CN116976018A - Method for predicting relative position deviation of milling tool system - Google Patents
Method for predicting relative position deviation of milling tool system Download PDFInfo
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Abstract
The application discloses a method for predicting relative position deviation of a milling tool system, which comprises the following steps: s1: utilizing the tool system dynamics model to construct a dynamics model of the milling tool system joint surface; s2: providing a dynamic stability analysis method for a joint surface of a milling tool system; s3: the method comprises the steps of solving the dynamic energy consumption of the joint surface of a milling tool system, researching the transmission and distribution of the energy consumption of the joint surface of the tool system, and providing an identification method for the transmission and distribution of the dynamic energy consumption of the joint surface of the milling tool system; s4: further characterizing the overall relative position deviation of the milling tool system, and providing a prediction method of the relative position deviation of the milling tool system. The prediction method for the relative position deviation of the milling tool system characterizes the overall relative position deviation of the tool system, predicts the overall position deviation of the tool system by using the artificial neural network, and further improves the stability of the tool system.
Description
The application relates to a division application of application number 202310230881.5, application day 2023, 03 and 11, and the application is named as a milling tool joint surface dynamics model and an energy consumption model construction method.
Technical Field
The application belongs to the technical field of milling, and particularly relates to a method for predicting relative position deviation of a milling tool system
Background
The numerical control machine tool is a working machine of equipment manufacturing industry, and the technical level of the numerical control machine tool is a symbol of national comprehensive national force. The numerical control machine tool system comprises a main shaft, a tool handle, a cutter and other important subsystems. The dynamic characteristics of the milling tool system can directly influence the milling precision, the tool system directly participates in the milling process in numerical control processing equipment, and the dynamic characteristics of the tool system directly influence the milling stability of a cutter and the surface processing precision of a workpiece. The main bonding surfaces in the tool system comprise a main shaft-tool shank bonding surface and a tool shank-tool bonding surface, and the dynamic properties of the bonding surfaces can directly influence the precision and quality of the milling surface, so that the problems of incapability of meeting the quality requirements of the milling surface and the like are solved, and therefore, the research of the dynamic model of the milling tool system is of great significance.
The dynamic model of the existing milling tool system only researches dynamics, and the influence of dynamic energy consumption on the milling tool system is not further researched, so that the stability of the whole milling tool system is influenced.
Disclosure of Invention
The present application is directed to a method for predicting relative positional deviation of a milling tool system, so as to solve the above-mentioned problems in the prior art.
In order to achieve the above purpose, the present application provides the following technical solutions: a method of predicting relative positional offset of a milling tool system, comprising the steps of:
s1: providing a method for constructing a dynamic model of a milling tool system joint surface, and constructing the dynamic model of the milling tool system joint surface by using the dynamic model of the tool system;
s2: carrying out contact stiffness calculation on the constructed dynamic model of the joint surface of the milling tool system, analyzing the dynamic stability of the joint surface of the milling tool system, and providing a dynamic stability analysis method of the joint surface of the milling tool system;
s3: the method comprises the steps of solving the dynamic energy consumption of the joint surface of a milling tool system, researching the transmission and distribution of the energy consumption of the joint surface of the tool system, and providing an identification method for the transmission and distribution of the dynamic energy consumption of the joint surface of the milling tool system;
s4: further characterizing the overall relative position deviation of the milling tool system, and providing a prediction method of the relative position deviation of the milling tool system.
Unlike the already disclosed technology: the existing dynamic modeling method of the milling tool system joint surface is limited to carrying out overall dynamic modeling on the milling tool system joint surface, the degree of freedom of the modeling method is low, the accuracy is influenced, the dynamic modeling of the tool system joint surface is carried out by utilizing a finite element method, multiple degrees of freedom can be realized, the accuracy of the obtained dynamic model of the tool system joint surface is higher, and the modeling method of the tool system joint surface dynamic model is further optimized.
According to the existing dynamic stability analysis method for the joint surface of the milling tool system, only the contact stiffness of the joint surface of the tool system is calculated, the stability of the tool system is analyzed through a mode, and the analysis method cannot meet the requirement of the stability of the tool system.
In the prior art, the dynamic analysis of the joint surface of the milling tool system does not further study the dynamic energy consumption of the joint surface of the milling tool system, and the application identifies the transmission and distribution of the energy consumption of the joint surface of the tool system by resolving the energy consumption of the joint surface of the tool system, thereby improving the stability of the joint surface of the tool system.
The existing tool system position error only researches the position error of the spindle system, does not research the position error of the whole spindle-tool shank-tool system, and does not research the relative position deviation of the milling tool system.
Compared with the prior art, the application has the beneficial effects that: the application provides a method for constructing a dynamic model of a milling tool joint surface and an energy consumption model.
According to the dynamic stability analysis method for the milling tool system joint surface, the contact stiffness of the tool system joint surface is solved, and the dynamic parameters of the tool system joint surface are further solved, so that the dynamic stability of the milling tool system joint surface is analyzed, and the stability of the tool system joint surface is improved.
According to the identification method for dynamic energy consumption transmission and distribution of the milling tool system joint surface, the energy consumption transmission and distribution of the tool system joint surface is further identified through calculation of the energy consumption of the tool system joint surface, and the stability of the tool system joint surface is improved.
The prediction method for the relative position deviation of the milling tool system characterizes the overall relative position deviation of the tool system, predicts the overall position deviation of the tool system by using the artificial neural network, and further improves the stability of the tool system.
Drawings
FIG. 1 is a schematic diagram of a dynamic model of a milling tool system of the present application; FIG. 2 is a schematic diagram of a particle dynamics system of the tool system interface of the present application; FIG. 3 is a schematic view of a dynamic model of the interface of the tool system of the present application; FIG. 4 is a schematic diagram of a tool system interface energy consumption model according to the present application; FIG. 5 is a schematic diagram of the energy consumption transmission path of the milling tool system of the present application; FIG. 6 is a schematic diagram of a relative positional displacement model of the tool system of the present application; FIG. 7 is a flow chart of a method for characterizing relative positional offset of a tool system according to the present application; FIG. 8 is a schematic diagram of an artificial neural network according to the present application; FIG. 9 is a schematic diagram of the cutting force distribution of 30s at 1290r/min according to the present application; FIG. 10 is a schematic view of the cutting force distribution of 30s at 1433r/min of the present application; FIG. 11 is a graph showing the cutting force distribution of the present application at a rotational speed of 1576r/min for 30 s; FIG. 12 is a schematic diagram of a Butterworth low pass filter of the present application; FIG. 13 is a schematic view of the feed direction cutting force distribution of the present application;
FIG. 14 is a schematic view of the radial cutting force distribution of the present application; FIG. 15 is a schematic view of the axial cutting force distribution of the present application; FIG. 16 is a graph showing the contact stiffness of the spindle-shank interface at 1290r/min in accordance with the present application; FIG. 17 is a graph showing the frequency domain signal of the contact stiffness of the spindle-shank interface at 1290 r/min; FIG. 18 is a graph showing the contact stiffness of the spindle-shank interface at different speeds in accordance with the present application; FIG. 19 is a graph showing the frequency domain signal of the contact stiffness of the spindle-shank interface at different rotational speeds in accordance with the present application; FIG. 20 is a schematic view of the energy consumption caused by the load out of the spindle-shank interface of the present application; FIG. 21 is a schematic representation of the energy consumption caused by the contact stiffness of the spindle-shank interface of the present application; FIG. 22 is a schematic diagram of energy consumption due to damping of the spindle-shank interface contact in accordance with the present application; FIG. 23 is a schematic view of the energy consumption distribution of the spindle-shank interface of the present application; FIG. 24 is a schematic diagram of relative displacement of the rotational speed only tool system at 1290r/min in accordance with the present application; FIG. 25 is a schematic diagram of the relative offset of the tool system at time 2.5s at 1290r/min in accordance with the present application; FIG. 26 is a diagram showing relative tool system offset at 5s at 1290r/min in accordance with the present application; FIG. 27 is a schematic diagram of the relative offset of the tool system at 7.5s at 1290r/min in accordance with the present application; FIG. 28 is a schematic diagram of the relative offset of the tool system at 10s time at 1290r/min in accordance with the present application; FIG. 29 is a schematic diagram of the relative offset of the rotational speed only tool system at a rotational speed of 1433r/min of the present application; FIG. 30 is a schematic diagram of the relative offset of the tool system at time 2.5s at 1433r/min of the present application; FIG. 31 is a diagram showing relative tool system offset at time 5s at 1433r/min according to the present application; FIG. 32 is a schematic diagram of the relative offset of the tool system at time 7.5s at 1433r/min of the present application; FIG. 33 is a schematic diagram of the relative offset of the tool system at time 10s at 1433r/min of the present application; FIG. 34 is a schematic diagram of relative displacement of the speed tool system at 1576r/min only; FIG. 35 is a schematic diagram of the relative offset of the tool system at time 2.5s at 1576r/min of the present application; FIG. 36 is a diagram showing relative tool system offset at time 5s at 1576r/min according to the present application; FIG. 37 is a schematic diagram of the relative displacement of the tool system at 7.5s at 1576 r/min; FIG. 38 is a schematic diagram of the relative displacement of the tool system at 10s at 1576 r/min; FIG. 39 is a schematic diagram of the relative spindle offset at 1290r/min in accordance with the present application; FIG. 40 is a schematic diagram of the relative spindle-shank offset at 1290r/min in accordance with the present application; FIG. 41 is a schematic diagram of the relative shank-tool offset at 1290r/min in accordance with the present application; FIG. 42 is a graph showing relative spindle offset at 1433r/min according to the present application; FIG. 43 is a schematic representation of the relative spindle-shank offset at 1433r/min of the present application; FIG. 44 is a schematic view of the relative shank-tool offset at 1433r/min of the present application; FIG. 45 is a schematic diagram of the relative spindle offset at 1576r/min according to the present application; FIG. 46 is a schematic diagram of the relative spindle-shank offset at 1576r/min of the present application; FIG. 47 is a schematic diagram of the relative shank-tool offset at 1576r/min of the present application; FIG. 48 is a graph showing the effect of cutting speed on tool system relative positional offset in accordance with the present application; FIG. 49 is a graphical representation of the effect of feed per tooth on the relative positional offset of the tool system in accordance with the present application; FIG. 50 is a graph showing the effect of depth of cut on relative positional offset of a tool system according to the present application; FIG. 51 is a schematic view of the effect of cutting width on tool system relative positional offset in accordance with the present application; FIG. 52 is a graph showing the determination coefficients of the relative position offset of the tool system at different cutting speeds according to the present application; FIG. 53 is a graph showing the determination coefficients of the relative position offset of the tool system for different feed rates per tooth according to the present application; FIG. 54 is a graph showing the determination coefficients of the relative positional offset of the tool system at different depths of cut according to the present application; FIG. 55 is a graph showing the determination coefficients of the relative positional offset of the tool system for different cutting widths according to the present application; FIG. 56 is a graph showing the comparison of predicted and actual values of tool system relative position offsets at different cutting speeds according to the present application; FIG. 57 is a graph showing the comparison of predicted and actual values of tool system relative position offsets for different tooth feeds according to the present application; FIG. 58 is a graph showing the comparison of predicted and actual values of tool system relative positional offset at different depths of cut according to the present application; FIG. 59 is a graph showing the comparison of predicted and actual values of tool system relative positional displacement for different cutting widths according to the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a milling tool joint surface dynamics model and an energy consumption model construction method as shown in fig. 1-59, which comprises the following steps:
s1: providing a method for constructing a dynamic model of a milling tool system joint surface, and constructing the dynamic model of the milling tool system joint surface by using the dynamic model of the tool system;
s2: carrying out contact stiffness calculation on the constructed dynamic model of the joint surface of the milling tool system, analyzing the dynamic stability of the joint surface of the milling tool system, and providing a dynamic stability analysis method of the joint surface of the milling tool system;
s3: the method comprises the steps of solving the dynamic energy consumption of the joint surface of a milling tool system, researching the transmission and distribution of the energy consumption of the joint surface of the tool system, and providing an identification method for the transmission and distribution of the dynamic energy consumption of the joint surface of the milling tool system;
s4: further characterizing the overall relative position deviation of the milling tool system, and providing a prediction method of the relative position deviation of the milling tool system.
1. Method for constructing dynamic model of joint surface of milling tool system
(1) Tool system dynamics model construction method
In the milling process, the milling tool system is subjected to the action of external load of cutting force under the vibration condition, the stability of the milling tool system is influenced, and in order to study the stability of the milling tool system, a dynamic model of the whole milling tool system is built by using the cutting force in the feeding direction, the radial direction and the axial direction, and the front view and the top view of the dynamic model of the milling tool system are shown in figure 1.
As can be seen from fig. 1, a dynamic model of the external loading of the cutting forces in relation to the feed direction, radial and axial direction is created with a front view and a top view of the milling tool system, where k x Modal stiffness in the feed direction of the milling tool system; c x Modal damping of the feed direction of the milling tool system; k (k) y The modal stiffness in radial direction of the milling tool system; c y Radial modal damping for milling tool systems; k (k) z Modal stiffness in the axial direction of the milling tool system; c z For the modal damping of the axial direction of the milling tool system, the dynamic equation set of the milling tool system according to the dynamic model of the milling tool system is shown as the following formula (2-4):
and (3) finishing to obtain a milling tool system dynamics equation, wherein the equation is shown in the formula (2-5):
m is in x Modal mass for feed direction of the milling tool system; f (F) x External load of cutting force for the feed direction of the milling tool system; m is m y A radial modal mass for the milling tool system; f (F) y External load for radial cutting forces of the milling tool system; m is m z Is the axial modal mass of the milling tool system; f (F) z For external axial loading of the milling tool system with cutting forces, the modal mass matrix M of the milling tool system 1 Modal damping matrix C 1 And modal stiffness matrix K 1 The method comprises the following steps of:
the milling tool system dynamics equation is:
(2) Tool system joint surface dynamics model construction method
Based on a milling tool system dynamics model, a dynamics model about external load of cutting force is built on a tool system joint surface by utilizing a finite element method, a spindle-tool shank and tool shank-tool joint surface is discretized into n mass points of the spindle-tool shank joint surface and the tool shank-tool joint surface, and a dynamics model about the mass points is built, as shown in fig. 2.
As can be seen from fig. 2, k 1i The unit rigidity of the combined surface of the main shaft and the cutter handle; c 1i Damping the unit of the combined surface of the main shaft and the cutter handle; ΔF (delta F) i Is an external load acting on the combined surface of the main shaft and the cutter handle; k (k) 2j The unit rigidity of the combined surface of the cutter handle and the cutter; c 2j Single for the shank-tool interfaceMeta-damping; ΔF (delta F) j External load of the combined surface of the cutter handle and the cutter; u (u) 1i The unit deformation displacement of the combined surface of the main shaft and the cutter handle; u (u) 2j And (3) for the unit deformation displacement of the combined surface of the cutter handle and the cutter, and then establishing an equation set according to the dynamic system of the upper graph as follows:
m is in 1i The unit spots are the main shaft-cutter handle joint surfaces; m is m 2j The unit dots are the combined surfaces of the cutter handle and the cutter;the unit deformation speed of the combined surface of the main shaft and the cutter handle; />Unit deformation acceleration for the combined surface of the main shaft and the cutter handle; />The unit deformation speed of the combined surface of the cutter handle and the cutter; />And (5) unit deformation acceleration of the combined surface of the cutter handle and the cutter.
And (3) finishing to obtain a tool system particle dynamics equation:
the unit mass matrix m of the tool system interface c Cell stiffness matrix k c And the unit damping matrix is c c The method comprises the following steps:
coupling the established particle dynamics model of the tool system joint surface into an overall dynamics model related to the tool system joint surface, wherein an overall mass matrix M obtained by coupling is as follows:
the overall stiffness matrix K is:
the overall damping matrix C is:
finally, the tool system joint surface dynamics equation is formed by coupling:
wherein M is the overall mass matrix of the joint surface of the tool system, C is the overall damping matrix of the joint surface of the tool system, K is the overall rigidity matrix of the joint surface of the tool system, and F is the external load of cutting force.
2. Dynamic stability analysis method for joint surface of milling tool system
(1) Tool system joint surface contact stiffness resolving method
In order to obtain the contact stiffness of the tool system joint surface, a dynamic model of the main shaft-tool handle joint surface and the tool handle-tool joint surface is established, and the contact stiffness of the joint surfaces is calculated respectively, as shown in fig. 3.
Taking a main shaft-cutter handle joint surface as an example, analyzing according to a unit particle dynamics equation obtained by dispersing the main shaft-cutter handle joint surface to obtain:
cell contact stiffness k 1i Equal to:
f in the formula ni Delta for cell contact stress i Is cell contact deformation.
Then the contact stiffness k 1 The method comprises the following steps:
the damping ratio ζ can be the damping coefficient c 1 Critical damping coefficient c r The ratio of (2) is:
in the middle ofω ni The natural vibration circle frequency is undamped for the unit.
Unit damping coefficient c 1i The method comprises the following steps:
c 1i ＝2m 1i ω ni ζ 1i (3-5)
the damping coefficient is:
(2) Tool system joint surface dynamic stability analysis method
When an external load can be expressed as a load with a sine or cosine law changing along with time and can be expressed as a simple harmonic load, to analyze the dynamic stability of the tool system joint surface, the dynamic equations of the spindle-tool shank joint surface and the tool shank-tool joint surface need to be solved respectively, and taking the dynamic equation of the spindle-tool shank joint surface as an example, the dynamic equation of the spindle-tool shank joint surface is expressed as follows:
dividing both sides by mass m 1i And let c 1i ＝2m 1i ω ni ζ 1i Obtaining:
u in the formula st ＝ΔF i /k 1i For the joint surface at DeltaF i Static deformation under action. Let the homogeneous solution of the motion differential equation be:
wherein omega is Di Is the natural vibration circular frequency omega with damping system Di The method comprises the following steps:
let the special solution of the motion differential equation be:
substituting the special solution into a power equation to obtain the solution:
in the method, in the process of the application,for the frequency ratio, D is the amplitude and α is the initial phase angle.
Wherein the general solution of the kinetic equation is u 1i ＝u c (t)+u p (t) defining an initial condition as u when t=0 1i (t)＝u 1i (0)，
And (3) solving to obtain:
the solution of the kinetic equation is:
the first two terms of the equation solution are transient response, the last term is steady state response, the first two indexes gradually decay towards zero due to the existence of damping, the motion state of the equation solution is stable, the equation solution is attributed to steady state response, and the final kinetic equation solution is as follows:
u 1i (t)＝Dsin(ω i t-α) (3-15)
according to the solution of the dynamic equation, the deformation displacement, the deformation speed and the deformation acceleration of the main shaft-tool handle joint surface can be obtained, and the dynamic stability of the main shaft-tool handle joint surface can be known by respectively analyzing the deformation displacement, the deformation speed and the deformation acceleration of the main shaft-tool handle joint surface.
3. Method for identifying dynamic energy consumption transmission and distribution of joint surface of milling tool system
(1) Tool system joint surface energy consumption resolving method
In order to obtain the energy consumption of the tool system joint surface, the energy consumption of the spindle-tool shank joint surface and the energy consumption of the tool shank-tool joint surface need to be respectively calculated, and then a tool system joint surface energy consumption model is constructed, and the energy consumption caused by external load, the energy consumption caused by contact stiffness and the energy consumption caused by contact damping of the spindle-tool shank joint surface are respectively calculated, as shown in fig. 4.
As can be seen from fig. 4, taking the energy consumption of the spindle-tool shank interface as an example, du1i is the deformation displacement of the spindle-tool shank interface unit; dPsi is energy consumption caused by external load of a main shaft-cutter handle joint surface particle; dPdi is energy consumption caused by mass point contact rigidity of a combined surface of the main shaft and the cutter handle; dPfi is energy consumption caused by mass point contact damping of a main shaft-cutter handle joint surface.
According to the previous dynamic equation solving result, the displacement of the unit mass point under the action of simple harmonic load is obtained as follows:
u 1i (t)＝Dsin(ω i t-α) (4-1)
the energy consumption caused by external load is external load delta F i And (3) the instantaneous consumed energy consumption of sin omega t, wherein the energy consumption distribution function caused by the instantaneous external load of the particles is as follows:
the energy consumption caused by the instantaneous external load of the particles is as follows:
the energy consumption caused by the external load of the main shaft-cutter handle joint surface is as follows:
the energy consumption distribution function caused by the contact stiffness of the mass points of the combined surface of the main shaft and the cutter handle is as follows:
the energy consumption caused by the contact stiffness of the particles is:
the energy consumption caused by the contact stiffness of the spindle-shank interface is:
the energy consumption distribution function caused by the contact damping of the mass points of the combined surface of the main shaft and the cutter handle is as follows:
the energy consumption caused by contact damping of particles is as follows:
the energy consumption caused by the contact damping of the main shaft-tool handle joint surface is as follows:
(2) Recognition method for energy consumption transmission and distribution of tool system joint surface
The energy consumption of the milling tool system joint surface is caused by the excitation of cutting force as input, so that a transmission path of the energy consumption of the milling tool system joint surface is constructed, the distribution of the energy consumption of the milling tool system joint surface can be further disclosed, and the transmission path of the energy consumption of the milling tool system joint surface is shown in fig. 5.
According to the energy consumption transmission path of the milling tool system joint surface, the energy consumption of the tool system joint surface caused by external load of cutting force can be known from the graph and is respectively transmitted into the main shaft-tool shank joint surface and the tool shank-tool joint surface through the milling tool system, and according to the dynamic response, the energy consumption caused by inertial force, the energy consumption caused by contact stiffness and the energy consumption output caused by contact damping are finally used as the distribution duty ratio of the energy consumption of the milling tool system, and the distribution of the energy consumption of the tool system joint surface can be known by comparing the obtained energy consumption distribution caused by the external load of the tool system joint surface, the energy consumption distribution caused by contact stiffness and the energy consumption distribution caused by contact stiffness.
4. Method for predicting relative position deviation of milling tool system
(1) Tool system relative position deviation characterization method
The internal energy consumption generated by the spindle-shank interface and the shank-tool interface is mainly reflected in the relative position offset of the tool system, and the relative position offset of the tool system is different with time because the end mill is used for intermittent cutting, so that a relative position offset model of the tool system is established for exploring the relative position offset of the tool system, as shown in fig. 6.
As can be seen from fig. 6, which is a model of relative position shift of the tool system, the deformation amount of the tool system is extracted according to the positive relative shift reference and the negative relative shift reference, and compared with the initial reference, so that it can be seen how much the relative position shift of the tool system is, and it can be seen from the figure that the shift amounts of the respective positions of the tool system are not the same, wherein the shift amount of the shank-tool system is the largest, and a method for characterizing the relative position of the milling tool system is provided for revealing the change characteristics of the relative position shift amount of the milling tool system, as shown in fig. 7.
By using the characterization method, the characteristic of the change of the relative position of the tool system during milling processing can be obtained, and the change of the relative position offset of the tool system is researched.
(2) Method for predicting relative position deviation of tool system
And calculating cutting forces under different cutting conditions by using a milling force calculation formula, and analyzing the influence of cutting parameters on the contact stiffness and the relative position offset.
According to the influence of the above process parameters on the relative position offset of the tool system, an artificial neural network is utilized to construct a prediction model of the relative position offset, and a typical artificial neural network structure is shown in fig. 8.
And training the relative position offset of the tool system under different process parameters by using the artificial neural network to obtain a prediction model of the relative position offset of the tool system.
Implementation example 1: method for constructing dynamic model of joint surface of milling tool system
In order to obtain the cutting force during milling, a milling experiment of an end mill is carried out, the milling cutter adopted in the experiment is a five-tooth integral hard alloy end mill (MC 122-20.0A5B-WJ30 TF) produced by a Walt company, a machine tool selected in the experiment is a three-axis numerical control milling machining center, a DynoWare client matched with the machine tool is utilized to extract data such as main cutting force, vibration signals and the like in the cutting process of the hard alloy milling cutter, the milling mode is down milling and dry milling, and a workpiece adopted in the experiment is titanium alloy;
the experimental technological parameter scheme adopts three groups of experimental schemes, wherein three rotating speeds of 1290r/min, 1433r/min and 1576r/min are adopted in the three groups of experimental schemes, technological parameters such as feeding quantity, cutting depth and width of each tooth are the same, wherein Deltazi is a cutter tooth axial error distribution sequence, deltari is a cutter tooth radial error distribution sequence, and Deltazi= (-0.004, -0.001, -0.013,0, -0.009); Δri= (0, -0.029, -0.039, -0.010, -0.018), as shown in tables 2-1 to 2-2 below.
Table 2-1 process parameters used in experiments
Table 2-2 error of cutter teeth used in experiments
Milling experiments are carried out by adopting the experimental schemes shown in tables 2-1 to 2-2, milling force data in the feeding direction, the milling depth direction and the milling width direction of the milling cutter are detected by using a Kistler rotary three-way dynamometer, the milling force data are converted into cutting force signals by using a cutting force acquisition system, and vibration acceleration signals in the three directions are obtained by using an acceleration sensor. Wherein the cutting force signal under the action of milling vibration for a cutting period of 0-30s is shown in fig. 9-11.
As can be seen from fig. 9 to 11, the cutting force distribution at different rotation speeds is nonlinear, in the experiment, the cutting force in the three directions of the cutting force acquisition system, namely, the cutting force in the cutting tool feeding direction, the milling depth direction and the milling width direction, is influenced by various disturbing signals, the fluctuation degree is large, so that the cutting force in the three directions of the cutting tool feeding direction, the milling depth direction and the milling width direction under the condition of only vibration is not accurately reflected, and in order to accurately obtain the cutting force in the three directions of the cutting tool feeding direction, the milling depth direction and the milling width direction under the vibration condition, the cutting force signals in the three directions of the cutting tool feeding direction, the milling depth direction and the milling width direction are filtered by using a butterworth low-pass filter, and the influence of the disturbing signals is removed, and the butterworth low-pass filter is shown in fig. 12.
The x, y, z axis cutting force distribution at different rotational speeds obtained by filtering the cutting force according to the experiment is shown in fig. 13-15 below.
And after the random waveform and other irrelevant factors in the cutting force data measured by the original experiment are filtered by using the Butterworth low-pass filter, the cutting force after filtering is more similar to the cutting force data only under the influence of the vibration waveform, so that the development of subsequent simulation and research is facilitated.
Implementation example 2: dynamic stability analysis method for joint surface of milling tool system
According to a contact stiffness solving formula, in order to obtain the contact stress and the contact deformation of the main shaft-tool handle joint surface, transient dynamics simulation is carried out on the milling tool system based on an experimental scheme.
The dynamic simulation of the main shaft-tool handle combination surface adopts finite element simulation software ANSYS, a Transient Structural module of the main shaft-tool handle combination surface is used for carrying out transient dynamic simulation on a milling tool system, a UG software is firstly used for constructing a main shaft-tool handle-tool model according to a milling experiment and guiding the main shaft-tool handle-tool model into a Transient Structural simulation module, then the main shaft-tool handle system is endowed with material properties according to actual conditions, constraint is applied to the main shaft-tool handle system, then the main shaft-tool handle system is subjected to grid division, boundary conditions are applied to the main shaft-tool handle system, and finally the main shaft-tool handle system is solved to obtain a dynamic simulation result of the main shaft-tool handle combination surface, and the material parameters of the main shaft-tool handle component are shown in a table 3-1.
TABLE 3-1 spindle-tool handle System Material parameters
The simulated boundary conditions are shown in table 3-2.
TABLE 3-2 simulation boundary conditions
As can be seen from Table 3-2, ΔF xi1 、ΔF yi1 、ΔF zi1 The cutting force of the x, y and z axes is experimentally measured under the rotating speed of 1290 r/min; ΔF (delta F) xi2 、ΔF yi2 、ΔF zi2 Cutting forces of x, y and z axes which are experimentally measured under the condition of 1433r/min rotating speed; ΔF (delta F) xi3 、ΔF yi3 、ΔF zi3 The cutting force of the x, y and z axes is experimentally measured under the rotating speed condition of 1576 r/min; applied to the cutter teeth involved in cutting in three times.
Dynamic contact stiffness distribution characteristics of the spindle-tool handle bonding surface system are obtained through transient dynamics simulation results, grid nodes of maximum values corresponding to stress strain on the spindle-tool handle bonding surface are obtained every 0.04s according to the simulation results of the spindle-tool handle bonding surface, 250 points are just obtained when the moment reaches 10s, a maximum contact stiffness curve graph of the spindle-tool handle bonding surface changing along with time is drawn, and stability of the spindle-tool handle bonding surface is analyzed by taking the simulation results under the rotating speed of 1290r/min as an example, as shown in fig. 16. It can be seen from fig. 16 that the contact stiffness of the spindle-shank interface reaches the trough at intervals, and the peak value of the contact stiffness shows nonlinear variation;
to analyze the dynamic change curve of the spindle-shank interface contact stiffness and the dynamic stability of the spindle-shank interface, the spindle-shank interface contact stiffness was frequency domain analyzed as shown in fig. 17. As can be seen from fig. 17, the main frequency of the contact stiffness of the spindle-shank junction surface under the rotation speed of 1290r/min is 2.2Hz, the amplitude is maximum, which indicates that the contact stiffness of the spindle-shank junction surface reaches a peak value once every 0.46s, which corresponds to the peak value of the contact stiffness curve of the spindle-shank, and the frequency domain signal of the contact stiffness of the spindle-shank junction surface changes steadily as a whole, which indicates that the contact stiffness of the spindle-shank junction surface is 2.2Hz during the cutting process, because the low-pass filtering frequency of the cutting force is about 3Hz, which indicates that the contact stiffness of the junction surface is mainly affected by the cutting force.
To further investigate the stability of the spindle-shank interface during milling, the contact stiffness at three different speeds 1290r/min, 1433r/min, 1576r/min was calculated using simulation solutions, as shown in FIG. 18. As can be seen from fig. 18, the contact stiffness of the bonding surface varies under the influence of different rotational speeds and cutting forces, the higher the rotational speed is, the greater the contact stiffness of the bonding surface is, and the frequency of the contact stiffness of the spindle-shank bonding surface varies under the influence of different rotational speeds and cutting forces;
to further ascertain the effect of rotational speed and cutting force on the spindle-shank interface, a frequency domain analysis of the spindle-shank interface contact stiffness curve is shown in fig. 19. As can be seen from fig. 19, the main frequencies and peaks of the signals of the frequency domain of the contact stiffness of the spindle-shank at the rotation speeds 1290r/min, 1433r/min and 1576r/min are different, because the cutting forces at the rotation speeds 1290r/min, 1433r/min and 1576r/min are different, the main frequency of the contact stiffness of the spindle-shank at the rotation speeds 1290r/min and 1576r/min is 1.67Hz, and the main frequency of the contact stiffness of the spindle-shank at the rotation speeds 1576r/min is 1Hz, which means that a peak appears at intervals of 0.46s of the contact stiffness of the spindle-shank at the rotation speeds 1290 r/min; at 1433r/min, a peak value occurs in the contact stiffness of the main shaft-cutter handle joint surface every 0.6 s; a peak value can appear on the contact stiffness of the spindle-tool handle joint surface every 1s at 1576r/min, but the signals of the frequency domain of the contact stiffness of the spindle-tool handle joint surface at 1290r/min, 1433r/min and 1576r/min are stable integrally, which indicates that the spindle-tool handle joint surface is stable integrally in the milling process.
Implementation example 3: method for identifying dynamic energy consumption transmission and distribution of joint surface of milling tool system
In order to study the influence law of the energy consumption of the main shaft-knife handle combination surface, the dynamic energy consumption of the main shaft-knife handle combination surface at different rotating speeds is drawn, as shown in figures 20-22.
As can be seen from fig. 20 to 22, as the rotation speed increases, the energy consumption caused by the external load of the spindle-shank interface, the energy consumption caused by the contact stiffness and the energy consumption caused by the contact damping gradually increase, indicating that the rotation speed and the cutting force are the main factors affecting the energy consumption of the spindle-shank interface.
The energy consumption distribution caused by the load outside the main shaft-tool handle joint surface, the energy consumption distribution caused by the contact damping and the energy consumption distribution caused by the contact stiffness are plotted into the joint surface energy consumption distribution curve, wherein the energy consumption value caused by the inertia force is almost close to zero, so that the energy consumption is not studied here, as shown in fig. 23.
It can be seen from fig. 23 that the energy consumption distribution of the spindle-shank junction is mainly the energy consumption due to the external load and the energy consumption due to the contact stiffness, the energy consumption due to the contact damping is small, and from the kinetic equation, the energy consumption due to the external load is constituted by the energy consumption due to the contact stiffness and the energy consumption due to the contact damping, and the energy consumption due to the inertial force is very small, so that the energy consumption due to the inertial force is not analyzed here, and from the spindle-shank energy consumption distribution diagram, the energy consumption due to the cutting force is mainly the energy consumption due to the contact stiffness of the spindle-shank junction due to the strain generated by the cutting force, and the energy consumption due to the contact damping is very small, indicating that the spindle-shank junction is mainly the energy consumption due to the deformation resistance during milling.
Implementation example 4: method for shifting relative positions of milling tool system
According to the relative position characterization method of the milling tool system, the relative position offset of the tool system, which changes with time at different rotation speeds, is obtained, as shown in fig. 24-38.
As can be seen from fig. 24 to 38, the relative positional shift amount varies with time, when the cutting force is not yet generated at the start of cutting, the shift amount of the tool system is relatively uniform under the influence of only the rotational speed, the shift amount of the spindle system and the shift amount of the shank-tool system at which the maximum shift amount occurs are not greatly different, but with time variation, when the cutting force is added, the shift amount of the spindle system approaches zero, and the shift amount is mainly concentrated in the spindle-shank system and the shank-tool system, wherein the shift amount reaches the maximum at the shank-tool system, and the maximum values of the positive relative shift and the negative relative shift are also different, which may be related to the direction of the cutting force at the time of cutting, and when the cutting time is 5s, the maximum shift amount of the tool system reaches the maximum at the time, and when the cutting time is 7.5s, the maximum shift amount of the tool system decreases toward the upper time, and when the cutting time is 10s, the maximum shift amount of the tool system increases again at the upper time, which is affected by the cutting force.
The maximum positive relative positional offset and the maximum negative relative positional offset are not the same, and for further explanation of the reasons, tool system spindle system, spindle-shank system, shank-tool system functional unit offsets are selected, as shown in fig. 39-47.
39-47, the tool system offset varies differently at different locations, with the spindle system functional unit offset being smaller, and expanding more than the ideal spindle system offset. The offset of the spindle-shank system functional units is mainly concentrated on both sides of the top and bottom ends to be expanded outwards compared with the offset of the ideal spindle system. The deviation of the functional unit of the cutter handle-cutter system is larger, and the shape is changed compared with the ideal state deviation of the cutter handle-cutter system, so that the cutter handle-cutter system is expanded outwards and contracted inwards, because the cutter handle-cutter system is close to the milling cutter, the cutting force directions of different cutter teeth of the milling cutter are different, the directions of the former cutter teeth can be positive when the cutter teeth participate in cutting, and the directions of the latter cutter teeth can be negative when the cutter teeth cut, so that the reason why the positive relative deviation and the negative relative position deviation are different in maximum value can be explained.
And (3) carrying out influence characteristic analysis of each process characteristic variable on the relative position offset by adopting a single factor analysis method, and determining parameters of the single factor design variables as shown in tables 5-1 to 5-4.
TABLE 5-1 design variable parameter Table
TABLE 5-2 design variable parameter Table
TABLE 5-3 design variable parameter Table
Tables 5-4 design variable parameter tables
The effect of process parameters on the relative positional offset of the tool system was analyzed according to the table above and is shown in fig. 48-51.
And training the relative position offset of the tool system under different process parameters by using an artificial neural network to obtain a determination coefficient R of the relative position offset of the tool system by the different process parameters as shown in figures 52-55.
52-55, the determination coefficients R of the relative position offset of the tool system by different process parameters are all close to 1 and above 0.99, which indicates that the prediction performance of the artificial neural network is better, and the relative position offset of the tool system under different process parameters can be effectively predicted. The tool system relative position offset predicted value and the actual value calculated according to the artificial neural network are compared with each other, for example, as shown in fig. 56-59.
56-59 show that the error between the predicted value and the actual value of the relative position offset of the tool system under different process parameters obtained by the artificial neural network is small, and the error is between 0.1% and 0.3%, which indicates that the prediction model of the relative position offset of the tool system obtained by the artificial neural network can accurately predict the relative position offset of the tool system under different process parameters.
In summary, compared with the prior art, the method for constructing the dynamic model of the joint surface of the milling tool system, provided by the application, has the advantages that the overall dynamic model of the external load of the cutting force is established for the spindle-tool handle-tool system, the dynamic model of the joint surface of the milling tool system is constructed by utilizing the established dynamic model of the tool system by utilizing the finite element method, and the modeling method of the dynamics of the joint surface of the milling tool system is optimized.
According to the dynamic stability analysis method for the milling tool system joint surface, the contact stiffness of the tool system joint surface is solved, and the dynamic parameters of the tool system joint surface are further solved, so that the dynamic stability of the milling tool system joint surface is analyzed, and the stability of the tool system joint surface is improved.
According to the identification method for dynamic energy consumption transmission and distribution of the milling tool system joint surface, the energy consumption transmission and distribution of the tool system joint surface is further identified through calculation of the energy consumption of the tool system joint surface, and the stability of the tool system joint surface is improved.
The prediction method for the relative position deviation of the milling tool system characterizes the overall relative position deviation of the tool system, predicts the overall position deviation of the tool system by using the artificial neural network, and further improves the stability of the tool system.
Claims (2)
1. A method for predicting relative positional displacement of a milling tool system, characterized in that the method for characterizing relative positional displacement of the tool system comprises: according to the positive relative offset reference and the negative relative offset reference, the deformation of the extracting tool system is compared with the initial reference;
the characteristic of the change of the relative position of the tool system during milling can be obtained by using the characterization method, and the change of the relative position offset of the tool system is researched;
according to the influence of the technological parameters on the relative position offset of the tool system, the prediction method of the relative position offset of the tool system utilizes an artificial neural network to construct a prediction model of the relative position offset;
and training the relative position offset of the tool system under different process parameters by using the artificial neural network to obtain a prediction model of the relative position offset of the tool system.
2. The method for predicting the relative positional shift of a milling tool system according to claim 1, wherein the cutting forces under different cutting conditions are calculated using a milling force calculation formula, and the influence of the cutting parameters on the contact stiffness and the relative positional shift is analyzed.
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