CN112364444A - Numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation - Google Patents
Numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation Download PDFInfo
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Abstract
The invention discloses a numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation, which comprises the following steps of: firstly, initializing simulation conditions; (II) setting initialization conditions of the finite element model of the numerical control machine tool to be optimized, comprising the following steps of: (1) importing the 3D bare base model of the numerical control machine tool to be optimized, and simplifying; (2) setting materials of the numerical control machine tool to be optimized and parts of the numerical control machine tool to be optimized; (3) setting a heat source; (4) setting a boundary condition; (5) finishing grid division; (6) completing the configuration of a solver; thirdly, generating a thermal equilibrium temperature field and a thermal deformation field of the numerical control machine tool to be optimized; fourthly, calculating the Poisson coefficient between the temperature and the thermal deformation of the numerical control machine tool to be optimized, and generating a distribution field of the numerical control machine tool to be optimized; and (V) measuring point optimization. The invention can make the correlation between the measuring point and the temperature meet the requirement, ensure the accuracy and the robustness of the thermal compensation model and greatly reduce the cost during the research and development.
Description
Technical Field
The invention relates to a temperature measuring point optimization technology, in particular to a numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation.
Background
The proportion of the thermal error to the total error of the machine tool is usually 40-80%, the proportion is larger in a more precise numerical control machine tool, the selection of the temperature measuring point is a key problem in the numerical control machine tool thermal error compensation technology, and the optimization of the temperature measuring point is a premise for realizing thermal error modeling and compensation. The temperature measuring point has the problems that the sample is too small for the temperature field of the whole machine and the temperature field information of the machine tool cannot be completely reflected. The measuring point indexes are optimized only by calculating the correlation coefficient between the measuring point and the thermal error, so that the measuring point indexes are too single and are easy to misjudge, and the phenomenon that the measuring point and the thermal error are over-correlated can be caused, so that the accuracy and the robustness of a thermal error model are influenced.
The temperature measuring point optimization of the existing numerical control machine tool has the following two schemes:
the first scheme is as follows: and (4) calculating the tightness degree of the temperature measuring point and the thermal error of the numerical control machine tool by adopting a grey correlation method to complete temperature measuring point optimization.
Scheme II: and calculating the correlation between the temperature variable and the thermal error of the numerical control machine tool by adopting a stepwise multiple regression method to select the measuring points.
In the first scheme, although the method for optimizing the temperature measuring points by using the grey correlation degree solves the problems that compared with a numerical control machine tool, the temperature of the measuring points is too small in sample and cannot completely reflect the temperature field information of the machine tool, the index for completing optimization is only that the tightness between the measuring points and the thermal error is too single, and misjudgment is easily caused; and in the second scheme, the method adopting stepwise multiple regression only considers the correlation between the temperature variable and the thermal error variable, and does not consider the coupling phenomenon between the variables. Possibly resulting in over-correlation of temperature variables in subsequent modeling processes. The traditional experimental method needs a large amount of measurement of temperature and thermal error data in the early stage, has higher requirements on the precision of an error sensor and has the defect of great waste of manpower and material resources.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation, which can make the correlation between the measuring point and the temperature meet the requirement, ensure the accuracy and the robustness of a thermal compensation model and greatly reduce the cost in all aspects during the research and development.
The invention achieves the technical aim through the following technical scheme.
A numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation is improved in that: the method comprises the following steps:
initializing simulation conditions, comprising the following steps:
(1) according to the type of the numerical control machine tool to be optimized, initializing a heat source R in the machining process of the numerical control machine tool to be optimized1、R2、R3…RnAnd according to the corresponding heating power P of the numerical control machine tool to be optimized1、P2、P3…PnCalculating the calorific value Q of the numerical control machine tool to be optimized1、Q2、Q3…Qn；
(2) Horizontal and vertical heat exchange areas S during initial heat transferr1、Sr2Corresponding heat transfer coefficient lambda1、λ2Calculating the heat loss Q of the numerical control machine tool to be optimizedL1、QL2；
(3) Initializing a Poisson coefficient r calculation formula and a judgment rule thereof:
analyzing the degree of correlation between the temperature field and the thermal error of the numerical control machine tool to be optimized by using a Poisson coefficient r, wherein the Poisson coefficient r is shown as a formula (1):
the poisson coefficient r specific judgment rule is shown in table 1:
TABLE 1 Poisson coefficient decision rule
(4) Initializing the area s in which the numerical control machine tool to be optimized can actually carry out measuring point arrangement1、s2、s3…snAt this pointAnalyzing the measuring points in the positions according to the Poisson coefficient distribution map obtained by simulation to obtain measuring points C with high correlation1、C2、C3…Cn；
(II) setting initialization conditions of the finite element model of the numerical control machine tool to be optimized, comprising the following steps of:
(1) importing the 3D bare base model of the numerical control machine tool to be optimized, and simplifying;
importing the 3D bare base model of the numerical control machine tool to be optimized into COMSOL Multiphysics, simplifying the 3D bare base model in the COMSOL Multiphysics, and deleting a secondary structure with little influence;
(2) setting materials of the numerical control machine tool to be optimized and parts of the numerical control machine tool to be optimized;
directly searching by using a built-in material library in COMSOL Multiphysics, or defining a hollow material by itself and keying in the constant-pressure heat capacity C of the materialpThermal conductivity lambda0Density ρ, Young's modulus E, Poisson's ratio nu;
(3) setting a heat source;
finishing the setting of the friction heat of the rolling bearing, the friction heat of the ball screw and the friction heat of the electric spindle according to the formulas (3) and (4);
the heat generation amount of the friction heat of the rolling bearing and the ball screw is represented by the formula (3):
in formula (3) QbRepresenting the heat generation amount of the bearing per unit time, n representing the rotating speed of the bearing, M representing the friction torque of the bearing, M0Refers to a frictional moment, M, independent of the load1Refers to the frictional torque associated with the load, where f1Taking 0.00055; at M0In the calculation formula, f0Refers to lubrication related factors, take 2; v. of0Refers to the kinematic viscosity, D, of the bearing lubricant at the operating temperaturemRefers to the average diameter of the bearing, and in M1In the calculation formula (c)P1Refers to the bearing load at the time of friction torque calculation;
loss power P of motor for generating heat of electric spindleLIs calculated, wherein the input power of the electric spindle is PinOutput power of PoutThe expression is as follows:
PL＝Pin-Pout (3)
(4) setting a boundary condition;
in a solid heat transfer physical field, setting natural convection heat exchange between the numerical control machine tool to be optimized and air, and calculating the formula (5) for the 3D bare base model, wherein the natural convection heat exchange exists between the side wall of the base and the air; the rest links which have no obvious influence on the numerical control machine tool to be optimized are all regarded as heat insulation states; in a solid mechanics physical field, setting boundary load and inputting a pressure value P of the boundary load;
the natural convection heat transfer coefficient h generated by the numerical control machine tool to be optimized and the surrounding environment is calculated by the Nouchert criterion, wherein NμRefers to the Nouchert quasi-coefficient, L is the characteristic dimension, and λ is the thermal conductivity, and the formula is as follows:
Nμ＝hL/λ(4)
(5) finishing grid division;
dividing grids by using free tetrahedrons;
(6) completing the configuration of a solver;
selecting a transient solver, and setting the relative tolerance of the solver to be 0.01; selecting a generalized alpha by a time stepping method, selecting a middle-level step by a step length, uniformly initializing in algebraic variable setting, and selecting a backward Euler method, wherein the initial step length fraction is 0.001;
thirdly, generating a thermal equilibrium temperature field and a thermal deformation field of the numerical control machine tool to be optimized;
performing numerical simulation according to the set finite element model to obtain the distribution of the dynamic temperature field and the thermal deformation field of the numerical control machine tool to be optimized in the process from machining to thermal balance;
fourthly, calculating the Poisson coefficient between the temperature and the thermal deformation of the numerical control machine tool to be optimized, and generating a distribution field of the numerical control machine tool to be optimized;
forming a temperature and thermal deformation error correlation coefficient field by introducing and calculating a Poisson coefficient between the two;
(V) measuring point optimization;
and selecting a region to be optimized which accords with the reality, and finishing the optimization of the measuring points by combining a correlation coefficient distribution field.
Compared with the prior art, the invention has the following positive effects:
1. by the method, the correlation between the measuring point and the temperature can meet the requirement, the accuracy and the robustness of the thermal compensation model are ensured, and the cost in all aspects during the research and development is greatly reduced.
2. The method solves the problems that in the traditional temperature measuring point optimization method, the sample is too small and the temperature field information of the machine tool cannot be completely reflected, and the coupling phenomenon among variables is not considered, and the method can directly select the measuring point through a numerical control machine tool temperature and thermal deformation correlation coefficient distribution field.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flow chart of initializing simulation conditions.
FIG. 3 is a flow chart of setting finite element initialization conditions.
Fig. 4 is a model diagram of a 3D bare base in example 1.
In FIG. 4, a 1-Z axis motor; 2-a lead screw bearing; 3-a main shaft; 4-a reduction gearbox; 5-a slide rail; 6-an objective table; 7-a spindle motor; 8-base.
FIG. 5 is a grid drawing diagram of a VL1160 numerically controlled machine tool in example 1.
FIG. 6 is a graph of the distribution field of the correlation coefficients of the VL1160 numerical control machine tool in example 1.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation shown in the attached figures 1-3 comprises the following steps:
initializing simulation conditions, comprising the following steps:
(1) according to the type of the numerical control machine tool to be optimized, initializing a heat source R in the machining process of the numerical control machine tool to be optimized1、R2、R3…RnAnd according to the corresponding heating power P of the numerical control machine tool to be optimized1、P2、P3…PnCalculating the calorific value Q of the numerical control machine tool to be optimized1、Q2、Q3…Qn；
(2) Horizontal and vertical heat exchange areas S during initial heat transferr1、Sr2Corresponding heat transfer coefficient lambda1、λ2Calculating the heat loss Q of the numerical control machine tool to be optimizedL1、QL2；
(3) Initializing a Poisson coefficient r calculation formula and a judgment rule thereof:
analyzing the degree of correlation between the temperature field and the thermal error of the numerical control machine tool to be optimized by using a Poisson coefficient r, wherein the Poisson coefficient r is shown as a formula (1):
the poisson coefficient r specific judgment rule is shown in table 1:
TABLE 1 Poisson coefficient decision rule
(4) Initializing the area s in which the numerical control machine tool to be optimized can actually carry out measuring point arrangement1、s2、s3…snAnalyzing the measuring points in the positions according to the Poisson coefficient distribution diagram obtained by simulation to obtain measuring points C with high correlation1、C2、C3…Cn；
And initially judging the influence degree of each measuring point on the thermal error, respectively establishing thermal error models of a plurality of different measuring points, and carrying out statistical analysis on model parameters to finally obtain the key temperature measuring point for establishing the thermal error model. The flow chart for initializing the simulation conditions is shown in fig. 2.
(II) setting initialization conditions of the finite element model of the numerical control machine tool to be optimized, preparing to input the initialization conditions into the finite element model, and performing numerical simulation of the finite element model, wherein the method comprises the following steps:
(1) importing the 3D bare base model of the numerical control machine tool to be optimized, and simplifying;
importing the 3D bare base model of the numerical control machine tool to be optimized into COMSOL Multiphysics, simplifying the 3D bare base model in the COMSOL Multiphysics, and deleting a secondary structure with little influence; the simplification principle is as follows: unnecessary fillets, chamfers, bosses, bolt holes and the like are deleted; replacing the ball, the motor rotor and the stator inside the bearing with a ring body which is in contact with the inner ring and the outer ring of the bearing; filling the grooves on the ball screw.
(2) Setting materials of the numerical control machine tool to be optimized and parts of the numerical control machine tool to be optimized;
directly searching by using a built-in material library in COMSOL Multiphysics, or defining a hollow material by itself and keying in the constant-pressure heat capacity C of the materialp(unit: J/(kg. K)), thermal conductivity. lambda0(unit: W/(m.K)), density ρ (unit: kg/m), Young's modulus E (unit: Pa), Poisson's ratio nu;
(3) setting a heat source;
in the normal operating process, the heat conduction of the numerical control machine tool to be optimized comprises the heat conduction of all parts due to different temperature rises, and the heat is conducted from the surface of the numerical control machine tool to be optimized and the surrounding air to form heat convection and heat radiation. Since the actual thermal radiation has a low degree of influence, only the thermal conduction and the thermal convection are considered in the setting of the heat transfer conditions of the numerically controlled machine tool to be optimized. Since the temperature field of the numerically-controlled machine tool to be optimized is a transient temperature field which changes along with the change of time, the temperature field can be expressed as:
t＝f(x,y,z,τ) (6)
in equation (2), x, y, z refer to spatial cartesian coordinates, and τ to temporal coordinates.
Transient analysis boundary conditions include: the method comprises the following steps of rolling bearing friction heat calculation, ball screw friction heat calculation, electric spindle friction heat calculation and air heat exchange coefficient calculation.
Finishing the setting of the friction heat of the rolling bearing, the friction heat of the ball screw and the friction heat of the electric spindle according to the formulas (3) and (4);
the heat generation amount of the friction heat of the rolling bearing and the ball screw is represented by the formula (3):
in formula (3) QbThe heat generation amount per unit time (unit: kW) of the bearing is shown, N is the bearing rotation speed (unit: r/min), M is the bearing friction torque (unit: N mm), and M is0Refers to a frictional moment, M, independent of the load1Refers to the frictional torque (unit:) related to the load, where f1Taking 0.00055; at M0In the calculation formula, f0Refers to lubrication related factors, take 2; v. of0Refers to the kinematic viscosity (unit: mm) of the bearing lubricant at the working temperature2/s)，DmRefers to the average diameter (unit: mm) of the bearing, and in M1Is P in the calculation formula1Refers to the bearing load (unit: N) at the time of friction torque calculation;
loss power P of motor for generating heat of electric spindleLIs calculated, wherein the input power of the electric spindle is PinOutput power of PoutThe expression is as follows:
PL＝Pin-Pout (8)
(4) setting a boundary condition;
in a solid heat transfer physical field, setting natural convection heat exchange between the numerical control machine tool to be optimized and air, and calculating the formula (5) for the 3D bare base model, wherein the natural convection heat exchange exists between the side wall of the base and the air; the rest links which have no obvious influence on the numerical control machine tool to be optimized are all regarded as heat insulation states; in a solid mechanics physical field, setting a boundary load, and inputting a pressure value P (unit: Pa) of the boundary load;
the natural convection heat transfer coefficient h generated by the numerical control machine tool to be optimized and the surrounding environment is calculated by the Nouchert criterion, wherein NμRefers to the Nouchert quasi-coefficient, L is the characteristic dimension, and λ is the thermal conductivity, and the formula is as follows:
Nμ＝hL/λ (9)
(5) finishing grid division;
dividing grids by using free tetrahedrons; because the links with complicated shapes exist in the thermal contact of the links of different parts, a free tetrahedral grid is firstly created for global drawing for saving computing resources, and then the joints of the parts are refined through a refining function to finish the grid drawing.
(6) Completing the configuration of a solver;
selecting a transient solver, and setting the relative tolerance of the solver to be 0.01; selecting a generalized alpha by a time stepping method, selecting a middle-level step by a step length, uniformly initializing in algebraic variable setting, and selecting a backward Euler method, wherein the initial step length fraction is 0.001;
thirdly, generating a thermal equilibrium temperature field and a thermal deformation field of the numerical control machine tool to be optimized;
performing numerical simulation according to the set finite element model to obtain the distribution of the dynamic temperature field and the thermal deformation field of the numerical control machine tool to be optimized in the process from machining to thermal balance;
fourthly, calculating the Poisson coefficient between the temperature and the thermal deformation of the numerical control machine tool to be optimized, and generating a distribution field of the numerical control machine tool to be optimized;
forming a temperature and thermal deformation error correlation coefficient field by introducing and calculating a Poisson coefficient between the two;
(V) measuring point optimization;
and selecting a region to be optimized which accords with the reality, and finishing the optimization of the measuring points by combining a correlation coefficient distribution field.
Example 1
In this embodiment, the numerical control machine tool to be optimized is a VL1160 numerical control machine tool, and the steps of the method are adopted to optimize the selection of the spindle temperature measuring point of the VL1160 numerical control machine tool, wherein:
(1) the 3D bare base model is shown in fig. 4.
(2) Initializing simulation conditions, heat source and heat exchange parameter tables are shown in tables 2-4, and areas s of measuring point arrangement1、s2、s3…snThe part areas of the parts 1, 2, 5, 7, 8 in fig. 4 are selected as the positions of the measuring points to be optimized.
TABLE 2 Heat Source parameter Table
TABLE 3 Motor parameter table
TABLE 4 coefficient of convective heat transfer
(3) And finishing the setting of finite element initialization conditions and numerical simulation. Setting the finite element initialization conditions according to the method shown in the step (II), setting the initial environment temperature of the 3D bare base model shown in fig. 4 to be 20 ℃, setting the material parameters of the whole VL1160 numerical control machine tool to be shown in table 5, setting the grid division to be shown in fig. 5, setting the boundary load pressure to be 1E6(Pa), and calculating the correlation coefficient r between the temperature T (DEG C) and the deformation u (um) to be shown in the formula (6), and setting the coefficient distribution field to be shown in fig. 6.
TABLE 5 import materials parameter Table
(4) And finishing the measurement point optimization. According to fig. 4 and table 1, in the preset optimized area, the temperature of the parts 1, 2 and 5 has a better correlation to the thermal deformation, and can be arranged as a measuring point area.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (1)
1. A numerical control machine tool temperature measuring point optimization method based on finite element model numerical simulation,
the method is characterized in that: the method comprises the following steps:
initializing simulation conditions, comprising the following steps:
(1) according to the type of the numerical control machine tool to be optimized, initializing a heat source R in the machining process of the numerical control machine tool to be optimized1、R2、R3…RnAnd according to the corresponding heating power P of the numerical control machine tool to be optimized1、P2、P3…PnCalculating the calorific value Q of the numerical control machine tool to be optimized1、Q2、Q3…Qn；
(2) Horizontal and vertical heat exchange areas S during initial heat transferr1、Sr2Corresponding heat transfer coefficient lambda1、λ2Calculating the heat loss Q of the numerical control machine tool to be optimizedL1、QL2；
(3) Initializing a Poisson coefficient r calculation formula and a judgment rule thereof:
analyzing the degree of correlation between the temperature field and the thermal error of the numerical control machine tool to be optimized by using a Poisson coefficient r, wherein the Poisson coefficient r is shown as a formula (1):
the poisson coefficient r specific judgment rule is shown in table 1:
TABLE 1 Poisson coefficient decision rule
(4) Initializing the area s in which the numerical control machine tool to be optimized can actually carry out measuring point arrangement1、s2、s3…snAnalyzing the measuring points in the positions according to the Poisson coefficient distribution diagram obtained by simulation to obtain measuring points C with high correlation1、C2、C3…Cn；
(II) setting initialization conditions of the finite element model of the numerical control machine tool to be optimized, comprising the following steps of:
(1) importing the 3D bare base model of the numerical control machine tool to be optimized, and simplifying;
importing the 3D bare base model of the numerical control machine tool to be optimized into COMSOL Multiphysics, simplifying the 3D bare base model in the COMSOL Multiphysics, and deleting a secondary structure with little influence;
(2) setting materials of the numerical control machine tool to be optimized and parts of the numerical control machine tool to be optimized;
directly searching by using a built-in material library in COMSOL Multiphysics, or defining a hollow material by itself and keying in the constant-pressure heat capacity C of the materialpThermal conductivity lambda0Density ρ, Young's modulus E, Poisson's ratio nu;
(3) setting a heat source;
finishing the setting of the friction heat of the rolling bearing, the friction heat of the ball screw and the friction heat of the electric spindle according to the formulas (3) and (4);
the heat generation amount of the friction heat of the rolling bearing and the ball screw is represented by the formula (3):
in formula (3) QbRepresenting the heat generation amount of the bearing per unit time, n representing the rotating speed of the bearing, M representing the friction torque of the bearing, M0Refers to a frictional moment, M, independent of the load1Refers to the frictional torque associated with the load, where f1Taking 0.00055; at M0In the calculation formula, f0Refers to lubrication related factors, take 2; v. of0Refers to the kinematic viscosity, D, of the bearing lubricant at the operating temperaturemRefers to the average diameter of the bearing, and in M1Is P in the calculation formula1Refers to the bearing load at the time of friction torque calculation;
loss power P of motor for generating heat of electric spindleLIs calculated, wherein the input power of the electric spindle is PinOutput power of PoutThe expression is as follows:
PL＝Pin-Pout (3)
(4) setting a boundary condition;
in a solid heat transfer physical field, setting natural convection heat exchange between the numerical control machine tool to be optimized and air, and calculating the formula (5) for the 3D bare base model, wherein the natural convection heat exchange exists between the side wall of the base and the air; the rest links which have no obvious influence on the numerical control machine tool to be optimized are all regarded as heat insulation states; in a solid mechanics physical field, setting boundary load and inputting a pressure value P of the boundary load;
the natural convection heat transfer coefficient h generated by the numerical control machine tool to be optimized and the surrounding environment is calculated by the Nouchert criterion, wherein NμRefers to the Nouchert quasi-coefficient, L is the characteristic dimension, and λ is the thermal conductivity, and the formula is as follows:
Nμ＝hL/λ (4)
(5) finishing grid division;
dividing grids by using free tetrahedrons;
(6) completing the configuration of a solver;
selecting a transient solver, and setting the relative tolerance of the solver to be 0.01; selecting a generalized alpha by a time stepping method, selecting a middle-level step by a step length, uniformly initializing in algebraic variable setting, and selecting a backward Euler method, wherein the initial step length fraction is 0.001;
thirdly, generating a thermal equilibrium temperature field and a thermal deformation field of the numerical control machine tool to be optimized;
performing numerical simulation according to the set finite element model to obtain the distribution of the dynamic temperature field and the thermal deformation field of the numerical control machine tool to be optimized in the process from machining to thermal balance;
fourthly, calculating the Poisson coefficient between the temperature and the thermal deformation of the numerical control machine tool to be optimized, and generating a distribution field of the numerical control machine tool to be optimized;
forming a temperature and thermal deformation error correlation coefficient field by introducing and calculating a Poisson coefficient between the two;
(V) measuring point optimization;
and selecting a region to be optimized which accords with the reality, and finishing the optimization of the measuring points by combining a correlation coefficient distribution field.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113009882A (en) * | 2021-03-15 | 2021-06-22 | 大连理工大学 | Numerical control machine tool thermal error adaptive compensation method |
| CN113297761A (en) * | 2021-05-21 | 2021-08-24 | 武汉武重机床有限公司 | Thermal deformation test compensation method for numerical control machine tool |
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