A tool edge design method and system based on asymmetric micro-edge geometry optimization

Abstract

The application discloses a kind of cutter edge design method and system based on asymmetric micro-blade geometry optimization, belong to metal efficient high-precision cutting processing technical field.This method takes asymmetric edge parameters of tool, cutting parameters as input, with tool wear extension curve and tool life and its promotion scheme as output, using the calibration method of simulation solution and experimental observation combination, establish more in line with tool and workpiece material combination wear rate model;And establish the wear simulation module including user-defined wear rate model subroutine, realize more accurate wear increment and tool wear extension solution;Many asymmetric edge cutting tool cutting wear simulation is included simultaneously, the quantitative relationship of micro-blade parameter and tool life is constructed, and a feasible method is provided for tool life improvement target.

Description

Technical Field

[0001] This invention relates to the field of high-efficiency and high-precision metal cutting technology, and more specifically to a tool cutting edge design method and system based on asymmetric micro-edge geometry optimization. Background Technology

[0002] In the field of metal cutting, tool wear and tool life mainly depend on four factors: (1) workpiece material properties; (2) tool-workpiece material contact surface conditions; (3) servo system dynamics; and (4) tool design and properties. Among these factors, the last one, namely tool design and properties, is undoubtedly the most critical. Cutting tools play an important role in modern manufacturing industries, and the performance of tools, such as wear resistance, plays a decisive role in tool application scenarios, productivity, and efficiency. Therefore, many scholars have conducted extensive research on the life and wear problems under the influence of tool design and its properties.

[0003] Kitagawa et al. (T. Kitagawa, A. Kubo, K. Maekawa, Temperature and wear of cutting tools in high-speed machining of Inconel 718 and Ti6Al6V2Sn, Wear, 202(1997) 142-148.) linked tool wear with measured cutting temperatures and found that appropriate tool coatings or cutting speed selection could effectively reduce cutting temperatures, thereby increasing tool life. Fulemova and Jand (J. Fulemova, Z. Janda, Influence of the Cutting Edge Radius and the Cutting Edge Preparation on Tool Life and Cutting Forces at Inserts with Wiper Geometry, Procedia Engineering, 69(2014) 565-573) confirmed through cutting experiments that selecting an appropriate cutting edge radius can effectively improve tool life, and they determined the optimal cutting edge radius to be 15 micrometers. Although many scholars have found that modifying the cutting edge geometry of a tool can bring about a significant improvement in tool life, existing methods are still limited to conducting a large number of cutting experiments. This is equivalent to enumerating the optimal combination within a given tool geometry design range, which has obvious shortcomings in terms of economic input and output.

[0004] Therefore, designing a tool cutting edge design method and system based on asymmetric micro-edge geometry optimization to effectively reduce the R&D cost of tool structure optimization and quickly obtain the optimal cutting edge passivation design for tools under different working conditions and materials is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a tool cutting edge design method and system based on asymmetric micro-edge geometry optimization, which can effectively reduce the R&D cost of tool structure optimization and quickly obtain the optimal cutting edge passivation design for tools under different working conditions and materials.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] On one hand, this invention discloses a tool cutting edge design method based on asymmetric micro-edge geometry optimization, comprising the following steps:

[0008] S1. Obtain input parameters, including tool cutting edge parameters, cutting condition parameters, and tool-workpiece material properties;

[0009] S2. Perform steady-state simulation of right-angle cutting based on the input parameters, and extract the material flow velocity v on the tool surface. s The distribution of normal pressure p and temperature T;

[0010] S3. Construct and call the calibrated wear rate model, and calculate the material flow velocity v on the tool surface. s The distributions of the positive pressure p and temperature T are substituted into the calibrated wear rate model;

[0011] S4. Calculate the wear value of each node on the tool surface per unit time increment, and update the tool edge profile at the current moment.

[0012] S5. Determine the current cutting time t c Has the total cutting time t been reached? tot ;

[0013] S6. If the current cutting time t c Total cutting time t not reached tot Then, the parameters for updating the tool profile will be input into the next unit time interval, repeating S1-S5 until the current cutting time t. c Reaching the total cutting time t tot Output and save tool life values ​​and tool wear propagation curves;

[0014] S7. Modify the tool edge parameters, repeat S1-S6, collect the tool wear expansion curves and tool life values ​​under all tool edge parameter combinations, compare the different tool wear expansion curves, and determine the optimal tool edge parameter combination under a certain working condition based on the tool life values.

[0015] Preferably, the step of constructing the calibrated wear rate model includes:

[0016] Based on right-angle cutting experiments and simulations, and combined with the tool flank wear width VB, the geometric wear rate and mechanistic wear rate were calculated.

[0017] Determine whether the geometric wear rate and the mechanistic wear rate are equal. If they are not equal, the mechanistic wear rate formula is corrected until the geometric wear rate and the mechanistic wear rate are equal. The corrected mechanistic wear rate formula is then output as the calibrated wear rate model.

[0018] Preferably, the geometric wear rate The calculation formula is as follows:

[0019]

[0020] Where α is the tool clearance angle, and λ is the angle between the worn area on the tool flank and the cutting plane. p dVB / dt is the depth of cut, and dVB / dt is the slope of the wear curve.

[0021] Preferably, the formula for the mechanistic wear rate is:

[0022]

[0023] Where A, B, and G are the material constants of the cutting tool and the workpiece, respectively.

[0024] Preferably, the material constants A, B, and G are corrected based on the equality of the geometric wear rate and the mechanistic wear rate to obtain the calibrated wear rate model.

[0025] On the other hand, the present invention also discloses a tool edge design system based on asymmetric micro-edge geometry optimization, used to implement the above-mentioned tool edge design method, including:

[0026] The parameter acquisition module is used to acquire different combinations of input parameters, including tool edge parameters, cutting condition parameters, and tool-workpiece material properties.

[0027] The finite element simulation wear module is used to perform steady-state simulation of right-angle cutting based on the different combinations of input parameters, and to extract the material flow velocity v on the tool surface. s The distribution of normal pressure p and temperature T;

[0028] The model invocation module is used to invoke the calibrated wear rate model and to determine the material flow velocity v on the tool surface. s The distributions of the normal pressure p and temperature T are substituted into the calibrated wear rate model;

[0029] The software post-processing module is used to calculate the wear value of each node on the tool surface per unit time increment under different combinations, and update the tool edge profile at the current moment.

[0030] The output module is used to determine the current cutting time t. c Has the total cutting time t been reached? tot The judgment results are output and saved, along with the tool life values ​​and tool wear propagation curves under different combinations. At the same time, the tool wear propagation curves are compared, and the optimal combination of tool edge parameters under a certain working condition is determined by combining the tool life values.

[0031] Preferably, the system further includes a wear simulation module for constructing the calibrated wear rate model, including:

[0032] The wear rate calculation unit is used to calculate the geometric wear rate and mechanistic wear rate based on right-angle cutting experiments and right-angle cutting simulations, combined with the tool flank wear width VB.

[0033] The judgment and output unit is used to judge whether the geometric wear rate and the mechanistic wear rate are equal. If they are not equal, the mechanistic wear rate formula is corrected until the geometric wear rate and the mechanistic wear rate are equal. The corrected mechanistic wear rate formula is then output as the calibrated wear rate model.

[0034] Preferably, the software post-processing module is further used to extract the tool flank wear width VB based on the updated tool cutting edge contour results, and to calibrate the wear rate model.

[0035] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a tool cutting edge design method and system based on asymmetric micro-edge geometry optimization, which has the following beneficial effects:

[0036] 1. By using a calibration method that combines simulation and experimental observation, a wear rate model that better reflects the combination of tool and workpiece materials is established;

[0037] 2. A wear simulation module containing a user-defined wear rate model subroutine was established, enabling more accurate solutions for wear increment and tool wear extension;

[0038] 3. It encompasses the simulation of cutting wear of numerous asymmetric cutting edge tools, establishes a quantitative relationship between micro-cutting edge parameters and tool life, and provides a practical method for improving tool life. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a method for improving tool life based on asymmetric micro-edge geometry optimization according to the present invention.

[0040] Figure 2(a) shows the wear profile change per unit time, where the shaded area represents the wear area; Figure 2(b) shows the asymmetric micro-edge of the tool.

[0041] Figures 3(a) and 3(b) show the experimental design for right-angle cutting used for material wear rate model calibration and preliminary verification, respectively;

[0042] Figure 4 This is a diagram showing the wear characteristics of the tool back side as observed in a right-angle cutting experiment.

[0043] Figure 5 Wear curves from four sets of right-angle cutting experiments;

[0044] Figure 6 A finite element simulation model of tool cutting was created using the commercial finite element software Third Wave AdvantEdge;

[0045] Figure 7 Comparison of tool simulation results with different preset flank wear values;

[0046] Figure 8 A comparison chart of the wear rate model after it has been imported into the finite element simulation wear module and the wear propagation results of another set of experimental records.

[0047] Figure 9 Fifteen cutting tools designed to investigate the wear propagation and life of different asymmetric cutting tools;

[0048] Figure 10 This is a comparison chart of the tool cutting edge and its temperature field with the number of frames of cutting time in one of the simulations.

[0049] Figure 11 From Figure 10 Comparison of the extracted tool profile with cutting time;

[0050] Figure 12 A comparison diagram showing the changes in the cutting edge profile of a tool during the initial stage of wear;

[0051] Figure 13 Comparison of the profiles of the simulated cutting edges of six cutting tools at the same moment during the initial stage of wear;

[0052] Figure 14 From Figure 13 Comparison of the distribution results of the maximum distance of blade shape change during the initial wear stage of the 6 extracted tools;

[0053] Figure 15 The middle section is a comparison chart of the distribution between the maximum distance of the initial tool wear change in the cutting edge and the percentage increase in the final tool life;

[0054] Figure 16Design drawing for milling experiment;

[0055] Figure 17 This is a schematic diagram showing the expansion and change of wear characteristics at four locations of one of the cutting tools throughout its service life.

[0056] Figure 18(a) shows the wear surface image of the tool in the stable wear stage obtained by experiment; Figure 18(b) shows the wear characteristics of the tool in the stable section obtained by simulation wear; and Figure 18(c) shows the contour comparison of the two.

[0057] Figure 19 A comparison chart of the simulated life curves and experimental measurement results for six cutting tools;

[0058] Figure 20 The simulation and experimental values ​​of the tool life improvement results for 6 tools are shown in a three-dimensional distribution diagram with respect to the initial cutting edge combination of the tools. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] This invention discloses a tool cutting edge design method and system based on asymmetric micro-edge geometry optimization. The method takes the tool's asymmetric cutting edge parameters and cutting parameters as inputs, and the tool wear propagation curve and tool life and its improvement scheme as outputs. This method effectively reduces the R&D cost of tool structure optimization and quickly obtains the optimal cutting edge passivation design for tools under different working conditions and materials.

[0061] On one hand, embodiments of the present invention provide a tool cutting edge design method based on asymmetric micro-edge geometry optimization, the process of which is as follows: Figure 1 As shown, the specific steps are as follows:

[0062] S1. Obtain input parameters, including tool cutting edge parameters: tool cutting edge rake face passivation length S γ and the passivation length S of the back face α Cutting parameters: feed rate f and cutting speed V during cutting. c And the tool-workpiece material properties; wherein, the method for obtaining the tool-workpiece material properties is: inputting the finite element software to construct a cutting simulation model, and selecting the properties of the tool W-Co cemented carbide and the workpiece material Inconel 718 nickel-based superalloy from the software's built-in material library.

[0063] Figure 2(b) shows the asymmetric micro-cutting edge of the tool, from which two characterizing parameters S of the asymmetric tool cutting edge can be seen. γ and S α , respectively, correspond to the blunting lengths of the rake face and flank face of the tool. Point C is assumed to be the intersection of the bisector of the cutting edge angle ∠AOB and the tool profile. This is to facilitate subsequent tool modeling and import into the cutting simulation model.

[0064] S2. Perform steady-state simulation of right-angle cutting based on input parameters, and extract the material flow velocity v on the tool surface. s The distribution of normal pressure p and temperature T;

[0065] S3. Construct and call the calibrated wear rate model, and include the material flow velocity v on the tool surface. s The distributions of normal pressure p and temperature T are substituted into the calibrated wear rate model.

[0066] The method for constructing the calibrated wear rate model is as follows:

[0067] S31. Based on right-angle cutting experiments and simulations, and combined with the tool flank wear width VB, calculate the geometric wear rate and mechanistic wear rate.

[0068] Specifically, using right-angle cutting experiments and simulations, the geometric wear rate of the tool under different initial wear values ​​was calculated. and mechanistic wear rate The following is an example using VB. t and VB t+1 This represents the result of the tool flank wear width VB at two consecutive time points t and t+1. Assuming the tool wear surface is flat, the increase in wear area between the two time points can be obtained through geometric relationships (refer to the wear profile change per unit time shown in Figure 2(a), where the shaded area is the wear area):

[0069]

[0070] In the above formula, variable S t and S t+1 Let α and λ represent the wear area of ​​the tool flank at the corresponding time, respectively. α is the tool clearance angle, and λ is the angle between the wear area of ​​the tool flank and the cutting plane. From this, the wear rate (volume loss per unit time) based on geometric relationships can be obtained as follows:

[0071]

[0072] In the above formula, Δt represents the increment per unit time, and a p For the cutting depth, since the wear value is very small, the above formula can be simplified by removing the square term.

[0073]

[0074] The above formula indicates that the tool wear rate can be approximately solved by the slope dVB / dt of the wear curve measured experimentally and the tool geometry. Figures 3(a) and 3(b) show the right-angle cutting experimental design used for material wear rate model calibration and preliminary verification, respectively, where the cutting tool has an initial blunt rounded cutting edge radius of 20 micrometers.

[0075] Figure 4 These are the tool flank wear characteristics observed in right-angle cutting experiments. Figure 4 (a) is the back face of the new cutter. Figure 4 (b) The results show that adhesive wear and abrasive wear are the main wear forms during cutting of this material. Observations of the tool wear mechanism through right-angle cutting experiments revealed that abrasive wear and adhesive wear are dominant. Therefore, the mechanistic wear rate can be obtained from the empirical model of the wear mechanism as follows:

[0076]

[0077] In the above formula This represents the wear rate derived from the wear mechanism, with variables p and v. s T represents the stress, relative velocity, and local temperature at the tool-workpiece contact surface, respectively. A, B, and G are model constants related to the combination of tool and workpiece materials.

[0078] The right side of the above formula contains the sum of two wear results: abrasive wear and adhesive wear.

[0079] S32. Determine whether the geometric wear rate and the mechanistic wear rate are equal. If they are not equal, correct the mechanistic wear rate formula until the geometric wear rate and the mechanistic wear rate are equal. Output the corrected mechanistic wear rate formula as the calibrated wear rate model.

[0080] Figure 5 The wear curves for the four sets of right-angle cutting experiments are as follows: Figure 5 As shown in the figure, this study uses the linear fitting slope of its wear stabilization stage to calculate the wear rate, specifically by substituting the slope into the geometric wear rate model established based on Figure 2.

[0081] Specifically, the experimental parameters for right-angle cutting are shown in Table 1.

[0082] Table 1 shows the right-angle cutting experiments used for wear rate model calibration.

[0083]

[0084]

[0085] Figure 6This is a finite element simulation model of tool cutting established using the commercial finite element software Third Wave AdvantEdge; the tool is user-defined and then the cutting model is generated by importing from an external source.

[0086] Figure 7 Comparison of tool simulation results with different preset flank wear values; among them Figure 7 In diagram (a), the tool wear width is preset to 200 micrometers on the flank face of the tool. Other widths have gradients of 50, 100, and 150 micrometers. After the simulation reaches steady state, according to... Figure 7 P1 to P2 are shown in (a) 20 Extract the material flow velocity between the worn area on the back face of the tool and the workpiece, in the range of Q1 to Q. 20 Extract the interface temperature and stress results along the path, and finally obtain Figure 7 The results for groups (b)-(d) are presented. Finally, the average value of each curve is calculated and substituted into the above-mentioned mechanism of wear rate. Simultaneously, corresponding right-angle cutting experiments were conducted to extract the corresponding wear propagation curves and calculate the geometric wear rate. Since the two should be equal, three equations are obtained. After solving for the three material constants in the model, the final wear mechanism model is obtained, which serves as the wear rate model after calibration:

[0087]

[0088] Furthermore, the method for calling the calibrated wear rate model is as follows:

[0089] By secondary development, the calibrated wear rate model is embedded into the user subroutine UserWearModel.f. The core solver for cutting simulation is recompiled to give it a new wear subroutine. In the software, the wear simulation module is selected, the user-defined wear model is chosen, and the three calibrated material constants are input. Then, in the wear model iterative solution interface, the wear unit time increment is input as 5 seconds, the wear smoothing angle is 150 degrees, and the total wear time is 100 minutes. This enables the calling of the calibrated wear rate model.

[0090] Figure 8 This diagram illustrates the comparison between the calibrated wear rate model imported into the finite element cutting simulation and another set of experimentally recorded wear propagation results. This demonstrates that the calibrated wear rate model is reasonable.

[0091] S4. Calculate the wear value of each node on the tool surface per unit time increment, and update the tool edge profile at the current moment.

[0092] Specifically, based on the material flow velocity v on the tool surface extracted from the simulation results in S3... sThe distribution of normal pressure p and temperature T is substituted into the wear rate model to solve for the wear increment on the tool surface per unit time. Based on this result, the model updates the tool edge profile after reaching the unit time increment.

[0093] S5. Extract the tool flank wear width VB based on the updated tool cutting edge profile, and determine the current cutting time t. c Has the total cutting time t been reached? tot .

[0094] S6. If the current cutting time t c Total cutting time t not reached tot Then, the parameters for updating the tool profile will be input into the next unit time interval, repeating S1-S5 until the current cutting time t. c Reaching the total cutting time t tot It outputs and saves tool life values ​​and tool wear propagation curves.

[0095] S7. Modify the tool edge parameters, repeat S1-S6, collect the tool wear expansion curves and tool life values ​​under all tool edge parameter combinations, compare the different tool wear expansion curves, and determine the optimal tool edge parameter combination under a certain working condition based on the tool life values.

[0096] Once all possible combinations are completed, the tool wear propagation curves of different initial cutting edges are compared to determine the cutting edge parameter combination that maximizes tool life under this working condition. Finally, tool preparation and cutting experiments are conducted to verify the optimal cutting edge combination.

[0097] Figure 9 Fifteen cutting tools were designed to investigate the wear propagation and life of different asymmetric cutting tools. Their cutting edge geometry encompasses the range achievable by existing passivation techniques, and the combinations of parameters also cover common waterfall and trumpet-shaped cutting edges, as shown in Table 2.

[0098] Table 2 shows the cutting edge dulling parameters and horizontal design used to investigate the influence of cutting edge geometry.

[0099]

[0100] The wear curves and final lifespans for different parameter combinations were obtained using the method proposed in the embodiments of the present invention. The final results are shown in Table 3.

[0101] Table 3. Statistics of Simulation Life Results for Tools with Different Cutting Edge Geometries

[0102]

[0103]

[0104] Figure 10The simulation compares the cutting edge and its temperature field with the number of frames of cutting time in one set of simulations. In 1144 frames, the cutting edge gradually wears down from the initial waterfall-shaped cutting edge to a final double chamfered shape.

[0105] Figure 11 From Figure 10 The image shows a comparison of the tool profile extracted from the image as a function of cutting time. The number of cutting iteration frames is converted into the tool cutting time, which allows us to see the relationship between tool life and wear propagation.

[0106] Figure 12 The image shows a comparison of the changes in the cutting edge profile of a tool during the initial stage of wear. It can be seen that the cutting edge profile changes drastically in the initial stage of wear, which coincides with the phenomenon of a large wear rate in this stage. However, in the stable wear stage, the cutting edge profile of the tool exhibits a more regular change.

[0107] Figure 13 The study presents a comparison of the simulated cutting edge profiles of six cutting tools at the same moment during the initial wear stage. It reveals that although the initial results of the six tools differ significantly, the wear width on the flank face of all six tools after the initial wear stage is between 20 and 13 micrometers, indicating that the cutting edge profiles tend to become more uniform. Simultaneously, by measuring the maximum distance between the initial cutting edge profile point and the post-wear profile point, it was found that the wear distance varies considerably among tools with different initial cutting edges.

[0108] Figure 14 From Figure 13 A comparison of the distribution results of the maximum distance of edge shape change during the initial wear stage of the six extracted tools reveals significant differences in the height and stability of the distribution curves corresponding to tools with different initial edge shapes. Among them, the edge combination S... γ =S α =20μm shows the lowest value and the most stable curve, indicating that the combined cutting edge has a relatively stable cutting shape change in the initial stage of wear.

[0109] Figure 15 The middle part compares the distribution of the maximum distance of the edge profile change in the initial wear of the tool with the percentage increase in the final tool life. By fitting the two sets of data, it can be found that as the tool passivation parameter K increases, the initial wear distance of the tool gradually decreases, while the tool life increases rapidly. This indicates that the edge profile change distance in the initial stage of wear is related to the change in tool life.

[0110] Figure 16 To verify the optimal cutting edge design obtained from the above simulation results, various cutting edge designs were carried out for the milling experiment, including standard tools and improved tools. The milling experiment conditions are shown in Table 4.

[0111] Table 4. Milling test conditions for nickel-based superalloy Inconel 718

[0112]

[0113] Figure 17 The study explored the wear characteristics of one of the cutting tools at four locations throughout its service life. It was found that pitting wear was the main wear form on the cutting edge until the tool failed. Furthermore, the study also found that the failure modes of the tool included chipping and detachment.

[0114] Figure 18(a) shows the wear surface image of the tool in the stable wear stage obtained by experiment, while Figure 18(b) shows the wear characteristics of the tool in the stable section obtained by simulation wear, and Figure 18(c) shows the contour comparison of the two. It can be seen that the simulation results are in good agreement with the experimental observations. Both show a wear surface with a similar negative chamfer shape on the back face. The simulation results show a certain wear tip on the wear surface. This may be because the simulation cannot consider the tool wear caused by built-up edge. The middle of the wear surface is subjected to huge pressure and temperature, which can easily generate built-up edge in actual cutting.

[0115] Figure 19 The life curves of the six cutting tools were compared with the experimental measurement results. It can be seen that the simulation results are in good agreement with the experimental measurement results. The cutting edge information of the six cutting tools is shown in Table 5.

[0116] Table 5. Statistics on the cutting edge information of 6 milling tools in the milling experiment.

[0117]

[0118]

[0119] Figure 20 The figure shows the three-dimensional distribution relationship between the simulation and experimental values ​​of the tool life improvement results and the initial cutting edge combination of the tool. It can be seen from the figure that improving the initial cutting edge passivation K value and radius within a certain range can effectively improve the tool life, with the highest improvement reaching 27%. These experimental results also match the tool life surface prediction constructed from the simulation results, thus further verifying the rationality and accuracy of the proposed tool life improvement method.

[0120] On the other hand, embodiments of the present invention also propose a tool edge design system based on asymmetric micro-edge geometry optimization, used to implement the above-mentioned tool edge design method, including:

[0121] The parameter acquisition module is used to acquire different combinations of input parameters, including tool edge parameters, cutting condition parameters, and tool-workpiece material properties.

[0122] The finite element simulation wear module is used to perform steady-state simulation of right-angle cutting based on different combinations of input parameters, and to extract the material flow velocity v on the tool surface. s The distribution of normal pressure p and temperature T;

[0123] The model call module is used to call the calibrated wear rate model and calculate the material flow velocity v on the tool surface. s The distributions of normal pressure p and temperature T are substituted into the calibrated wear rate model;

[0124] The software post-processing module is used to calculate the wear value of each node on the tool surface per unit time increment under different combinations, and update the tool edge profile at the current moment.

[0125] The output module is used to determine the current cutting time t. c Has the total cutting time t been reached? tot The judgment results are output and saved, along with the tool life values ​​and tool wear propagation curves under different combinations. At the same time, the tool wear propagation curves are compared, and the optimal combination of tool edge parameters under a certain working condition is determined by combining the tool life values.

[0126] Furthermore, the system also includes a wear simulation module for constructing a calibrated wear rate model, including:

[0127] The wear rate calculation unit is used to calculate the geometric wear rate and mechanistic wear rate based on right-angle cutting experiments and simulations, combined with the initial tool wear value VB.

[0128] The judgment and output unit is used to determine whether the geometric wear rate and the mechanistic wear rate are equal. If they are not equal, the mechanistic wear rate formula is corrected until the geometric wear rate and the mechanistic wear rate are equal. The corrected mechanistic wear rate formula is then output as the calibrated wear rate model.

[0129] Furthermore, the software post-processing module is also used to extract the initial tool wear value VB based on the updated tool edge profile results.

[0130] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0131] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tool cutting edge design method based on asymmetric micro-edge geometry optimization, characterized in that, Includes the following steps: S1. Obtain input parameters, including tool cutting edge parameters, cutting condition parameters, and tool-workpiece material properties; S2. Perform steady-state simulation of right-angle cutting based on the input parameters, and extract the material flow velocity on the tool surface. positive pressure p and temperature T Distribution; S3. Construct and call the calibrated wear rate model to measure the material flow rate on the tool surface. The distributions of the normal pressure p and temperature T are substituted into the calibrated wear rate model; The method for constructing the calibrated wear rate model is as follows: S31. Based on right-angle cutting experiments and simulations, and combined with the tool flank wear width VB, calculate the geometric wear rate and the mechanistic wear rate; Using right-angle cutting experiments and simulations, the geometric wear rate of the tool under different initial wear values ​​was calculated. and mechanistic wear rate ,by and Indicates the width of the wear on the flank face of the tool. At two consecutive moments and The following results, assuming the tool wear surface is flat, show the increase in wear area between two time points obtained through geometric relationships: ； variables in the formula and These represent the wear area of ​​the flank face at the corresponding time points. It is the back angle of the cutting tool. The angle between the worn area on the tool's flank and the cutting plane is used to obtain the geometric wear rate: ； In the formula, Indicates the increment per unit of time. For the cutting depth, since the wear value is very small, the formula for calculating the geometric wear rate is simplified by removing the square term: ； In the formula, It is the slope of the wear curve; S32. Determine whether the geometric wear rate and the mechanistic wear rate are equal. If they are not equal, correct the mechanistic wear rate formula until the geometric wear rate and the mechanistic wear rate are equal. Output the corrected mechanistic wear rate formula as the calibrated wear rate model. S4. Calculate the wear value of each node on the tool surface per unit time increment, and update the tool edge profile at the current moment. S5. Determine the current cutting time. Has the total cutting time been reached? ; S6, if the current cutting time Total cutting time not reached Then, the parameters for updating the tool profile will be input into the next unit time interval, repeating S1-S5 until the current cutting time. Reaching the total cutting time Output and save tool life values ​​and tool wear propagation curves; S7. Modify the tool edge parameters, repeat S1-S6, collect the tool wear expansion curves and tool life values ​​under all tool edge parameter combinations, compare the different tool wear expansion curves, and determine the optimal tool edge parameter combination under a certain working condition based on the tool life values.

2. The tool cutting edge design method based on asymmetric micro-edge geometry optimization according to claim 1, characterized in that, The formula for the wear rate mechanism is: ； in, These are the material constants for the cutting tool and the workpiece.

3. The tool cutting edge design method based on asymmetric micro-edge geometry optimization according to claim 2, characterized in that, The material constant is determined by the equality of the geometric wear rate and the mechanistic wear rate. A,B,G The model is then corrected to obtain the calibrated wear rate model.

4. A tool edge design system based on asymmetric micro-edge geometry optimization, used to implement the tool edge design method based on asymmetric micro-edge geometry optimization as described in any one of claims 1-3, characterized in that, include: The parameter acquisition module is used to acquire different combinations of input parameters, including tool edge parameters, cutting condition parameters, and tool-workpiece material properties. The finite element simulation wear module is used to perform steady-state simulation of right-angle cutting based on the different combinations of input parameters, and to extract the material flow velocity on the tool surface. positive pressure p and temperature T Distribution; The model invocation module is used to invoke the calibrated wear rate model and calculate the material flow rate on the tool surface. positive pressure p and temperature T The distribution is substituted into the calibrated wear rate model; The software post-processing module is used to calculate the wear value of each node on the tool surface per unit time increment under different combinations, and update the tool edge profile at the current moment. The output module is used to determine the current cutting time. Has the total cutting time been reached? The judgment results are output and saved, along with the tool life values ​​and tool wear propagation curves under different combinations. At the same time, the tool wear propagation curves are compared, and the optimal combination of tool edge parameters under a certain working condition is determined by combining the tool life values.

5. The tool cutting edge design system based on asymmetric micro-edge geometry optimization according to claim 4, characterized in that, The system also includes a wear simulation module for obtaining the calibrated wear rate model, including: The wear rate calculation unit is used to calculate the geometric wear rate and mechanistic wear rate based on right-angle cutting experiments and right-angle cutting simulations, combined with the tool flank wear width VB. The judgment and output unit is used to judge whether the geometric wear rate and the mechanistic wear rate are equal. If they are not equal, the mechanistic wear rate formula is corrected until the geometric wear rate and the mechanistic wear rate are equal. The corrected mechanistic wear rate formula is then output as the calibrated wear rate model.

6. The tool cutting edge design system based on asymmetric micro-edge geometry optimization according to claim 5, characterized in that, The software post-processing module is also used to extract the tool flank wear width VB based on the updated tool cutting edge contour results, and to calibrate the wear rate model.

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