Method and system for planning small-deformation machining path of alloy part

Abstract

The invention discloses an alloy part small-deformation machining path planning method and system, and the method comprises the steps: obtaining machining parameters, and building a part rigidity evolution prediction model based on a material removal process; carrying out residual stress testing on the surface of the blank, and constructing a part residual stress initial distribution model; performing multi-physics field coupling on the part rigidity evolution prediction model based on the material removal process and a constructed part residual stress initial distribution model to obtain a coupling model, simulating the material removal process under a preset processing path, and correcting part deformation predicted by the coupling model through simulation experiment data; and a small-deformation machining path is obtained based on the mapping relation between the part deformation and the machining path. Through staged rigidity calculation and stress partitioning, the deformation prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical processing, and in particular to a method and system for planning a small deformation processing path for alloy parts. Background Art

[0002] In the field of precision machining, alloy parts (such as titanium alloys, nickel-based high-temperature alloys, and alloys for turbine runners) have residual stresses due to casting, forging, and welding processes before machining. Furthermore, due to the different cooling rates of different parts during the heat treatment of the blank, the residual stress distribution inside the parts varies. The material removal process is accompanied by dynamic changes in part stiffness and redistribution of residual stress, often leading to machining deformation problems. Traditional machining path planning methods have the following drawbacks: 1. The influence of stiffness evolution differences caused by the material removal process on machining deformation is not considered. 2. Ignoring the mechanism of the effect of the initial distribution difference of residual stress on the sensitivity of machining path; 3. There is a lack of analytical methods for the coupling between stiffness field and residual stress field.

[0003] Therefore, how to reduce the deformation of parts by optimizing the machining path during the cutting process has become an urgent problem to be solved.

[0004] In the prior art, CN2022103756246 discloses a finite element-based method for predicting machining deformation, but does not address closed-loop control of dynamic residual stress redistribution. CN2019109107596 proposes machining path optimization based on stiffness change curves, but does not address the coupling effect between stiffness and residual stress, resulting in low prediction accuracy. Therefore, a small-deformation machining path planning method that can simultaneously characterize the synergistic effects of stiffness evolution and residual stress redistribution is urgently needed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for planning a machining path for small deformation of alloy parts, so as to solve the problem of low prediction accuracy.

[0006] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for planning a machining path for a small deformation alloy part, comprising: Obtain processing parameters and establish a prediction model for part stiffness evolution based on the material removal process; Conduct residual stress tests on the blank surface and build an initial residual stress distribution model for the part; Based on the part stiffness evolution prediction model during the material removal process and the initial distribution model of the residual stress of the part, a multi-physics field coupling is performed to obtain a coupling model. The material removal process under the preset machining path is simulated, and the part deformation predicted by the coupling model is corrected by the simulated experimental data. Based on the mapping relationship between part deformation and machining path, a small deformation machining path is obtained.

[0007] Furthermore, the acquisition of processing parameters and establishment of a part stiffness evolution prediction model based on the material removal process include: Based on finite element analysis (FEA), combined with the Kirchhoff plate model and machining parameters, a staged stiffness matrix calculation model is established. By updating the mesh element properties of the material removal area in real time, the dynamic attenuation process of the part stiffness during machining is simulated. The machining time step is synchronized with the tool path, and the stiffness field distribution is updated after each set cutting stroke.

[0008] Furthermore, the machining parameters include cutting depth and feed speed.

[0009] Furthermore, the residual stress test on the blank surface and the construction of the initial distribution model of the residual stress of the part include: The blind hole method is used to test the residual stress on the blank surface. The strain released by drilling is measured by strain gauges, and the initial residual stress field is inversely calculated. Based on the stress test results, the part surface is divided into tensile stress zone, compressive stress zone and transition zone, and each zone is marked with a different color in the 3D model.

[0010] Furthermore, the part stiffness evolution prediction model based on the material removal process is coupled with the initial distribution model of the residual stress of the part through multi-physics field coupling to obtain a coupled model, including: Construct a stiffness-stress interaction matrix whose elements are determined by the product of the stiffness field gradient and the residual stress field gradient, and solve the coupled equations iteratively: [K]{u} ={F}+[C]{σ res} Where [K] is the stiffness matrix, {u} is the displacement vector, {F} is the cutting force vector, [C] is the coupling coefficient matrix, {σ res} is the residual stress vector.

[0011] Furthermore, simulating the material removal process under the preset machining path and correcting the part deformation predicted by the coupling model using the simulated experimental data include: The enumeration method is used to simulate the material removal process of typical machining paths, and the maximum deformation under each path is recorded. A comparative experiment is carried out on a five-axis machining center, and a laser displacement sensor is used to monitor the deformation of parts online. An error compensation database of experimental data and simulation results is established.

[0012] Furthermore, the obtaining of a small deformation processing path based on the mapping relationship between the part deformation amount and the processing path includes: Based on the support vector regression algorithm, the path-deformation mapping relationship model is trained with the machining path parameters as input and the deformation extreme value and residual stress standard deviation as output; the machining path parameters include the tool direction, cutting sequence and layer thickness.

[0013] In a second aspect, the present invention provides a system for planning a small deformation machining path for alloy parts, comprising: The first model building module is used to obtain processing parameters and establish a part stiffness evolution prediction model based on the material removal process; The second model building module is used to test the residual stress on the blank surface and build an initial distribution model of the residual stress of the part; The coupling module is used to perform multi-physics field coupling between the part stiffness evolution prediction model based on the material removal process and the initial distribution model of the residual stress of the part. The coupling model is obtained to simulate the material removal process under the preset processing path and the part deformation predicted by the coupling model is corrected by the simulated experimental data. The output module is used to obtain a small deformation processing path based on the mapping relationship between the part deformation amount and the processing path.

[0014] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for planning a path for small deformation processing of an alloy part when executing the computer program.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for planning a small deformation processing path of an alloy part are implemented.

[0016] Compared with the prior art, the present invention has the following technical effects: The present invention improves deformation prediction accuracy through staged stiffness calculation and stress partitioning; The present invention adopts a stiffness-stress coupling model to solve the error accumulation problem in traditional single-field analysis; The present invention adopts dynamic path planning to significantly reduce the deformation of complex structural parts during machining. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 : Flowchart of machining path planning method; Figure 2 : Schematic diagram of residual stress partitioning; Figure 3 : Schematic diagram of typical processing path; Figure 4 : Diagram of the mechanism of part deformation controlled by machining path. DETAILED DESCRIPTION

[0018] The present invention will be further described below with reference to the accompanying drawings: Example 1, please refer to Figure 1 The present invention aims to solve the problem of insufficient machining deformation control accuracy caused by ignoring the interaction between stiffness evolution and residual stress redistribution in the existing technology, and provides a dynamic planning method for small deformation machining paths based on multi-physical field coupling analysis.

[0019] A small deformation machining path planning method for alloy parts, comprising: Obtain processing parameters and establish a prediction model for part stiffness evolution based on the material removal process; Conduct residual stress tests on the blank surface and build an initial residual stress distribution model for the part; Based on the part stiffness evolution prediction model during the material removal process and the initial distribution model of the residual stress of the part, a multi-physics field coupling is performed to obtain a coupling model. The material removal process under the preset machining path is simulated, and the part deformation predicted by the coupling model is corrected by the simulated experimental data. Based on the mapping relationship between part deformation and machining path, a small deformation machining path is obtained.

[0020] In embodiment 2, the present invention provides a method for planning a small deformation machining path for an alloy part, comprising: 1. Construction of stiffness evolution prediction model: Based on finite element analysis, combining finite element theory (Kirchhoff plate model) and machining parameters (cut depth, feed rate), a staged stiffness matrix calculation model was established. By updating the mesh element properties in the material removal area in real time, the dynamic attenuation of part stiffness during machining was simulated.

[0021] In the embodiment, ABAQUS software is used to set the processing time step to be synchronized with the tool path, and the stiffness field distribution is updated every time a 0.5 mm cutting stroke is completed.

[0022] 2. Modeling and partitioning of initial residual stress distribution: The blind hole method (ASTM E837 standard) was used to test the residual stress on the blank surface. The strain released by drilling was measured by strain gauges, and the initial residual stress field was inversely calculated.

[0023] Based on the stress test results, the part surface is divided into a tensile stress zone (σ>50MPa), a compressive stress zone (σ<-30MPa) and a transition zone (-30MPa≤σ≤50MPa). Each zone is marked with a different color in the 3D model.

[0024] 3. Multi-physics coupling analysis: Construct a stiffness-stress interaction matrix whose elements are determined by the product of the stiffness field gradient and the residual stress field gradient. Solve the coupled equations iteratively: [K]{u}={F}+[C]{σres} Among them, [K] is the stiffness matrix, {u} is the displacement vector, {F} is the cutting force vector, [C] is the coupling coefficient matrix, and {σ_res} is the residual stress vector.

[0025] 4. Processing path simulation and experimental verification: The enumeration method is used to simulate the material removal process of typical machining paths (such as inner ring spiral path and outer ring reciprocating path), and the maximum deformation under each path is recorded.

[0026] Comparative experiments were conducted on a five-axis machining center (such as the DMU 80 monoBLOCK), and a laser displacement sensor (accuracy 0.1μm) was used to monitor part deformation online, and an error compensation database was established between the experimental data and simulation results.

[0027] 5. Small Deformation Path Mapping Model: Based on the support vector regression (SVR) algorithm, the path-deformation mapping relationship model is trained with the machining path parameters (tool direction, cutting sequence, and layer thickness) as input and the extreme value of deformation and the standard deviation of residual stress as output.

[0028] Take the processing of impulse turbine runner (material: 06Cr13Ni4Mo stainless steel) as an example: Step 1: The initial residual stress test shows that the root of the runner blade is in the compressive stress area (-120MPa to -80MPa) and the tip is in the tensile stress area (60MPa to 90MPa); Step 2: Simulation shows that the outer ring path causes a deformation of 0.12 mm in the blade root area, and the inner ring path causes a deformation of 0.08 mm in the blade tip area; Step 3: After coupled analysis and optimization, a hybrid path (outer ring cutting in the blade root area and inner ring cutting in the blade tip area) is adopted. The final deformation is controlled within 0.05 mm, and the surface residual compressive stress is stabilized in the range of -50 MPa to -30 MPa.

[0029] In yet another embodiment of the present invention, a system for planning a path for small deformation processing of an alloy part is provided, which can be used to implement the above-mentioned method for planning a path for small deformation processing of an alloy part. Specifically, the system includes: The first model building module is used to obtain processing parameters and establish a part stiffness evolution prediction model based on the material removal process; The second model building module is used to test the residual stress on the blank surface and build an initial distribution model of the residual stress of the part; The coupling module is used to perform multi-physics field coupling between the part stiffness evolution prediction model based on the material removal process and the initial distribution model of the residual stress of the part. The coupling model is obtained to simulate the material removal process under the preset processing path and the part deformation predicted by the coupling model is corrected by the simulated experimental data. The output module is used to obtain a small deformation processing path based on the mapping relationship between the part deformation amount and the processing path.

[0030] The module division in the embodiments of the present invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in various embodiments of the present invention may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The integrated modules may be implemented in either hardware or software functional modules.

[0031] In another embodiment of the present invention, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a small deformation processing path planning method for alloy parts.

[0032] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor may load and execute the one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for small deformation machining path planning for alloy parts described in the above-mentioned embodiment.

[0033] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0034] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0035] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0036] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for planning a small deformation machining path for alloy parts, characterized in that: include: Obtain processing parameters and establish a prediction model for part stiffness evolution based on the material removal process; Conduct residual stress tests on the blank surface and build an initial residual stress distribution model for the part; Based on the part stiffness evolution prediction model during the material removal process and the initial distribution model of the residual stress of the part, a multi-physics field coupling is performed to obtain a coupling model. The material removal process under the preset machining path is simulated, and the part deformation predicted by the coupling model is corrected by the simulated experimental data. Based on the mapping relationship between part deformation and machining path, a small deformation machining path is obtained.

2. A method for planning a small deformation machining path for an alloy part according to claim 1, characterized in that: The process of obtaining processing parameters and establishing a part stiffness evolution prediction model based on the material removal process includes: Based on finite element analysis (FEA), combined with the Kirchhoff plate model and machining parameters, a staged stiffness matrix calculation model is established. By updating the mesh element properties of the material removal area in real time, the dynamic attenuation process of the part stiffness during machining is simulated. The machining time step is synchronized with the tool path, and the stiffness field distribution is updated after each set cutting stroke.

3. A method for planning a small deformation machining path for an alloy part according to claim 2, characterized in that: Machining parameters include cutting depth and feed speed.

4. The method for planning a small deformation machining path for an alloy part according to claim 1, wherein: The residual stress test on the blank surface and the construction of the initial residual stress distribution model of the part include: The blind hole method is used to test the residual stress on the blank surface. The strain released by drilling is measured by strain gauges, and the initial residual stress field is inversely calculated. Based on the stress test results, the part surface is divided into tensile stress zone, compressive stress zone and transition zone, and each zone is marked with a different color in the 3D model.

5. The method for planning a small deformation machining path for an alloy part according to claim 1, wherein: The part stiffness evolution prediction model based on the material removal process is coupled with the initial distribution model of residual stress of the part through multi-physics field coupling to obtain a coupled model, including: Construct a stiffness-stress interaction matrix whose elements are determined by the product of the stiffness field gradient and the residual stress field gradient, and solve the coupled equations iteratively: [K]{u} ={F}+[C]{σ res } Where [K] is the stiffness matrix, {u} is the displacement vector, {F} is the cutting force vector, [C] is the coupling coefficient matrix, {σ res } is the residual stress vector.

6. The method for planning a small deformation machining path for an alloy part according to claim 1, wherein: The simulation of the material removal process under the preset machining path and the correction of the part deformation predicted by the coupling model using the simulated experimental data include: The enumeration method is used to simulate the material removal process of typical machining paths, and the maximum deformation under each path is recorded. A comparative experiment is carried out on a five-axis machining center, and a laser displacement sensor is used to monitor the deformation of parts online. An error compensation database of experimental data and simulation results is established.

7. The method for planning a small deformation machining path for an alloy part according to claim 1, wherein: The method of obtaining a small deformation processing path based on the mapping relationship between the part deformation amount and the processing path includes: Based on the support vector regression algorithm, the path-deformation mapping relationship model is trained with the machining path parameters as input and the deformation extreme value and residual stress standard deviation as output; the machining path parameters include the tool direction, cutting sequence and layer thickness.

8. A small deformation processing path planning system for alloy parts, characterized in that: include: The first model building module is used to obtain processing parameters and establish a part stiffness evolution prediction model based on the material removal process; The second model building module is used to test the residual stress on the blank surface and build an initial distribution model of the residual stress of the part; The coupling module is used to perform multi-physics field coupling between the part stiffness evolution prediction model based on the material removal process and the initial distribution model of the residual stress of the part. The coupling model is obtained to simulate the material removal process under the preset processing path and the part deformation predicted by the coupling model is corrected by the simulated experimental data. The output module is used to obtain a small deformation processing path based on the mapping relationship between the part deformation amount and the processing path.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for planning a small deformation processing path for an alloy part as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for planning a small deformation machining path for an alloy part as claimed in any one of claims 1 to 7 are implemented.

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