Construction method of alloy cutting model

Abstract

The invention discloses a construction method of an alloy cutting model, and relates to the technical field of electric digital data processing. The construction method of the alloy cutting model comprises the following steps: acquiring an alloy material sample according to alloy material characteristic parameters; obtaining a cutting tool wear degree index of the alloy material sample; obtaining an elastic-plastic reference index of the alloy material sample; modeling and simulating to construct an alloy cutting model to obtain an optimal parameter combination; and obtaining a comprehensive cutting index of the alloy cutting model. According to the method, an alloy material sample is obtained through characteristic parameters of an alloy material, then a corresponding cutting tool wear degree index is obtained through a scanning electron microscope, a tensile test and a compression test are performed to obtain a corresponding elastic-plastic reference index, and then an optimal parameter combination is obtained through modeling simulation; and finally, the cutting comprehensive index of the alloy cutting model is obtained, the effect of improving the cutting efficiency of the alloy material is achieved, and the problem that the cutting efficiency of the alloy material is not high in the prior art is solved.

Description

[0001] This application is a divisional application of the application filed on September 27, 2024, with application number 202411356811.5 and invention name “Method for constructing alloy cutting model”. Technical Field

[0002] The invention relates to the technical field of electrical digital data processing, and in particular to a method for constructing an alloy cutting model. Background Art

[0003] With the development of industrial manufacturing technology, the demand for improving production efficiency and reducing costs is increasing. Alloy materials have the characteristics of high strength and high hardness, but are generally difficult to process. The processing characteristics of alloy materials pose challenges to the selection and design of cutting tools. By constructing a cutting model, the influence of different tool parameters on the cutting process can be evaluated, thereby optimizing tool design and improving cutting efficiency and tool life.

[0004] The machining process of alloy materials is prone to problems such as tool wear and poor surface quality, which affect the machining quality. Constructing a cutting model can help analyze factors such as tool wear, cutting temperature distribution, and cutting deformation during the cutting process, provide a basis for optimizing the machining process, and improve the machining quality. In the machining of alloy materials, reducing material waste and improving material utilization are important economic and environmental considerations. Constructing a cutting model can help optimize machining paths and cutting parameters, reduce material waste, and improve material utilization. With the development of numerical simulation technology, the cutting process can be more accurately predicted and simulated with the help of computer simulation and numerical simulation technology. Therefore, constructing an alloy cutting model helps to optimize cutting process parameters and improve the efficiency of cutting.

[0005] The existing system is mainly based on experimental data, theoretical models and numerical simulations, taking into account material properties, cutting parameters, and mechanical and thermal behaviors during the cutting process, and using a combination of experimental, theoretical and numerical simulation methods to construct an alloy cutting model.

[0006] For example, the invention patent with announcement number: CN104392090B announces a method for constructing an end milling cutting force and cutting deformation model for aluminum alloy materials, including: establishing a milling force prediction model based on average cutting force, and establishing a quadratic polynomial model of the cutting force coefficient, which is a key factor in solving the milling force, about four cutting parameters: feed per tooth, axial cutting depth, cutting speed and single-side cutting width, according to the analytical relationship between the instantaneous undeformed chip layer thickness and the transient milling force. The single-side cutting width represents the change in the cutting-in and cutting-out angles caused by different tool paths during the end milling process. By carrying out a four-factor four-level end milling cutting force measurement test, the coefficients in the cutting force coefficient model are regressed using the least squares method to obtain the cutting parameter effect. The influence of cutting force coefficient is considered, and a milling force prediction model based on average cutting force is established; a milling force prediction model based on bevel cutting mechanism is established, and analytical calculation is carried out on the relationship between cutting force and cutting parameters in bevel cutting. Based on the Johnson-Cook material constitutive model of cast aluminum alloy, the finite element simulation method is used to predict and solve the basic quantities of shear angle cutting, and the cutting force coefficient in the milling force prediction model is calculated, and a milling force prediction model based on bevel cutting mechanism is established; based on the established milling force prediction model based on average cutting force and the milling force prediction model based on bevel cutting mechanism, single-tooth and multi-tooth transient milling force predictions are carried out respectively and compared with experimental data, and the causes of cutting force errors are analyzed in combination with the model establishment process.

[0007] For example, the invention patent with announcement number: CN113868912B announces a method for identifying and correcting parameters of the JC constitutive model of a titanium alloy, including: selecting a titanium alloy specimen for a right-angle cutting test, recording a first cutting force, and measuring a chip thickness value; calculating specimen parameters of the titanium alloy specimen based on the cutting parameters and chip thickness value of the right-angle cutting test; solving model parameters of the titanium alloy JC constitutive model based on the specimen parameters and the initial parameters of the titanium alloy JC constitutive model; performing a simulated right-angle cutting test based on the model parameters, and extracting a second cutting force in the simulation test; calculating the error between the first cutting force and the second cutting force, and when the error is less than an error threshold, constructing a titanium alloy JC constitutive model based on the model parameters.

[0008] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, the machining process of alloy materials is prone to low precision of experimental equipment, resulting in low efficiency in cutting the alloy materials. Summary of the invention

[0009] The embodiment of the present application solves the problem of low efficiency in alloy material cutting in the prior art by providing a method for constructing an alloy cutting model, thereby improving the efficiency in alloy material cutting.

[0010] The embodiment of the present application provides a method for constructing an alloy cutting model, comprising the following steps: obtaining an alloy material sample according to characteristic parameters of the alloy material; measuring the degree of wear of the cutting tool corresponding to the alloy material sample after cutting by a scanning electron microscope, and obtaining a cutting tool wear degree index according to the measurement results, performing a tensile test and a compression test on the alloy material sample, and obtaining a stress-strain curve of the alloy material sample according to the results of the tensile test and the compression test, thereby obtaining an elastic-plastic reference index of the alloy material sample; setting parameters by a preset cutting speed range, a preset feed speed range, and a preset cutting depth range by computer-aided engineering software and numerical simulation software, submitting the simulation results to the simulation software for modeling and simulation to obtain an alloy cutting model, wherein the alloy cutting model is used to determine a preset processing parameter combination according to the actual cutting conditions of the alloy material sample, thereby obtaining an optimal parameter combination, wherein the optimal parameter combination includes an optimal cutting tool wear index and an optimal elastic-plastic reference index; obtaining a comprehensive cutting index of the alloy material according to the obtained optimal parameter combination of the cutting tool of the alloy material sample and in combination with the corresponding cutting tool wear degree index and the elastic-plastic reference index, wherein the comprehensive cutting index is used to evaluate the wear degree and elastic-plastic difference of the alloy material during the cutting process.

[0011] Furthermore, a specific method for obtaining the cutting tool wear index of the alloy material sample is as follows: the alloy material sample is cut by a lathe, and the load of the cutting tool of the alloy material sample during the machining process is measured by a force sensor; the material mass of the cutting tool of the alloy material sample before and after the cutting process is measured by a precision analytical balance, and the corresponding wear amount is obtained accordingly; the tip wear degree and edge deformation degree of the cutting tool corresponding to the alloy material sample after the cutting process are measured by a scanning electron microscope to obtain the corresponding wear length; the cutting force coefficient of the alloy material sample is obtained in combination with the actual conditions of the cutting process, and the corresponding cutting tool wear index is obtained in combination with the wear length, load and wear amount of the cutting tool of the alloy material sample.

[0012] Furthermore, the cutting tool wear index is calculated using the following formula: ; In the formula, represents the cutting tool wear index corresponding to the i-th alloy material sample, , i represents the number of the alloy material sample, m represents the total number of alloy material samples, represents the cutting force coefficient of the i-th alloy material sample, represents the cutting tool load of the ith alloy material sample, represents the wear length of the cutting tool of the i-th alloy material sample, represents the wear amount of the cutting tool of the i-th alloy material sample, Indicates the preset wear depth, represents the preset wear amount, and e represents a natural constant.

[0013] Furthermore, the specific process of obtaining the elastic-plastic reference index of the alloy material sample is as follows: the slope of the stress-strain curve is obtained by the least squares method, thereby obtaining the elastic modulus of the alloy material sample, and the elastic modulus is the proportional coefficient between stress and strain; the elastic-plastic reference index of the alloy material sample is obtained by combining the obtained elastic modulus, stress and strain of the alloy material sample.

[0014] Furthermore, the elastic-plastic reference index is calculated using the following formula: ; In the formula, represents the elastic-plastic reference index of the i-th alloy material sample, i , m represents the total number of alloy material samples, i represents the number of alloy material samples, represents the elastic modulus of the ith alloy material sample, represents the stress of the ith alloy material sample, represents the strain of the ith alloy material sample, Indicates the preset elastic modulus.

[0015] Furthermore, the specific method for obtaining the cutting comprehensive index is as follows: obtaining the performance difference weight of the alloy material sample according to the subjective weighting method, wherein the performance difference weight includes the wear weight and the elastic-plastic weight; obtaining the cutting tool wear coefficient of the alloy material sample according to the cutting tool wear index and the optimal cutting tool wear index of the alloy material sample; obtaining the elastic-plastic coefficient of the alloy material sample according to the elastic-plastic reference index and the optimal elastic-plastic reference index of the alloy material sample; obtaining the cutting comprehensive index of the alloy material according to the obtained cutting tool wear coefficient and the elastic-plastic coefficient of the alloy material sample in combination with the performance difference weight.

[0016] The embodiment of the present application provides a system for constructing an alloy cutting model, including an alloy material sample acquisition module, a wear degree measurement module, an elastic-plastic measurement module, an alloy cutting model construction module, and a cutting comprehensive index acquisition module; wherein the alloy material sample acquisition module is used to obtain an alloy material sample according to the characteristic parameters of the alloy material; the wear degree measurement module is used to measure the wear degree of the cutting tool corresponding to the alloy material sample after cutting through a scanning electron microscope, and obtain the cutting tool wear degree index of the alloy material sample according to the measurement result, and the cutting tool wear degree index is used to measure the wear degree of the cutting tool of the alloy material sample; the elastic-plastic measurement module is used to perform tensile test and compression test on the alloy material sample, and obtain the composite cutting index according to the results of the tensile test and the compression test. The stress-strain curve of the gold material sample is used to obtain the elastic-plastic reference index of the alloy material sample; the alloy cutting model construction module is used to obtain the alloy cutting model through modeling and simulation by computer-aided engineering software and numerical simulation software, and the alloy cutting model is used to determine the preset processing parameter combination according to the actual cutting conditions of the alloy material sample, and obtain the optimal parameter combination according to the optimal parameter combination of the cutting tool and the optimal elastic-plastic reference index; the cutting comprehensive index acquisition module is used to obtain the cutting comprehensive index of the alloy material according to the optimal parameter combination of the cutting tool of the alloy material sample obtained and combined with the corresponding cutting tool wear degree index and elastic-plastic reference index, and the cutting comprehensive index is used to evaluate the wear degree and elastic-plastic difference of the alloy material during the cutting process.

[0017] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Obtain alloy material samples through characteristic parameters of alloy materials, and then measure the degree of wear of cutting tools corresponding to alloy material samples after cutting through scanning electron microscope to obtain cutting tool wear degree index of alloy material samples, and perform tensile test and compression test on alloy material samples to obtain stress-strain curve of alloy material samples, and obtain elastic-plastic reference index of alloy material samples accordingly, and then perform modeling and simulation through computer-aided engineering software and numerical simulation software, and finally obtain comprehensive cutting index of alloy cutting model according to the optimal parameter combination of cutting tools of alloy material samples, thereby realizing evaluation of comprehensive cutting ability of alloy cutting model, and then realizing improvement of cutting processing efficiency of alloy materials, and effectively solving the problem of low cutting processing efficiency of alloy materials in prior art.

[0018] 2. By obtaining the cutting tool wear index and the elastic-plastic reference index of the alloy material sample, and obtaining the optimal parameter combination of the cutting tool of the alloy material sample based on modeling and simulation, and finally obtaining the comprehensive cutting index of the alloy cutting model based on the optimal parameter combination, the alloy cutting process can achieve the best effect, thereby achieving a more accurate evaluation of the cutting ability of the alloy cutting model.

[0019] 3. The slope of the stress-strain curve is obtained by the least squares method to obtain the elastic modulus of the alloy material sample, and the elastic-plastic reference index of the alloy material sample is obtained by combining the elastic modulus, stress and strain of the alloy material sample, thereby realizing the evaluation of the elastic and plastic behavior of the alloy cutting model, and then realizing a more accurate evaluation of the comprehensive cutting capability of the alloy cutting model. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a method for constructing an alloy cutting model provided in an embodiment of the present application; Figure 2 A statistical chart showing changes in the comprehensive cutting index of the alloy material provided in the embodiment of the present application; Figure 3 A schematic diagram of the structure of a system for constructing an alloy cutting model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The embodiment of the present application solves the problem of low efficiency of cutting processing in the prior art by providing a method for constructing an alloy cutting model. First, an alloy material sample is obtained according to the characteristic parameters of the alloy material, and the degree of wear of the cutting tool corresponding to the alloy material sample after cutting is measured by a scanning electron microscope. Then, a cutting tool wear degree index is obtained according to the measurement results, and a tensile test and a compression test are performed on the alloy material sample. Then, the stress-strain curve of the alloy material sample is obtained according to the results of the tensile test and the compression test, and the elastic-plastic reference index of the alloy material sample is obtained accordingly. Then, the alloy cutting model is obtained by modeling and simulating through computer-aided engineering software and numerical simulation software, and the optimal parameter combination is obtained accordingly. Finally, the comprehensive cutting index of the alloy material is obtained according to the optimal parameter combination of the cutting tool of the obtained alloy material sample and combined with the corresponding cutting tool wear degree index and the elastic-plastic reference index, thereby improving the cutting efficiency of the alloy material.

[0022] The technical solution in the embodiment of the present application is to solve the problem of low efficiency of alloy material cutting in the above-mentioned prior art, and the overall idea is as follows: The corresponding cutting tool wear index is obtained by measuring the wear degree of the cutting tool corresponding to the alloy material sample after cutting. Then, tensile and compression tests are carried out to obtain the elastic-plastic reference index of the alloy material sample. Then, the alloy cutting model is obtained through modeling and simulation to obtain the optimal parameter combination. Finally, the cutting tool wear index and the elastic-plastic reference index are combined to obtain the comprehensive cutting index of the alloy material, thereby achieving the effect of improving the cutting efficiency of the alloy material.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0024] like Figure 1 As shown, it is a flow chart of the method for constructing the alloy cutting model provided in the embodiment of the present application. The method for constructing the alloy cutting model provided in the embodiment of the present application comprises the following steps: obtaining an alloy material sample according to the characteristic parameters of the alloy material, the characteristic parameters including the strength, hardness and plasticity of the alloy material, measuring the degree of wear of the cutting tool corresponding to the alloy material sample after cutting by a scanning electron microscope, and obtaining the cutting tool wear degree index of the alloy material sample according to the measurement result, the cutting tool wear degree index is used to measure the degree of wear of the cutting tool of the alloy material sample, performing a tensile test and a compression test on the alloy material sample, and obtaining the stress-strain curve of the alloy material sample according to the results of the tensile test and the compression test, thereby obtaining the elastic-plastic reference index of the alloy material sample, the tensile test is used to evaluate the tensile properties of the alloy material sample, the compression test is used to evaluate the compressive properties of the alloy material sample, and the stress-strain curve The line is used to describe the deformation degree of the alloy material sample, and the elastic-plastic reference index is used to measure the elastic-plasticity of the alloy material sample; the alloy cutting model is obtained by modeling and simulation through computer-aided engineering software and numerical simulation software. The alloy cutting model is used to determine the preset processing parameter combination according to the actual cutting conditions of the alloy material sample, and the optimal parameter combination is obtained accordingly. The optimal parameter combination includes the optimal cutting tool wear index and the optimal elastic-plastic reference index. The preset processing parameter combination is used to maximize the cutting efficiency. The optimal cutting tool wear index represents the minimum wear degree of the cutting tool, and the optimal elastic-plastic reference index represents the optimal elastic-plasticity of the alloy material sample. The cutting comprehensive index of the alloy material is obtained according to the optimal parameter combination of the cutting tool of the alloy material sample obtained and combined with the corresponding cutting tool wear index and elastic-plastic reference index. The cutting comprehensive index is used to evaluate the wear degree and elastic-plastic difference of the alloy material during the cutting process.

[0025] In this embodiment, obtaining the characteristic parameters of the alloy material requires strength testing, hardness testing and plasticity testing. The alloy material is hardness tested with a universal testing machine and the corresponding strength index is recorded. The alloy material is hardness tested with a Rockwell hardness tester and the corresponding hardness index is recorded. A compression test is performed, a stress-strain curve is recorded, and the corresponding plasticity index is recorded. A universal testing machine is a machine used to perform physical property tests on materials, parts, components, etc. It can apply loads such as force, stress, displacement or torque in a controlled environment to evaluate the strength, stiffness, wear resistance, ductility and other performance indicators of the material. A Rockwell hardness tester is an instrument commonly used to measure the hardness of a material. It evaluates the hardness of a material by applying a certain load on the surface of the material and measuring the diameter of the dent or mark on the surface of the material under the load. The cutting efficiency of the alloy material is improved.

[0026] Furthermore, the process of obtaining alloy material samples is as follows: a hardness test is performed on the alloy material to evaluate the corresponding hardness, and the first group of samples are obtained by comparing and screening with the preset hardness requirement. The hardness test is used to test the hardness of the alloy material; a strength test is performed on the second group of samples to evaluate the corresponding strength, and the second group of samples are obtained by comparing and screening with the preset strength requirement. The strength test is used to test the strength of the alloy material; a plasticity test is performed on the third group of samples to evaluate the corresponding plasticity, and the alloy material samples are obtained by comparing with the preset plasticity requirement. The plasticity test is used to test the plasticity of the alloy material.

[0027] In this embodiment, the preset hardness data is determined by taking the average value after statistics on the historical hardness data of the alloy material, the preset strength data is determined by taking the average value after statistics on the historical strength data of the alloy material, and the preset plasticity data is determined by taking the average value after statistics on the historical plasticity data of the alloy material. The hardness of the first group of samples meets the preset hardness data, the strength of the second group of samples meets the preset strength data, and the plasticity of the third group of samples meets the preset plasticity data; thereby improving the cutting processing efficiency.

[0028] Furthermore, a specific method for obtaining the cutting tool wear index of the alloy material sample is as follows: the alloy material sample is cut by a lathe, and the load of the cutting tool of the alloy material sample during the machining process is measured by a force sensor; the material mass of the cutting tool of the alloy material sample before and after the cutting process is measured by a precision analytical balance, and the corresponding wear amount is obtained accordingly; the tip wear degree and edge deformation degree of the cutting tool corresponding to the alloy material sample after the cutting process are measured by a scanning electron microscope to obtain the corresponding wear length; the cutting force coefficient of the alloy material sample is obtained in combination with the actual conditions of the cutting process, and the corresponding cutting tool wear index is obtained in combination with the wear length, load and wear amount of the cutting tool of the alloy material sample. The cutting force coefficient is used to describe the relationship between the cutting force and the cutting conditions in the cutting process.

[0029] In this embodiment, a force sensor is a device for measuring and detecting force, which usually converts force into an electrical signal or other easily processed form; a precision analytical balance is a high-precision balance for accurately measuring mass, which is usually used in laboratories, scientific research institutions and production processes for mass measurement requiring extremely high precision. They have higher accuracy and sensitivity than ordinary balances, and are usually equipped with a series of functions and features to ensure accurate mass measurement; a scanning electron microscope is a high-resolution microscope that uses an electron beam instead of light to form an image of the sample surface, has a high magnification and high depth clarity, and can observe the surface morphology, structure and composition of the sample at the microscopic scale; the cutting tool wear index of the alloy material sample is obtained; and the processing efficiency of alloy cutting is improved.

[0030] Furthermore, the cutting tool wear index can be obtained by collecting the cutting tool wear data of alloy material tests reported in the literature and randomly selecting some data points as the initial cluster centers, and then using the K-means clustering algorithm to iteratively update the center points until convergence. The data points at this time are used as cluster centers, and the K-means clustering algorithm is applied to the data set to assign the data points to clusters. The average value of the group of cluster data represents the cutting tool wear index, where the group of clusters has data with similar cutting tool wear patterns. The cutting tool wear index can also be calculated more accurately by formula. The cutting tool wear index is calculated using the following formula: ; In the formula, represents the cutting tool wear index corresponding to the i-th alloy material sample, , i represents the number of the alloy material sample, m represents the total number of alloy material samples, represents the cutting force coefficient of the i-th alloy material sample, represents the cutting tool load of the ith alloy material sample, represents the wear length of the cutting tool of the i-th alloy material sample, represents the wear amount of the cutting tool of the i-th alloy material sample, Indicates the preset wear depth, represents the preset wear amount, and e represents a natural constant.

[0031] In this embodiment, let , represents the wear depth value of the i-th alloy material sample, then the calculation formula of the cutting tool wear degree index corresponding to the i-th alloy material sample can be simplified as: , The range is 0-1, Range 0-1000 (Newton), The range is 0-100 (micrometers), The range is 0-100 (micrometers). The preset wear depth is determined by taking the average value of the historical wear depth data of the cutting tools of the alloy material samples. The preset wear volume is determined by taking the average value of the historical wear volume data of the cutting tools of the alloy material samples. The degree of wear and the amount of wear are not necessarily proportional. The degree of wear usually describes the wear of the cutting tool surface, which can be the surface unevenness and the failure of the cutting edge, while the amount of wear refers to the amount of material lost by the cutting tool. In some cases, the degree of wear and the amount of wear may be proportional, especially when the wear is uniform and there is no other form of damage. The statistical table of the change of the wear degree index of the cutting tool corresponding to the alloy material samples is shown in Table 1: Table 1 Statistical table of changes in cutting tool wear index corresponding to alloy material samples

[0032] It can be seen from the above table that the cutting tool wear index cannot be directly obtained through a single data, but needs to be obtained through a comprehensive analysis of the cutting force coefficient, the load of the cutting tool, the wear length of the cutting tool and the wear amount of the cutting tool. When the preset wear depth and wear amount are the same, the greater the cutting force coefficient, the load of the cutting tool, the wear length of the cutting tool and the wear amount of the cutting tool, the greater the cutting tool wear index. The cutting tool wear index has a moderate negative correlation with the cutting force coefficient, the wear length of the cutting tool and the wear amount of the cutting tool, while the correlation with the load of the cutting tool is weak; the cutting force coefficient has a strong negative correlation with the load of the cutting tool, the wear length of the cutting tool and the wear amount of the cutting tool; the load of the cutting tool has a moderate negative correlation with the wear length of the cutting tool and the wear amount of the cutting tool; the wear length of the cutting tool has a strong positive correlation with the wear amount of the cutting tool.

[0033] Furthermore, the specific process of obtaining the stress-strain curve is as follows: statistics and comparison of the data of tensile and compressive deformation of the alloy material sample in the historical time period are performed to obtain preset tensile force data and preset pressure data, and the preset tensile force range and preset pressure range are obtained accordingly, the preset tensile force data represents the range of variation of the tensile force applied to the alloy material sample, and the preset pressure data represents the range of variation of the pressure applied to the alloy material sample; a tensile test is performed on the alloy material sample by a tensile testing machine, and a tensile force is gradually applied to the alloy material sample within the range corresponding to the preset tensile force data, and the tensile force applied next time is automatically adjusted according to the tensile force result applied last time by an adaptive learning algorithm, and the stress and strain amount in the tensile test are recorded by a strain gauge and a force sensor; a compression test is performed on the alloy material sample by a compression testing machine, and pressure is gradually applied to the alloy material sample within the range corresponding to the preset pressure data, and the pressure applied next time is automatically adjusted according to the pressure result applied last time by an adaptive learning algorithm, and the stress and strain amount in the compression test are recorded by a strain gauge and a force sensor; the recorded stress and strain amount data are plotted into a curve to obtain the stress-strain curve of the alloy material sample.

[0034] In this embodiment, the tensile testing machine is a device used to test the performance and behavior of alloy materials under tensile loading. It is a commonly used device in material mechanics testing and can measure the stress-strain relationship of alloy materials under stress. The compression testing machine is a device used to evaluate the performance and behavior of alloy materials under compressive loading. Similar to the tensile testing machine, it is also a commonly used device in material mechanics testing and is used to measure the stress-strain relationship of alloy materials under stress, thereby evaluating the mechanical properties of the material. The preset pressure data is determined by taking the average value of the historical pressure data of the alloy material, and the preset tensile force data is determined by taking the average value of the historical tensile force data of the alloy material. The cutting processing efficiency is improved.

[0035] Furthermore, the specific process of obtaining the elastic-plastic reference index of the alloy material sample is as follows: the slope of the stress-strain curve is obtained by the least squares method, thereby obtaining the elastic modulus of the alloy material sample, and the elastic modulus is the proportional coefficient between stress and strain; the elastic-plastic reference index of the alloy material sample is obtained by combining the obtained elastic modulus, stress and strain of the alloy material sample.

[0036] In this embodiment, each strain value corresponds to a stress value, and the slope of the stress-strain curve is obtained by minimizing the residual sum of squares by the least squares method. The value of the slope is the required elastic modulus. The elastic modulus reflects the stress-strain relationship of the alloy material. The stress-strain curve is a graph that describes the mechanical properties of the alloy material under force loading. It shows the relationship between the strain of the alloy material when it is subjected to external force and the stress generated. This curve is usually an important performance indicator for evaluating the strength, toughness, and plastic deformation ability of the material. The cutting efficiency is improved by obtaining the elastic-plastic reference index of the alloy material sample.

[0037] Furthermore, the elastic-plastic reference index can be obtained by collecting the elastic-plastic reference index data of alloy material tests reported in the literature and randomly selecting some data points as the initial cluster centers, and then using the K-means clustering algorithm to iteratively update the center points until convergence. The data points at this time are used as cluster centers, and the K-means clustering algorithm is applied to the data set to assign the data points to clusters. The average value of the cluster data represents the elastic-plastic reference index, wherein the cluster has data with similar elastic-plastic reference index patterns. The elastic-plastic reference index can also be calculated more accurately by formula. The elastic-plastic reference index is calculated using the following formula: ; In the formula, represents the elastic-plastic reference index of the i-th alloy material sample, i , m represents the total number of alloy material samples, i represents the number of alloy material samples, represents the elastic modulus of the ith alloy material sample, represents the stress of the ith alloy material sample, represents the strain of the ith alloy material sample, Indicates the preset elastic modulus.

[0038] In this embodiment, let is the elastic modulus of the i-th alloy material sample, then the calculation formula of the elastic-plastic reference index of the i-th alloy material sample can be simplified as: ; The expression of the stress of the alloy material sample is ,in is the stress data of the ith alloy material sample, is the reference stress data of the ith alloy material sample, is the stress data reference deviation, and the expression of the strain of the alloy material sample is ,in is the strain data of the ith alloy material sample, is the reference strain data of the ith alloy material sample, The reference deviation of the strain data is used to improve the cutting efficiency.

[0039] It should be understood that the preset elastic modulus data is determined by taking the average value after statistics of the historical elastic modulus data of the alloy material, the stress data is obtained by measuring the i-th alloy material sample, the strain data is obtained by measuring the i-th alloy material sample, the reference stress data is obtained by taking the average value after analyzing and counting the stress data of the alloy material sample, the reference strain data is obtained by taking the average value after analyzing and counting the strain data of the alloy material sample, the stress data reference deviation is obtained by comparing the stress data of the alloy material sample with the reference stress data through the reference deviation calculation formula and calculating the deviation between them, and the strain data reference deviation is obtained by comparing the strain data of the alloy material sample with the reference strain data through the reference deviation calculation formula and calculating the deviation between them.

[0040] Furthermore, the specific process of obtaining the optimal parameter combination is as follows: an alloy cutting model is constructed by using CAD software and running numerical simulation software, and the cutting tool wear index of the alloy material sample and the elastic-plastic reference index of the alloy material sample are modeled and simulated; the cutting speed, feed speed, and cutting depth data of the alloy material sample in a historical time period are statistically obtained to obtain the preset cutting speed, feed speed, and cutting depth data, and the preset cutting speed range, preset feed speed range, and preset cutting depth range are obtained accordingly, the preset cutting speed data represents the range of variation of the cutting speed applied to the alloy material sample, the preset feed speed data represents the range of variation of the feed speed applied to the alloy material sample, and the preset cutting depth data represents the range of variation of the cutting depth applied to the alloy material sample; the obtained cutting tool wear index and elastic-plastic reference index of the alloy material sample are iterated by the gradient descent method to gradually adjust the parameter combination until the gradient descent method converges to obtain the optimal parameter combination.

[0041] In this embodiment, CAD software is a tool for creating, modifying, analyzing and optimizing designs. Numerical simulation software is a type of software used to solve engineering and scientific problems. CAD software and numerical simulation software are usually complementary to each other in engineering design and analysis. CAD software is used to create designs, while numerical simulation software is used to verify whether the designs meet the requirements, optimize the designs, and predict the performance of products under specific working conditions. The preset cutting speed data is determined by taking the average value of the historical cutting speed data of the alloy materials, the preset feed speed data is determined by taking the average value of the historical feed speed data of the alloy materials, and the preset cutting depth data is determined by taking the average value of the historical cutting depth data of the alloy materials. The cutting efficiency is improved by obtaining the optimal parameter combination of the alloy material samples.

[0042] Furthermore, a specific method for obtaining the comprehensive cutting index is as follows: the performance difference weights of the alloy material samples are obtained according to the subjective weighting method, the performance difference weights represent the influence of the elastic-plasticity of the alloy material samples and the corresponding degree of wear of the cutting tool on the cutting performance of the alloy material samples, the performance difference weights include wear weights and elastic-plastic weights, the wear weights represent the influence of the degree of wear of the cutting tool corresponding to the alloy material sample on the cutting performance of the alloy material sample, and the elastic-plastic weights represent the influence of the elastic-plasticity of the alloy material sample on the cutting performance of the alloy material sample; the cutting tool wear degree of the alloy material sample is obtained according to the cutting tool wear degree index of the alloy material sample and the optimal cutting tool wear degree index. Coefficient, cutting tool wear coefficient represents the wear deviation of the cutting tool of the alloy material sample, and the cutting tool wear coefficient is the ratio of the difference between the cutting tool wear index and the best cutting tool wear index to the best cutting tool wear index; the elastic-plastic coefficient of the alloy material sample is obtained according to the elastic-plastic reference index and the best elastic-plastic reference index of the alloy material sample, the elastic-plastic coefficient represents the elastic-plastic deviation of the alloy material sample, and the elastic-plastic coefficient is the ratio of the difference between the elastic-plastic reference index and the best elastic-plastic reference index of the alloy material sample to the best elastic-plastic reference index; the cutting comprehensive index of the alloy material is obtained according to the obtained cutting tool wear coefficient and elastic-plastic coefficient of the alloy material sample and combined with the performance difference weight.

[0043] In this embodiment, if Figure 2As shown, it is a statistical graph of the changes in the comprehensive cutting index of the alloy material provided in the embodiment of the present application. It can be seen from the figure that when the cutting tool wear coefficient of the alloy material sample increases, the comprehensive cutting index of the alloy material will decrease, and when the elastic-plastic coefficient of the alloy material sample increases, the comprehensive cutting index of the alloy material will increase, because the elastic-plastic coefficient of the alloy material sample is in the numerator of the exponential term, and its increase will cause γ to increase. When the cutting tool wear coefficient of the alloy material sample or the elastic-plastic coefficient of the alloy material sample is 0, γ will depend on the value of e, but usually, it will tend to 1 because the value of the index is close to 0; when the cutting tool wear coefficient of the alloy material sample or the elastic-plastic coefficient of the alloy material sample reaches its maximum value, γ will be more affected because the value of the index will be closer to negative infinity. The cutting comprehensive index of alloy materials can be calculated by collecting the cutting comprehensive index data of alloy materials tested reported in the literature and randomly selecting some data points as the initial cluster centers, and then using the K-means clustering algorithm to iteratively update the center points until convergence. The data points at this time are used as cluster centers. The K-means clustering algorithm is applied to the data set to assign the data points to clusters. The average value of the cluster data represents the cutting comprehensive index. The cluster has data with similar cutting comprehensive index patterns. The cutting comprehensive index can also be calculated more accurately by formula. The cutting comprehensive index is calculated using the following formula: ; In the formula, Indicates the comprehensive cutting index of alloy materials. represents the cutting tool wear coefficient of the i-th alloy material sample, represents the elastic-plastic coefficient of the i-th alloy material sample, e represents the natural constant, is the wear weight, is the elastic-plastic weight, i , m represents the total number of alloy material samples, i represents the number of alloy material samples, The range is 0-10, The range is 0-10.

[0044] Specifically, the expression of the cutting tool wear coefficient of the i-th alloy material sample is: ,in, The optimal cutting tool wear index of the cutting tool of the i-th alloy material sample is represented by the expression of the elastic-plastic coefficient of the i-th alloy material sample: ,in, Represents the optimal elastic-plastic reference index of the i-th alloy material sample.

[0045] It should be understood that the best cutting tool wear index is obtained by collecting the best cutting tool wear index data of alloy material tests reported in the literature and randomly selecting some data points as the initial cluster centers, and then using the K-means clustering algorithm to iteratively update the center points until convergence. The data points at this time are used as cluster centers, and the K-means clustering algorithm is applied to the data set to assign the data points to clusters. The average value of the group of cluster data represents the best cutting tool wear index, where the group of clusters has data with similar best cutting tool wear index patterns. The method for obtaining the best elastic-plastic reference index is similar, specifically by collecting the data reported in the literature. The best elastic-plastic reference index data of alloy material tests are obtained and some data points are randomly selected as the initial cluster centers. Then the K-means clustering algorithm is used to iteratively update the center points until convergence. The data points at this time are used as cluster centers. The K-means clustering algorithm is applied to the data set to assign data points to clusters. The average value of this group of cluster data represents the best elastic-plastic reference index, where this group of clusters has data with similar best elastic-plastic reference index patterns. The wear weight is obtained by subjective weighting based on existing material test data, literature reports and empirical estimates, and the elastic-plastic weight is obtained by subjective weighting based on existing material test data, literature reports and empirical estimates.

[0046] like Figure 3As shown, it is a structural schematic diagram of the alloy cutting model construction system provided in the embodiment of the present application. The alloy cutting model construction system provided in the embodiment of the present application includes: an alloy material sample acquisition module, a wear degree measurement module, an elastic-plasticity measurement module, an alloy cutting model construction module, and a cutting comprehensive index acquisition module; wherein the alloy material sample acquisition module is used to obtain the alloy material sample according to the characteristic parameters of the alloy material, and the characteristic parameters include the strength, hardness, and plasticity of the alloy material; the wear degree measurement module is used to measure the wear degree of the cutting tool corresponding to the alloy material sample after cutting through a scanning electron microscope, and obtain the cutting tool wear degree index of the alloy material sample according to the measurement result, and the cutting tool wear degree index is used to measure the wear degree of the cutting tool of the alloy material sample; the elastic-plasticity measurement module is used to perform tensile test and compression test on the alloy material sample, and obtain the stress-strain curve of the alloy material sample according to the results of the tensile test and the compression test, and obtain the elastic-plasticity reference index of the alloy material sample accordingly, and the tensile test is used to evaluate the tensile properties of the alloy material sample. The compression test is used to evaluate the compression performance of the alloy material sample, the stress-strain curve is used to describe the deformation degree of the alloy material sample, and the elastic-plastic reference index is used to measure the elastic-plasticity of the alloy material sample; the alloy cutting model construction module is used to obtain the alloy cutting model through modeling and simulation by computer-aided engineering software and numerical simulation software. The alloy cutting model is used to determine the preset processing parameter combination according to the actual cutting conditions of the alloy material sample, and obtain the optimal parameter combination accordingly. The optimal parameter combination includes the optimal cutting tool wear index and the optimal elastic-plastic reference index. The preset processing parameter combination is used to maximize the cutting efficiency. The optimal cutting tool wear index indicates the minimum wear degree of the cutting tool, and the optimal elastic-plastic reference index indicates the optimal elastic-plasticity of the alloy material sample; the cutting comprehensive index acquisition module is used to obtain the cutting comprehensive index of the alloy material according to the optimal parameter combination of the cutting tool of the alloy material sample obtained and combined with the corresponding cutting tool wear index and elastic-plastic reference index. The cutting comprehensive index is used to evaluate the wear degree and elastic-plastic difference of the alloy material during the cutting process.

[0047] In this embodiment, the alloy material sample is obtained by performing strength test, hardness test and plasticity test on the alloy material to obtain the corresponding strength index, hardness index and plasticity index, and the alloy material sample is screened and obtained; the wear degree is measured by measuring the wear degree of the cutting tool corresponding to the alloy material sample after cutting through a scanning electron microscope, and obtaining the cutting tool wear degree index of the alloy material sample according to the measurement result; the elastic-plasticity measurement is to perform tensile test and compression test on the alloy material sample to obtain the stress-strain curve of the alloy material sample, and obtain the elastic-plastic reference index of the alloy material sample based on this; the alloy cutting model is constructed by using CAD software to create a geometric model of the alloy material and the tool, and using numerical simulation software to simulate the trajectory, cutting speed and feed speed of the tool during the cutting process, so as to construct the alloy cutting model to obtain the optimal parameter combination of the cutting tool of the alloy material sample; the cutting comprehensive index is obtained by combining the optimal parameter combination of the cutting tool of the alloy material sample with the corresponding cutting tool wear degree index and elastic-plastic reference index to obtain the cutting comprehensive index of the alloy material; the cutting processing efficiency is improved.

[0048] To summarize, the embodiment of the present application obtains an alloy material sample through the characteristic parameters of the alloy material, and then measures the degree of wear of the cutting tool corresponding to the alloy material sample after cutting through a scanning electron microscope to obtain the cutting tool wear degree index of the alloy material sample, and performs tensile test and compression test on the alloy material sample to obtain the stress-strain curve of the alloy material sample, and obtains the elastic-plastic reference index of the alloy material sample based on this, and then performs modeling and simulation through computer-aided engineering software and numerical simulation software, and finally obtains the alloy cutting model cutting comprehensive index according to the optimal parameter combination of the cutting tool of the obtained alloy material sample, thereby realizing the evaluation of the comprehensive cutting ability of the alloy cutting model, and then realizing the improvement of the cutting efficiency of the alloy material, which effectively solves the problem of low cutting efficiency of alloy materials in the prior art.

[0049] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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.

[0051] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0053] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0054] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing an alloy cutting model, characterized in that: The following steps are involved: S1, obtaining an alloy material sample according to characteristic parameters of the alloy material, and evaluating the hardness, strength and plasticity of the sample; S2, measuring the wear degree of the cutting tool corresponding to the alloy material sample by scanning electron microscope, and obtaining the wear degree index of the cutting tool; S3, performing tensile and compression tests on the alloy material sample, obtaining a stress-strain curve, and calculating an elastic-plastic reference index based on the curve; S4, simulating the range of cutting speed, feed speed and cutting depth by computer-aided design (CAD) software and numerical simulation software to determine the best processing parameter combination, which includes an optimized cutting tool wear index and an elastic-plastic reference index; S5, obtaining a comprehensive cutting index of the alloy material according to the obtained optimal parameter combination of the cutting tool of the alloy material sample and combining the corresponding cutting tool wear index and elastic-plastic reference index, wherein the comprehensive cutting index is used to evaluate the wear degree and elastic-plastic difference of the alloy material during the cutting process; The specific method for obtaining the cutting tool wear index of the alloy material sample is as follows: The alloy material sample is cut by a lathe, and the load of the cutting tool of the alloy material sample during the machining process is measured by a force sensor; The material mass of the cutting tool of the alloy material sample before and after cutting is measured by a precision analytical balance, and the corresponding wear amount is obtained accordingly; The wear degree of the cutting tool tip and the deformation degree of the cutting edge of the alloy material sample after cutting are measured by scanning electron microscope to obtain the corresponding wear length; The cutting force coefficient of the alloy material sample is obtained in combination with the actual conditions of the cutting process, and the corresponding cutting tool wear degree index is obtained in combination with the wear length, load and wear amount of the cutting tool of the alloy material sample.

2. A method for constructing an alloy cutting model as claimed in claim 1, characterized in that: The cutting tool wear index is calculated using the following formula: ； In the formula, represents the cutting tool wear index corresponding to the i-th alloy material sample, , i represents the number of the alloy material sample, m represents the total number of alloy material samples, represents the cutting force coefficient of the i-th alloy material sample, represents the cutting tool load of the ith alloy material sample, represents the wear length of the cutting tool of the i-th alloy material sample, represents the wear amount of the cutting tool of the i-th alloy material sample, Indicates the preset wear depth, represents the preset wear amount, and e represents a natural constant.

3. A method for constructing an alloy cutting model as claimed in claim 2, characterized in that: The process of obtaining the alloy material sample is as follows: Perform hardness testing on the alloy material to evaluate the corresponding hardness, collect hardness data in real time and compare with the preset hardness requirements to obtain the first group of samples; Perform strength tests on the first group of samples to evaluate the corresponding strength, collect strength data in real time and compare them with the preset strength requirements to obtain the second group of samples; The second group of samples is subjected to plasticity testing to evaluate the corresponding plasticity, and the plasticity data is collected in real time and compared with the preset plasticity requirements to obtain alloy material samples.

4. A method for constructing an alloy cutting model as claimed in claim 3, characterized in that: The specific process of obtaining the stress-strain curve is as follows: The data of tensile and compressive deformation of alloy material samples in the historical time period are statistically analyzed and compared to obtain preset tensile force data and preset pressure data, and thereby obtain a preset tensile force range and a preset pressure range; A tensile test is performed on the alloy material sample by a tensile testing machine, and a tensile force is gradually applied to the alloy material sample within a range corresponding to the preset tensile force data. The tensile force applied next time is automatically adjusted according to the tensile force result applied last time by an adaptive learning algorithm, and the stress and strain in the tensile test are recorded by a strain gauge and a force sensor at the same time; The alloy material sample is subjected to compression test by means of a compression testing machine, and pressure is gradually applied to the alloy material sample within the corresponding range of the preset pressure data. The pressure applied next time is automatically adjusted according to the pressure result applied last time by means of an adaptive learning algorithm, and the stress and strain amount in the compression test are recorded by means of strain gauges and force sensors. The recorded stress and strain data are plotted into a graph to obtain the stress-strain curve of the alloy material sample.

5. The method for constructing a cutting model of an alloy material sample according to claim 4, characterized in that: The specific process of obtaining the elastic-plastic reference index of the alloy material sample is as follows: The slope of the stress-strain curve is obtained by the least square method, thereby obtaining the elastic modulus of the alloy material sample; The elastic-plastic reference index of the alloy material sample is obtained by combining the elastic modulus, stress and strain of the alloy material sample.

6. A method for constructing an alloy cutting model as claimed in claim 5, characterized in that: The elastic-plastic reference index is calculated using the following formula: ； In the formula, represents the elastic-plastic reference index of the i-th alloy material sample, i , m represents the total number of alloy material samples, i represents the number of alloy material samples, represents the elastic modulus of the ith alloy material sample, represents the stress of the ith alloy material sample, represents the strain of the ith alloy material sample, Indicates the preset elastic modulus.

7. A method for constructing an alloy cutting model as claimed in claim 6, characterized in that: The specific process of obtaining the optimal parameter combination is as follows: An alloy cutting model is constructed by using CAD software and running numerical simulation software, and the cutting tool wear index of the alloy material sample and the elastic-plastic reference index of the alloy material sample are modeled and simulated; The cutting speed, feed speed and cutting depth data of the alloy material samples in the historical time period are statistically analyzed and compared to obtain the preset cutting speed, feed speed and cutting depth data, and the preset cutting speed range, the preset feed speed range and the preset cutting depth range are obtained accordingly; The cutting tool wear index and elastic-plastic reference index of the obtained alloy material samples are iterated by the gradient descent method to gradually adjust the parameter combination until the gradient descent method converges to obtain the optimal parameter combination.

8. A method for constructing an alloy cutting model as claimed in claim 7, characterized in that: The specific method for obtaining the cutting comprehensive index is as follows: Obtaining performance difference weights of alloy material samples according to a subjective weighting method, wherein the performance difference weights include wear weights and elastic-plastic weights; The cutting tool wear coefficient of the alloy material sample is obtained according to the cutting tool wear index of the alloy material sample and the optimal cutting tool wear index; The elastic-plastic coefficient of the alloy material sample is obtained according to the elastic-plastic reference index and the optimal elastic-plastic reference index of the alloy material sample; The comprehensive cutting index of the alloy material is obtained based on the cutting tool wear coefficient and elastic-plastic coefficient of the obtained alloy material samples and combined with the performance difference weight.

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