阅读说明：本技术 盘铣刀加工参数优化方法及装置、电子设备及存储介质 (Disc milling cutter machining parameter optimization method and device, electronic equipment and storage medium ) 是由 姜广君 李华强 李琦 陈红霞 孙洪华 于 2020-05-11 设计创作，主要内容包括：本发明公开了一种盘铣刀加工参数优化方法及装置、电子设备及存储介质,优化方法包括根据不同设定参数条件下盘铣刀可靠性试验得到的刀具的使用寿命建立刀具寿命模型；设定参数包括主轴转速、每齿进给量和切削深度；获取不同设定参数条件下盘铣刀可靠性试验得到的对应刀具的切削力和材料去除率；根据切削力和材料去除率获取不同设定参数条件下盘铣刀可靠性试验刀具的灰色关联度；以刀具的灰色关联度为目标函数,以设定参数和刀具的使用寿命为约束条件建立盘铣刀加工参数优化模型,并根据盘铣刀加工参数优化模型和刀具寿命模型获取盘铣刀的最优加工参数。通过本发明的技术方案,盘铣刀能够以较小切削力获得较大材料去除率的同时保证刀具寿命。(The invention discloses a method and a device for optimizing machining parameters of a disc milling cutter, electronic equipment and a storage medium, wherein the optimization method comprises the steps of establishing a cutter life model according to the service life of a cutter obtained by a disc milling cutter reliability test under different set parameter conditions; setting parameters including the rotating speed of a main shaft, the feed amount of each tooth and the cutting depth; obtaining cutting force and material removal rate of a corresponding cutter obtained by a disc milling cutter reliability test under different set parameter conditions; obtaining the grey correlation degree of the cutter for the reliability test of the disc milling cutter under different set parameter conditions according to the cutting force and the material removal rate; and establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and taking the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter life model. By the technical scheme of the invention, the disc milling cutter can obtain a larger material removal rate with a smaller cutting force and simultaneously ensure the service life of the cutter.)

1. A method for optimizing machining parameters of a disc milling cutter is characterized by comprising the following steps:

establishing a cutter life model according to the service life of the cutter obtained by a disc milling cutter reliability test under different set parameter conditions; the set parameters comprise the rotating speed of the main shaft, the feed amount per tooth and the cutting depth;

obtaining cutting force and material removal rate corresponding to the cutter obtained by a disc milling cutter reliability test under different set parameter conditions;

obtaining the grey correlation degree of the cutter in the disc milling cutter reliability test under different set parameter conditions according to the cutting force and the material removal rate;

and establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter service life model.

2. The disc milling cutter machining parameter optimization method according to claim 1, wherein the tool life model is:

wherein T is the service life of the tool and represents a gamma function, and Z-r₁lnv-r₂lnf-r₃lnd，v is the spindle speed, f is the feed per tooth, d is the depth of cut, β is the shape parameter, η is the scale parameter, r is₁、r₂、r₃Is a tool life model parameter.

3. The disc milling cutter machining parameter optimization method according to claim 2, wherein the establishing of the tool life model according to the service lives of the tools obtained by the disc milling cutter reliability tests under different set parameters comprises:

according to the service life of the cutter obtained by a disc milling cutter reliability test under different set parameter conditions, processing a log-likelihood function of the service life of the cutter by using a Markov chain Monte Carlo method;

obtaining set model parameters in the tool life model;

the log-likelihood function of tool life is:

wherein L is the product of the probabilities of the life of all the cutters, n is the number of reliability tests of the disc milling cutter, and T_iAnd t_iMachining parameters v set for reliability tests of the disc milling cutter of the ith group respectively_i、f_i、d_iAnd i is a positive integer corresponding to the service life of the cutter and the fault time of the cutter.

4. The disc milling cutter machining parameter optimization method according to claim 1, wherein the material removal rate is:

wherein b is the width of the disc milling cutter, N is the number of the disc milling cutters, D is the diameter of the disc milling cutter, v_i、f_iAnd d_iThe main shaft rotating speed, the feed per tooth and the cutting depth Q which are respectively set for the reliability test of the ith group of the disc milling cutter_iMachining parameters v set for reliability test of the disc milling cutter of the ith group_i、f_i、d_iAnd i is a positive integer corresponding to the material removal rate.

5. The disc milling cutter machining parameter optimization method according to claim 1, wherein obtaining the grey correlation of the cutter reliability test under different setting parameters according to the cutting force and the material removal rate comprises:

respectively normalizing the cutting force and the material removal rate, wherein the normalized cutting force and the normalized material removal rate are respectively as follows:

wherein, F_iAnd Q_iRespectively the cutting force and the material removal rate obtained by the reliability test of the ith group of the disc milling cutter,andnormalized cutting force and material removal rate, respectively, F₀＝minF_i，Q₀＝maxQ_iI is a positive integer;

respectively obtaining the grey correlation coefficients of the cutting force and the material removal rate, wherein the grey correlation coefficients of the cutting force and the material removal rate are respectively as follows:

wherein the content of the first and second substances,anda grey scale correlation coefficient for the cutting force and a grey scale correlation coefficient for the material removal rate,m_F＝minΔF_i，M_F＝maxΔF_i，m_Q＝minΔQ_i，M_Q＝maxΔQ_iξ is a discriminating function and ξ∈ [0,1 ]]；

Obtaining the grey correlation degree of the cutter, wherein the grey correlation degree of the cutter is as follows:

wherein, γ_iFor the grey correlation of the cutter obtained by the reliability test of the disc milling cutter of the ith group, theta is a weight coefficient, and theta ∈ [0,1 ]]。

6. The disc milling cutter machining parameter optimization method according to claim 1, wherein the disc milling cutter machining parameter optimization model is:

maxγ＝g(v,f,d)

wherein v is_min、v_max、f_min、f_max、d_min、d_maxThe lower limit value and the upper limit value T which are respectively corresponding to the main shaft rotating speed v, the feed amount f of each tooth and the cutting depth d_min、T_maxAnd g (v, f, d) is a gray correlation function established for the gray correlation degree of the cutter obtained by the reliability test of the disc milling cutter.

7. The disc milling cutter machining parameter optimization method according to claim 6, wherein the obtaining of the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the tool life model comprises:

expanding the grey correlation degree of the cutter obtained by the reliability test of the disc milling cutter under different set parameter conditions to a continuous area by adopting a radial basis function neural network so as to obtain the objective function of the disc milling cutter processing parameter optimization model;

processing the objective function by adopting a particle swarm optimization algorithm to obtain the optimal processing parameters of the disc milling cutter; the optimal machining parameters comprise an optimal spindle rotating speed, an optimal feed per tooth and an optimal cutting depth;

obtaining the optimized service life of the cutter according to the optimal machining parameters and the cutter service life model, and verifying whether the optimized service life is T_minAnd T_maxThe range of the formed interval.

8. A disc milling cutter machining parameter optimizing apparatus, comprising:

the service life model establishing module is used for establishing a cutter service life model according to the service life of the cutter obtained by the reliability test of the disc milling cutter under different set parameter conditions; the set parameters comprise the rotating speed of the main shaft, the feed amount per tooth and the cutting depth;

the cutter parameter acquisition module is used for acquiring cutting force and material removal rate corresponding to the cutter, which are obtained by a disc milling cutter reliability test under different set parameter conditions;

the correlation degree obtaining module is used for obtaining the grey correlation degree of the cutter in the disc milling cutter reliability test under different set parameter conditions according to the cutting force and the material removal rate;

and the parameter optimization module is used for establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and taking the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter life model.

9. An electronic device, comprising a processor and a memory, wherein the processor executes the steps of the disc milling cutter machining parameter optimization method according to any one of claims 1 to 8 by calling a program or instructions stored in the memory.

10. A storage medium storing a program or instructions for causing a computer to perform the steps of the disc milling cutter machining parameter optimization method according to any one of claims 1 to 8.

Technical Field

The embodiment of the invention relates to the technical field of cutters, in particular to a method and a device for optimizing machining parameters of a disc milling cutter, electronic equipment and a storage medium.

Background

The machine manufacturing industry is the material basis of national economy, social development and national defense construction, is an important component of the total value of national production, and is an important mark of national comprehensive strength. With the development of the machine manufacturing industry, the machine manufacturing industry has new characteristics of dynamic and changeable market demands, shortened product updating period, increased variety specifications, reduced batch, sustainable development and the like. To adapt to this new situation, high efficiency, high quality, high flexibility, greening and informatization have become necessary trends in the development of machine manufacturing industry.

In the machine manufacturing industry, although various advanced special machining methods are emerging along with the technological progress, the milling process still occupies an indispensable position at present, such as a blisk of an aircraft engine, and a multi-axis numerical control disc milling method is currently more applied. However, in the disc milling process, if the machining parameters are not properly selected, the tool often has poor rigidity and low reliability due to the overlong overhanging length, and the tool deflects and vibrates under the action of the mirror image cutting force, so that the machining quality is reduced. Therefore, the method focuses on optimizing the machining parameters of the disc milling cutter, improves the production efficiency of cutting machining, reduces the production cost, and has very important significance for the rapid development of the machine manufacturing industry.

Disclosure of Invention

In view of this, the invention provides a method and an apparatus for optimizing processing parameters of a disc milling cutter, an electronic device and a storage medium, which can ensure the service life of a cutter while obtaining a larger material removal rate with a smaller cutting force during the processing of the disc milling cutter, and are beneficial to improving the processing efficiency and reducing the production cost.

In a first aspect, an embodiment of the present invention provides a method for optimizing machining parameters of a disc milling cutter, including:

establishing a cutter life model according to the service life of the cutter obtained by a disc milling cutter reliability test under different set parameter conditions; the set parameters comprise the rotating speed of the main shaft, the feed amount per tooth and the cutting depth;

obtaining cutting force and material removal rate corresponding to the cutter obtained by a disc milling cutter reliability test under different set parameter conditions;

obtaining the grey correlation degree of the cutter in the disc milling cutter reliability test under different set parameter conditions according to the cutting force and the material removal rate;

and establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter service life model.

Optionally, the tool life model is:

wherein T is the service life of the tool and represents a gamma function, and Z-r₁lnv-r₂lnf-r₃lnd，v is the spindle speed, f is the feed per tooth, d is the depth of cut, β is the shape parameter, η is the scale parameter, r is₁、r₂、r₃Is a tool life model parameter.

Optionally, the establishing a tool life model according to the service life of the tool obtained by the reliability test of the disc milling cutter under the condition of different set parameters includes:

according to the service life of the cutter obtained by a disc milling cutter reliability test under different set parameter conditions, processing a log-likelihood function of the service life of the cutter by using a Markov chain Monte Carlo method;

obtaining set model parameters in the tool life model;

the log-likelihood function of tool life is:

wherein L is the product of the probabilities of the life of all the cutters, n is the number of reliability tests of the disc milling cutter, and T_iAnd t_iMachining parameters v set for reliability tests of the disc milling cutter of the ith group respectively_i、f_i、d_iAnd i is a positive integer corresponding to the service life of the cutter and the fault time of the cutter.

Optionally, the material removal rate is:

wherein b is the width of the disc milling cutter, N is the number of the disc milling cutters, D is the diameter of the disc milling cutter, v_i、f_iAnd d_iThe main shaft rotating speed, the feed per tooth and the cutting depth Q which are respectively set for the reliability test of the ith group of the disc milling cutter_iMachining parameters v set for reliability test of the disc milling cutter of the ith group_i、f_i、d_iAnd i is a positive integer corresponding to the material removal rate.

Optionally, obtaining a grey correlation of the cutter in a disc milling cutter reliability test under different set parameters according to the cutting force and the material removal rate, including:

respectively normalizing the cutting force and the material removal rate, wherein the normalized cutting force and the normalized material removal rate are respectively as follows:

wherein, F_iAnd Q_iRespectively the cutting force and the material removal rate obtained by the reliability test of the ith group of the disc milling cutter,andnormalized cutting force and material removal rate, respectively, F₀＝min F_i，Q₀＝max Q_iI is a positive integer;

respectively obtaining the grey correlation coefficients of the cutting force and the material removal rate, wherein the grey correlation coefficients of the cutting force and the material removal rate are respectively as follows:

wherein the content of the first and second substances,anda grey scale correlation coefficient for the cutting force and a grey scale correlation coefficient for the material removal rate,m_F＝minΔF_i，M_F＝maxΔF_i，m_Q＝minΔQ_i，M_Q＝maxΔQ_iξ is a discriminating function and ξ∈ [0,1 ]]；

Obtaining the grey correlation degree of the cutter, wherein the grey correlation degree of the cutter is as follows:

wherein, γ_iFor the grey correlation of the cutter obtained by the reliability test of the disc milling cutter of the ith group, theta is a weight coefficient, and theta ∈ [0,1 ]]。

Optionally, the optimized model of machining parameters of the disc milling cutter is as follows:

maxγ＝g(v,f,d)

wherein v is_min、v_max、f_min、f_max、d_min、d_maxThe lower limit value and the upper limit value T which are respectively corresponding to the main shaft rotating speed v, the feed amount f of each tooth and the cutting depth d_min、T_maxAnd g (v, f, d) is a gray correlation function established for the gray correlation degree of the cutter obtained by the reliability test of the disc milling cutter.

Optionally, the obtaining of the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the tool life model includes:

expanding the grey correlation degree of the cutter obtained by the reliability test of the disc milling cutter under different set parameter conditions to a continuous area by adopting a radial basis function neural network so as to obtain the objective function of the disc milling cutter processing parameter optimization model;

processing the objective function by adopting a particle swarm optimization algorithm to obtain the optimal processing parameters of the disc milling cutter; the optimal machining parameters comprise an optimal spindle rotating speed, an optimal feed per tooth and an optimal cutting depth;

obtaining the optimized service life of the cutter according to the optimal machining parameters and the cutter service life model, and verifying whether the optimized service life is T_minAnd T_maxThe range of the formed interval.

In a second aspect, an embodiment of the present invention further provides a device for optimizing processing parameters of a disc milling cutter, including:

the service life model establishing module is used for establishing a cutter service life model according to the service life of the cutter obtained by the reliability test of the disc milling cutter under different set parameter conditions; the set parameters comprise the rotating speed of the main shaft, the feed amount per tooth and the cutting depth;

the cutter parameter acquisition module is used for acquiring cutting force and material removal rate corresponding to the cutter, which are obtained by a disc milling cutter reliability test under different set parameter conditions;

the correlation degree obtaining module is used for obtaining the grey correlation degree of the cutter in the disc milling cutter reliability test under different set parameter conditions according to the cutting force and the material removal rate;

and the parameter optimization module is used for establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and taking the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter life model.

In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a processor and a memory, where the processor executes the steps of the disc milling cutter machining parameter optimization method according to the first aspect by calling a program or an instruction stored in the memory.

In a fourth aspect, embodiments of the present invention also provide a storage medium storing a program or instructions for causing a computer to execute the steps of the disc milling cutter machining parameter optimization method according to the first aspect.

The embodiment of the invention provides a method and a device for optimizing machining parameters of a disc milling cutter, electronic equipment and a storage medium, wherein the method for optimizing the machining parameters of the disc milling cutter comprises the steps of establishing a cutter life model according to the service life of a cutter obtained by a reliability test of the disc milling cutter under different set parameter conditions; the set parameters comprise the rotating speed of the main shaft, the feed amount of each tooth and the cutting depth; obtaining cutting force and material removal rate of a corresponding cutter obtained by a disc milling cutter reliability test under different set parameter conditions; obtaining the grey correlation degree of the cutter for the reliability test of the disc milling cutter under different set parameter conditions according to the cutting force and the material removal rate; and establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and taking the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter life model. Therefore, the machining parameters of the disc milling cutter are determined through the established disc milling cutter machining parameter optimization model, so that the disc milling cutter can obtain a larger material removal rate with smaller cutting force in the machining process, the service life of the cutter is ensured, and the improvement of the machining efficiency and the reduction of the production cost are facilitated.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.

Fig. 1 is a flowchart of a method for optimizing machining parameters of a disc milling cutter according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a device for optimizing processing parameters of a disc milling cutter according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, the present invention will be further described in detail with reference to the accompanying drawings and examples. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.

Fig. 1 is a flowchart of a method for optimizing machining parameters of a disc milling cutter according to an embodiment of the present invention. The disc milling cutter processing parameter optimization method can be applied to scenes in which the processing parameters of the disc milling cutter need to be optimized, and can be executed by the disc milling cutter processing parameter optimization device provided by the embodiment of the invention. As shown in fig. 1, the method for optimizing machining parameters of a disc milling cutter comprises the following steps:

s110, establishing a cutter life model according to the service life of the cutter obtained by a disc milling cutter reliability test under different set parameter conditions; wherein the set parameters comprise the rotating speed of the main shaft, the feed per tooth and the cutting depth.

Specifically, the set parameters may include a spindle rotation speed, a feed per tooth and a cutting depth, the spindle rotation speed is a spindle rotation speed of the tool lathe, the feed per tooth corresponds to a feed speed, the feed speed refers to a relative displacement of the workpiece and the milling cutter in a feed direction in unit time or a feed amount of the tool when the spindle rotates for one circle, the cutting depth is inversely proportional to the feed number, and a larger cutting depth may be selected to reduce the feed number under the conditions of process system rigidity and machine power allowance. And performing reliability test on the disc milling cutter by using different main shaft rotating speeds, feeding amount per tooth and cutting depth to obtain the service life of the cutter under the corresponding test scene, and establishing a cutter life model according to the service life of the cutter obtained by the reliability test of the disc milling cutter under different set parameters.

Optionally, the tool life model is:

wherein T is the service life of the cutter and represents a gamma function, v is the rotating speed of the main shaft, f is the feed per tooth, d is the cutting depth, and Z is-r₁lnv-r₂lnf-r₃lnd，β is the shape parameter, η is the scale parameter, r₁、r₂、r₃Is a tool life model parameter. In particular, the representative gamma function is,r₁、r₂、r₃is the regression coefficient of the proportional hazards model, specifically r₁Is the regression coefficient of the cutting speed of the tool, r₂Is a regression coefficient of the tool feed, r₃The gamma distribution is a statistical continuous probability function, shape, for the regression coefficient of the cutting depth of the toolThe shape parameter β determines the basic shape of its probability density curve, and the scale parameter η functions to scale up or down the probability density curve, but does not affect the shape of the density curve distribution.

Optionally, a tool life model is established according to the service lives of the tools obtained through the disc milling cutter reliability tests under different setting parameter conditions, and the log-likelihood function of the tool life can be processed by using a markov chain monte carlo method according to the service lives of the tools obtained through the disc milling cutter reliability tests under different setting parameter conditions, so as to obtain the setting model parameters in the tool life model.

The log-likelihood function of tool life is:

wherein L is the continuous product of the probability densities of the service lives of all cutters, n is the number of reliability tests of the disc milling cutter, and T_iAnd t_iThe processing parameters v are respectively set for the reliability test of the ith group of disc milling cutters_i、f_i、d_iAnd i is a positive integer corresponding to the service life of the cutter and the fault time of the cutter.

Specifically, the disc milling cutter is subjected to reliability test by using different spindle rotation speeds, feed per tooth and cutting depth, so that the service life of the cutter under the corresponding test scene is obtained, and the machining parameters selected by the reliability test and the obtained service life of the cutter are shown in table 1.

TABLE 1 tool Life data

The data in Table 1 obtained by the test are substituted into the log-likelihood function of the tool life, i.e. the different T_iAnd Z_i＝-r₁lnv_i-r₂lnf_i-r₃lnd_iSubstituting the log likelihood function of the tool life into the log likelihood function of the tool life so that the likelihood function of the tool life includes five parameters lambda, β, r₁、r₂And r₃Solving set model parameters in the tool life model by using a Markov chain Monte Carlo method, wherein the set model parameters in the tool life model comprise lambda, β and r₁、r₂And r₃The results are shown in Table 2.

TABLE 2 Markov chain Monte Carlo method analysis results

From table 2, the tool life model can be found as:

and S120, obtaining the cutting force and the material removal rate of the corresponding cutter obtained by the reliability test of the disc milling cutter under the condition of different set parameters.

Specifically, the cutting force corresponding to different spindle rotating speeds, feeding amount per tooth and cutting depth is measured, illustratively, reliability tests are performed on the disc milling cutter by using different spindle rotating speeds, feeding amount per tooth and cutting depth, the cutting force can be measured by using a peculiar rock measuring instrument, and the corresponding material removal rate and the corresponding cutter service life can be calculated.

Optionally, the material removal rate is:

wherein b is the width of the disc milling cutter, N is the number of the disc milling cutters, D is the diameter of the disc milling cutter, v_i、f_iAnd d_iThe main shaft rotating speed, the feed per tooth and the cutting depth Q which are respectively set for the reliability test of the ith group of disc milling cutters_iMachining parameter v set for reliability test of ith group of disc milling cutters_i、f_i、d_iCorresponding to the material removal rate, i is a positive integer.

Specifically, the disc milling cutter was subjected to a reliability test using different spindle speeds, feed per tooth and cutting depth, and the cutting force, material removal rate and tool life obtained by the test are shown in table 3.

TABLE 3 test data

S130, obtaining the grey correlation degree of the cutter in the reliability test of the disc milling cutter under different set parameter conditions according to the cutting force and the material removal rate.

Specifically, the grey correlation of the cutter for the reliability test of the disc milling cutter under different set parameters is obtained according to the cutting force and the material removal rate, and the cutting force and the material removal rate can be normalized respectively, where the normalized cutting force and the normalized material removal rate are respectively:

wherein, F_iAnd Q_iRespectively the cutting force and the material removal rate obtained by the reliability test of the ith group of disc milling cutters,andnormalized cutting force and material removal rate, respectively, F₀＝minF_i，Q₀＝maxQ_iAnd i is a positive integer, namely the embodiment of the invention minimizes the cutting force and maximizes the material removal rate.

Then, obtaining the grey correlation coefficients of the cutting force and the material removal rate respectively, wherein the grey correlation coefficients of the cutting force and the material removal rate are respectively as follows:

wherein the content of the first and second substances,andthe grey correlation coefficient of the cutting force and the grey correlation coefficient of the material removal rate respectively,m_F＝minΔF_i，M_F＝maxΔF_i，m_Q＝minΔQ_i，M_Q＝maxΔQ_iξ is a discriminating function and ξ∈ [0,1 ]]ξ is equal to or greater than 0 and equal to or less than 1.

And finally, obtaining the grey correlation degree of the cutter, wherein the grey correlation degree of the cutter is as follows:

wherein, γ_iFor the grey correlation of the cutter obtained by the reliability test of the ith group of disc milling cutters, theta is a weight coefficient, and theta ∈ [0,1 ]]I.e., θ is equal to or greater than 0 and equal to or less than 1, for example, the differentiating function ξ may be selected to be 0.5, the weighting factor θ may be selected to be 0.5, and the obtained normalized data, the gray-related factor and the gray-related degree of the tool are shown in table 4.

TABLE 4 normalized data, Grey correlation coefficient and Grey correlation degree of tool

S140, establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and taking the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter life model.

Optionally, the optimized model of the machining parameters of the disc milling cutter is as follows:

maxγ＝g(v,f,d)

wherein v is_min、v_max、f_min、f_max、d_min、d_maxThe lower limit value and the upper limit value T which are respectively corresponding to the main shaft rotating speed v, the feed amount f of each tooth and the cutting depth d_min、T_maxAnd g (v, f, d) is a gray correlation function established for the gray correlation degree of the cutter obtained by the reliability test of the disc milling cutter, and s.t. represents a constraint condition.

Optionally, the optimal processing parameters of the disc milling cutter are obtained according to the disc milling cutter processing parameter optimization model and the cutter life model, the gray correlation degree of the cutter obtained by the disc milling cutter reliability test under different set parameter conditions can be expanded to a continuous region by adopting a radial basis function to obtain an objective function of the disc milling cutter processing parameter optimization model, then the objective function is processed by adopting a particle swarm optimization algorithm to obtain the optimal processing parameters of the disc milling cutter, the optimal processing parameters comprise the optimal spindle rotation speed, the optimal feed per tooth and the optimal cutting depth, finally the optimal service life of the cutter is obtained according to the optimal processing parameters and the cutter life model, and whether the optimal service life is T or not is verified_minAnd T_maxThe range of the formed interval.

In the embodiment of the invention, the established tool machining parameter optimization model is as follows:

maxγ＝g(v,f,d)

since the data in table 4 is calculated in the discrete domain using the formula, it can be extended into the continuous domain using the radial basis function neural network, resulting in the optimization model objective function γ being g (v, f, d). And then processing the objective function by adopting a particle swarm optimization algorithm to obtain the optimal processing parameters of the disc milling cutter, wherein the obtained optimal processing parameters are v 70m/min, f 0.075mm/tooth and d 43mm, the gray correlation degree gamma is 0.6828 to be the maximum, and the optimal processing parameters v, f and d are substituted into a tool life modelT is obtained to be 44.17min, and the requirement on the service life of the cutter in the constraint condition is met.

In the milling process of the disc milling cutter, the cutting force and the material removal rate influence the processing cost and the processing efficiency, the service life of a cutter is shortened when the cutting force is too large, and the material removal rate is influenced when the cutting force is too small, so that the processing efficiency is low; on the other hand, the service life of the tool is an important index when the reliability of the tool is evaluated, and the selection of the machining parameters is important.

Aiming at the problems of low service life, poor reliability, low processing quality, low efficiency and the like of a cutter caused by the difficulty in selection of the current disc milling cutter processing parameters, on one hand, starting from a cutter reliability test, the influence of the main shaft rotating speed, the feed per tooth and the cutting depth on the cutter service life is fully considered, and a cutter service life model is established and is used as a constraint condition of a processing parameter optimization model. On the other hand, the important influences of the cutting force and the material removal rate on the machining cost and the machining efficiency are considered, the multi-objective optimization problem of the cutting force and the material removal rate is converted into a grey correlation value sequence by using a grey correlation technology, and an objective function of a machining parameter optimization model is established. Therefore, the embodiment of the invention takes the cutting force and the material removal rate as the parameter optimization target, establishes the disc milling cutter processing parameter optimization model by taking the service life of the cutter as one of the optimization constraint conditions, determines the optimal milling scheme, and can ensure that the disc milling cutter selects proper processing parameters in the processing process, thereby ensuring the service life of the cutter while obtaining larger material removal rate by smaller cutting force, effectively improving the processing efficiency and reducing the production cost. In addition, the embodiment of the invention can also be expanded to the selection of other cutter processing parameters, thereby improving the milling processing efficiency and promoting the development of the machine manufacturing industry.

The embodiment of the invention also provides a device for optimizing the machining parameters of the disc milling cutter, and fig. 2 is a schematic structural diagram of the device for optimizing the machining parameters of the disc milling cutter provided by the embodiment of the invention. As shown in fig. 2, the disc milling cutter machining parameter optimizing apparatus includes: the service life model establishing module 210 is used for establishing a service life model of the cutter according to the service life of the cutter obtained by a disc milling cutter reliability test under different set parameter conditions, and the set parameters comprise the rotating speed of a main shaft, the feed amount of each tooth and the cutting depth; the cutter parameter obtaining module 220 is used for obtaining the cutting force and the material removal rate of the corresponding cutter obtained by the reliability test of the disc milling cutter under different set parameter conditions; the correlation degree obtaining module 230 is configured to obtain a gray correlation degree of the cutter for the reliability test of the disc milling cutter under different setting parameters according to the cutting force and the material removal rate; the parameter optimization module 240 is configured to establish a disc milling cutter processing parameter optimization model by using the gray correlation degree of the tool as an objective function and using the set parameter and the service life of the tool as constraint conditions, and obtain the optimal processing parameter of the disc milling cutter according to the disc milling cutter processing parameter optimization model and the tool life model.

The disc milling cutter processing parameter optimizing device provided by the embodiment of the invention determines the processing parameters of the disc milling cutter through the established disc milling cutter processing parameter optimizing model, so that the disc milling cutter can obtain a larger material removal rate with a smaller cutting force in the processing process, the service life of the cutter is ensured, and the disc milling cutter processing parameter optimizing device is beneficial to improving the processing efficiency and reducing the production cost.

Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, the electronic apparatus includes: at least one processor 310, at least one memory 320, and at least one communication interface 330. The various components in the electronic device are coupled together by a bus system 340. A communication interface 330 for information transmission with an external device. It will be appreciated that the bus system 340 is used to enable communications among the components connected. The bus system 340 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, the various buses are labeled as bus system 340 in fig. 3.

It will be appreciated that the memory 320 in this embodiment may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. In some embodiments, memory 320 stores elements, executable units or data structures, or a subset thereof, or an expanded set thereof as follows: an operating system and an application program. The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs, including various application programs such as a Media Player (Media Player), a Browser (Browser), etc., are used to implement various application services. The program for implementing the method for optimizing the machining parameters of the disc milling cutter provided by the embodiment of the application can be contained in an application program.

In the embodiment of the present invention, the processor 310 is configured to execute the steps of the disc milling cutter machining parameter optimization method provided by the embodiment of the present invention by calling the program or the instruction stored in the memory 320, which may be specifically a program or an instruction stored in an application program.

The disc milling cutter machining parameter optimization method provided by the embodiment of the application can be applied to the processor 310, or implemented by the processor 310. The processor 310 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 310. The Processor 310 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The steps of the method for optimizing the machining parameters of the disc milling cutter provided by the embodiment of the invention can be directly embodied as the execution of a hardware decoding processor, or the combination of hardware and software units in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 320, and the processor 310 reads the information in the memory 320 and performs the steps of the method in combination with the hardware thereof.

The electronic device may further include a solid component or a plurality of solid components to control the machining parameters of the disc milling cutter according to instructions generated by the processor 310 when executing the optimization method for the machining parameters of the disc milling cutter provided by the embodiment of the present application. The various physical components cooperate with the processor 310 and the memory 320 to implement the functionality of the electronic device in this embodiment.

Embodiments of the present invention also provide a storage medium, such as a computer-readable storage medium, storing a program or instructions, where the program or instructions are used to enable a computer to execute a method for optimizing machining parameters of a disc milling cutter, where the method includes:

establishing a cutter life model according to the service life of the cutter obtained by a disc milling cutter reliability test under different set parameter conditions; the set parameters comprise the rotating speed of the main shaft, the feed amount of each tooth and the cutting depth;

obtaining cutting force and material removal rate of a corresponding cutter obtained by a disc milling cutter reliability test under different set parameter conditions;

obtaining the grey correlation degree of the cutter for the reliability test of the disc milling cutter under different set parameter conditions according to the cutting force and the material removal rate;

and establishing a disc milling cutter machining parameter optimization model by taking the grey correlation degree of the cutter as an objective function and taking the set parameters and the service life of the cutter as constraint conditions, and acquiring the optimal machining parameters of the disc milling cutter according to the disc milling cutter machining parameter optimization model and the cutter life model.

Optionally, the computer executable instructions, when executed by the computer processor, may be further used to implement the technical solution of the disc milling cutter machining parameter optimization method provided in any embodiment of the present application.

From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods of the embodiments of the present application.

Those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.

Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.