Spherical tooth tip blade safety evaluation method and system based on data analysis

Abstract

The application discloses a spherical tooth tip blade safety evaluation method and system based on data analysis, and relates to the technical field of cutting processing.The application comprises the following steps: collecting blade surface morphology, microscopic lattice structure and cutting dynamic load data through a white light interferometer, EBSD and a dynamometer; establishing a macro finite element model and embedding a microscopic crystal plasticity submodel; matching macro and microscopic stress fields, calculating lattice orientation conversion matrix and Schmid factor, and extracting slip system characteristic parameters; determining crack initiation, calculating an extension path in combination with an anisotropy criterion, and reconstructing three-dimensional crack morphology; calculating a cumulative damage factor, dividing three-level safety threshold values, and outputting a visual report and a life prediction value.The application establishes a coupling equation of macro stress field and microscopic crystal orientation, integrates the mechanical response characteristics of a material at different scales, and improves the physical authenticity of crack initiation and extension prediction.

Description

Technical Field

[0001] This invention belongs to the field of cutting technology, and in particular relates to a data analysis-based method and system for safety assessment of spherical tooth tip inserts. Background Technology

[0002] Spherical inserts are key tools in the machining field. Their spherical tips directly contact the workpiece and bear complex dynamic loads during cutting, making them the core area determining machining accuracy and tool life. These inserts are typically made of high-strength, high-wear-resistant materials, and their surface morphology, micrograin orientation, and mechanical response characteristics in the tip region significantly influence cutting performance. However, due to the combined effects of alternating stress and frictional heat during service, the tip is prone to localized stress concentration caused by grain anisotropy, leading to fatigue crack initiation and propagation, thus affecting machining safety and stability.

[0003] The safety performance of spherical tooth tip inserts is crucial to machining quality and efficiency. Traditional safety assessment methods are mostly based on single-scale analysis, focusing either on macroscopic mechanical response or microscopic grain characteristics. They are difficult to effectively integrate multi-dimensional data such as insert surface morphology, grain orientation, and cutting load. This results in a disconnect between the characterization of macroscopic stress characteristics and microscopic anisotropic behavior, failing to accurately reveal the evolution of mechanical behavior in the complex service environment of the tooth tip region. Consequently, crack prediction and safety assessment results deviate from actual working conditions. To address these issues, the following solutions are proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a data analysis-based safety assessment method and system for spherical tooth tip cutting tools. By establishing a coupling equation between the macroscopic stress field and the microscopic crystal orientation, the mechanical response characteristics of materials at different scales are integrated, which solves the problem that existing methods are mostly based on single-scale analysis, resulting in deviations between crack prediction and safety assessment results and actual working conditions.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0006] This invention relates to a data analysis-based safety assessment method for spherical toothed cutting blades, the assessment method comprising:

[0007] Step S1, Data Acquisition: Simultaneously acquire the three-dimensional morphology, grain orientation distribution, and real-time cutting load data of the blade surface using a white light interferometer, EBSD, and dynamic force gauge, and construct a multi-dimensional feature parameter matrix;

[0008] Step S2, Macro-micro coupling modeling: Establish a macro-dynamic constitutive model and embed a crystal plastic sub-model in the tooth tip region to reconstruct the grain structure, define the slip system activation criterion, and realize a unified characterization of the macro- and micro-mechanical behavior of the material;

[0009] Step S3, Cross-scale data fusion: Using sub-model technology and intrinsic stress correction equation, the macroscopic stress field is matched with the microscopic lattice orientation to quantify the dynamic influence of grain anisotropy on principal stress distribution.

[0010] Step S4, Crack Dynamic Prediction: Determine crack initiation, calculate crack path using anisotropic propagation criterion, and reconstruct three-dimensional crack morphology and propagation velocity field using level set method;

[0011] Step S5, Safety Assessment: The degree of blade damage is quantified by the damage factor accumulation model, the safety level is divided according to the threshold, and the three-dimensional crack cloud map, life prediction curve and key early warning grain information are output to realize the visualization of the full-process assessment results.

[0012] Furthermore, step S1, data acquisition, specifically includes the following steps:

[0013] Step S11, Surface morphology data acquisition: A white light interferometer is used to scan the blade surface with a grid density of 0.5mm×0.5mm, and the height distribution z(x,y), local radius of curvature R(x,y) and surface roughness Ra(x,y) of the three-dimensional morphology are acquired simultaneously.

[0014] Step S12, Microstructure Data Acquisition: In the selected area, the microstructure of the grains is analyzed using an X-ray backscatter diffractometer, and the grain orientation matrix g, grain boundary ∑ value classification, and residual stress σ are acquired. res (x,y,z) distribution;

[0015] Step S13, Dynamic load data acquisition: The dynamic load spectrum Fx / Fy / Fz during the cutting process is captured in real time by a triaxial piezoelectric force meter at a high frequency of 20kHz.

[0016] This design uses a white light interferometer, an X-ray backscatter diffractometer, and a dynamic force gauge to systematically collect real-time data on the surface morphology, microstructure, and cutting load of the cutting blade. This provides high-precision, multi-dimensional initial input for multi-scale modeling, ensuring the physical realism and dynamic response basis of the model analysis.

[0017] Furthermore, step S2, macro-micro coupling modeling, specifically includes the following steps:

[0018] Step S21, Macroscopic Finite Element Modeling: A three-dimensional solid model of the cutting tool is established in ABAQUS software. C3D10M second-order tetrahedral elements are used for mesh generation, and the Johnson-Cook dynamic constitutive equation is loaded to simulate the macroscopic stress-strain response of the cutting tool during the cutting process. The equation is as follows:

[0019]

[0020] In the formula, α is the flow stress, A is the initial yield strength of the material, B is the strain hardening modulus, ε is the equivalent plastic strain, n is the strain hardening exponent, and C is the strain rate sensitivity coefficient. Given the current strain rate, For reference strain rate, T * Here, m is the normalized temperature, and m is the temperature softening index.

[0021] Step S22, Microcrystalline Plasticity Modeling: A finite element sub-model of crystal plasticity is embedded in the critical region at the tooth tip (within 0.5 mm of the tooth tip). The grain topology matching the actual EBSD data is reconstructed using the Voronoi algorithm. Based on the slip system activation criterion, the critical decomposition shear stress threshold of each grain slip system is defined. The slip system activation criterion is as follows:

[0022]

[0023] In the formula, τ α The decomposed shear stress of the α-th slip system, σ is the macroscopic stress tensor, μ α Let be the orientation tensor of the α-th slip system. Let α be the critical decomposition shear stress of the α-th slip system, where α is the slip system number;

[0024] This design establishes a macroscopic finite element model (ABAQUS) and a microscopic crystal plasticity submodel (CPFEM) to simulate the overall stress distribution of the blade at the global scale and analyze the activation behavior of the slip system at the local grain scale, thereby achieving coupled modeling of cross-scale mechanical response and providing a macro-micro linkage computational framework for crack prediction.

[0025] Furthermore, step S3, cross-scale data fusion, specifically includes the following steps:

[0026] Step S31, Stress Field Matching: Based on the sub-model technique, the stress field σ calculated by the macroscopic finite element method is matched. macro Compared with the intrinsic stress Δσ measured by EBSD eigen Superposition correction is performed to transfer the stress field from the macroscopic to the microscopic scale. The superposition correction formula is as follows:

[0027] σ micro =σ macro +Δσ eigen ;

[0028] In the formula, σ micro For microscale correction of stress, σ macro For macroscopic finite element calculation of stress, Δσ eigen This is the intrinsic stress correction based on EBSD;

[0029] Step S32, Lattice Orientation Mapping: The local coordinate system of each grain is mapped to the global coordinate system through the grain orientation transformation matrix Q, quantifying the spatial orientation relationship between the slip system and the principal stress field. The transformation matrix is ​​Q:

[0030]

[0031] In the formula, φ is the rotation angle of the grain about the initial global coordinate system Z-axis, θ is the tilt angle of the grain about the new X′ axis after the first rotation, and ψ is the final rotation angle of the grain about the new Z″ axis after the second rotation.

[0032] Step S33, Feature Parameter Extraction: Combining the Schmid factor The calculation and dynamic analysis of the normal to the slip plane within each grain are performed. The angle between the slip direction λ and the principal stress;

[0033] In the formula, m α The Schmid factor for the α-th slip system. λ is the angle between the normal direction of the slip surface and the direction of the principal stress, and λ is the angle between the slip direction and the direction of the principal stress.

[0034] This design performs coordinate transformation and parameter mapping between macroscopic stress field and microscopic lattice orientation data. Through stress correction, grain coordinate system transformation and Schmid factor calculation, a cross-scale data correlation model is constructed to quantify the contribution of grain anisotropy to crack behavior and provide a unified set of multi-source characteristic parameters for dynamic prediction.

[0035] Furthermore, step S4, crack dynamic prediction, specifically includes the following steps:

[0036] Step S41, Crack Initiation Determination: Determine the crack initiation conditions according to the formula, which is:

[0037]

[0038] In the formula, N is the number of crack initiation cycles, N0 is the base number of cycles, and Δγ P γ is the equivalent plastic slip. f denoted as the critical slip at material fracture, and k as the fatigue damage index;

[0039] Step S42, Crack propagation path calculation: Establish an anisotropic propagation criterion, introduce a lattice correction term, and dynamically track the crack propagation path along grain boundaries or transgranularly by calculating the crack propagation rate. The formula is:

[0040]

[0041] In the formula, Let C, m, and n be the crack propagation rate, and ΔK be the material constant according to Paris law. eff Let ΔK be the magnitude of the equivalent stress intensity factor. th ΔK is the stress intensity factor threshold. I ,ΔK II These are the amplitudes of the type I and type II stress intensity factors, respectively, and β is the lattice anisotropy coefficient;

[0042] Step S43, 3D Crack Reconstruction: The level set method is used to dynamically track the geometric changes of the 3D crack front. The crack propagation velocity field is characterized by the level set function to handle the topological evolution of complex crack paths. The level set equation is:

[0043]

[0044] V n =0.5C(ΔK) eff ) m ;

[0045] In the formula, φ is the level set function. V is the rate of change of the level set function over time. n The normal propagation velocity at the crack front. Let C be the gradient magnitude of the level set function, C be the material-dependent fatigue crack propagation rate coefficient, and ΔK be the gradient magnitude of the level set function. eff The effective stress intensity factor amplitude is m, and the material fatigue crack propagation index is m.

[0046] This design, based on the improved Tanaka-Mura equation and anisotropic propagation criterion, combined with the stress intensity factor correction formula and level set method, simulates the entire life cycle evolution of cracks from initiation threshold determination, multi-mode propagation path calculation to three-dimensional morphology reconstruction, achieving accurate spatiotemporal dynamic prediction of crack behavior.

[0047] Furthermore, step S5, the security assessment, specifically includes the following steps:

[0048] Step S51, Damage Factor Calculation: Calculate the service damage factor for each load segment based on the crack propagation rate integral. The formula is:

[0049]

[0050] In the formula, D is the cumulative damage factor, and t i t represents the service time or number of cycles for the i-th load segment. fi Let q be the failure time or cycle number corresponding to the i-th load segment, and q be the nonlinear damage accumulation exponent. This represents the crack propagation rate.

[0051] Step S52, Security Level Classification: Based on the D value, three levels of early warning are defined, specifically as follows:

[0052] Safe zone: D < 0.3;

[0053] Warning zone: 0.3 < D < 0.7;

[0054] Danger zone: D≥0.7;

[0055] Step S53, Evaluation Report Generation: Integrate the crack path prediction results to generate a comprehensive evaluation report that includes a 3D crack propagation cloud map, real-time damage factor evolution curve, key early warning grain locations, and a weighted sum of remaining lifetime prediction values. The report uses the Unity3D engine to achieve dynamic interactive visualization of multi-dimensional data.

[0056] This design transforms crack prediction results into actionable indicators that quantify safety levels, remaining lifespan, and risk locations through damage factor accumulation calculation, three-level early warning threshold division, and three-dimensional visualization report generation, providing an intuitive engineering assessment basis for tool maintenance decisions.

[0057] A data analysis-based safety assessment system for spherical toothed cutting blades, comprising a data acquisition module, a data fusion and processing unit, a multi-scale modeling module, a dynamic prediction module, and an assessment output module;

[0058] The output of the data acquisition module is unidirectionally connected to the input of the data fusion processing unit. The output of the data fusion processing unit is unidirectionally connected to the input of the multi-scale modeling module. The output of the multi-scale modeling module is unidirectionally connected to the input of the dynamic prediction module. The output of the dynamic prediction module is unidirectionally connected to the input of the evaluation output module.

[0059] Furthermore, the data acquisition module is used to simultaneously acquire the surface morphology of the cutting blade, the micro-grain orientation (electron backscatter diffraction data), and the cutting dynamic load (three-dimensional force signal) through a white light interferometer, an EBSD probe, and a three-dimensional force gauge, providing multi-dimensional raw data input for subsequent analysis;

[0060] The data fusion processing unit is used to perform spatiotemporal registration and feature fusion on heterogeneous data. Through algorithms such as lattice orientation matrix transformation and stress field correction equation, macroscopic mechanical data are associated with microstructural features, and cross-scale feature parameters (such as anisotropy coefficient and corrected stress intensity factor) are extracted.

[0061] The multi-scale modeling module is used to construct a macroscopic and microscopic coupled numerical model. It uses ABAQUS to establish a macroscopic finite element model of the blade and embeds a crystal plastic sub-model (CPFEM) based on the real grain structure in the key area. Through the cross-scale interface, the stress field is transmitted bidirectionally, revealing the control mechanism of material anisotropy on crack behavior.

[0062] The dynamic prediction module is used to calculate the crack initiation location, propagation path and three-dimensional morphology evolution in real time, and combined with the LSTM neural network to predict the influence of cutting load changes on crack dynamics, and output quantitative propagation rate and direction parameters.

[0063] The evaluation output module is used to integrate the damage factor calculation model and the safety level threshold, generate a three-dimensional visualization report through the Unity3D engine, dynamically display the crack propagation process, remaining life prediction and early warning information, support process parameter optimization decisions, and realize real-time audible and visual alarm feedback for abnormal states.

[0064] The present invention has the following beneficial effects:

[0065] 1. This invention integrates the mechanical response characteristics of materials at different scales by establishing a coupling equation between the macroscopic stress field and the microscopic crystal orientation. Based on the dynamic mapping relationship between the lattice slip coefficient and the principal stress direction, a stress intensity factor correction equation incorporating grain anisotropy characteristics is constructed, which can quantify the non-uniform distribution characteristics of the stress field at the crack tip. By introducing multi-source microscopic data such as grain boundary type and residual stress distribution, the model can analyze the correlation between crack propagation path and grain orientation, overcoming the limitations of single-scale analysis. This design improves the physical realism of crack initiation and propagation prediction, providing support for revealing the damage evolution mechanism of materials under complex loads.

[0066] 2. This invention constructs a real-time updated crack evolution prediction framework by integrating high-frequency dynamic cutting force data with multi-scale stress field calculation results. Based on a GPU-accelerated parallel computing architecture, the model can simultaneously complete the iterative calculation of the activation state of the micro-slip system when dynamic loads are input, realizing online correction of the crack propagation rate. Combined with the three-dimensional crack reconstruction algorithm of the level set method, it can describe the morphological evolution process of the crack front in the continuous time domain. This dynamic coupling mechanism ensures the rapid response capability of the prediction model to transient loads, providing a reliable technical means for real-time monitoring of tool damage status.

[0067] 3. This invention employs crystal plasticity finite element method and Voronoi grain generation method to reproduce the polycrystalline topology of cemented carbide cutting tools; by defining slip system activation criteria and anisotropic propagation criteria, the model can analyze the influence of different grain orientations on crack deflection behavior; combined with the spatial mapping relationship between Schmid factor distribution and local residual stress, the system can identify sensitive grain boundary regions where cracks preferentially propagate; this microscale refined modeling method provides a high-resolution analytical basis for damage tolerance assessment of defective materials.

[0068] 4. This invention establishes a unified standard for classifying tool safety status by defining quantitative indicators of damage factors and three-level warning thresholds. Based on the remaining life prediction algorithm and three-dimensional visualization technology, the system can automatically generate a full-element assessment report that includes crack propagation path, damage accumulation trend and key warning areas. This design achieves standardization from data acquisition to decision output, provides a basis for formulating preventive maintenance strategies, and reduces the subjective risk of human experience judgment.

[0069] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0070] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a flowchart illustrating the data analysis-based safety assessment method for spherical toothed cutting blades of the present invention.

[0072] Figure 2 This is a framework diagram of the data analysis-based safety assessment system for spherical toothed cutting blades of the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Please see Figure 1 As shown, this invention is a data analysis-based safety assessment method for spherical toothed cutting blades, comprising:

[0075] Step S1, Data Acquisition:

[0076] Step S11, Surface morphology data acquisition: A white light interferometer is used to scan the blade surface with a grid density of 0.5mm×0.5mm, and the height distribution z(x,y), local radius of curvature R(x,y) and surface roughness Ra(x,y) of the three-dimensional morphology are acquired simultaneously.

[0077] Step S12, Microstructure Data Acquisition: In the selected area, the microstructure of the grains is analyzed using an X-ray backscatter diffractometer, and the grain orientation matrix g, grain boundary ∑ value classification, and residual stress σ are acquired.res (x,y,z) distribution;

[0078] Step S13, Dynamic load data acquisition: The dynamic load spectrum Fx / Fy / Fz during the cutting process is captured in real time by a triaxial piezoelectric force gauge at a high frequency of 20kHz.

[0079] Step S2, Macro-micro coupling modeling:

[0080] Step S21, Macroscopic Finite Element Modeling: A three-dimensional solid model of the cutting tool is established in ABAQUS software. C3D10M second-order tetrahedral elements are used for mesh generation, and the Johnson-Cook dynamic constitutive equation is loaded to simulate the macroscopic stress-strain response of the cutting tool during the cutting process. The equation is as follows:

[0081]

[0082] In the formula, α is the flow stress, A is the initial yield strength of the material, B is the strain hardening modulus, ε is the equivalent plastic strain, n is the strain hardening exponent, and C is the strain rate sensitivity coefficient. Given the current strain rate, For reference strain rate, T * Here, m is the normalized temperature, and m is the temperature softening index.

[0083] Step S22, Microcrystalline Plasticity Modeling: A finite element sub-model of crystal plasticity is embedded in the tooth tip. The grain topology matching the actual EBSD data is reconstructed using the Voronoi algorithm. The critical decomposition shear stress threshold for each grain slip system is defined based on the slip system activation criterion. The slip system activation criterion is as follows:

[0084]

[0085] In the formula, τ α The decomposed shear stress of the α-th slip system, σ is the macroscopic stress tensor, μ α Let be the orientation tensor of the α-th slip system. Let α be the critical decomposition shear stress of the α-th slip system, where α is the slip system number.

[0086] Step S3, Cross-scale data fusion:

[0087] Step S31, Stress Field Matching: Based on the sub-model technique, the stress field σ calculated by the macroscopic finite element method is matched. macro Compared with the intrinsic stress Δσ measured by EBSD eigen Superposition correction is performed to transfer the stress field from the macroscopic to the microscopic scale. The superposition correction formula is as follows:

[0088] σ micro =σ macro +Δσ eigen ;

[0089] In the formula, σ micro For microscale correction of stress, σ macro For macroscopic finite element calculation of stress, Δσ eigen This is the intrinsic stress correction based on EBSD;

[0090] Step S32, Lattice Orientation Mapping: The local coordinate system of each grain is mapped to the global coordinate system through the grain orientation transformation matrix Q, quantifying the spatial orientation relationship between the slip system and the principal stress field. The transformation matrix is ​​Q:

[0091]

[0092] In the formula, φ is the rotation angle of the grain about the initial global coordinate system Z-axis, θ is the tilt angle of the grain about the new X′ axis after the first rotation, and ψ is the final rotation angle of the grain about the new Z″ axis after the second rotation.

[0093] Step S33, Feature Parameter Extraction: Combining the Schmid factor The calculation and dynamic analysis of the normal to the slip plane within each grain are performed. The angle between the slip direction λ and the principal stress;

[0094] In the formula, m α The Schmid factor for the α-th slip system. λ is the angle between the normal direction of the slip surface and the direction of the principal stress, and λ is the angle between the slip direction and the direction of the principal stress.

[0095] Step S4, Crack Dynamic Prediction:

[0096] Step S41, Crack Initiation Determination: Determine the crack initiation conditions according to the formula, which is:

[0097]

[0098] In the formula, N is the number of crack initiation cycles, N0 is the base number of cycles, and Δγ P γ is the equivalent plastic slip. f denoted as the critical slip at material fracture, and k as the fatigue damage index;

[0099] Step S42, Crack propagation path calculation: Establish an anisotropic propagation criterion, introduce a lattice correction term, and dynamically track the crack propagation path along grain boundaries or transgranularly by calculating the crack propagation rate. The formula is:

[0100]

[0101] In the formula, Let C, m, and n be the crack propagation rate, and ΔK be the material constant according to Paris law.eff Let ΔK be the magnitude of the equivalent stress intensity factor. th ΔK is the stress intensity factor threshold. I ,ΔK II These are the amplitudes of the type I and type II stress intensity factors, respectively, and β is the lattice anisotropy coefficient;

[0102] Step S43, 3D Crack Reconstruction: The level set method is used to dynamically track the geometric changes of the 3D crack front. The crack propagation velocity field is characterized by the level set function to handle the topological evolution of complex crack paths. The level set equation is:

[0103]

[0104] V n =0.5C(ΔK) eff ) m ;

[0105] In the formula, φ is the level set function. V is the rate of change of the level set function over time. n The normal propagation velocity at the crack front. Let C be the gradient magnitude of the level set function, C be the material-dependent fatigue crack propagation rate coefficient, and ΔK be the gradient magnitude of the level set function. eff denoted as the effective stress intensity factor amplitude, and m as the material fatigue crack propagation index.

[0106] Step S5, Safety Assessment:

[0107] Step S51, Damage Factor Calculation: Calculate the service damage factor for each load segment based on the crack propagation rate integral. The formula is:

[0108]

[0109] In the formula, D is the cumulative damage factor, and t i t represents the service time or number of cycles for the i-th load segment. fi Let q be the failure time or cycle number corresponding to the i-th load segment, and q be the nonlinear damage accumulation exponent. This represents the crack propagation rate.

[0110] Step S52, Security Level Classification: Based on the D value, three levels of early warning are defined, specifically as follows:

[0111] Safe zone: D < 0.3;

[0112] Warning zone: 0.3 < D < 0.7;

[0113] Danger zone: D≥0.7;

[0114] Step S53, Evaluation Report Generation: Integrate the crack path prediction results to generate a comprehensive evaluation report that includes a 3D crack propagation cloud map, real-time damage factor evolution curve, key early warning grain locations, and a weighted sum of remaining lifetime prediction values. The report uses the Unity3D engine to achieve dynamic interactive visualization of multi-dimensional data.

[0115] Please see Figure 2 As shown, the present invention is a data analysis-based safety assessment system for spherical tooth tip cutting tools, including a data acquisition module, a data fusion processing unit, a multi-scale modeling module, a dynamic prediction module, and an assessment output module;

[0116] The output of the data acquisition module is unidirectionally connected to the input of the data fusion processing unit. The output of the data fusion processing unit is unidirectionally connected to the input of the multi-scale modeling module. The output of the multi-scale modeling module is unidirectionally connected to the input of the dynamic prediction module. The output of the dynamic prediction module is unidirectionally connected to the input of the evaluation output module.

[0117] The data acquisition module is used to simultaneously acquire the surface morphology, micrograin orientation and cutting dynamic load of the cutting tool through a white light interferometer, an EBSD probe and a three-dimensional force gauge, providing multi-dimensional raw data input for subsequent analysis;

[0118] The data fusion processing unit is used to perform spatiotemporal registration and feature fusion on heterogeneous data. Through algorithms such as lattice orientation matrix transformation and stress field correction equation, macroscopic mechanical data are associated with microstructural features to extract cross-scale feature parameters.

[0119] The multi-scale modeling module is used to construct a numerical model that couples macroscopic and microscopic dimensions. It uses ABAQUS to establish a macroscopic finite element model of the blade and embeds a crystal plasticity sub-model based on the real grain structure in the key region. It realizes bidirectional stress field transmission through cross-scale interface and reveals the control mechanism of material anisotropy on crack behavior.

[0120] The dynamic prediction module is used to calculate the crack initiation location, propagation path and three-dimensional morphology evolution in real time. It combines LSTM neural network to predict the impact of cutting load changes on crack dynamics and outputs quantified propagation rate and direction parameters.

[0121] The evaluation output module integrates the damage factor calculation model with the safety level threshold, generates a three-dimensional visualization report through the Unity3D engine, dynamically displays the crack propagation process, remaining life prediction and early warning information, supports process parameter optimization decisions, and realizes real-time audible and visual alarm feedback for abnormal states.

[0122] One specific application of this embodiment is:

[0123] Implementation objects and conditions:

[0124] Tooling parameters: Φ6mm diameter ball end mill, cutting edge material is WC-10%Co cemented carbide

[0125] Material constants: Johnson-Cook parameters:

[0126] A=4800MPa, B=520MPa, n=0.24, C=0.017, m=1.56;

[0127] Cutting conditions: Milling 45# steel, spindle speed 8000 r / min, feed rate 0.1 mm / tooth, depth of cut 0.3 mm;

[0128] Implementation steps:

[0129] 1. Multi-source data acquisition:

[0130] 1.1 Surface morphology data: The blade tip region (0.5 × 0.5 mm) was measured using a white light interferometer (Zygo NewView 9000). 2 Maximum surface roughness Ra = 0.32 μm; Radius of curvature distribution: R min =2.8μm,R avg =5.6μm;

[0131] 1.2 Microstructure data: EBSD scanning (step size 0.2μm) showed that the average grain size d = 1.5μm and the proportion of grain boundaries ∑3 was 62%.

[0132] Residual stress field: (compressive stress);

[0133] 1.3 Dynamic Load Data:

[0134] The three-dimensional force gauge records the cutting force time-domain signal (sampled at 20kHz):

[0135] F x =120±15N,F y =85±10N,F z =40±8N;

[0136] 2. Multi-scale modeling:

[0137] 2.1 Macroscopic Finite Element Model:

[0138] Mesh generation: Global model element size 50μm, with local mesh refinement to 5μm at the tooth tips;

[0139] Dynamic load input: (S ij (For the deviatoric stress tensor);

[0140] 2.2 Crystal Plastic Submodel:

[0141] Embedded tooth tip area (300×300×200μm) 3 It contains 2048 Voronoi grains;

[0142] Activation threshold of the slip system:

[0143] (Basis slip), 1.8 GPa (cylindrical slip);

[0144] 3. Data fusion:

[0145] 3.1 Stress Matching Correction: Intrinsic Stress Correction Items:

[0146]

[0147] 3.2 Lattice Orientation Mapping: Typical grain orientation (Euler angles φ = 32°, θ = 15°, ψ = 58°), transformation matrix:

[0148]

[0149] 3.3 Schmid factor calculation: Basis slip system (0001)<11-20>:

[0150] m α =cos28°×cos43°=0.67;

[0151] 4. Crack dynamic prediction:

[0152] 4.1 Crack Initiation Criterion: Critical Slip γ f =0.15, calculate the number of germination cycles:

[0153]

[0154] 4.2 Calculation of the propagation path: Anisotropy coefficient β = 0.38, principal stress direction angle θ = 52°:

[0155] ΔK eff =9.7;

[0156] Expansion rate:

[0157]

[0158] 4.3 Three-dimensional crack reconstruction: Initial crack length a0 = 50 μm, after 3000 cycles:

[0159]

[0160] 5. Safety assessment:

[0161] 5.1 Calculation of damage factors:

[0162] Lifespan at each stage:

[0163]

[0164] 5.2 Security Level Determination: D = 0.21 < 0.3, currently in the safe zone;

[0165] 5.3 Summary of the Assessment Report:

[0166] Remaining life prediction: 1.8 × 10 4 The next loop;

[0167] High-risk grain ID: Grain 1423 (Schmid factor 0.82);

[0168] Recommended testing cycle: every 5 x 10 3 Re-inspection after each cutting cycle.

[0169] This embodiment uses coupled analysis of measured dynamic load (tangential force 120N) and microscopic residual stress (-1.2GPa) to predict the crack propagation of WC-10%Co inserts after 3000 cycles to be 53.15μm and the damage factor to be 0.21, thus verifying the effectiveness of the MSCDPM model in the safety assessment of cemented carbide cutting tools.

[0170] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," 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, 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.

[0171] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data analysis-based safety assessment method for spherical toothed cutting blades, characterized in that, The evaluation method includes the following steps: Step S1, Data Acquisition: Simultaneously acquire the three-dimensional morphology, grain orientation distribution, and real-time cutting load data of the blade surface using a white light interferometer, EBSD, and dynamic force gauge, and construct a multi-dimensional feature parameter matrix; Step S2, Macro-micro coupling modeling: Establish a macro-dynamic constitutive model and embed a crystal plastic sub-model in the tooth tip region to reconstruct the grain structure, define the slip system activation criterion, and realize a unified characterization of the macro- and micro-mechanical behavior of the material; Step S3, Cross-scale data fusion: Using sub-model technology and intrinsic stress correction equation, the macroscopic stress field is matched with the microscopic lattice orientation to quantify the dynamic influence of grain anisotropy on principal stress distribution. Step S4, Crack Dynamic Prediction: Determine crack initiation, calculate crack path using anisotropic propagation criterion, and reconstruct three-dimensional crack morphology and propagation velocity field using level set method; Step S5, Safety Assessment: The degree of blade damage is quantified by the damage factor accumulation model, the safety level is divided according to the threshold, and the three-dimensional crack cloud map, life prediction curve and key early warning grain information are output to realize the visualization of the full-process assessment results.

2. The data analysis-based safety assessment method for spherical toothed cutting blades according to claim 1, characterized in that, Step S1, data acquisition, specifically includes the following steps: Step S11, Surface morphology data acquisition: A white light interferometer is used to scan the blade surface with a grid density of 0.5mm×0.5mm, and the height distribution z(x,y), local radius of curvature R(x,y) and surface roughness Ra(x,y) of the three-dimensional morphology are acquired simultaneously. Step S12, Microstructure Data Acquisition: In the selected area, the microstructure of the grains is analyzed using an X-ray backscatter diffractometer, and the grain orientation matrix g, grain boundary ∑ value classification, and residual stress σ are acquired. res (x,y,z) distribution; Step S13, Dynamic load data acquisition: The dynamic load spectrum Fx / Fy / Fz during the cutting process is captured in real time by a triaxial piezoelectric force gauge at a high frequency of 20kHz.

3. The data analysis-based safety assessment method for spherical toothed cutting blades according to claim 1, characterized in that, Step S2, macro-micro coupling modeling, specifically includes the following steps: Step S21, Macroscopic Finite Element Modeling: A three-dimensional solid model of the cutting tool is established in ABAQUS software. C3D10M second-order tetrahedral elements are used for mesh generation, and the Johnson-Cook dynamic constitutive equation is loaded to simulate the macroscopic stress-strain response of the cutting tool during the cutting process. The equation is as follows: In the formula, α is the flow stress, A is the initial yield strength of the material, B is the strain hardening modulus, ε is the equivalent plastic strain, n is the strain hardening exponent, and C is the strain rate sensitivity coefficient. Given the current strain rate, For reference strain rate, T * Here, m is the normalized temperature, and m is the temperature softening index. Step S22, Microcrystalline Plasticity Modeling: A finite element sub-model of crystal plasticity is embedded in the tooth tip. The grain topology matching the actual EBSD data is reconstructed using the Voronoi algorithm. The critical decomposition shear stress threshold for each grain slip system is defined based on the slip system activation criterion. The slip system activation criterion is as follows: In the formula, τ α The decomposed shear stress of the α-th slip system, σ is the macroscopic stress tensor, μ α Let be the orientation tensor of the α-th slip system. Let α be the critical decomposition shear stress of the α-th slip system, where α is the slip system number.

4. The data analysis-based safety assessment method for spherical toothed cutting blades according to claim 1, characterized in that, Step S3, cross-scale data fusion, specifically includes the following steps: Step S31, Stress Field Matching: Based on the sub-model technique, the stress field σ calculated by the macroscopic finite element method is matched. macro Compared with the intrinsic stress Δσ measured by EBSD eigen Superposition correction is performed to transfer the stress field from the macroscopic to the microscopic scale. The superposition correction formula is as follows: s micro =s macro +Ds eigen ; In the formula, σ micro For microscale correction of stress, σ macro For macroscopic finite element calculation of stress, Δσ eigen This is the intrinsic stress correction based on EBSD; Step S32, Lattice Orientation Mapping: The local coordinate system of each grain is mapped to the global coordinate system through the grain orientation transformation matrix Q, quantifying the spatial orientation relationship between the slip system and the principal stress field. The transformation matrix is ​​Q: In the formula, φ is the rotation angle of the grain about the initial global coordinate system Z-axis, θ is the tilt angle of the grain about the new X′ axis after the first rotation, and ψ is the final rotation angle of the grain about the new Z″ axis after the second rotation. Step S33, Feature Parameter Extraction: Combining the Schmid factor The calculation and dynamic analysis of the normal to the slip plane within each grain are performed. The angle between the slip direction λ and the principal stress; In the formula, m α The Schmid factor for the α-th slip system. λ is the angle between the normal direction of the slip surface and the direction of the principal stress, and λ is the angle between the slip direction and the direction of the principal stress.

5. The data analysis-based safety assessment method for spherical toothed cutting blades according to claim 1, characterized in that, Step S4, dynamic crack prediction, specifically includes the following steps: Step S41, Crack Initiation Determination: Determine the crack initiation conditions according to the formula, which is: In the formula, N is the number of crack initiation cycles, N0 is the base number of cycles, and Δγ P γ is the equivalent plastic slip. f denoted as the critical slip at material fracture, and k as the fatigue damage index; Step S42, Crack propagation path calculation: Establish an anisotropic propagation criterion, introduce a lattice correction term, and dynamically track the crack propagation path along grain boundaries or transgranularly by calculating the crack propagation rate. The formula is: In the formula, Let C, m, and n be the crack propagation rate, and ΔK be the material constant according to Paris law. eff Let ΔK be the magnitude of the equivalent stress intensity factor. th ΔK is the stress intensity factor threshold. I ,ΔK II These are the amplitudes of the type I and type II stress intensity factors, respectively, and β is the lattice anisotropy coefficient; Step S43, 3D Crack Reconstruction: The level set method is used to dynamically track the geometric changes of the 3D crack front. The crack propagation velocity field is characterized by the level set function to handle the topological evolution of complex crack paths. The level set equation is: V n =0.5C(ΔK eff ) m ; In the formula, φ is the level set function. V is the rate of change of the level set function over time. n The normal propagation velocity at the crack front. Let C be the gradient magnitude of the level set function, C be the material-dependent fatigue crack propagation rate coefficient, and ΔK be the gradient magnitude of the level set function. eff denoted as the effective stress intensity factor amplitude, and m as the material fatigue crack propagation index.

6. The data analysis-based safety assessment method for spherical toothed cutting blades according to claim 1, characterized in that, Step S5, the security assessment, specifically includes the following steps: Step S51, Damage Factor Calculation: Calculate the service damage factor for each load segment based on the crack propagation rate integral. The formula is: In the formula, D is the cumulative damage factor, and t i t represents the service time or number of cycles for the i-th load segment. fi Let q be the failure time or cycle number corresponding to the i-th load segment, and q be the nonlinear damage accumulation exponent. This represents the crack propagation rate. Step S52, Security Level Classification: Based on the D value, three levels of early warning are defined, specifically as follows: Safe zone: D < 0.3; Warning zone: 0.3 < D < 0.7; Danger zone: D≥0.7; Step S53, Evaluation Report Generation: Integrate the crack path prediction results to generate a comprehensive evaluation report that includes a 3D crack propagation cloud map, real-time damage factor evolution curve, key early warning grain locations, and a weighted sum of remaining lifetime prediction values. The report uses the Unity3D engine to achieve dynamic interactive visualization of multi-dimensional data.

7. A data analysis-based safety assessment system for spherical toothed cutting blades, characterized in that, The evaluation system includes a data acquisition module, a data fusion and processing unit, a multi-scale modeling module, a dynamic prediction module, and an evaluation output module; The output of the data acquisition module is unidirectionally connected to the input of the data fusion processing unit. The output of the data fusion processing unit is unidirectionally connected to the input of the multi-scale modeling module. The output of the multi-scale modeling module is unidirectionally connected to the input of the dynamic prediction module. The output of the dynamic prediction module is unidirectionally connected to the input of the evaluation output module.

8. The data analysis-based safety assessment method for spherical toothed cutting blades according to claim 7, characterized in that, The data acquisition module is used to simultaneously acquire the surface morphology of the blade, micrograin orientation and cutting dynamic load through a white light interferometer, an EBSD probe and a triaxial force gauge, providing multi-dimensional raw data input for subsequent analysis; The data fusion processing unit is used to perform spatiotemporal registration and feature fusion on heterogeneous data. Through algorithms such as lattice orientation matrix transformation and stress field correction equation, macroscopic mechanical data are associated with microstructural features, and cross-scale feature parameters are extracted. The multi-scale modeling module is used to construct a macroscopic and microscopic coupled numerical model. It uses ABAQUS to establish a macroscopic finite element model of the blade, embeds a crystal plasticity sub-model based on the real grain structure in the key area, and realizes bidirectional stress field transmission through cross-scale interface, revealing the control mechanism of material anisotropy on crack behavior. The dynamic prediction module is used to calculate the crack initiation location, propagation path and three-dimensional morphology evolution in real time, and combined with the LSTM neural network to predict the influence of cutting load changes on crack dynamics, and output quantitative propagation rate and direction parameters. The evaluation output module is used to integrate the damage factor calculation model and the safety level threshold, generate a three-dimensional visualization report through the Unity3D engine, dynamically display the crack propagation process, remaining life prediction and early warning information, support process parameter optimization decisions, and realize real-time audible and visual alarm feedback for abnormal states.

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