Cutter full life cycle cutting parameter prediction method and system based on stored energy field
By establishing a finite element model of the stored energy field, the wear of the tool throughout its entire life cycle can be predicted. This solves the problem that the existing technology has failed to delve into the relationship between microscopic energy accumulation and wear, and realizes accurate prediction and full life cycle tracking of tool wear, providing a basis for adaptive adjustment of cutting parameters.
Patent Information
- Application Number
- CN202511412023.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-29
- Publication Date
- 2025-12-23
AI Technical Summary
Existing technologies have failed to effectively establish the intrinsic relationship between tool wear and the microscopic energy accumulation (stored energy field) of workpiece materials, neglected the physical nature of wear, lacked a unified prediction model for the entire life cycle, made it difficult to dynamically reflect the evolution of cutting characteristics, and failed to coordinate the prediction of key parameters such as cutting force and temperature.
By establishing a finite element model with an embedded energy storage field subroutine, the distribution of the energy storage field in the cutting deformation zone is calculated. Combined with the finite element simulation method, the tool wear is predicted, and the distribution of the energy storage field at different wear stages is simulated. A tool wear model is established, a wear cloud map is generated, the tool geometry is updated, and the energy storage field law at each stage is obtained.
It achieves accurate wear prediction based on microscopic energy accumulation, dynamically tracks tool health status, provides a theoretical basis for adaptive adjustment of cutting parameters at different wear stages, improves the scientificity and accuracy of the prediction model, and simplifies the analysis of complex coupled problems.
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Figure CN121189102A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of metal cutting, in particular to a tool full life cycle cutting parameter prediction method and system based on stored energy field. BACKGROUND
[0002] The statements in this section merely provide background technology related to the present disclosure and do not necessarily constitute prior art.
[0003] Metal cutting is one of the core processes in manufacturing industry. The tool, as a key component directly executing the cutting task, its wear state directly determines the machining precision, surface quality and production cost of the workpiece. Therefore, it is of great significance to accurately predict and monitor the tool wear state and optimize the cutting parameters accordingly for realizing intelligent and efficient advanced manufacturing.
[0004] The tool wear is essentially a complex physical and chemical process of tool material under extreme working conditions of high temperature, high pressure and severe friction. The traditional tool wear prediction methods can be mainly divided into two categories: one is the model based on experience, which relies on a large amount of experimental data, and has poor universality and limited prediction accuracy; the other is the monitoring method based on sensor signals (such as collecting cutting force, vibration, acoustic emission signals, etc.), which can realize online monitoring, but often has problems such as signal lag, many interference factors, and difficulty in revealing the internal physical nature of wear.
[0005] More importantly, most of the existing prediction methods are limited to the macro parameter correlation level and fail to delve into the material micro deformation mechanism. During the cutting process, the workpiece material undergoes severe plastic deformation under the action of the tool, and a kind of energy called "stored energy" is accumulated inside. This stored energy is the driving force for the change of material microstructure (such as dislocation multiplication and grain boundary evolution), directly affecting the mechanical behavior and thermodynamic state of the material, and then closely related to the wear rate of the tool. However, the existing methods fail to establish the internal relationship between tool macro wear and workpiece material micro energy accumulation (stored energy field), ignoring the physical nature of wear. At the same time, there is a lack of unified prediction model that can penetrate the whole life cycle of the tool from initial wear, normal wear to rapid wear, making it difficult to dynamically reflect the evolution law of cutting characteristics at different wear stages. It also fails to comprehensively utilize the mapping relationship between stored energy field and temperature field, stress field, and microstructure to achieve collaborative and accurate prediction of key process parameters such as cutting force and cutting temperature. SUMMARY
[0006] The purpose of the present application is to provide a tool full life cycle cutting parameter prediction method and system based on stored energy field, which aims to solve at least one of the technical problems in the above background technology.
[0007] To achieve the above purpose, the present application provides the following solutions: The first aspect of the present application provides a tool full life cycle cutting parameter prediction method based on stored energy field, comprising: Obtaining initial parameters of the cutting process, including workpiece material properties, cutting parameters and tool geometric parameters; Based on the initial parameters, a finite element model embedded with a stored energy field subroutine is established, and the stored energy field distribution of the cutting deformation zone is obtained by solving the finite element model; Based on the stored energy field distribution, a tool wear model is established to predict tool wear and generate a tool wear cloud map; Based on the predicted tool wear, the tool geometric morphology is updated, the stored energy field distribution under different tool wear stages is simulated, and the stored energy field distribution law at different wear stages is obtained; Based on the stored energy field distribution law and the mapping relationship between stored energy and flow stress, energy balance relationship, displacement density and subgrain size, the cutting deformation zone temperature field, stress field and microstructure distribution are obtained based on the finite element simulation method.
[0008] Further, the calculation of the stored energy field is based on the following formula: ; Further, when establishing the finite element model, the material properties are set as user materials, and the corresponding mechanical constants of the subroutine are added; the field output variables include state / field / user / time variables, which are state-dependent variables used to store stored energy field information; Based on the stored energy field distribution, a tool wear model is established to predict tool wear and generate a tool wear cloud map.
[0009] Further, the stored energy field density value and temperature value of at least one predetermined region in the cutting deformation zone are extracted from the finite element result file; The stored energy field density value and temperature value are input into a preset tool wear rate calculation model together with the cutting time parameter, and the wear amount of the tool surface is calculated; Based on the calculated wear amount, a wear cloud map of the tool relief surface wear zone is generated.
[0010] Further, the stored energy field distribution under different tool wear stages is simulated to obtain the stored energy field distribution law at different wear stages, specifically including: According to the predicted tool wear, tool geometric models corresponding to the initial wear stage, normal wear stage and rapid wear stage are established in turn; The establishment and solving steps of the finite element model are re-executed for each wear stage tool geometric model to obtain the distribution data of the stored energy field, temperature field and equivalent stress field of the cutting deformation zone at each wear stage.
[0011] Further, the distribution rule comprises: With the wear stage evolving from initial stage, normal stage to sharp stage, the peak values of the stored energy field and the temperature field of the first deformation zone, the second deformation zone and the third deformation zone in the cutting deformation zone all present an increasing trend; In the second deformation zone, with the wear stage evolving from initial stage, normal stage to sharp stage, the peak value of the equivalent stress field is lower than that of the first deformation zone due to the intensification of material thermal softening effect, or presents a trend of first increasing and then decreasing.
[0012] The second aspect of the present application provides a cutting parameter prediction system for the whole life cycle of a tool based on a stored energy field, comprising: A parameter acquisition module is configured to acquire initial parameters of a cutting process, including workpiece material properties, cutting parameters and tool geometric parameters; A stored energy field calculation module is configured to establish a finite element model embedded with a stored energy field subroutine based on the initial parameters, and obtain a stored energy field distribution of a cutting deformation zone by solving the finite element model; A tool wear prediction module is configured to establish a tool wear model based on the stored energy field distribution, predict a tool wear amount, and generate a tool wear cloud chart; A stage simulation module is configured to update a tool geometric appearance based on the predicted tool wear amount, simulate the stored energy field distribution under different tool wear stages, and obtain a stored energy field distribution rule of different wear stages; A multi-field coupling analysis module is configured to obtain a temperature field, a stress field and a microstructure distribution of the cutting deformation zone based on the stored energy field distribution rule and the mapping relationship between the stored energy and the flow stress, the energy balance relationship, the displacement density and the subgrain size based on a finite element simulation method.
[0013] The third aspect of the present application provides an electronic device, comprising a memory, a processor and a program stored in the memory and running on the processor, wherein the processor implements the steps in the cutting parameter prediction method for the whole life cycle of a tool based on a stored energy field as described in the first aspect of the present application when executing the program.
[0014] The fourth aspect of the present application provides a computer readable storage medium having a program stored thereon, wherein the program is executed by a processor to implement the steps in the cutting parameter prediction method for the whole life cycle of a tool based on a stored energy field as described in the first aspect of the present application.
[0015] The fifth aspect of the present application provides a computer program product comprising software code, wherein the program in the software code implements the steps in the cutting parameter prediction method for the whole life cycle of a tool based on a stored energy field as described in the first aspect of the present application.
[0016] Compared with the prior art, the tool full life cycle cutting parameter prediction method and system based on storage energy field provided by the application have the following beneficial effects: (1) The application provides a specific calculation formula of the storage energy field by establishing a finite element model embedded with a storage energy field subroutine, and directly calculates the storage energy field distribution of the cutting deformation zone through finite element simulation. This makes the wear prediction based on the physical basis of material micro-plastic deformation energy accumulation, realizes quantitative and accurate prediction of tool wear from the more essential scalar energy, solves the technical problems of traditional methods ignoring the micro-physical nature of tool wear and insufficient prediction accuracy, and significantly improves the scientificity and accuracy of the prediction model.
[0017] (2) The application continuously updates the tool geometric appearance, simulates the storage energy field distribution under different tool wear stages, and clearly divides the initial, normal and rapid wear stages. Through iterative updating of the tool geometric model and re-simulation, the storage energy field law of each stage is dynamically obtained. The health status of the tool is tracked and predicted throughout the whole process, which provides a theoretical basis for self-adaptive adjustment of cutting parameters in different wear stages and ensures the machining quality, and solves the technical problem that the existing model cannot cover the tool full life cycle wear stage.
[0018] (3) The application establishes the mapping relationship between the storage energy field and the cutting deformation zone temperature field, stress field and microstructure, takes the storage energy field as the core bridge, and systematically establishes the correlation model of the storage energy field with temperature, stress and microstructure. Using single storage energy field data, cutting force, cutting temperature, stress field and even workpiece surface microstructure evolution and other key parameters can be cooperatively predicted, overcoming the technical problem of complex coupling of multiple physical fields and effectively simplifying the analysis difficulty of complex coupling problems, and providing comprehensive prediction of the cutting process. BRIEF DESCRIPTION OF DRAWINGS
[0019] The drawings accompanying the specification of the present disclosure serve to provide a further understanding of the present disclosure, and the illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure, and do not constitute an improper limitation on the present disclosure.
[0020] Figure 1 is a flowchart of a tool full life cycle cutting parameter prediction method based on storage energy field provided by the embodiment one of the application; Figure 2 is a cutting finite element model provided by the embodiment one of the application; Figure 3 is a tool wear cloud chart provided by the embodiment one of the application; Figure 4 is a tool wear schematic diagram provided by the embodiment one of the application; Figure 5is the cutting force of the tool in the initial wear stage provided by the embodiment one of the present application; Figure 6 is the cutting deformation zone storage energy field distribution cloud picture of the tool in the initial wear stage provided by the embodiment one of the present application; Figure 7 is the cutting deformation zone temperature distribution cloud picture of the tool in the initial wear stage provided by the embodiment one of the present application; Figure 8 is the cutting deformation zone equivalent stress distribution cloud picture of the tool in the initial wear stage provided by the embodiment one of the present application; Figure 9 is the cutting deformation zone subgrain size distribution cloud picture of the tool in the initial wear stage provided by the embodiment one of the present application; Figure 10 is the cutting deformation zone storage energy field distribution cloud picture of the tool in the normal wear stage provided by the embodiment one of the present application; Figure 11 is the cutting deformation zone temperature distribution cloud picture of the tool in the normal wear stage provided by the embodiment one of the present application; Figure 12 is the cutting deformation zone equivalent stress distribution cloud picture of the tool in the normal wear stage provided by the embodiment one of the present application; Figure 13 is the cutting deformation zone subgrain size distribution cloud picture of the tool in the normal wear stage provided by the embodiment one of the present application; Figure 14 is the cutting deformation zone storage energy field distribution cloud picture of the tool in the sharp wear stage provided by the embodiment one of the present application; Figure 15 is the cutting deformation zone temperature distribution cloud picture of the tool in the sharp wear stage provided by the embodiment one of the present application; Figure 16 is the cutting deformation zone equivalent stress distribution cloud picture of the tool in the sharp wear stage provided by the embodiment one of the present application; Figure 17 is the cutting deformation zone subgrain size distribution cloud picture of the tool in the sharp wear stage provided by the embodiment one of the present application; Figure 18 is the cutting parameter prediction system based on storage energy field of the tool in the whole life cycle provided by the embodiment two of the present application. DETAILED DESCRIPTION
[0021] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
[0022] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and "comprising", when used in this specification, specify the presence of stated features, integers, steps, or components, but do not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof.
[0023] The embodiments in the present application and the features in the embodiments can be combined with each other in the case of no conflict.
[0024] All data of the present embodiment are acquired on the basis of compliance with laws and regulations and user consent, and legal application of the data.
[0025] Before the embodiments of the present application are described in detail, the special terms involved in the present application are explained and defined as follows: 1, Storage energy field: In the present application, it refers to the energy that is stored in the form of crystal defects (such as dislocations, grain boundaries) in the internal material during plastic deformation and cannot be converted into heat energy. Among them, the storage energy refers to the storage energy accumulated by the metal during plastic deformation, which mainly includes the storage energy contributed by the statistical storage dislocation and the storage energy contributed by the sub-grain boundary formed by the geometrically necessary dislocation. The field is a spatial distribution function, that is, the storage energy values of different position points in the cutting deformation zone constitute a "field". The storage energy field of the workpiece material cutting deformation zone is the driving force of tool wear (for example, the high storage energy area corresponds to severe plastic deformation, which leads to the easier dissolution, diffusion or fracture of the tool material).
[0026] 2, Tool life cycle: It refers to the entire time process from the first time the tool is put into cutting (the cutting edge is intact) to the wear reaches the dulling standard (failure).
[0027] 3, Storage energy field subroutine: It is a computer program embedded in the finite element software for calculating the storage energy of each integral point in the deformation process.
[0028] 4, Cutting deformation zone: It includes the first deformation zone, the second deformation zone and the third deformation zone. Specifically, the first deformation zone (shear zone) is the area where the workpiece material forms a chip by concentrated shear slip. The second deformation zone (tool-chip contact zone) is the area where the bottom layer of the chip and the rake face of the tool are in severe friction. The third deformation zone (tool-work contact zone) is the area where the machined surface and the relief surface of the tool are in contact.
[0029] 5. Tool wear stage: including initial wear stage, normal wear stage and rapid wear stage. From the macro-morphological characteristics, the initial wear stage is blade edge micro-collapse, the normal wear stage is uniform wear band, and the rapid wear stage is rapid expansion of wear band and may be accompanied by groove wear.
[0030] 6. The statistical storage dislocation and geometrically necessary dislocation are respectively the dislocations formed by mutual capture of dislocations in uniform deformation of materials and the dislocations formed to adapt to strain gradient in non-uniform deformation process.
[0031] Embodiment one As Figure 1 The embodiment provides a tool full life cycle cutting parameter prediction method based on storage energy field, which comprises the following steps: obtaining initial parameters of a cutting process, including workpiece material properties, cutting parameters and tool geometric parameters; based on the initial parameters, establishing a finite element model embedded with a storage energy field subroutine, and obtaining the storage energy field distribution of a cutting deformation zone by solving the finite element model; based on the storage energy field distribution, establishing a tool wear model, predicting tool wear amount, and generating a tool wear cloud chart; based on the predicted tool wear amount, updating tool geometric morphology, simulating the storage energy field distribution under different tool wear stages, and obtaining the storage energy field distribution law of different wear stages; based on the storage energy field distribution law and the mapping relationship between storage energy and flow stress, energy balance relationship, displacement density and subgrain size, obtaining the temperature field, stress field and microstructure distribution of the cutting deformation zone based on the finite element simulation method.
[0032] Specifically, the calculation of the storage energy field is based on the following formula: ; wherein, is the energy coefficient of dislocation interaction, is the shear modulus, b is the Burgers vector, is the average dislocation density, is the subgrain size, G is the subgrain boundary specific energy.
[0033] The subgrain boundary specific energy G may be expressed by a subgrain boundary orientation angle,
[0034] In the formula, k is the ratio of the initial subgrain boundary angle to the critical angle of high-angle grain boundary, is the specific energy of high-angle grain boundary.
[0035] Geometrically required dislocation density The increase in subgrain boundary orientation angle leads to The increase in energy density leads to subgrain boundary energy storage. accumulation, The evolution can be represented as ; In the formula, The initial subgrain boundary angle, b This is the Burgers vector.
[0036] The mapping relationship between stored energy and flow stress, energy balance, dislocation density, and subgrain size is as follows: ; ; ; ; ; In the formula, For the flow stress of the material, For flow stress terms that are independent of strain hardening, and This represents the plastic work and heat generated during material deformation. Es represents the stored energy, where Es = Ess + EsG, and Ess is the stored energy contributed by statistically stored dislocations, while EsG is the stored energy contributed by subgrain boundaries. The energy coefficient for dislocation interactions. Here, b is the shear modulus, G is the Burgers vector, ρ is the subgrain boundary specific energy, ds is the statistical storage dislocation density, and ds is the subgrain size. and All of these are material constants to be fitted.
[0037] Specifically, when establishing the finite element model, the material properties are set to user materials, and the mechanical constants corresponding to the subroutine are added; the field output variables include state-dependent variables from the state / field / user / time variables, which are used to store the stored energy field information; Based on the energy storage field distribution, a tool wear model is established to predict the tool wear amount and generate a tool wear cloud map.
[0038] Specifically, the stored energy field density value and temperature value of at least one predetermined region within the cutting deformation zone are extracted from the finite element result file; The stored energy field density value and temperature value, along with the cutting time parameter, are input into a preset tool wear rate calculation model to calculate the wear amount on the tool surface. Based on the calculated wear amount, a wear cloud of the tool relief face wear zone is generated.
[0039] The calculation formula of the tool wear rate calculation model is as follows: ; ; ; ; wherein, 、 、 is the volume of abrasive wear, adhesive wear and diffusion wear, is the dislocation density, R is the gas constant, A is the effective contact area of the tool chip, s is the cutting distance, T is the absolute temperature, is a proportional coefficient indicating the proportion of stored energy that can be used to assist in reducing the activation energy, 、 、 is the relevant wear coefficient, 、 is the critical temperature for distinguishing the main tool wear type (obtained according to the actual tool cutting material).
[0040] Specifically, the distribution of the stored energy field in different tool wear stages is simulated to obtain the distribution law of the stored energy field in different wear stages, which specifically includes: According to the predicted tool wear amount, tool geometry models corresponding to the initial wear stage, the normal wear stage and the rapid wear stage are sequentially established; The establishment and solving steps of the finite element model are re-executed for the tool geometry model of each wear stage to obtain the distribution data of the stored energy field, the temperature field and the equivalent stress field of the cutting deformation zone in each wear stage.
[0041] Specifically, the distribution law includes: With the evolution of the wear stage from the initial stage, the normal stage to the rapid stage, the peak values of the stored energy field and the temperature field of the first deformation zone, the second deformation zone and the third deformation zone in the cutting deformation zone all show an increasing trend; In the second deformation zone, with the evolution of the wear stage from the initial stage, the normal stage to the rapid stage, the peak value of the equivalent stress field is lower than that of the first deformation zone due to the intensification of material thermal softening effect, or shows a trend of first increasing and then decreasing.
[0042] In one specific embodiment, as Figure 2As shown, a two-dimensional cutting model is established in the Abaqus software, materials and cutting parameters are set according to relevant simulation, then a job file is created to submit operation, and an odb result file is obtained by solving. In the parameter setting process, the material properties of the embodiment are checked, the corresponding mechanical constants are added to the subroutine, and the state dependent variables in the state / field / user / time variables are checked, except for the CSTRESS, TEMP, S, U, RF and other variables of the field output variable of the embodiment; when creating the job file, the energy storage field subroutine is added, and the subroutine VUMAT supporting the user-defined material constitutive and energy calculation in the explicit analysis is selected. The VUMAT subroutine is a subroutine provided by the ABAQUS software, and the elastic-plastic deformation characteristics of the material can be defined by writing the subroutine, and the interface of the subroutine is as follows, SUBROUTINE VUMAT( C Read only - 1 NBLOCK, NDIR, NSHR, NSTATEV, NFIELDV, NPROPS, LANNEAL, 2 STEPTIME, TOTALTIME, DT, CMNAME, COORDMP, CHARLENGTH, 3 PROPS, DENSITY, STRAININC, RELSPININC, 4 TEMPOLD, STRETCHOLD, DEFGRADOLD, FIELDOLD, 5 STRESSOLD, STATEOLD, ENERINTERNOLD, ENERINELASOLD, 6 TEMPNEW, STRETCHNEW, DEFGRADNEW, FIELDNEW, C Write only - 7 STRESSNEW, STATENEW, ENERINTERNNEW, ENERINELASNEW) INCLUDE 'VABA_PARAM.INC' DIMENSION PROPS(NPROPS), DENSITY(NBLOCK), COORDMP(NBLOCK), 1 CHARLENGTH(NBLOCK), STRAININC(NBLOCK, NDIR+NSHR), 2 RELSPININC(NBLOCK, NSHR), TEMPOLD(NBLOCK), 3 STRETCHOLD(NBLOCK, NDIR+NSHR),DEFGRADOLD(NBLOCK, NDIR+NSHR+NSHR), 4 FIELDOLD(NBLOCK, NFIELDV), STRESSOLD(NBLOCK, NDIR+NSHR), 5 STATEOLD(NBLOCK, NSTATEV), ENERINTERNOLD(NBLOCK), 6 ENERINELASOLD(NBLOCK), TEMPNEW(NBLOCK), 7 STRETCHNEW(NBLOCK, NDIR+NSHR),DEFGRADNEW(NBLOCK, NDIR+NSHR+NSHR), 8 FIELDNEW(NBLOCK, NFIELDV), STRESSNEW(NBLOCK, NDIR+NSHR), 9 STATENEW(NBLOCK, NSTATEV), ENERINTERNNEW(NBLOCK), 1 ENERINELASNEW(NBLOCK) The main read and update variables are as follows: STEPTIME is the analysis step time, DT is the time increment, PROPS is the material parameter array input through the abaqus material interface, TEMPOLD, STRESSOLD and STATEOLD are the temperature, stress and state dependent variable information of each node before the start of the analysis step, TEMPNEW, STRESSNEW and STATENEW are the updated information after the end of the analysis step. The STATENEW array contains the storage energy, microstructure and other information of each node. The calculation flow of the material subroutine is as follows: The subroutine starts→reads the stress, temperature and state dependent variable information of each finite element analysis node before the start of the analysis step→based on the mapping relationship between the storage energy and the dislocation density and the subgrain size, the storage energy after the analysis step is calculated by the explicit update algorithm→based on the mapping relationship between the storage energy and the flow stress, the flow stress after the analysis step is calculated, and based on the energy balance relationship, the temperature of each node is calculated→the temperature, stress component and state dependent variable information of each analysis node are updated→the subroutine ends, and the updated information is returned to the main program The key statements of the subroutine in the above flow are as follows: (1) When reading the information of the last analysis step, the information of temperature, stress and state-dependent variables can be directly called through the TEMPOLD, STRESSOLD and STATEOLD arrays. The material state parameter call can be realized through a statement like EMOD=PROPS(1), where the left side of the statement is the self-defined parameter, and the number in the parentheses is the sequence in the material information input of the abaqus software. (2) The storage energy updating statement is as follows: If the variable ESH0 is less than the variable ESTC: Set the value of STATENEW(K, 3) as: ESH0 + 2 × EAA1 × sqrt(ESH0) ÷ EKALPHA × DEQPL Otherwise (i.e., ESH0 is greater than or equal to ESTC): Set the value of STATENEW(K, 3) as: ESH0 + [2 × (EAA1 + (EDG + EKALPHA × sqrt(ESTORED0)) × EAA2) × sqrt(ESH0) ÷ EKALPHA - 2 × EAA2 × ESH0] × DEQPL Calculate the value of EGB as: EGAMGB0 × STATENEW(K, 9) ÷ ETHETAC × (1 - log(STATENEW(K, 9) ÷ ETHETAC)) (where log represents natural logarithm) Update STATENEW(K, 7): Set the value of STATENEW(K, 7) as: 3 × EGB ÷ STATENEW(K, 5) Update STATENEW(K, 8): Set the value of STATENEW(K, 8) as: STATENEW(K, 7) + STATENEW(K, 3) Where STATENEW(K, 3), STATENEW(K, 7) and STATENEW(K, 8) are the updated values of storage energy contributed by statistical dislocations, storage energy contributed by subgrain boundaries and total storage energy, respectively.
[0043] (3) The flow stress updating is realized by calculating the hardening rate, and the key statement for calculating the hardening rate is: If ESH0 < ESTC: Calculate HARD1 = EAA1 x (EGT / EG) + EH4 Else: Calculate HARD1 = (EGT / EG) x [EAA1 - EAA2 x (EKALPHA x (sqrt(ESH0) - sqrt(ESTORED0)) - EDG)] + EH4 Calculate the flow stress key statement as, Calculate SIGDIF = SMISES - SYIEL0 Initialize FACYLD = 0 If SIGDIF > 0: Set FACYLD = 1 Calculate DEQPL = FACYLD x SIGDIF ÷ (EG3T + HARD1) Calculate SYIELD = SYIEL0 + DEQPL x HARD1 Calculate FACTOR = SYIELD ÷ (SYIELD + EG3T x DEQPL) (4) The key statement of stress component update is as follows: Update STRESSNEW(K,1) = S11 x FACTOR + SMEAN Update STRESSNEW(K,2) = S22 x FACTOR + SMEAN Update STRESSNEW(K,3) = S33 x FACTOR + SMEAN Update STRESSNEW(K,4) = S12 x FACTOR If NSHR > 1: Update STRESSNEW(K,5) = S13 x FACTOR Update STRESSNEW(K,6) = S23 x FACTOR The program is written in Python language to extract the stored energy field and temperature field information in the odb file, and the tool wear cloud diagram of tool wear is obtained based on the tool wear model of the stored energy field, as Figure 4 The tool with different wear is obtained to simulate different wear stages of the tool.
[0044] The geometric model of tool wear in the embodiment is exemplified as Figure 2 , 3, the initial wear stage VB is 0.05mm, KT is 0.01mm, KM is 0.1mm, and the friction coefficient is set to 0.8; the normal wear stage VB is 0.15mm, KT is 0.04mm, KM is 0.12mm, and the friction coefficient is set to 0.9; and the sharp wear stage VB is 0.25mm, KT is 0.06mm, KM is 0.18mm, and the friction coefficient is set to 1.0.
[0045] When creating the job file, the stored energy field subroutine is embedded for extracting the stored energy field distribution of the cutting deformation zone. The stored energy field calculation model used in this example is E = (G * b) / (2 * pi) * ln (d / b), wherein G is the energy coefficient of dislocation interaction, b is the Burgers vector, d is the average dislocation density, b is the grain size, and G is the specific energy of the subgrain boundary.
[0046] As shown in Figures 6 to 17 , the odb file is solved, the strain of the first deformation zone is small, the statistical stored dislocation density dominates the stored energy accumulation, and as the tool wear degree increases, the tool rake angle decreases, the tool and chip contact surface friction angle increases, which leads to the decrease of the shear angle, so that the strain and strain rate increase, which promotes the accumulation of statistical stored dislocation in the first deformation zone, so the overall stored energy field of the first deformation zone presents an increasing trend with the aggravation of tool wear. The stress field presents a similar change rule as the stored energy field with the increase of the tool wear degree, and the increase of the stress in the first deformation zone and the decrease of the shear angle will lead to the increase of the cutting force.
[0047] As shown in Figures 6 to 17 , the strain of the second deformation zone is much larger than that of the first deformation zone, and the geometrically necessary dislocation density dominates the stored energy accumulation. The increase of the tool wear degree leads to the increase of the tool-chip contact length and the friction coefficient, resulting in the increase of the stored energy with the increase of the tool wear degree. Because the plastic deformation increases and the dislocation density increases, the second deformation zone needs to dissipate more heat, so the cutting temperature gradually increases with the increase of the tool wear degree, As shown in Figures 6 to 17 , due to the increase of tool wear, the tool clearance angle and the contact area with the workpiece surface increase, and the stored energy of the workpiece surface in the third deformation zone increases significantly, and the cutting temperature increases.
[0048] Embodiment Two As shown in Figure 18 , the embodiment provides a tool full life cycle cutting parameter prediction system based on stored energy field, comprising: A parameter acquisition module is configured to acquire initial parameters of a cutting process, including workpiece material properties, cutting parameters and tool geometric parameters. A stored energy field calculation module is configured to establish a finite element model embedded with a stored energy field subroutine based on the initial parameters, and obtain the stored energy field distribution of the cutting deformation zone by solving the finite element model. a tool wear prediction module configured to establish a tool wear model based on the stored energy field distribution, predict a tool wear amount, and generate a tool wear cloud map; a stage simulation module configured to update a tool geometry based on the predicted tool wear amount, simulate the stored energy field distribution under different tool wear stages, and obtain a stored energy field distribution law at different wear stages; a multi-field coupling analysis module configured to obtain a temperature field, a stress field, and a microstructure distribution in a cutting deformation zone based on the stored energy field distribution law and a mapping relationship between the stored energy and a flow stress, an energy balance relationship, a displacement density, and a subgrain size, and based on a finite element simulation method.
[0049] Embodiment Three The embodiment three of the present application provides an electronic device.
[0050] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor, and the processor implements the steps in the tool full life cycle cutting parameter prediction method based on a stored energy field according to the embodiment one of the present application when executing the program.
[0051] The detailed steps are the same as those in the tool full life cycle cutting parameter prediction method based on a stored energy field provided in the embodiment one, and will not be repeated here.
[0052] Embodiment Four The embodiment four of the present application provides a computer readable storage medium.
[0053] A computer readable storage medium has a program stored thereon, and the program is executed by a processor to implement the steps in the tool full life cycle cutting parameter prediction method based on a stored energy field according to the embodiment one of the present application.
[0054] The detailed steps are the same as those in the tool full life cycle cutting parameter prediction method based on a stored energy field provided in the embodiment one, and will not be repeated here.
[0055] Embodiment Five The embodiment five of the present application provides a computer program product.
[0056] A computer program product includes software code, and a program in the software code executes the steps in the tool full life cycle cutting parameter prediction method based on a stored energy field according to the embodiment one of the present application.
[0057] The detailed steps are the same as those in the tool full life cycle cutting parameter prediction method based on a stored energy field provided in the embodiment one, and will not be repeated here.
[0058] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon. Embodiments of the present application can be implemented in various computer languages including, but not limited to, Java, JavaScript, and the like.
[0059] The present application is described in reference to the flowchart illustrations and / or block diagrams according to the embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.
[0060] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.
[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.
[0062] While preferred embodiments of the application have been described, modifications and variations can be apparent to those skilled in the art once aware of the general underlying concepts. Accordingly, the appended claims are intended to embrace all modifications and variations of the preferred embodiments which fall within the scope of the application.
[0063] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.
[0064] The above description is merely that of the preferred embodiments of the present application and is not intended to limit the present application. The present application can be carried out in other various modifications and alterations within the scope of the present application. Accordingly, the scope of the present application should be gauged by the claims and their legal equivalents together with the full scope of all equivalents to which such claims are entitled.
Claims
1. A method for predicting cutting parameters throughout the entire lifecycle of a cutting tool based on a stored energy field, characterized in that, include: Obtain the initial parameters of the cutting process, including workpiece material properties, cutting parameters, and tool geometry parameters; Based on the initial parameters, a finite element model with an embedded energy storage field subroutine is established, and the energy storage field distribution in the cutting deformation zone is obtained by solving the finite element model. Based on the energy storage field distribution, a tool wear model is established to predict the tool wear amount and generate a tool wear cloud map. Based on the predicted tool wear, the tool geometry is updated, and the stored energy field distribution under different tool wear stages is simulated to obtain the stored energy field distribution law at different wear stages. Based on the distribution law of the stored energy field and the mapping relationship between stored energy and flow stress, energy balance, displacement density and subgrain size, the temperature field, stress field and microstructure distribution of the cutting deformation zone are obtained by finite element simulation method.
2. The method as described in claim 1, characterized in that, The calculation of the energy storage field is based on the following formula: ； in, This represents the energy coefficient of dislocation interactions. Shear modulus b For Burgers vector, For the average dislocation density, Subgrain size, G The energy of the subgrain boundary.
3. The method as described in claim 1, characterized in that, When establishing the finite element model, the material properties are set to user materials, and the mechanical constants corresponding to the subroutines are added; the field output variables include state-dependent variables from the state / field / user / time variables, which are used to store the stored energy field information; Based on the energy storage field distribution, a tool wear model is established to predict the tool wear amount and generate a tool wear cloud map.
4. The method as described in claim 1, characterized in that, Extract the stored energy field density and temperature values of at least one predetermined region within the cutting deformation zone from the finite element result file; The stored energy field density value and temperature value, along with the cutting time parameter, are input into a preset tool wear rate calculation model to calculate the wear amount on the tool surface. Based on the calculated wear amount, a wear cloud map of the wear band on the tool's flank face is generated.
5. The method as described in claim 1, characterized in that, The energy storage field distribution under different tool wear stages was simulated to obtain the distribution law of energy storage field under different wear stages, specifically including: Based on the predicted tool wear, tool geometric models corresponding to the initial wear stage, normal wear stage, and rapid wear stage are established sequentially. For each wear stage, the steps of establishing and solving the finite element model are repeated for the tool geometry model to obtain the distribution data of the stored energy field, temperature field and equivalent stress field of the cutting deformation zone under each wear stage.
6. The method as described in claim 5, characterized in that, The distribution patterns include: As the wear stage evolves from the initial and normal stages to the rapid stages, the peak values of the stored energy field and temperature field in the first deformation zone, the second deformation zone, and the third deformation zone in the cutting deformation zone all show an increasing trend. In the second deformation zone, as the wear stage evolves from the initial and normal stages to a rapid one, the peak value of its equivalent force field is lower than that of the first deformation zone due to the intensified thermal softening effect of the material, or it shows a trend of first increasing and then decreasing.
7. A tool lifecycle cutting parameter prediction system based on a stored energy field, characterized in that, include: The parameter acquisition module is used to acquire the initial parameters of the cutting process, including workpiece material properties, cutting parameters, and tool geometry parameters. The energy storage field calculation module is used to establish a finite element model with an embedded energy storage field subroutine based on the initial parameters, and to obtain the energy storage field distribution in the cutting deformation zone by solving the finite element model. The tool wear prediction module is used to establish a tool wear model based on the stored energy field distribution, predict the tool wear amount, and generate a tool wear cloud map; The stage simulation module is used to update the tool geometry based on the predicted tool wear amount, simulate the storage energy field distribution under different tool wear stages, and obtain the storage energy field distribution law of different wear stages. The multi-field coupling analysis module is used to obtain the temperature field, stress field and microstructure distribution of the cutting deformation zone based on the distribution law of the stored energy field and the mapping relationship between stored energy and flow stress, energy balance, displacement density and subgrain size, using the finite element simulation method.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the tool lifecycle cutting parameter prediction method based on the stored energy field as described in any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the tool lifecycle cutting parameter prediction method based on the stored energy field as described in any one of claims 1 to 6.
10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the tool lifecycle cutting parameter prediction method based on the stored energy field as described in any one of claims 1 to 6.