative position constraint model in the embodiments of this application), and perform local refinement and mesh generation on the straight-hole nozzle and blade in the pre-processing software. Figure 2 Import the mesh file into the first simulation software (CFD). In CFD, set the inlet as a pressure inlet and the outlet as a pressure outlet. The fluid domain adopts the RNG k-ε turbulence model with standard wall functions. Spatial discretization uses element-based least squares gradient calculation, a higher-order pressure term scheme, and a second-order upwind scheme of momentum / turbulence scalar (see [link to CFD]). Figure 3 Simultaneously, the same mesh / geometry is imported into a second simulation software (Discrete Element Method (DEM) simulation module), the particle size distribution, physical property parameters, and contact parameters (friction coefficient, coefficient of restitution, contact stiffness, etc.) of the blasted particles are set, and the Archard wear model incorporating impact effects is selected to calculate the local material removal amount:

[0129]

[0130] in For localized volume loss, The wear coefficient is... For normal force, The tangential slip distance, For material hardness. CFD and DEM are bidirectionally coupled through a coupling interface file, employing a multi-step sub-cycle strategy with non-isochronous step sizes (DEM sub-step size). CFD step size In each synchronization interval, the velocity field, pressure field, local volume fraction, and particle momentum source are exchanged to achieve the conservation and numerical stability of physical quantities.

[0131] Specifically, after the coupled solution is completed, the wear depth distribution cloud maps of the rake face and flank face are exported from the second simulation software (preferably saved simultaneously in high-depth numerical matrix and lossless image format, see...). Figure 5 , Figure 6 . Figure 5 The wear depth distribution cloud map of the rake face after the simulation of the second simulation software provided in an embodiment of this application is completed. Figure 6 The second simulation software provided in one embodiment of this application outputs a wear depth distribution cloud map of the flank face after simulation, and outputs it in pseudo-color processing and depth matrix form to retain micron-level information. The exported cloud map is processed using an image digitization workflow (grayscale conversion → histogram equalization → adaptive threshold binarization → morphological denoising → Canny edge detection → contour tracing) to extract the wear contour boundaries of the front / back face. Figure 7 , Figure 8 . Figure 7 A schematic diagram of the wear contour boundary curve of the rake face after image processing software is provided in an embodiment of this application; Figure 8 (A schematic diagram of the wear contour boundary curve of the flank face after image processing software provided in an embodiment of this application). Image pixel coordinates... By pixel scale factor Offset from origin Mapped to physical coordinates:

[0132]

[0133] To improve accuracy, sub-pixel fitting is performed on the contour points to obtain a discrete point set with depth values. Several feature sampling windows are then defined on the cutting surface for this discrete point set, and outliers are removed (e.g., based on IQR or z-score). Within each window, the window-level average wear depth is calculated using area or Gaussian kernel weighting. The overall average wear depth of the cutting edge is obtained by weighted averaging:

[0134]

[0135] Define the average wear depth of the rake / flank face as and Construct normalized asymmetric coefficients:

[0136]

[0137] The equivalent passivation characterization values ​​before and after (e.g., calculated by converting the cross-sectional area or equivalent radius between the fitted curve and the original blade line) are presented graphically. Figure 9 , Figure 10 . Figure 9 This is a diagram showing the equivalent passivation value of the rake face after data extraction by the data analysis software provided in an embodiment of this application. Figure 10 This image shows the equivalent passivation value of the flank face after data extraction by data analysis software provided in an embodiment of this application. It is used to calculate the cutting edge shape factor. Shape factor. It can be obtained from one of the following formulas:

[0138] Weighted ratio form:

[0139]

[0140] Or a linear mapping based on asymmetric coefficients:

[0141]

[0142] in This represents the equivalent passivation value for the rake face and the flank face. For wear volume, weight With calibration coefficient The matching engineering evaluation scale can be determined through calibration experiments or regression.

[0143] Understandably, the above complete workflow achieves a closed loop from process parameters and geometric constraints to coupled simulation and then to image / data representation: Figure 2 Display grid division, Figure 3 , Figure 4 The simulation states at both ends of the coupled solution are shown. Figure 5 , Figure 6 This is the original depth contour map after simulation. Figure 7 , Figure 8 For the contour extraction results, Figure 9 , Figure 10 This is a data-driven output of the equivalent passivation value and shape factor. The resulting shape factor... It can serve as a quantitative indicator for predicting tool passivation uniformity, cutting edge asymmetry, and potential service life; to improve engineering usability, key model parameters (such as...) should be addressed. Sensitivity analysis and experimental calibration were performed on the friction coefficient, particle size distribution, turbulence model selection and mesh density, and uncertainty and confidence interval were calculated through repeated simulation and measurement to ensure the reliability and comparability of the shape factor.

[0144] This method combines CFD-DEM bidirectional coupling, multi-scale mesh refinement, and high-order numerical schemes to achieve high-fidelity simulation of the fluid-particle coupling effect on the cutting edge during sandblasting passivation. Therefore, it offers several technical advantages: First, it can reconstruct the impact, slip, and wear mechanisms between abrasive particles and the cutting edge at the microscale, thus obtaining more accurate predictions of the cutting edge morphology evolution than simple geometric or empirical estimations. Second, by employing a multi-step sub-cycle with non-isochronous step sizes and a locally adaptive mesh strategy, it significantly reduces overall computational overhead and improves simulation efficiency while ensuring DEM collision accuracy and CFD stability. Third, by introducing numerical measures such as RNG turbulence correction, element-based least squares gradient, and high-order pressure schemes, this method provides more stable and convergent flow field inputs in the near-wall and jet exit zones, thereby improving the reliability of discrete phase motion and wear calculations. It also features parameterizable relative position constraints (jet distance, offset angle, etc.) and particle / material property settings, facilitating batch processing and sensitivity analysis, thus providing quantitative basis for process optimization.

[0145] Understandably, this method also has significant engineering application value: on the one hand, it can reduce a large number of blind experiments, reduce labor time and material consumption, and make sandblasting passivation process design more efficient and controllable; on the other hand, through the export of image-based wear cloud maps and the calculation of numerical equivalent passivation values ​​and shape factors, it can provide standardized and comparable quantitative indicators for tool quality evaluation, passivation uniformity determination, and life prediction; in addition, this method is easy to couple with experimental calibration procedures (such as single-particle impact or white light interferometry), can improve the quantitative accuracy of the results through calibration coefficients, and can output confidence intervals and uncertainty assessments, enhancing its adoptability and reliability in industrial inspection, process adjustment, and predictive maintenance.

[0146] Figure 11 This is a schematic diagram of a cutting edge characterization system based on multiphase flow coupling modeling provided in an embodiment of this application. Figure 11 The cutting edge characterization system 10 based on multiphase flow coupling modeling shown includes at least the following modules: modeling module 11, model import module 12, coupling module 13, depth statistics module 14, and cutting edge characterization module 15.

[0147] In this embodiment, the modeling module 11 is used to perform geometric modeling of the nozzle and the blade to be tested; and to mesh the geometric model according to the inlet and outlet boundaries, solid region and fluid region to form a mesh model file. For details, please refer to the attached document. Figures 1 to 10 The details and their corresponding descriptions are not repeated here.

[0148] In this embodiment, the model import module 12 is used to import the mesh model file into the first simulation software, set the inlet boundary as a pressure inlet and the outlet boundary as a pressure outlet, and configure the fluid domain parameters and spatial discretization solution settings, including the fluid material and turbulence model; and to import the mesh model file into the second simulation software, setting the physical property parameters of the blasting particles, nozzle and blade, as well as the contact parameters between each contact pair. For details, please refer to the accompanying documentation. Figures 1 to 10 The details and their corresponding descriptions are not repeated here.

[0149] In this embodiment, the coupling module 13 is used to import the coupling interface file, enabling the first simulation software and the second simulation software to be connected. By setting the coupling time step and number of time steps between the first and second simulation software, iterative calculations begin until the simulation is completed. For details, please refer to the accompanying documentation. Figures 1 to 10 The details and their corresponding descriptions are not repeated here.

[0150] In this embodiment, the depth statistics module 14 is used to capture and export the wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting; extract the coordinate information of the wear contour pixels based on the wear depth distribution cloud maps to form discrete point set data about the wear contour; calculate the average wear depth of the rake face and the average wear depth of the flank face based on the discrete point set data. For details, please refer to the accompanying documentation. Figures 1 to 10 The details and their corresponding descriptions are not repeated here.

[0151] In this embodiment, the cutting edge characterization module 15 is used to obtain the shape factor of the cutting edge of the insert under test based on the average wear depth of the rake face and the average wear depth of the flank face. For details, please refer to the attached document. Figures 1 to 10 The details and their corresponding descriptions are not repeated here.

[0152] Figure 12 This is an electronic device 20 provided in one embodiment of this application. For example... Figure 12 As shown, the electronic device 20 includes at least the following components: a processor 21 and a memory 22.

[0153] In this embodiment, the memory 22 is used to store executable instructions of the processor 21, which, when configured to execute instructions, implement... Figure 1 The image shows a cutting edge characterization method based on multiphase flow coupling modeling.

[0154] In one embodiment of this application, the program operating in the electronic device 20 may be a program that controls a central processing unit (CPU) or similar device to achieve the functions of the above-described embodiments of the present invention (a program that enables the computer to function). The information processed by these devices is then temporarily stored in random access memory (RAM) during processing, and subsequently stored in various ROMs such as read-only memory (Flash ROM) and hard disk drives (HDDs), and read, corrected, and written by the CPU as needed.

[0155] It should be noted that a portion of the electronic device 20 described above can also be implemented using a computer. In this case, the program for implementing the control function can be recorded on a computer-readable recording medium, and the program recorded on the recording medium can be read into the computer and executed.

[0156] It should be noted that the term "computer" as used here refers to a computer built into electronic device 20, employing hardware including an operating system and peripheral devices. Furthermore, "computer-readable recording media" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard drives built into the computer.

[0157] Furthermore, a "computer-readable recording medium" can include: a medium that dynamically stores a program for a short period of time, such as a communication line used when transmitting a program via a network such as the Internet or a communication line such as a telephone line; or a medium that stores a program for a fixed period of time, such as volatile memory inside a computer that serves as a server or client in this case. In addition, the aforementioned program can be a program used to implement the above-mentioned functions, or it can be a program that can implement the above-mentioned functions by combining with programs already recorded in the computer.

[0158] It is understood that the embodiments of this application provide a cutting edge characterization method, system, and device based on multiphase flow coupling modeling. Through bidirectional coupling simulation of CFD and DEM, it can accurately reproduce the microscopic effect of multiphase flow on the cutting edge of cemented carbide cutting tools during sandblasting passivation, overcoming the limitations of traditional experimental methods such as high cost, difficult observation, and great interference from environmental factors. By integrating the multiphase flow force model and the material wear model, it realizes the quantitative simulation of the evolution of the micromorphology of the cutting edge, and combines image processing technology to extract accurate geometric contour data from the wear cloud map, thereby realizing the standardized numerical characterization of the cutting edge shape factor K. It effectively solves the technical problems of low accuracy and difficulty in quantitative characterization of asymmetric cutting edges, and provides a scientific, efficient, and high-precision digital analysis means for optimizing tool passivation processes and predicting tool life.

[0159] Those skilled in the art should recognize that the above embodiments are only used to illustrate this application and are not intended to limit this application. Any appropriate changes and variations made to the above embodiments within the essential spirit and scope of this application fall within the scope of protection claimed in this application.

Claims

1. A method of edge characterization based on multiphase flow coupling modeling, characterized in that, The method includes: Perform geometric modeling of the nozzle and the blade under test; The geometric model is meshed based on the inlet / outlet boundaries, solid regions, and fluid regions to form a mesh model file; Import the mesh model file into the first simulation software, set the inlet boundary as a pressure inlet and the outlet boundary as a pressure outlet, and configure the fluid domain parameters and spatial discretization solution settings, including fluid material and turbulence model. Import the mesh model file into the second simulation software and set the physical property parameters of the sandblasting particles, nozzle and blade, as well as the contact parameters between each contact pair; Import the coupling interface file to connect the first simulation software with the second simulation software. Set the coupling time step and number of time steps between the first simulation software and the second simulation software, and start iterative calculation until the simulation is completed. Wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting were extracted and exported respectively. The coordinate information of the wear contour pixels is extracted from the wear depth distribution cloud map to form discrete point set data about the wear contour; The average wear depth of the rake face and the average wear depth of the flank face are calculated based on the discrete point set data. This includes: defining a feature sampling window in the discrete point set data to exclude abnormal discrete points generated by the randomness of discrete element particle collisions; performing a statistical weighted average operation on all coordinate points of the rake face and the flank face within the feature sampling window to obtain a continuous wear feature quantity characterizing the evolution of the macroscopic morphology; and calculating a shape factor reflecting the degree of offset of the cutting edge profile to one side based on the average wear depth values ​​of the rake face and the flank face through asymmetric coefficient transformation, thereby realizing a quantitative characterization of the microscopic morphology of the complex cutting edge. The shape factor of the cutting edge of the test insert is obtained based on the average wear depth of the rake face and the average wear depth of the flank face. This includes: performing image digitization processing on the exported wear depth distribution cloud map, identifying the bright wear areas in the wear depth distribution cloud map using grayscale and binarization algorithms, and automatically tracking and extracting the pixel coordinates of the wear edge using a boundary contour scanning program; mapping the extracted pixel coordinates to physical space coordinates, obtaining the feature quantity describing the wear degree through numerical integration or average value calculation methods, and defining it as the equivalent passivation characterization value of the rake face and the equivalent passivation characterization value of the flank face; and completing the numerical characterization of the asymmetric cutting edge contour of the test insert by calculating the ratio of the equivalent passivation characterization value of the rake face to the equivalent passivation characterization value of the flank face.

2. The method of claim 1, wherein the modeling of multiphase flow coupling is based on a blade edge characterization. The configuration includes fluid domain parameters and spatial discretization solution settings for fluid materials and turbulence models, including: The renormalization group k-ε model in the turbulence model is selected to correct the pulsation characteristics of the fluid domain, and the standard wall function is used to constrain the surface flow field near the blade under test. In the spatial discretization solution setting, the gradient is calculated using the element-based least squares method, a high-precision scheme is used for the pressure term, and the momentum, turbulent kinetic energy and turbulent dissipation rate are all discretized using the upwind scheme to ensure the convergence and accuracy of the flow field calculation.

3. The method of claim 2, wherein the modeling of the multiphase flow coupling is based on a blade edge characterization. The settings include the physical properties of the sandblasting particles, nozzle, and blade, as well as the contact parameters between each contact pair, including: When configuring contact parameters, a contact model and a standard rolling friction model are selected for the interaction between the sandblasting particles, the nozzle, and the blade under test. When calculating the evolution of the blade surface, the Archard wear model combined with the relative wear model is selected. The amount of material removed from the surface of the blade under test is characterized by calculating the tangential cumulative force generated by particle impact, thereby simulating the morphological changes during the sandblasting passivation process.

4. The edge characterization method based on multiphase flow coupling modeling according to claim 3, characterized in that, The method further includes: In the discrete phase physical model, fluid dynamics are transmitted to the second simulation software through the coupling interface file. The fluid dynamics include at least Suffman lift, virtual mass force and pressure gradient force to correct the trajectory of particles in the liquid medium. A bidirectional coupling time synchronization strategy is set up. Based on the difference between fluid flow velocity and particle collision frequency, the time steps of the first simulation software and the second simulation software are set to non-equidistant time steps. Data exchange between discrete phase and continuous phase is realized through multi-step loop.

5. The edge characterization method based on multiphase flow coupling modeling according to claim 1, characterized in that, The geometric modeling of the nozzle and the blade under test includes: Establish a relative position constraint model between the nozzle outlet and the cutting edge of the blade under test in a three-dimensional spatial domain; Based on the actual passivation process requirements, the spray distance and offset angle are set in the relative position constraint model to construct a geometric space field that can simulate the interaction between the abrasive particle motion vector and the cutting edge surface normal vector.

6. The edge characterization method based on multiphase flow coupling modeling according to claim 1, characterized in that, The process of separately capturing and exporting wear depth distribution cloud maps of the rake and flank faces of the test insert after sandblasting includes: In the second simulation software, pseudo-color processing technology is used to transform the force distribution on the surface of the blade geometry into a depth distribution image with color gradient changes. The depth distribution image is output using a specific image format, and grayscale histogram equalization is performed on the image using image digitization processing tools to enhance the visual contrast between the edge of the worn area and the unworn substrate. The boundary tracing algorithm is used to extract pixel contours from the binarized image and establish a mapping relationship between pixel coordinates and three-dimensional physical coordinates.

7. A cutting edge characterization system based on multiphase flow coupling modeling, applied to the cutting edge characterization method based on multiphase flow coupling modeling as described in any one of claims 1 to 6, characterized in that, The system includes: The modeling module is used to perform geometric modeling of the nozzle and the blade under test; and to mesh the geometric modeling according to the inlet and outlet boundaries, solid region and fluid region to form a mesh model file; The model import module is used to import the mesh model file into the first simulation software, set the inlet boundary as a pressure inlet and the outlet boundary as a pressure outlet, and configure the fluid domain parameters and spatial discretization solution settings, including fluid materials and turbulence models; and to import the mesh model file into the second simulation software, setting the physical property parameters of the sandblasting particles, nozzles and blades, as well as the contact parameters between each contact pair. The coupling module is used to import the coupling interface file, so that the first simulation software and the second simulation software are connected. By setting the coupling time step and the number of time steps between the first simulation software and the second simulation software, iterative calculation begins until the simulation is completed. The depth statistics module is used to capture and export the wear depth distribution cloud maps of the rake face and flank face of the test insert after sandblasting; extract the coordinate information of the wear contour pixels based on the wear depth distribution cloud maps to form discrete point set data about the wear contour; and calculate the average wear depth of the rake face and the average wear depth of the flank face based on the discrete point set data. The cutting edge characterization module is used to obtain the shape factor of the cutting edge of the insert under test based on the average wear depth of the rake face and the average wear depth of the flank face.

8. An electronic device, characterized in that, include: processor; And a memory having computer-readable instructions stored thereon for controlling the processor to execute the edge characterization method based on multiphase flow coupling modeling as described in any one of claims 1 to 6.

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