A method, apparatus, and medium for additive manufacturing component machining risk prediction

Abstract

This invention provides a method, apparatus, and medium for predicting machining risks in additive manufacturing components. The method includes acquiring a three-dimensional geometric model and mechanical performance parameters of the component to be processed; performing geometric feature analysis to identify key risk areas; constructing a finite element model, using a refined mesh for key risk areas and a coarser mesh for non-key risk areas, and inputting the mechanical performance parameters into the finite element model; applying equivalent clamping forces to the finite element model according to the clamping method during processing, and applying equivalent cutting forces according to the processing area, while setting boundary conditions; simulating and solving the finite element model after applying the load to obtain equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed; and identifying areas with machining cracking risks from the equivalent stress distribution data and equivalent plastic strain distribution data based on preset risk judgment criteria. This invention can improve the accuracy of pre-prediction of machining cracking risks in weak areas.

Description

Technical Field

[0001] This invention belongs to the field of additive manufacturing technology, specifically relating to a method, device, and medium for predicting machining risks of additive manufacturing components. Background Technology

[0002] Selective laser melting (SLM) additive manufacturing technology achieves the integrated forming of complex structural parts by melting and solidifying metal powder layer by layer. However, the formed parts usually still need to undergo machining such as turning and milling to achieve the final dimensional accuracy and surface quality requirements.

[0003] In the process of machining and structural integrity assessment, there are risks and deficiencies in the prediction and prevention of structural damage such as micro-cracks or plastic deformation. Current technologies for quality control of SLM parts mainly employ methods such as forming process monitoring (e.g., detecting uneven powder distribution, identifying spheroidization spatter) and post-forming non-destructive testing (e.g., using industrial CT to screen internal porosity). Meanwhile, existing cutting simulation technologies primarily analyze the machining process itself, such as tool life and cutting temperature, through the optimization of machining process parameters.

[0004] However, the aforementioned existing technologies have significant shortcomings in application: because the existing simulation material data mainly uses traditional forging data, it does not consider the unique characteristics of SLM forming materials, such as anisotropy, high residual stress, and weak interlayer bonding, which makes it impossible to reflect the impact of the machining process on the structural integrity of the SLM parts themselves. When SLM parts with the above characteristics are machined, under the combined action of clamping force and cutting force, weak areas such as cantilever structures and thin-walled features are prone to local stress exceeding limits, thereby causing damage that leads to hidden quality risks. Existing technologies lack systematic means to predict such hidden damage. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device and medium for predicting the machining risk of additive manufacturing components, so as to improve the accuracy of predicting the risk of machining cracks in weak areas.

[0006] In a first aspect, the present invention provides a method for predicting machining risks of additively manufactured components, the method comprising the following steps: Obtain the three-dimensional geometric model of the component to be processed, as well as the mechanical property parameters of the material used in the component under SLM forming state; the mechanical property parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation and anisotropy coefficient, where the anisotropy coefficient is the ratio of the first yield strength perpendicular to the forming direction to the second yield strength parallel to the forming direction, and the mechanical property parameters are obtained by actual measurement of standard samples formed in the same batch of SLM forming. Perform geometric feature analysis on the three-dimensional geometric model to identify structural parts that conform to the preset geometric structure features as key risk parts; the preset geometric structure features include at least one of the following: cantilever structure, thin-walled structure with a wall thickness less than the preset value, single connection structure, geometrically discontinuous structure, and structure where the clamping and positioning surface is located. Construct a finite element model of the component to be processed, using a finer mesh in critical risk areas and a coarser mesh in non-critical risk areas, and input the mechanical performance parameters into the finite element model; Based on the clamping method during machining, an equivalent clamping force is applied in the finite element model, and an equivalent cutting force is applied according to the machining area, while setting boundary conditions. The finite element model after the load is applied is simulated and solved to obtain the equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed; Based on the preset risk assessment criteria, regions with machining-induced cracking risk are identified from equivalent stress distribution data and equivalent plastic strain distribution data; the risk assessment criteria are at least related to the yield strength or interlaminar bond strength of the material used in the component to be processed.

[0007] Optionally, in thin-walled structures with a wall thickness less than the preset value, the preset value is 3mm; Geometric discontinuities include at least one of sharp corners, grooves, steps, and variable cross-sections; The structure containing the clamping and positioning surface is the clamping position of the fixture and its adjacent area.

[0008] Optionally, the size of the finer mesh can range from 0.5 mm to 1.5 mm; The size range for coarser grids is 2mm to 5mm.

[0009] Optionally, an equivalent clamping force may be applied in the finite element model according to the clamping method during machining, including: When the clamping method is three-jaw chuck clamping, apply a radial pressure of 800N to 1200N; When clamping is done with a flat-jaw vise, apply a normal pressure of 1500N to 2500N; When the clamping method is plate clamping, each plate is subjected to a normal pressure of 500N to 1000N; When the clamping method is vacuum suction cup clamping, apply a uniform adsorption pressure of -0.08MPa to -0.06MPa.

[0010] Optionally, when milling is performed in the machining area, applying an equivalent cutting force based on the machining area includes: Apply the main cutting force, radial force, and axial force to the machining area; The formula for calculating the main cutting force is:

[0011]

[0012]

[0013] in, The main cutting force represents the cutting resistance generated in the main motion direction when the tool cuts the material of the structural component; Indicates radial force; Indicates axial force; This is the cutting force coefficient, representing the cutting force per unit area; The depth of cut represents the axial dimension into which the tool penetrates the material of the workpiece in a single cut. Feed per tooth represents the relative linear displacement of the tool in the feed direction for each tooth it rotates. The number of teeth on the cutting tool represents the total number of teeth on the milling cutter that actually participate in the cutting operation. The radial force proportionality coefficient has a value ranging from 0.3 to 0.5. It is the axial force proportionality coefficient with a value ranging from 0.2 to 0.4.

[0014] Optionally, when drilling is performed in the machining area, applying an equivalent cutting force based on the machining area includes: Apply drilling axial force and drilling torque to the machining area; The formula for calculating the axial force in drilling is: ; The formula for calculating drilling torque is: ; in, This is the axial force during drilling, representing the force applied along the central axis of the drill bit to resist the feed during the drilling process. The axial force coefficient represents a proportional constant of the axial resistance generated per unit area during drilling. The drill bit diameter represents the maximum outer diameter of the drill bit's cutting edge. Feed per revolution indicates the distance the drill bit penetrates axially into the material of the structural component in one complete revolution. The drilling torque is the rotational torque required to overcome the friction between the cutting edge of the drill bit and the metal and the chips. is the torque coefficient, representing a proportional constant that generates a resistance torque during drilling.

[0015] Optional, cutting force coefficient Axial force coefficient and torque coefficient The cutting force coefficient is determined based on the material being processed; specifically, when the material being processed is 316L stainless steel, the cutting force coefficient is... The value ranges from 2200 MPa to 2500 MPa; when the workpiece is an AlSi10Mg aluminum alloy, the cutting force coefficient is... The value ranges from 900 MPa to 1100 MPa; when the workpiece is Ti6Al4V titanium alloy, the cutting force coefficient is... The value ranges from 2200 MPa to 2600 MPa; when the workpiece is an Inconel 718 nickel-based alloy, the cutting force coefficient is... The value range is from 2800MPa to 3200MPa.

[0016] Optionally, based on preset risk assessment criteria, regions with machining-induced cracking risk are identified from equivalent stress distribution data and equivalent plastic strain distribution data, including regions that meet any of the following assessment conditions: The equivalent stress in this region is greater than or equal to the material's yield strength; The equivalent stress in this region is greater than or equal to 0.9 times the material yield strength; The equivalent plastic strain in this region is greater than zero; The maximum principal stress in this region is greater than or equal to the interlaminar bond strength of the material; the interlaminar bond strength is taken as 80% to 90% of the tensile strength parallel to the forming direction.

[0017] Secondly, the present invention provides a machining risk prediction device for additive manufacturing components, comprising: The acquisition module is used to acquire the three-dimensional geometric model of the component to be processed, as well as the mechanical property parameters of the material used for the component in the SLM forming state. The mechanical property parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation and anisotropy coefficient, where the anisotropy coefficient is the ratio of the first yield strength perpendicular to the forming direction to the second yield strength parallel to the forming direction. The mechanical property parameters are obtained by actual measurement of standard samples formed by SLM in the same batch. The feature analysis module is used to perform geometric feature analysis on the three-dimensional geometric model and identify structural parts that conform to the preset geometric structure features as key risk parts. The preset geometric structure features include at least one of the following: cantilever structure, thin-walled structure with a wall thickness less than the preset value, single connection structure, geometric discontinuity structure, and structure where the clamping and positioning surface is located. The model building module is used to build a finite element model of the component to be processed. It uses a refined mesh in critical risk areas and a coarser mesh in non-critical risk areas, and inputs the mechanical performance parameters into the finite element model. The load application module is used to apply equivalent clamping force in the finite element model according to the clamping method during machining, and to apply equivalent cutting force according to the machining area, while setting boundary conditions. The simulation solution module is used to simulate and solve the finite element model after applying load, and obtain the equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed; The risk identification module is used to identify areas with machining-induced cracking risk from equivalent stress distribution data and equivalent plastic strain distribution data based on preset risk judgment criteria; the risk judgment criteria are at least related to the yield strength or interlaminar bond strength of the material used in the component to be processed.

[0018] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0019] The present invention has at least the following beneficial effects: By acquiring the elastic modulus, yield strength, anisotropy coefficient, and other mechanical property parameters obtained from the measured standard SLM forming samples of the same batch, a simulation database consistent with the actual material state of SLM parts was established, overcoming the shortcomings of traditional methods that use forging data, which lead to excessive stress prediction deviations. Based on this, geometric feature analysis was performed on the 3D model of the components, automatically identifying weak points with abrupt stiffness changes, such as cantilever structures, thin-walled structures, single-connection parts, geometrically discontinuous regions, and structures near the clamping and positioning surfaces. During finite element modeling, mesh refinement was performed on these key risk areas, thereby accurately capturing stress concentration peaks while controlling the computational scale. The simulation fully applied the equivalent clamping force corresponding to the actual clamping method of the machining center, as well as the milling or drilling equivalent cutting force calculated based on cutting parameters and material properties, realistically reproducing the multi-source loads borne by the parts during machining. Multi-level risk assessment criteria were established by combining the yield strength and interlaminar bond strength of the SLM material, identifying areas with cracking risk and their risk levels from the equivalent stress distribution and equivalent plastic strain distribution. This method enables the quantitative prediction and location of hidden damage risks before cutting operations. It not only effectively solves the problem of difficulty in predicting hidden damage to complex components under the combined action of cutting and clamping in existing technologies, but also improves the accuracy of identifying the risk of machining cracks in weak areas. Furthermore, it moves the quality control checkpoint forward, significantly reducing scrap rate and trial-and-error costs. Attached Figure Description

[0020] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0021] Figure 1 This is a flowchart of a method for predicting machining risks of additive manufacturing components in one embodiment of this application; Figure 2This is a structural diagram of the additive manufacturing component machining risk prediction device according to one embodiment of this application; Figure 3 This is a structural diagram of a terminal device in one embodiment of this application. Detailed Implementation

[0022] The technical solution of the present invention will now be described in detail and completely with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be noted that the terms "upper", "lower", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] In existing technologies, quality control of SLM-formed parts focuses on monitoring the forming process and non-destructive testing after forming, without addressing the mechanical effects of the machining process. Furthermore, cutting simulations using forging data cannot reflect the anisotropy, residual stress, and weak interlayer bonding characteristics of SLM materials, making it impossible to predict the machining-induced cracking risk in structurally weak areas. This invention provides a method, apparatus, and medium for predicting machining risks in additive manufacturing components, which will be described in detail below with reference to specific embodiments.

[0025] Example 1 This embodiment uses a 316L stainless steel flange structure for aviation applications as an example. This part is integrally formed using SLM technology, with the flange and body connected via a neck, and the flange root having a right-angle transition. After forming, the flange end face needs to be milled, and M6 threaded holes (tapping) need to be machined on the flange. Before this, the additive manufacturing component machining risk prediction method provided by this invention is used for risk prediction and control.

[0026] like Figure 1 As shown, the additive manufacturing component machining risk prediction method provided by the present invention includes steps 11 to 16.

[0027] Step 11: Obtain the three-dimensional geometric model of the component to be processed, as well as the mechanical property parameters of the material used in the component under SLM forming state.

[0028] In this embodiment of the invention, the mechanical property parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation, and anisotropy coefficient, wherein the anisotropy coefficient is the ratio of the first yield strength perpendicular to the forming direction to the second yield strength parallel to the forming direction, and the mechanical property parameters are obtained by actual measurement of standard samples formed in the same batch of SLM.

[0029] In one feasible implementation, a three-dimensional geometric model of the 316L stainless steel flange structure for aviation can be obtained from the design or engineering end. The file format can be STL, STEP, or IGES, which contains accurate geometric topology information, for subsequent import into finite element simulation software.

[0030] To accurately obtain the actual parameters, while producing the flange structure using SLM forming, standard tensile specimens were prepared according to GB / T 228.1-2021 "Metallic materials, tensile testing—Part 1: Tests at room temperature" using the same batch of powder and the same set of process parameters. The test directions were strictly divided into two groups: parallel to the forming direction (XY direction) and perpendicular to the forming direction (Z direction). Five specimens were tested in each group, and the arithmetic mean was used as the final material property parameters. Through actual testing of the same batch of specimens, the mechanical property parameters of the 316L stainless steel material in the SLM forming state were obtained. The measured basic data are: elastic modulus 193 GPa, Poisson's ratio 0.3, tensile strength 480 MPa, and elongation of the material.

[0031] Due to the layer-by-layer melting and solidification process, the mechanical properties of the material differ between the directions perpendicular and parallel to the forming direction. Actual measurements show that the second yield strength parallel to the forming direction is 170 MPa, while the first yield strength perpendicular to the forming direction (interlayer direction) is relatively weak, conservatively estimated at 155 MPa. Based on this, the anisotropy coefficient (perpendicular yield strength / parallel yield strength) of this batch of material is calculated to be approximately 0.91. This detailed data containing anisotropic characteristics will be input into the finite element model, assigning corresponding material properties to different directions.

[0032] Step 12: Perform geometric feature analysis on the three-dimensional geometric model to identify structural parts that conform to the preset geometric structure features, as key risk parts.

[0033] In this embodiment of the invention, the preset geometric structure features include at least one of the following: cantilever structure, thin-walled structure with a wall thickness less than a preset value, single connection structure, geometrically discontinuous structure, and structure in which the clamping and positioning surface is located.

[0034] In one feasible implementation, after obtaining the three-dimensional geometric model of the component to be processed, a comprehensive geometric topology analysis is performed on the model using feature recognition algorithms of three-dimensional modeling software (such as SolidWorks, UG, CATIA, etc.) combined with manual assistance, in order to extract and mark key risk areas that are prone to damage during subsequent machining.

[0035] Specifically, the identification steps for cantilever structures are as follows: Investigate parts with one end fixed and the other end suspended. This is because during processing, once the cantilever end is subjected to force, its root connection will generate extremely high bending stress.

[0036] The identification steps for thin-walled structures with a wall thickness less than a preset value are as follows: screen the support transition areas in the structure with a wall thickness of less than 3mm. Due to their thin walls, these areas have extremely poor structural rigidity and are very prone to deformation or cracking during processing.

[0037] The identification steps for a single-connection structure are as follows: Locate areas such as flanges or stiffening plates that are connected to the main body via only one connection point, where the cross-section changes abruptly at the connection. These areas often exhibit significant stress concentration. For example, a flange connected to the main body solely by a neck section is a typical single-connection location.

[0038] The steps for identifying geometrically discontinuous structures are as follows: extract areas in the model that are prone to stress concentration, such as sharp corners, grooves, steps, and variable cross-sections. For example, the identified flange neck connection has a right-angle transition without rounded corners for a smooth transition, constituting a serious geometrically discontinuous area.

[0039] The identification steps for the structure containing the clamping positioning surface are as follows: predict and mark the clamping position of the fixture and its adjacent area. This is because during clamping, the clamping force will act directly on this area, resulting in extremely high local stress.

[0040] Step 13: Construct a finite element model of the component to be processed, using a finer mesh in critical risk areas and a coarser mesh in non-critical risk areas, and input the mechanical performance parameters into the finite element model.

[0041] In this embodiment of the invention, SolidWorks software (or other commonly used finite element simulation software in the field, such as ANSYS, Abaqus, etc.) is used for modeling and analysis. A finite element model of the flange structure is established in SolidWorks to prepare a digital foundation for subsequent load application and solution.

[0042] A differentiated mesh density configuration scheme was adopted when generating the finite element mesh. Specifically, a refined mesh was used for the clearly identified critical risk area, the "flange root," to ensure sufficient mesh density in this critical region. The size range of the refined mesh was strictly controlled between 0.5 mm and 1.5 mm. For non-critical risk areas such as the flange body, a coarser mesh was used to save computational resources, with the size range of the coarser mesh set between 2 mm and 5 mm.

[0043] Accurately input the material property data obtained from the standard specimen measurements in step 11 into the finite element simulation software. Given the significant anisotropy of SLM materials, different mechanical properties need to be set in the software's material property definition interface for those parallel to the forming direction (XY direction) and perpendicular to the forming direction (Z direction). For the 316L flange model in this embodiment, input basic constants such as the elastic modulus of 193 GPa and Poisson's ratio of 0.3. Simultaneously, in the direction most critical for determining structural cracking risk (the direction perpendicular to the interlaminar layer), set the yield strength parameter and input a value of 155 MPa.

[0044] Step 14: Apply equivalent clamping force to the finite element model according to the clamping method during machining, apply equivalent cutting force according to the machining area, and set boundary conditions.

[0045] After completing the finite element mesh generation and material property input, in order to realistically reproduce the stress state of the machining (machining center) site in the digital space, in this embodiment of the invention, the machining clamping method and actual cutting process parameters are transformed into equivalent loads and boundary constraints in the finite element model.

[0046] The specific implementation steps for this 316L stainless steel flange structural component for aviation applications are as follows: First, specify the fixed position of the part during machining to the software. Based on the actual clamping conditions, apply a complete fixed constraint to the clamping points of the flange body (e.g., the bottom of the step or the clamping reference surface), restricting its translation and rotation in six degrees of freedom (X, Y, Z). If auxiliary support blocks are provided on the flange end face or bottom during actual machining, apply normal constraints to the corresponding auxiliary support points, restricting only its vertical displacement.

[0047] Clamping force is the enormous static pressure exerted by the fixture on the workpiece during machining. Based on the actual clamping scheme of the flange on the machining center, an equivalent force is applied to the corresponding surface of the three-dimensional model: if a flat-jaw vise is used to clamp the main body, a normal pressure is applied to the contact surface of the jaws, with a value set to 1500N to 2500N.

[0048] In other feasible implementations, the equivalent clamping force varies depending on the type of clamp used. For example, when using a flat-jaw vise, a normal pressure of 1500 N to 2500 N is applied. When using a pressure plate clamp, a normal pressure of 500 N to 1000 N is applied to each pressure plate. When using a vacuum suction cup clamp, a uniform adsorption pressure of -0.08 MPa to -0.06 MPa is applied.

[0049] It should be noted that cutting force is the dynamic resistance applied to the part by the tool when removing material. For the two processes "milling the flange end face" and "machining the M6 ​​threaded hole" in this embodiment of the invention, corresponding equivalent loads are applied respectively: For milling the flange end face, specifically, a three-dimensional spatial force consisting of the main cutting force, radial force, and axial force is applied in the milling area. The formula for calculating the main cutting force is:

[0050]

[0051]

[0052] in, The main cutting force represents the cutting resistance generated in the main motion direction when the tool cuts the material of the structural component (for 316L stainless steel workpiece, the cutting force coefficient). The value range is from 2200MPa to 2500MPa. This represents the radial force (with a value of 0.3). Up to 0.5 ); This represents the axial force (with a value of 0.3). Up to 0.5 ); The depth of cut represents the axial dimension into which the tool penetrates the material of the workpiece in a single cut. Feed per tooth represents the relative linear displacement of the tool in the feed direction for each tooth it rotates. The number of teeth on the cutting tool represents the total number of teeth on the milling cutter that actually participate in the cutting operation. The radial force proportionality coefficient has a value ranging from 0.3 to 0.5. It is the axial force proportionality coefficient with a value ranging from 0.2 to 0.4.

[0053] It should be noted that in actual implementation, if the end face milling allowance is extremely small and the cutting force in this area is relatively small after evaluation, it can be ignored and simplified in the model.

[0054] In another feasible implementation, to ensure high fidelity of the simulation, the corresponding cutting force parameters must be precisely matched according to the actual material type of the component to be processed. Specifically, when the material to be processed is Alsi10Mg aluminum alloy, this material is relatively soft and easy to cut, and the cutting force coefficient... The value range is set to 900MPa to 1100MPa. When the workpiece is Ti6Al4V titanium alloy, due to its poor thermal conductivity and high chemical reactivity, it is prone to tool sticking, resulting in high cutting resistance and a high cutting force coefficient. The value range is set to 2200MPa to 2600MPa. When the workpiece is Inconel 718 nickel-based alloy, it is a typical high-temperature, difficult-to-machine material with extremely high strength and a cutting force coefficient. The value range is set to 2800MPa to 3200MPa. Based on the same principle, the axial force coefficient... and torque coefficient It was also matched based on the experimental database of 316L material.

[0055] Machining (drilling and tapping) of M6 threaded holes on flanges. Specifically, axial drilling force and torque are applied to the inner wall and bottom area of ​​the hole. The theoretical formula for axial drilling force is: The formula for calculating drilling torque is: .in, This is the axial force during drilling, representing the force applied along the central axis of the drill bit to resist the feed during the drilling process. The axial force coefficient represents a proportional constant of the axial resistance generated per unit area during drilling. The drill bit diameter represents the maximum outer diameter of the drill bit's cutting edge. Feed per revolution indicates the distance the drill bit penetrates axially into the material of the structural component in one complete revolution. The drilling torque is the rotational torque required to overcome the friction between the cutting edge of the drill bit and the metal and the chips. is the torque coefficient, representing a proportional constant that generates a resistance torque during drilling.

[0056] In this specific practical example, according to the mechanical design manual, tapping an M6 thread generates a basic axial force of approximately 300N. A safety factor of 1.07 is used to cover the uncertainties in actual machining. Finally, an equivalent axial load of 320N is directly applied to the corresponding threaded hole area to be machined in the simulation model.

[0057] Step 15: Simulate and solve the finite element model after applying the load to obtain the equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed.

[0058] After completing mesh generation, material property assignment, boundary condition constraints, and application of equivalent cutting and clamping forces in the finite element model, this implementation method will start the numerical calculation engine to solve the component under virtual machining conditions, so as to transform the physical load into visualized mechanical response data inside the component.

[0059] Specifically, in finite element simulation software (such as SolidWorks Simulation), the statics analysis module is selected, and the solver parameters are set according to the nonlinear characteristics of the clamping and cutting process (if necessary, large displacement or nonlinear material options are enabled). Subsequently, the finite element model containing the stress states of the milled end face and the drilling and tapping of the M6 ​​thread is submitted for background calculation.

[0060] After the calculation, the system automatically extracts the equivalent stress (Von Mises stress) distribution data of the entire component to be processed and generates an intuitive equivalent stress distribution cloud map. In specific implementation, the key risk area of ​​"flange root" identified in step 12 is the focus. In the simulation results of this example, the generated distribution data successfully captured that under the combined action of clamping force and 320N tapping axial force, a significant stress concentration phenomenon occurred at a certain circumferential position of the flange root, with its equivalent stress peak reaching 167MPa.

[0061] In addition to stress data, the system simultaneously extracts equivalent plastic strain distribution data across the entire structural component, generating an equivalent plastic strain distribution map. This data is used to observe whether irreversible plastic deformation (i.e., whether the strain value is greater than zero) occurs in the material under processing loads. This is of crucial guiding significance for assessing extremely weak localized tears or permanent microscopic damage.

[0062] It should be noted that traditional machining process planning (such as using CAM programming software) only focuses on whether geometric interference (tool collision), overcutting, or undercutting occurs in the tool path; its essence is "geometric subtractive simulation." It completely ignores the enormous mechanical stress that the remaining structure endures under the dynamic action of clamping and cutting forces as material is removed. The additive manufacturing component machining risk prediction method provided by this invention breaks down the industry barriers between traditional manufacturing process planning and structural mechanics analysis. It does not simply run a conventional static calculation, but freezes the complex, dynamic "machining process" into the most extreme mechanical transient. By extracting the two key underlying data—equivalent stress and equivalent plastic strain—it can clearly and quantitatively show how the stress field at the flange root abruptly surges under the action of the milling cutter and drill bit, thus completely changing the previous blind trial-and-error mode of "discovering problems only after pressure testing following machining."

[0063] Step 16: Based on the preset risk assessment criteria, identify areas with machining-induced cracking risk from the equivalent stress distribution data and equivalent plastic strain distribution data.

[0064] In this embodiment of the invention, the risk assessment criteria are at least related to the yield strength or interlaminar bond strength of the material used in the component to be processed.

[0065] In one feasible implementation, the system pre-sets multi-dimensional risk assessment criteria. For any mesh region in the simulation results, it is identified as having a risk of machining-induced cracking if any of the following criteria are met: Condition 1: If the equivalent stress in a certain region is greater than or equal to the material's yield strength, then that region is considered to have a risk of machining-induced cracking. This is because the yield strength is the critical value at which a material transitions from elastic deformation to plastic deformation; exceeding this value will cause permanent deformation of the material, making it highly susceptible to microcracks.

[0066] Condition 2 states that if the equivalent stress in a certain region is greater than or equal to 0.9 times the material's yield strength, then that region is considered to have a potential risk of machining-induced cracking. This provides a safety margin buffer.

[0067] Condition 3: If the equivalent plastic strain in a certain region is greater than zero, then the region is determined to have a risk of machining-induced cracking.

[0068] Condition 4: If the maximum principal stress in a certain region is greater than or equal to the interlaminar bond strength, then that region is considered to have a risk of machining-induced cracking. For the 316L material in this example, the interlaminar bond strength is taken as 80% to 90% of the tensile strength parallel to the forming direction (480 MPa in this example).

[0069] In practice, the flange simulation data obtained in step 15 is automatically substituted into the aforementioned judgment criteria for point-by-point comparison. For example, the comparison reveals that at a certain circumferential position at the flange root, due to the "single connection structure" and "right-angle transition," the equivalent stress peak reaches 167 MPa under the superposition of the 320N tapping axial force and clamping force. Comparing this 167 MPa stress peak with the preset 316L (vertical direction) yield strength of 155 MPa, it is found that the local stress exceeds the material yield strength by 7.7%, satisfying "Condition 1." Therefore, the system clearly determines that there is a high risk of machining-induced cracking in the flange root area.

[0070] After the comparison is completed, the system automatically outputs the spatial coordinates of the risk area, the risk level (based on the proportion exceeding the yield strength), and the specific stress value, and accurately marks the hidden risk area in the three-dimensional stress cloud map with a bright color (such as red warning color).

[0071] It is worth mentioning that in traditional machining simulations, the evaluation of machining quality usually only focuses on superficial aspects such as "whether the cutting temperature is too high" and "whether the tool is worn." Even when there is some analysis involving structural stress, it is mostly a simple comparison of tensile strength or the use of the uniaxial yield criterion of conventional forgings. However, the machining risk prediction method for additive manufacturing components provided by this invention addresses the two major pain points of SLM additive manufacturing: "numerous microscopic defects" and "significant anisotropy," and pioneers a set of composite failure criteria. It not only introduces "0.9 times the yield strength" as a safety margin warning under dynamic impact, but also proposes a unique criterion of "interlaminar bond strength (80%-90% of tensile strength)." This is because in actual machining, cutting forces often tear SLM parts along the weak interlaminar surfaces, and this microscopic peeling is completely imperceptible to the naked eye. Through this set of pre-set judgment criteria that are rigorous and closely aligned with the physical characteristics of 3D printing, this method completely transforms post-remediation into pre-warning.

[0072] In one feasible implementation, based on the risk assessment results, one or more of the following prevention and control measures may be taken: (1) Process optimization. Reduce the depth of cut from 0.5mm to 0.3mm. Reduce the feed rate from 0.12mm / tooth to 0.08mm / tooth. Optimize the toolpath by using climb milling to avoid conventional milling. Use high-speed cutting to reduce cutting force. Increase the number of cuts from a single machining operation to multiple machining operations. For drilling, use a pecking drill method with multiple feeds.

[0073] (2) Structural optimization. Increase the transition fillet radius from R1mm to R3mm. Add reinforcing ribs with a thickness of 2mm to 3mm and a height of 5mm to 10mm. Add temporary process supports below weak areas, which will be removed after processing. Increase the wall thickness from 2mm to 3mm.

[0074] (3) Sequence optimization. Stress relief is achieved through heat treatment before machining, specifically vacuum annealing: vacuum level: -3Pa, heating temperature 1050℃, heating rate 300℃ / H; holding time 2 hours; furnace cooling to 200℃, followed by argon gas filling and rapid cooling to 50℃ before removal from the furnace, with an inlet pressure of 2 bar and an inlet flow rate of 10 L / min. Rough machining is performed first, followed by finish machining, leaving a rough machining allowance of 0.2 mm to 0.3 mm. Non-critical areas are machined first, followed by critical areas.

[0075] Selection principle: Prioritize measures with minimal changes and low cost. If process optimization can solve the problem, there is no need to modify the structure; if the structure itself has deficiencies, then consider optimizing the structure.

[0076] The optimized structure or process is verified by secondary simulation. The verification criteria are: the equivalent stress in the optimized risk area is less than the material yield strength, the equivalent plastic strain in the optimized risk area is equal to zero, and the safety margin is controlled so that the equivalent stress is not greater than 0.85 times the material yield strength.

[0077] Perform SLM molding and machining according to the optimized plan, and conduct pressure testing or non-destructive testing on the finished products. Produce 2 to 3 products for verification, and confirm the effectiveness of the plan after all products pass the verification.

[0078] If the simulation results after optimization still do not meet the safety standards, return to selecting other optimization measures or combinations of optimization until the requirements are met.

[0079] In one embodiment of the present invention, it was verified that the products remanufactured according to the optimized scheme were subjected to a water pressure test after processing at the machining center. The test pressure was 1.5 times the working pressure and the pressure was maintained for 30 minutes. All products passed the test without leakage.

[0080] Example 2 This embodiment, based on Embodiment 1, abandons the conventional linear elasticity or simple ideal elastoplasticity assumptions and introduces a constitutive model based on anisotropic tensor damage evolution and thermo-mechanical coupling to predict the risk of machining-induced cracking. This embodiment uses a complex thin-walled casing of a high-strength titanium alloy (Ti6Al4V) for aerospace applications as the implementation object. This component is formed using SLM technology and requires high-speed five-axis milling after forming.

[0081] Traditional simulations only input a single yield strength scalar. In this embodiment, an anisotropic constitutive equation incorporating continuous damage mechanics (CDM) is embedded in the finite element software through a user-defined material subroutine (UMAT / VUMAT).

[0082] Specifically, considering the non-uniform distribution of micropores and microcracks within the SLM material, a second-order damage tensor is introduced. The actual effective stress tensor it bears Expressed as: .in, This represents a fourth-order unit tensor. This represents the nominal stress tensor.

[0083] Subsequently, a modified Hill 48 yield criterion was introduced to transform the measured anisotropy coefficients (i.e., the ratio of yield strength perpendicular to the forming direction to yield strength parallel to the forming direction in Example 1) into a dimensionless anisotropy coefficient matrix. Yield function Defined as:

[0084] in, To account for the current yield stress due to work hardening effect, This is the equivalent isotropic hardening parameter.

[0085] High-speed machining generates not only mechanical stress but also extremely high cutting heat, and the thermal softening effect drastically alters the local stiffness of thin-walled regions. In this embodiment of the invention, within a defined milling area, the local cutting resistance is calculated based on a modified Johnson-Cook constitutive model, and then converted into the equivalent principal cutting force input into the finite element model. Flow stress of materials The formula is:

[0086] in, Indicates the reference strain rate. Indicates room temperature. Indicates the melting point of the material. Indicates the thermal softening coefficient. Indicates the strain hardening index. Represents equivalent plastic strain. Indicates strain rate. This indicates the transient cutting temperature.

[0087] Cutting heat flow boundary conditions, converted from friction and plastic work, are simultaneously applied in the cutting contact area of ​​the finite element model to simulate a thermo-mechanical coupling environment. Depending on the clamping method, an equivalent clamping force (such as a pressure plate normal pressure of 500N to 1000N) is applied to the constrained parts.

[0088] During the simulation process, the system not only outputs equivalent stress and equivalent plastic strain, but more importantly, it calculates the damage tensor. The dynamic evolution of the stress. Considering the unique property of SLM parts where the interlayer bond strength is 80% to 90% of the tensile strength parallel to the forming direction, this implementation strongly couples the Z-axis (forming direction) stress triaxiality into the damage evolution equation. :

[0089] in, Represents the damage accumulation rate tensor; Indicates the equivalent effective stress; , , It represents the material-specific damage dissipation constant. This represents the principal tensile stress in the normal direction (i.e., the direction perpendicular to the SLM interlayer interface). Indicates the interlayer weakening amplification factor ( (This is a fitting constant). When there is a very large clamping tension or cutting bending moment in the Z direction, this factor is amplified exponentially, accelerating the accumulation of interlayer damage. It represents the equivalent plastic strain rate. This represents the damage evolution direction tensor.

[0090] After the finite element method is solved, the system extracts the cumulative damage tensor of each mesh node. The risk assessment criteria have been upgraded from macroscopic scalar assessment to microscopic damage assessment. Extract the principal components of the damage tensor in the forming direction (Z direction). The following conditions are considered as indicating a risk of machining-induced cracking: .in, This indicates the critical damage threshold of this batch of SLM materials (usually) (This can be deduced from the plastic strain at fracture in a standard tensile test). If the principal components Mark the spatial coordinates Interlayer delamination and cracking will occur at this location.

[0091] Unlike Example 1, which uses the classic macroscopic phenomenological criterion of "equivalent stress ≥ yield strength", this example creatively lowers the prediction dimension to the level of continuous damage mechanics (CDM), through partial differential evolution equations. Describes the real process of microcrack initiation and propagation under cutting force.

[0092] Furthermore, Example 1 only performed a final comparison between Z-direction stress and interlaminar bond strength in the determination criteria. The method in this example constructs... This is a nonlinear amplification factor specifically for the SLM forming direction (Z direction). Under the same cutting force, the model automatically calculates that the damage rate in the Z direction is much greater than that in the XY direction, which can further improve the accuracy of machining crack risk prediction.

[0093] Example 3 like Figure 2 As shown, the present invention also provides a machining risk prediction device for additive manufacturing components, the device 200 comprising: The acquisition module 201 is used to acquire the three-dimensional geometric model of the component to be processed, as well as the mechanical property parameters of the material used in the component to be processed in the SLM forming state. The mechanical property parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation and anisotropy coefficient, wherein the anisotropy coefficient is the ratio of the first yield strength perpendicular to the forming direction to the second yield strength parallel to the forming direction. The mechanical property parameters are obtained by actual measurement of standard samples formed by SLM in the same batch. The feature analysis module 202 is used to perform geometric feature analysis on the three-dimensional geometric model and identify structural parts that conform to the preset geometric structure features as key risk parts. The preset geometric structure features include at least one of the following: cantilever structure, thin-walled structure with a wall thickness less than the preset value, single connection structure, geometric discontinuity structure, and structure where the clamping and positioning surface is located. The model building module 203 is used to build a finite element model of the component to be processed. A fine mesh is used in the critical risk areas, and a coarser mesh is used in the non-critical risk areas. The mechanical performance parameters are then input into the finite element model. The load application module 204 is used to apply an equivalent clamping force in the finite element model according to the clamping method during machining, and to apply an equivalent cutting force according to the machining area, while setting boundary conditions. The simulation solution module 205 is used to simulate and solve the finite element model after the load is applied, and to obtain the equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed. The risk identification module 206 is used to identify areas with machining crack risk from equivalent stress distribution data and equivalent plastic strain distribution data based on preset risk judgment criteria; the risk judgment criteria are at least related to the yield strength or interlaminar bond strength of the material used in the component to be processed.

[0094] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. Their specific functions and technical effects can be found in the method embodiments section, and will not be repeated here. Those skilled in the art will understand that, for the sake of convenience and brevity, the division of the above-mentioned functional units and modules is only used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] like Figure 3 As shown, embodiments of the present invention provide a terminal device, such as... Figure 3 As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 3The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0096] Specifically, when the processor D100 executes the computer program D102, it acquires the three-dimensional geometric model of the component to be processed, as well as the mechanical property parameters of the material used in the SLM forming state; it performs geometric feature analysis on the three-dimensional geometric model, identifies structural parts that conform to preset geometric structural features as key risk parts; it constructs a finite element model of the component to be processed, using a refined mesh in key risk parts and a coarser mesh in non-key risk areas, and inputs the mechanical property parameters into the finite element model; it applies equivalent clamping force to the finite element model according to the clamping method during processing, and applies equivalent cutting force according to the processing area, while setting boundary conditions; it performs simulation and solution on the finite element model after applying the load, and obtains the equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed; based on the preset risk judgment criteria, it identifies areas with machining crack risk from the equivalent stress distribution data and equivalent plastic strain distribution data. By acquiring the measured mechanical properties such as elastic modulus, yield strength, and anisotropy coefficient of standard SLM forming samples from the same batch, a simulation database consistent with the actual material state of SLM parts was established, overcoming the shortcomings of traditional methods that use forging data, which lead to excessive stress prediction deviations. Based on this, geometric feature analysis was performed on the 3D model of the components, automatically identifying weak points with abrupt stiffness changes, such as cantilever structures, thin-walled structures, single-connection parts, geometrically discontinuous regions, and structures near the clamping and positioning surfaces. During finite element modeling, mesh refinement was performed on these key risk areas, thereby accurately capturing stress concentration peaks while controlling the computational scale. The simulation fully applied the equivalent clamping force corresponding to the actual clamping method of the machining center, as well as the milling or drilling equivalent cutting force calculated based on cutting parameters and material properties, realistically reproducing the multi-source loads borne by the parts during machining. Multi-level risk assessment criteria were established by combining the yield strength and interlaminar bond strength of the SLM material, identifying areas with cracking risk and their risk levels from the equivalent stress distribution and equivalent plastic strain distribution. This method enables the quantitative prediction and location of hidden damage risks before cutting operations. It not only effectively solves the problem of difficulty in predicting hidden damage to complex components under the combined action of cutting and clamping in existing technologies, but also improves the accuracy of identifying the risk of machining cracks in weak areas. Furthermore, it moves the quality control checkpoint forward, significantly reducing scrap rate and trial-and-error costs.

[0097] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0098] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0099] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0100] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0101] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0102] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A method of additive manufacturing component machining risk prediction, characterized by, include: A three-dimensional geometric model of the component to be processed is obtained, as well as the mechanical property parameters of the material used in the component under SLM forming condition. The mechanical property parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation and anisotropy coefficient, wherein the anisotropy coefficient is the ratio of the first yield strength perpendicular to the forming direction to the second yield strength parallel to the forming direction. The mechanical property parameters are obtained by actual measurement of standard samples formed by SLM in the same batch. Geometric feature analysis is performed on the three-dimensional geometric model to identify structural parts that conform to the preset geometric structure features, which are then identified as key risk areas. The preset geometric structure features include at least one of the following: cantilever structure, thin-walled structure with a wall thickness less than the preset value, single connection structure, geometrically discontinuous structure, and structure where the clamping and positioning surface is located. A finite element model of the component to be processed is constructed, with a refined mesh used in the critical risk areas and a coarser mesh used in the non-critical risk areas, and the mechanical performance parameters are input into the finite element model. An equivalent clamping force is applied to the finite element model according to the clamping method during machining, and an equivalent cutting force is applied according to the machining area, while setting boundary conditions. The finite element model after the load is applied is simulated and solved to obtain the equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed; Based on the preset risk assessment criteria, regions with machining-induced cracking risk are identified from the equivalent stress distribution data and the equivalent plastic strain distribution data; The risk assessment criteria are at least related to the yield strength or interlaminar bond strength of the material used in the component to be processed.

2. The method of claim 1, wherein, In the thin-walled structure where the wall thickness is less than a preset value, the preset value is 3mm; The geometric discontinuity includes at least one of sharp corners, grooves, steps, and variable cross-sections; The structure containing the clamping and positioning surface is the clamping position of the fixture and its adjacent area.

3. The method of claim 2, wherein, The size of the refined mesh ranges from 0.5 mm to 1.5 mm; The size range of the coarser mesh is 2mm to 5mm.

4. The method of claim 3, wherein, Applying an equivalent clamping force to the finite element model according to the clamping method during processing includes: When the clamping method is three-jaw chuck clamping, apply a radial pressure of 800N to 1200N; When the clamping method is a flat-jaw vise clamping, a normal pressure of 1500N to 2500N is applied; When the clamping method is plate clamping, each plate is subjected to a normal pressure of 500N to 1000N; When the clamping method is vacuum suction cup clamping, a uniform adsorption pressure of -0.08MPa to -0.06MPa is applied.

5. The method of claim 4, wherein, When milling is performed on the machining area, applying an equivalent cutting force according to the machining area includes: Apply the main cutting force, radial force, and axial force to the machining area; The formula for calculating the main cutting force is: wherein, is the main cutting force, representing the cutting resistance generated by the tool in the main motion direction when cutting the material of the workpiece; is the radial force; is the axial force; is the cutting force coefficient, representing the cutting force per unit area; is the cutting depth, representing the axial dimension of the tool single cutting into the material of the workpiece; is the feed per tooth, representing the relative linear displacement of the tool in the feed direction per rotation of a tooth of the tool; is the number of teeth of the tool, representing the total number of teeth of the milling cutter actually participating in the cutting operation; is the radial force proportionality coefficient, whose value ranges between 0.3 and 0.5; is the axial force proportionality coefficient, whose value ranges between 0.2 and 0.

4.

6. The method for predicting machining risks of additive manufacturing components according to claim 4, characterized in that, When drilling is performed in the machining area, applying an equivalent cutting force according to the machining area includes: Apply drilling axial force and drilling torque to the machining area; The formula for calculating the axial force in the borehole is: ; The formula for calculating the drilling torque is: ; in, The axial force in the borehole represents the force resisting the feed applied along the central axis of the drill bit during the drilling process. The axial force coefficient represents a proportional constant of the axial resistance generated per unit area during drilling. The drill bit diameter represents the maximum outer diameter of the drill bit's cutting edge. Feed per revolution indicates the distance the drill bit penetrates axially into the material of the structural component in one complete revolution. The drilling torque is expressed as the rotational torque required to overcome the friction of the drill bit's cutting edge cutting metal and chip removal. is the torque coefficient, representing a proportional constant that generates a resistance torque during drilling.

7. The method for predicting machining risks of additive manufacturing components according to claim 5 or 6, characterized in that, Cutting force coefficient Axial force coefficient and torque coefficient Determined based on the material being processed; among which, When the workpiece is 316L stainless steel, the cutting force coefficient The value range is from 2200MPa to 2500MPa; When the workpiece is an AlSi10Mg aluminum alloy, the cutting force coefficient The value range is from 900MPa to 1100MPa; When the workpiece is a Ti6Al4V titanium alloy, the cutting force coefficient The value range is from 2200MPa to 2600MPa; When the workpiece is an Inconel 718 nickel-based alloy, the cutting force coefficient The value range is from 2800MPa to 3200MPa.

8. The method for predicting machining risks of additive manufacturing components according to claim 1, characterized in that, Based on the preset risk assessment criteria, regions with machining-induced cracking risk are identified from the equivalent stress distribution data and the equivalent plastic strain distribution data, including regions that meet any of the following assessment conditions: The equivalent stress in this region is greater than or equal to the material's yield strength; The equivalent stress in this region is greater than or equal to 0.9 times the material yield strength; The equivalent plastic strain in this region is greater than zero; The maximum principal stress in this region is greater than or equal to the interlayer bond strength of the material. The interlayer bonding strength is taken as 80% to 90% of the tensile strength parallel to the forming direction.

9. A machining risk prediction device for additive manufacturing components, characterized in that, include: The acquisition module is used to acquire the three-dimensional geometric model of the component to be processed, as well as the mechanical property parameters of the material used for the component in the SLM forming state. The mechanical property parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation and anisotropy coefficient, wherein the anisotropy coefficient is the ratio of the first yield strength perpendicular to the forming direction to the second yield strength parallel to the forming direction. The mechanical property parameters are obtained by actual measurement of standard samples formed in the same batch of SLM forming. The feature analysis module is used to perform geometric feature analysis on the three-dimensional geometric model and identify structural parts that conform to preset geometric structure features as key risk parts. The preset geometric structure features include at least one of the following: cantilever structure, thin-walled structure with a wall thickness less than the preset value, single connection structure, geometrically discontinuous structure, and structure where the clamping and positioning surface is located. The model building module is used to build a finite element model of the component to be processed, using a refined mesh in the critical risk areas and a coarser mesh in the non-critical risk areas, and inputting the mechanical performance parameters into the finite element model; The load application module is used to apply an equivalent clamping force to the finite element model according to the clamping method during machining, and to apply an equivalent cutting force according to the machining area, while setting boundary conditions. The simulation solution module is used to simulate and solve the finite element model after the load is applied, and to obtain the equivalent stress distribution data and equivalent plastic strain distribution data of the component to be processed. The risk identification module is used to identify areas with machining-induced cracking risk from the equivalent stress distribution data and the equivalent plastic strain distribution data based on preset risk judgment criteria. The risk assessment criteria are at least related to the yield strength or interlaminar bond strength of the material used in the component to be processed.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

Images