CN111360582A - Tool wear state identification method - Google Patents

Tool wear state identification method Download PDF

Info

Publication number
CN111360582A
CN111360582A CN202010051530.4A CN202010051530A CN111360582A CN 111360582 A CN111360582 A CN 111360582A CN 202010051530 A CN202010051530 A CN 202010051530A CN 111360582 A CN111360582 A CN 111360582A
Authority
CN
China
Prior art keywords
cutting
tool
angle
wear state
shear
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010051530.4A
Other languages
Chinese (zh)
Other versions
CN111360582B (en
Inventor
彭芳瑜
赵晟强
周林
孙豪
闫蓉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huazhong University of Science and Technology
Original Assignee
Huazhong University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huazhong University of Science and Technology filed Critical Huazhong University of Science and Technology
Priority to CN202010051530.4A priority Critical patent/CN111360582B/en
Publication of CN111360582A publication Critical patent/CN111360582A/en
Application granted granted Critical
Publication of CN111360582B publication Critical patent/CN111360582B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
    • B23Q17/0957—Detection of tool breakage

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Numerical Control (AREA)

Abstract

The invention belongs to the field of tool state identification, and particularly discloses a tool wear state identification method, which comprises the following steps: s1, axially dispersing the cutting edge of the cutter into K cutting microelements, constructing a cutting force analysis model representing the relation between the cutting force borne by the cutting microelements and the shearing force coefficient, the plough shearing force coefficient and the rear cutter face friction force coefficient, wherein the shearing force coefficient and the plough shearing force coefficient are obtained according to the arc radius of the cutting edge, and the rear cutter face friction force coefficient is obtained according to the wear width of the rear cutter face; s2, obtaining a cutting force and wear state mapping relation data set according to the cutting force analysis model; and S3, determining the arc radius of the cutting edge and the wear width of the flank according to the actual cutting force data through the cutting force and wear state mapping relation data set, and finishing tool wear state identification. The invention realizes two indexes of the abrasion state quantity of the cutter in actual processing: and the arc radius of the cutting edge and the wear width of the flank face can be accurately predicted at the same time.

Description

Tool wear state identification method
Technical Field
The invention belongs to the field of tool state identification, and particularly relates to a tool wear state identification method.
Background
The cutter abrasion in the cutting process seriously affects the cutting force, temperature and processing vibration in the processing process, and finally affects the manufacturing efficiency and processing quality of products. The general forward idea for researching the wear state of the cutter is as follows: establishing a precision milling cutting force analytical model considering cutter abrasion, setting geometric parameters and processing parameters of a workpiece and a cutter, inputting a cutter abrasion state quantity, and calculating and solving the cutting force in the processing process through the established analytical model.
In the precision milling processing of a complex curved surface, the wear form of the cutter generally comprises the increase of the width of the wear belt of the rear cutter surface and the expansion of the obtuse circle radius of the cutting edge, and can be embodied by two indexes of the arc radius of the cutting edge and the wear width of the rear cutter surface. For a flat bed knife, the wear width of the flank face and the blunt radius of the cutting edge of the knife can be measured by an optical microscope and other instruments, and the acquisition is relatively easy, but for a ball-end knife used in the precision machining of a complex curved surface, the acquisition of the arc radius of the cutting edge and the wear width of the flank face is very difficult, and the measurement can be usually performed only by a very expensive optical and contact profiler, and the measurement technology difficulty is large. In the existing tool wear research, generally, only the change of the wear width of the flank face of the tool is considered, but the change of the arc radius of the cutting edge is not considered, and the tool wear research method which ignores the change of the arc radius of the cutting edge can seriously affect the research result.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a tool wear state identification method, which aims to establish a precision milling cutting force analysis model considering the cutting edge arc radius and the flank wear width by taking precision milling precision machining of a complex curved surface as a main research scene, further obtain a cutting force and wear state mapping relation data set, determine the cutting edge arc radius and the flank wear width according to actual cutting force data through the data set, and accurately identify the tool wear state quantity in actual machining.
In order to achieve the above object, the present invention provides a method for identifying a wear state of a tool, comprising the following steps:
s1, dispersing the cutting edge of the cutter into K cutting microelements along the axial direction, and constructing a cutting force analysis model as follows:
Figure BDA0002371348200000021
wherein, Ft(k,t,z)、Fr(k,t,z)、Fa(k, t, z) are respectively the cutting force applied to the cutting micro element on the kth tooth with the axial height of z at the time t; ktc(re)、Krc(re)、Kac(re) Respectively the arc radius r of the cutting edgeeCorresponding tangential, radial, axial shear coefficient, Kte(re)、Kre(re)、Kae(re) Respectively the arc radius r of the cutting edgeeCorresponding tangential, radial, axial shear coefficient, Ktw(VB)、Krw(VB)、Kaw(VB) is the friction coefficient of the tangential, radial and axial flank face corresponding to the flank face wear width VB respectively, and h (k, t, z) is the instantaneous undeformed chip thickness corresponding to the cutting infinitesimal at the time t, where the axial height of the cutter on the kth tooth is z;
s2, obtaining a plurality of groups of corresponding data of cutting edge arc radius, flank wear width and cutting force borne by the cutting element according to the cutting force analytic model, and further obtaining a plurality of groups of corresponding data of cutting edge arc radius, flank wear width and cutting force borne by the cutter, namely a cutting force and wear state mapping relation data set;
and S3, determining the arc radius of the cutting edge and the wear width of the flank according to the actual cutting force data through the cutting force and wear state mapping relation data set, and finishing tool wear state identification.
Further preferably, the calculation formula of the instantaneous undeformed chip thickness h (k, t, z) corresponding to the cutting infinitesimal at which the axial height of the tool on the kth tooth is z at the time t is as follows:
h(k,t,z)＝ftsin(φ)sin(κ)
wherein f istIs the feed per tooth at time t, phi is the instantaneous radial contact angle of the cutter tooth at time t, and kappa is the instantaneous axial contact angle of the cutter tooth at time t.
More preferably, the tangential, radial and axial shear force coefficients are calculated as follows:
Figure BDA0002371348200000031
wherein τ is shear plane shear flow stress, i is bevel cut bevel angle, βnIs the normal friction angle of the tool, phinAt normal shear angle, ηcα for chip flow anglenThe calculation formula of the tool normal front angle is as follows:
Figure BDA0002371348200000032
where h is the instantaneous undeformed chip thickness, θfIn order to obtain a chip separation angle,
Figure BDA0002371348200000033
constant, α is the tool rake angle.
As a further preference, the chip flow angle is equal to ηcEqual to the bevel cutting bevel angle i.
Preferably, the tangential, radial and axial shear coefficients are calculated as follows:
Figure BDA0002371348200000041
wherein τ is shear plane shear flow stress, φnIs a normal shear angle, i is a bevel cutting inclination angle; r0Radius of sector formed by tool, chip and workpiece together, η0、γ0、α0Are all angles in the sector, which are calculated by：
Figure BDA0002371348200000042
Wherein, mu0The ratio of the frictional stress below the sector to the shear flow force of the workpiece material, ρ0Is the bow angle, αavgIs the average of the tool rake angles.
More preferably, the tangential, radial and axial clearance coefficient is calculated by the following formula:
Figure BDA0002371348200000043
wherein σw(x) And τw(x) The normal stress and the shear stress of a flank wear region are respectively, and i is an oblique angle cutting inclination angle.
More preferably, VB is less than or equal to VB*Normal stress sigma of flank wear zonew(x) And shear stress τw(x) The calculation formula of (a) is as follows:
Figure BDA0002371348200000051
Figure BDA0002371348200000052
when VB > VB*Normal stress sigma of flank wear zonew(x) And shear stress τw(x) The calculation formula of (a) is as follows:
Figure BDA0002371348200000053
Figure BDA0002371348200000054
wherein, VB*Critical flank wear width, μωIs the coefficient of friction between the flank of the tool and the surface of the machined workpiece,σ0and τ0Respectively the normal stress and the shear stress of the cutting edge area of the tool nose.
More preferably, in S3, a cutting force proxy model is trained from the cutting force/wear state mapping relationship data set, and actual cutting force data is input to the cutting force proxy model to obtain the edge arc radius and the flank wear width, thereby completing tool wear state recognition.
Preferably, in S3, a gaussian random process model is trained through the cutting force and wear state mapping relationship data set, and the trained gaussian random process model is a cutting force proxy model.
More preferably, in S2, 300 to 1000 sets of data of the arc radius of the cutting edge, the flank wear width, and the total cutting force applied to the tool are obtained.
Generally, compared with the prior art, the above technical solution conceived by the present invention mainly has the following technical advantages:
1. the invention provides a method for identifying the wear state of a cutter in an actual processing scene by taking precise milling and precise processing of a complex curved surface as a main research scene, wherein a precise milling and cutting force analysis model is established to obtain training data of the wear state and the cutting force of the cutter, and further an agent model for identifying the wear state of the cutter is established to realize accurate identification of the wear state quantity of the cutter in actual processing; on the aspect of monitoring the cutter state, the prediction of the cutter abrasion state quantity can be realized by using the proxy model, and the requirement of identifying the cutter abrasion state in the actual processing field is met.
2. The cutting edge arc radius and the flank face abrasion width are very difficult to obtain, and the flank face abrasion and the cutting edge arc radius have different sensitivity degrees to the cutting force, and the invention establishes a cutting force proxy model by establishing a precise milling cutting force analytic model considering the cutting edge arc radius and the flank face abrasion width, and then establishes two indexes of the abrasion state quantity of the cutter: and simultaneously predicting the arc radius of the cutting edge and the wear width of the rear cutter face.
3. The invention selects a Gaussian random process model as a proxy model, the Gaussian random process model is a probability method suitable for both linear problems and non-linear problems, approximation of real data and the model is realized by selecting different kernel function combinations, and when the Gaussian random process model is applied to actual problems, a confidence interval can be given while a mean value is output, so that the effectiveness of a prediction result is enhanced.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for identifying a wear state of a tool according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a theoretical model of a slip line field during cutting according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The method for identifying the wear state of the cutter provided by the embodiment of the invention, as shown in fig. 1, comprises the following steps:
s1, constructing a cutting force analysis model considering the wear state of the cutter:
considering shearing force, plough cutting force and friction force caused by flank wear in the cutting process, establishing a cutting force analytical model of precise milling, particularly dispersing a cutting edge of a cutter into K cutting microelements along the axial direction, regarding each cutting microelement, the cutting motion can be regarded as an independent oblique angle cutting process, the total cutting force borne by the cutter is the sum of the cutting forces borne by the cutting microelements participating in cutting, and regarding each cutting microelement, establishing the cutting force analytical model as follows:
Figure BDA0002371348200000071
wherein, Ft(k,t,z)、Fr(k,t,z)、Fa(k, t, z) are respectively the tangential, radial and axial cutting forces of the cutting micro-element with the axial height of z on the kth tooth at the time t; ktc(re)、Krc(re)、Kac(re) Respectively the arc radius r of the cutting edgeeCorresponding tangential, radial, axial shear coefficient, Kte(re)、Kre(re)、Kae(re) Respectively the arc radius r of the cutting edgeeCorresponding tangential, radial, axial shear coefficient, Ktw(VB)、Krw(VB)、Kaw(VB) is the tangential, radial and axial flank face friction coefficients corresponding to the flank face wear width VB; h (k, t, z) is the instantaneous undeformed chip thickness corresponding to the cutting infinitesimal at which the axial height of the tool on the kth tooth is z at time t, and the calculation formula is as follows:
h(k,t,z)＝ftsin(φ)sin(κ)
wherein f istIs the feed per tooth at time t, phi is the instantaneous radial contact angle of the cutter tooth at time t, and kappa is the instantaneous axial contact angle of the cutter tooth at time t.
Specifically, the following explains the cutting force solving process of the cutting element in detail:
(1) coefficient of shear force
The precise milling process has strong scale effect, and in order to better understand the influence mechanism of the scale effect on the precise milling process, an oblique angle cutting model is adopted, and the tangential, radial and axial shear force coefficients are expressed as follows:
Figure BDA0002371348200000081
where τ is shear plane shear flow stress and i is bevel cutting inclination equal to milling cutter helix angle λ, βnIs the normal friction angle of the tool, phinAt normal shear angle, ηcAs chip flow angle, αnFor a normal rake angle of the tool, and for further simplification of the model, a chip flow rule is adopted, the chip flow angle being equal to ηcEqual to the bevel cutting bevel angle i.
Further, a tool normal rake angle α is calculated using the average rake modelnThe calculation formula is as follows:
Figure BDA0002371348200000082
wherein r iseIs the arc radius of the cutting edge, h is the instantaneous undeformed chip thickness, thetafThe angle of separation of the cutting scraps is 37.6 degrees,
Figure BDA0002371348200000083
is constant, take 2 and α is the tool rake angle.
Further, the shear plane shear flow stress τ is calculated as follows:
Figure BDA0002371348200000084
wherein, αtTaking 0.5 as a model constant, G as a shear modulus, b as a Burger vector, 0.304nm and u as a scale effect term correction factor, and 0.5; tau isrefFor the reference shear flow stress on the shear plane during cutting, the formula is calculated as follows:
Figure BDA0002371348200000085
wherein A, B, C, m and n are J-C constitutive model coefficients, and are calibrated by a right-angle cutting experiment; epsilonABAnd
Figure BDA0002371348200000091
respectively equivalent strain and equivalent strain rate on shear plane AB,
Figure BDA0002371348200000092
for reference plastic strain rate, take 1; t isABTemperature, T, at shear plane ABmIs the melting point of the workpiece material, T0At room temperature, it is generally 25 ℃.
(2) Coefficient of plowing and shearing force
Because the arc radius of the cutting edge of the precision milling cutter and the feeding amount of each cutting tooth are in the same scale, the plough cutting effect caused by the arc radius of the cutting edge is not negligible, particularly when the feeding amount of each tooth is close to or even smaller than the arc radius of the cutting edge of the cutter, the plough cutting effect is very strong, the plough cutting force of the cutting edge is solved according to a slip line field theoretical model, and as shown in fig. 2, the coefficients of the tangential, radial and axial plough cutting forces are expressed as follows:
Figure BDA0002371348200000093
wherein τ is shear plane shear flow stress, φnIs a normal shear angle, i is a bevel cutting inclination angle; r0The radius of the sector formed by the tool, the chip and the workpiece in FIG. 2 is η0、γ0、α0Are the angles in the sector, which are calculated by:
Figure BDA0002371348200000094
wherein r iseIs the radius of the cutting edge arc, αavgIs the average value of the rake angle of the tool; mu.s0The ratio of the frictional stress below the sector to the shear flow force of the workpiece material was taken to be 0.95; rho0The bow angle can be determined according to finite element simulation analysis and is taken as 20 degrees.
(3) Coefficient of flank face friction
The friction force caused by the abrasion of the tool flank is introduced into the precise milling cutting force modeling, and the corresponding tangential, radial and axial flank friction force coefficients are expressed as follows:
Figure BDA0002371348200000101
where VB is the flank wear width, σw(x) And τw(x) Respectively the normal stress and the shear stress of a wear area of the rear cutter face, wherein i is an oblique angle cutting inclination angle;
further, normal stress andthe shear stress is distributed in a nonlinear way in the wear region of the rear cutter face and is in a nonlinear way with the wear width VB of the critical rear cutter face*Related, the critical flank wear width VB*Can be obtained by abrasion test; and further:
when VB is less than or equal to VB*Normal stress sigma of flank wear zonew(x) And shear stress τw(x) The calculation formula of (a) is as follows:
Figure BDA0002371348200000102
Figure BDA0002371348200000103
when VB > VB*Normal stress sigma of flank wear zonew(x) And shear stress τw(x) The calculation formula of (a) is as follows:
Figure BDA0002371348200000104
Figure BDA0002371348200000111
wherein x is an independent variable in a functional formula and represents the value of the wear width of the flank face, muωTaking the friction coefficient between the rear cutter face of the cutter and the surface of the machined workpiece as 1; sigma0And τ0Respectively is the normal stress and the shear stress of the blade tip cutting edge area, and the calculation formula is as follows:
σ0＝τ(1+2α0+2γ0+sin(2η0))
τ0＝τcos(2η0)
s2, constructing a cutting force and wear state mapping relation data set:
obtaining a plurality of groups of corresponding data of cutting edge arc radius, flank wear width and cutting force borne by the cutting infinitesimal according to the cutting force analytical model, and further obtaining a plurality of groups of corresponding data of cutting edge arc radius, flank wear width and total cutting force borne by the cutter, namely a cutting force and wear state mapping relation data set;
specifically, the method comprises the following steps:
(1) for the cutting force analysis model, basic parameters of materials such as J-C constitutive parameters and the like are calibrated by a right-angle cutting experiment, cutter geometric parameters such as a milling cutter helical angle, a cutter normal rake angle, a cutter normal friction angle, a cutter normal shear angle and the like are set, and processing parameters such as feed quantity of each tooth are set; selecting a tool wear state parameter as a main input quantity of a data set, and obtaining cutting force applied to a cutting infinitesimal tangent, a radial direction and an axial direction at a position where the axial height of the tool on a kth tooth at the time t is z through a cutting force analytical model;
(2) the numerical integration is carried out on the cutter infinitesimal to obtain the tangential, radial and axial cutting forces borne by the cutting edge of the integral cutter at the moment t, and the three cutting forces are taken as main output values of a data set, and the specific calculation formula is as follows:
Figure BDA0002371348200000121
wherein, Ft(t)、Fr(t)、Fa(t) is the cutting force applied to the tool in tangential, radial and axial directions, zmaxFor maximum axial height of cutting element, zminIs the minimum value of the axial height of the cutting micro element;
therefore, a data set of the mapping relation between the cutting force and the wear state is established, and preferably, the data set comprises 300-1000 groups of data, and 440 groups of data are taken.
S3, constructing a cutting force proxy model and identifying the tool wear state:
a Gaussian random process model, a Kriging model or a linear regression model can be used as the cutting force agent model, and the Gaussian random process model is preferably used as the cutting force agent model; the tangential, radial and axial cutting forces of the cutter are used as input values of the cutting force surrogate model, and the arc radius r of the cutting edgeeAnd the flank wear width VB is used as an output value of the cutting force proxy model; 20% (88 sets in total) of the data set of the mapping relationship between the cutting force and the wear state was randomly selected as the cutAnd (3) taking the rest 80% of data sets (352 sets) of the test data set of the force agent model as a training data set of the cutting force agent model, training the cutting force agent model through the training data set, inputting the test data set into the trained cutting force agent model, and identifying the tool abrasion loss based on the cutting force agent model.
Specifically, in a random process, when any two or more random variables are subjected to multidimensional joint gaussian distribution, the random process is called a gaussian random process, or may be referred to as a normal process for short. The gaussian random process finds out the law by processing the variation of the training data, and estimates the prior distribution by using the training data, thereby realizing the estimation of the posterior distribution.
Deducing a Gaussian random process model according to a weight space method to obtain a prediction form of a Gaussian random process; the weight space method has a probabilistic meaning, and from the linear relation, a model based on a gaussian random process can be written in the following form, wherein:
f(x)＝xTω
y＝f(x)+ε＝xTω+ε
where x represents an n-dimensional input vector, ω ═ ω [ ω ]1,...,ωn]TRepresenting the weight vector of the linear model, f representing the linear mapping relation, y being the observed value, epsilon being Gaussian noise, epsilon obeying Gaussian distribution
Figure BDA0002371348200000131
Wherein
Figure BDA0002371348200000132
Representing the variance of the gaussian noise.
Suppose the observation sample is X ═ X1,...,xn]T,Y＝[y1,...,yn]TAccording to the linear regression theory, the prior distribution function, the training sample set and other information, the likelihood function of the observation sample obtained by combining the Bayesian theory is as follows:
Figure BDA0002371348200000133
assuming that the prior distribution of the weight vector omega follows a gaussian distribution,
Figure BDA0002371348200000134
wherein
Figure BDA0002371348200000135
Representing the variance of the weight vector; according to bayes' theorem, the posteriori of the weight vector is proportional to the product of the likelihood function and the prior function, and the probability distribution of the weight coefficients is:
Figure BDA0002371348200000136
wherein the content of the first and second substances,
Figure BDA0002371348200000137
if X is*Representing the test specimen, f*Is defined as f (X)*) And representing the probability distribution function of the test sample, the probability distribution formula of the test set is as follows:
Figure BDA0002371348200000138
deducing a probability distribution formula of a test set according to the weight space method, inputting a training data set, and training a cutting force agent model through a Gaussian regression process; and predicting the output data of the test data set by using the trained model and a probability distribution formula of a Gaussian regression process test set, so as to realize the prediction of the tool wear state quantity under the actual processing working condition.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A tool wear state identification method is characterized by comprising the following steps:
s1, dispersing the cutting edge of the cutter into K cutting microelements along the axial direction, and constructing a cutting force analysis model as follows:
Figure FDA0002371348190000011
wherein, Ft(k,t,z)、Fr(k,t,z)、Fa(k, t, z) are respectively the cutting force applied to the cutting micro element on the kth tooth with the axial height of z at the time t; ktc(re)、Krc(re)、Kac(re) Respectively the arc radius r of the cutting edgeeCorresponding tangential, radial, axial shear coefficient, Kte(re)、Kre(re)、Kae(re) Respectively the arc radius r of the cutting edgeeCorresponding tangential, radial, axial shear coefficient, Ktw(VB)、Krw(VB)、Kaw(VB) is the friction coefficient of the tangential, radial and axial flank face corresponding to the flank face wear width VB respectively, and h (k, t, z) is the instantaneous undeformed chip thickness corresponding to the cutting infinitesimal at the time t, where the axial height of the cutter on the kth tooth is z;
s2, obtaining a plurality of groups of corresponding data of cutting edge arc radius, flank wear width and cutting force borne by the cutting element according to the cutting force analytic model, and further obtaining a plurality of groups of corresponding data of cutting edge arc radius, flank wear width and cutting force borne by the cutter, namely a cutting force and wear state mapping relation data set;
and S3, determining the arc radius of the cutting edge and the wear width of the flank according to the actual cutting force data through the cutting force and wear state mapping relation data set, and finishing tool wear state identification.
2. The tool wear state identification method according to claim 1, wherein the calculation formula of the instantaneous undeformed chip thickness h (k, t, z) corresponding to the cutting infinitesimal at the axial height z of the tool on the kth tooth at time t is as follows:
h(k,t,z)＝ftsin(φ)sin(κ)
wherein f istIs the feed per tooth at time t, phi is the instantaneous radial contact angle of the cutter tooth at time t, and kappa is the instantaneous axial contact angle of the cutter tooth at time t.
3. The tool wear state identification method of claim 1, wherein the tangential, radial, and axial shear force coefficients are calculated as follows:
Figure FDA0002371348190000021
wherein τ is shear plane shear flow stress, i is bevel cut bevel angle, βnIs the normal friction angle of the tool, phinAt normal shear angle, ηcα for chip flow anglenThe calculation formula of the tool normal front angle is as follows:
Figure FDA0002371348190000022
where h is the instantaneous undeformed chip thickness, θfIn order to obtain a chip separation angle,
Figure FDA0002371348190000023
constant, α is the tool rake angle.
4. The tool wear state identification method of claim 3, wherein the chip flow angle is equal to ηcEqual to the bevel cutting bevel angle i.
5. The tool wear state identification method of claim 1, wherein the tangential, radial, axial plow shear coefficients are calculated as follows:
Figure FDA0002371348190000024
wherein τ is shear plane shear flow stress, φnIs a normal shear angle, i is a bevel cutting inclination angle; r0Radius of sector formed by tool, chip and workpiece together, η0、γ0、α0Are the angles in the sector, which are calculated by:
Figure FDA0002371348190000031
wherein, mu0The ratio of the frictional stress below the sector to the shear flow force of the workpiece material, ρ0Is the bow angle, αavgIs the average of the tool rake angles.
6. The tool wear state identification method of claim 1, wherein the tangential, radial, and axial clearance coefficient of friction is calculated as follows:
Figure FDA0002371348190000032
wherein σw(x) And τw(x) The normal stress and the shear stress of a flank wear region are respectively, and i is an oblique angle cutting inclination angle.
7. The tool wear state identification method of claim 6, wherein VB is less than or equal to VB*Normal stress sigma of flank wear zonew(x) And shear stress τw(x) The calculation formula of (a) is as follows:
Figure FDA0002371348190000033
Figure FDA0002371348190000041
when VB > VB*Normal stress sigma of flank wear zonew(x) And shear stress τw(x) The calculation formula of (a) is as follows:
Figure FDA0002371348190000042
Figure FDA0002371348190000043
wherein, VB*Critical flank wear width, μωCoefficient of friction, σ, between the flank of the tool and the surface of the machined workpiece0And τ0Respectively the normal stress and the shear stress of the cutting edge area of the tool nose.
8. The tool wear state recognition method according to claim 1, wherein in step S3, a cutting force proxy model is obtained by training the cutting force/wear state mapping relationship data set, and the tool wear state recognition is completed by inputting actual cutting force data into the cutting force proxy model to obtain the cutting edge arc radius and the flank wear width.
9. The tool wear state identification method according to claim 8, wherein in S3, a gaussian random process model is trained through the cutting force and wear state mapping relation data set, and the trained gaussian random process model is a cutting force proxy model.
10. The tool wear state identification method according to any one of claims 1 to 9, wherein in S2, data of 300 to 1000 sets of corresponding cutting edge arc radius, flank wear width, and total cutting force applied to the tool are obtained.
CN202010051530.4A 2020-01-17 2020-01-17 Tool wear state identification method Active CN111360582B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010051530.4A CN111360582B (en) 2020-01-17 2020-01-17 Tool wear state identification method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010051530.4A CN111360582B (en) 2020-01-17 2020-01-17 Tool wear state identification method

Publications (2)

Publication Number Publication Date
CN111360582A true CN111360582A (en) 2020-07-03
CN111360582B CN111360582B (en) 2021-07-27

Family

ID=71200262

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010051530.4A Active CN111360582B (en) 2020-01-17 2020-01-17 Tool wear state identification method

Country Status (1)

Country Link
CN (1) CN111360582B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113094842A (en) * 2021-04-29 2021-07-09 中国工程物理研究院机械制造工艺研究所 Residual stress field modeling method for disc-shaped thin-wall component
CN113601265A (en) * 2021-09-01 2021-11-05 重庆科技学院 Method for estimating energy consumption ratio of front tool face and rear tool face of cutter in machining
CN113927368A (en) * 2021-09-23 2022-01-14 苏州大学 Micro milling cutter cutting edge wear monitoring method based on cutting force coefficient curve inflection point identification
CN114161227A (en) * 2021-12-28 2022-03-11 福州大学 Cutter wear loss monitoring method based on simulation feature and signal feature fusion
CN115213735A (en) * 2022-09-20 2022-10-21 南京航空航天大学 System and method for monitoring cutter state in milling process
CN115741234A (en) * 2022-11-24 2023-03-07 成都飞机工业（集团）有限责任公司 Measuring method for cutter face abrasion loss of milling cutter

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0905492A2 (en) * 1997-09-30 1999-03-31 Sumitomo Electric Industries, Ltd. Noncontact temperature distribution measuring apparatus
CN104392090A (en) * 2014-09-26 2015-03-04 北京理工大学 Construction method of aluminium alloy material end milling-cutting force and cutting processing deformation model
CN105069257A (en) * 2015-09-01 2015-11-18 华中科技大学 Free-form surface micro-milling cutting force modeling method
CN109434564A (en) * 2018-12-21 2019-03-08 哈尔滨理工大学 A kind of cutter wear state monitoring method based on deep neural network
CN110293451A (en) * 2019-07-03 2019-10-01 哈尔滨理工大学 A kind of monoblock type flat-bottom end mill wear of the tool flank rate determines method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0905492A2 (en) * 1997-09-30 1999-03-31 Sumitomo Electric Industries, Ltd. Noncontact temperature distribution measuring apparatus
CN104392090A (en) * 2014-09-26 2015-03-04 北京理工大学 Construction method of aluminium alloy material end milling-cutting force and cutting processing deformation model
CN105069257A (en) * 2015-09-01 2015-11-18 华中科技大学 Free-form surface micro-milling cutting force modeling method
CN109434564A (en) * 2018-12-21 2019-03-08 哈尔滨理工大学 A kind of cutter wear state monitoring method based on deep neural network
CN110293451A (en) * 2019-07-03 2019-10-01 哈尔滨理工大学 A kind of monoblock type flat-bottom end mill wear of the tool flank rate determines method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
曹翔等: "基于高斯过程回归方法的钛合金铣削刀具磨损预测", 《制造技术与机床》 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113094842A (en) * 2021-04-29 2021-07-09 中国工程物理研究院机械制造工艺研究所 Residual stress field modeling method for disc-shaped thin-wall component
CN113601265A (en) * 2021-09-01 2021-11-05 重庆科技学院 Method for estimating energy consumption ratio of front tool face and rear tool face of cutter in machining
CN113601265B (en) * 2021-09-01 2022-05-27 重庆科技学院 Method for estimating energy consumption ratio of front tool face and rear tool face of cutter in machining
CN113927368A (en) * 2021-09-23 2022-01-14 苏州大学 Micro milling cutter cutting edge wear monitoring method based on cutting force coefficient curve inflection point identification
CN113927368B (en) * 2021-09-23 2023-03-07 苏州大学 Micro milling cutter cutting edge wear monitoring method based on cutting force coefficient curve inflection point identification
CN114161227A (en) * 2021-12-28 2022-03-11 福州大学 Cutter wear loss monitoring method based on simulation feature and signal feature fusion
CN114161227B (en) * 2021-12-28 2024-05-03 福州大学 Cutter abrasion loss monitoring method based on simulation feature and signal feature fusion
CN115213735A (en) * 2022-09-20 2022-10-21 南京航空航天大学 System and method for monitoring cutter state in milling process
CN115741234A (en) * 2022-11-24 2023-03-07 成都飞机工业（集团）有限责任公司 Measuring method for cutter face abrasion loss of milling cutter

Also Published As

Publication number Publication date
CN111360582B (en) 2021-07-27

Similar Documents

Publication Publication Date Title
CN111360582B (en) Tool wear state identification method
Kuram et al. Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling
Fernández-Abia et al. Behavior of austenitic stainless steels at high speed turning using specific force coefficients
Kumar et al. Comparative study on machinability improvement in hard turning using coated and uncoated carbide inserts: part II modeling, multi-response optimization, tool life, and economic aspects
Frifita et al. Optimization of machining parameters in turning of Inconel 718 Nickel-base super alloy
CN109277883B (en) Ultrahigh-strength titanium alloy roughness prediction method based on cutting parameter weight
Feng et al. Inverse analysis of the cutting force in laser-assisted milling on Inconel 718
Sheikh-Ahmad et al. New tool life criterion for delamination free milling of CFRP
Sharma et al. Force evaluation and machining parameter optimization in milling of aluminium burr composite based on response surface method
Sur et al. Single and multi-objective optimization for cutting force and surface roughness in peripheral milling of Ti6Al4V using fixed and variable helix angle tools
Abhang et al. Optimization of machining process parameters using moora method
CN110293451A (en) A kind of monoblock type flat-bottom end mill wear of the tool flank rate determines method
CN107168245A (en) A kind of accurate Forecasting Methodology of chamfered edge circular bit cutting force for considering cutting edge effect
Motta et al. Machine learning models for surface roughness monitoring in machining operations
Han et al. Mechanistic modelling of worn drill cutting forces with drill wear effect coefficients
Krishnan et al. Experimental optimization of cutting parameters in turning of brass alloy using Taguchi method
Sun et al. The effect of micro-texture on wear resistance of WC/Co-based tools during cutting Ti-6Al-4V
Gurbuz et al. Investigation of the effects of different chip breaker forms on the cutting forces using artificial neural networks
Tomov et al. Modelling of multiple surface roughness parameters during hard turning: A comparative study between the kinematical-geometrical copying approach and the design of experiments method (DOE).
Aristimuño et al. An optimization methodology for material databases to improve cutting force predictions when milling martensitic stainless steel JETHETE-M152
Manohar et al. Application of experimental design and analysis of mathematical models for turning Inconel 718 using coated carbide tools
Loc et al. Optimization of cutting parameters on surface roughness and productivity when milling wood materials
Kumar et al. Optimization of duplex stainless steel dry turning parameters using desirability function
Zhujani et al. Mathematical modeling and optimization of machining parameters in CNC turning process of Inconel 718 using the Taguchi method
Nguyen Lam Study on multi-objective optimization of X12M steel milling process by reference ideal method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant