CN103823991A

CN103823991A - Heavy-duty tool thermal error prediction method taking environmental temperature into account

Info

Publication number: CN103823991A
Application number: CN201410088166.3A
Authority: CN
Inventors: 李斌; 谭波; 毛新勇; 毛宽民; 任光远; 彭芳瑜; 刘红奇
Original assignee: Huazhong University of Science and Technology
Current assignee: Wuhan Hengli Huazhen Technology Co Ltd
Priority date: 2014-03-11
Filing date: 2014-03-11
Publication date: 2014-05-28
Anticipated expiration: 2034-03-11
Also published as: CN103823991B

Abstract

The invention discloses a heavy-duty tool thermal deformation prediction method taking environmental temperature into account. The method specifically comprises the following steps of predicting the tool thermal deformation caused by the internal heat source of a tool, predicting tool thermal deformation caused by the external heat source of the tool, summing the tool thermal deformation caused by the internal and external heat sources to obtain the final tool thermal deformation. Time-lag thermal deformation error caused by the environmental temperature is taken into account during the prediction of the tool thermal deformation caused by the external heat source of the tool, and multiple regression modelling based on the least square method principle is performed during the prediction of the tool thermal deformation caused by the internal heat source of the tool. The joint action effects reflecting the influence of the environment temperature nonlinear lag and the influence of the internal heat source on the heavy-duty tool are combined by the method, and the thermal deformation error under any environmental condition and processing condition can be effectively predicted in real time.

Description

Heavy machine tool thermal error prediction method considering environmental temperature

Technical Field

The invention relates to the technical field of thermal error compensation of numerical control machines, in particular to a thermal error prediction method of a heavy machine tool considering environmental temperature.

Background

Under the non-constant temperature condition, the numerical control machine tool is subjected to the combined action of an internal heat source and an external environment during machining to generate non-linear temperature response and complex structure thermal deformation, so that the machining precision is reduced and even the precision is invalid. The prediction and compensation of the thermal error are important means for improving the processing precision of the numerical control machine tool and the thermal stability of the machine tool, and as the influence mechanism analysis of an internal heat source and the thermal error reduction method become mature, the proportion of the thermal error caused by the action of the environmental temperature on the machine tool to the total error gradually rises, and the thermal error becomes an important influence factor of the thermal deformation of the heavy machine tool.

Thermal error modeling generally refers to establishing a mapping relationship between thermal deformation and corresponding temperature of a machine tool, and common thermal error prediction models include: least square model, Bayesian network model, support vector machine model, gray system model, artificial intelligence model, fuzzy system model, etc. The temperature information includes body temperature and the environment temperature of the machine tool, and the method for determining the temperature measuring point required by modeling is as follows: a large number of temperature measuring points are arranged in a machine tool and the environment according to experience, and then measuring point selection, grouping, optimization and the like are carried out according to a certain strategy to obtain the optimal or closest linear point arrangement. In the current research, the research on the influence of the ambient temperature on the thermal error of the machine tool is limited to experimental statistics and qualitative analysis. In the aspect of reducing the influence of the ambient temperature on the structural thermal deformation of the machine tool, the method mainly adopted is to optimize the thermal structure of the machine tool, control the ambient temperature, improve the heat dissipation condition, optimize the heat source arrangement and the like.

With the development of an optimal point distribution identification method and a thermal error nonlinear modeling theory, the research on the influence mechanism of a heat source inside a machine tool and a thermal error reduction method is mature day by day, and a multiple linear regression model of thermal deformation caused by the heat source inside the machine tool can be established easily by using the optimal or recent linear point distribution temperature.

But the environmental temperature is different from the optimal distribution change characteristic of the machine tool body, the response of the heavy machine tool to the environmental temperature fluctuation always has lag, the nearest linear environmental temperature distribution does not exist, and the lag time is changed along with the climate and seasonal changes. However, the current linear and nonlinear thermal error prediction models cannot reflect the characteristic of lagging with the temperature, so that the universality of the current thermal error prediction models in different environments is limited, and even the compensation accuracy is invalid. On the other hand, current research and analysis of ambient temperature effects is limited to experimental measurements, phenomenological explanations, and qualitative discussions, and their thermal lag effects cannot be used in thermal error modeling and compensation.

Disclosure of Invention

The invention provides a thermal deformation prediction method of a heavy machine tool with consideration of ambient temperature, which considers the combined effect of reflecting the influence of the nonlinear hysteresis of the ambient temperature and the influence of an internal heat source on the heavy machine tool and can realize real-time effective prediction of thermal deformation errors under any ambient conditions and machining conditions.

In order to solve the technical problems, the invention adopts the technical scheme that:

a method for predicting thermal deformation of a heavy machine tool by considering environmental temperature specifically comprises the following steps: predicting machine tool thermal deformation delta L caused by machine tool internal heat source_inAnd predicting the thermal deformation amount DeltaL of the machine tool caused by the external heat source of the machine tool_extThe amount of thermal deformation Δ L of the machine tool caused by internal and external heat sources_in、ΔL_extSuperposing to obtain the final thermal deformation of the machine tool;

the thermal deformation quantity Delta L of the machine tool caused by the external heat source of the machine tool_extThe prediction is as follows:

real-time prediction of ambient temperature t of machine tool_e(x) And determining the surface temperature t of the machine tool according to the heat exchange balance relationship between the environment temperature and the surface temperature of the machine tool_b(x) X represents a time variable; calculating the time x₁To time x₂Machine tool thermal deformation amount Delta L caused by external heat source_ext＝αL_X(t_b(x₂)-t_b(x₁) Alpha is machine tool materialCoefficient of thermal expansion, L_XThe nominal size of the machine tool in the direction to be measured;

the thermal deformation quantity Delta L of the machine tool caused by the heat source in the machine tool_inThe prediction is as follows:

firstly, measuring the comprehensive thermal deformation error Delta L of the machine tool_XCombined with the amount of thermal deformation Δ L of the machine tool caused by the external heat source of the machine tool_extCalculating thermal deformation error Delta L caused by internal heat source of machine tool_in′＝ΔL_X+ΔL_extWill calculate Δ L_inThe temperature of a measuring point on the surface of the machine tool is used as an input, and a least square regression method is adopted to fit to obtain a prediction formula of thermal deformation error caused by a heat source in the machine tool

Wherein, Δ L_inPredicted value of thermal deformation error, delta t, caused by heat source inside machine tool_iIs the temperature difference of the distribution point temperature, k_iAnd C are the coefficients and constants determined by the fitting, respectively.

Further, the ambient temperature t at which the machine tool is located_e(x) The prediction formula of (c) is:

determining the surface temperature of the machine tool according to the heat exchange balance relationship between the environment temperature and the surface temperature of the machine tool

Wherein,

(x) The average value of the sampling environment temperature in one period is obtained;

T_max(x) The maximum value of the environmental temperature obtained by sampling and measuring in one period is obtained;

β_nthe weight is the contribution component of the temperature wave of different frequency components to the total temperature change;

ω₀is the fundamental frequency;

φ_0ninitial phases of temperature waves of different frequency components;

α_nis a lag time coefficient between the seasonal temperature and a phase component transformed with the seasonal temperature in the phases of the temperature waves of different frequency components;

τ_ris a time constant;

omega is the fundamental frequency of the temperature wave;

the phase angles of the respective orders are lags between the machine tool surface temperature response and the ambient temperature. The invention has the beneficial effects that:

in the current thermal error comprehensive prediction, the model input variables comprise the ambient temperature, the machine tool body temperature, machine tool coordinate parameters and the like. The environment temperature is measured in real time, and does not contain historical temperature information and thermal information, and the thermal deformation error of the machine tool is influenced by the current environment temperature and is also related to the past machine tool state and the past environment state. Therefore, the technical problems to be solved at present are as follows: how to establish the high-robustness comprehensive prediction model, the model can reflect the combined effect of the environment temperature nonlinear hysteresis influence and the internal heat source influence on the heavy machine tool, and the method is used for predicting the thermal deformation error of any processing condition under any environment condition in real time.

The invention provides a thermal error prediction comprehensive model modeling method for a heavy machine tool under combined action of an internal heat source and an external heat source. According to the method, time-lag thermal deformation errors caused by the environment temperature are analyzed and modeled, and thermal deformation caused by an internal heat source is subjected to multivariate regression modeling based on the least square principle.

Furthermore, the invention considers the periodicity and the non-periodicity of the environmental temperature fluctuation and the characteristics of the fluctuation changing along with seasons and years, simultaneously considers the objective fact that the current thermal state of the machine tool is influenced by the current environmental temperature and the historical temperature state, provides a new research idea, realizes the Fourier series decomposition of the environmental temperature by combining time series analysis, uses the predicted temperature with the analysis form of definite time, frequency and phase information for thermal error modeling to replace the actually measured temperature data, and is beneficial to more accurately and quantitatively describing the time lag response characteristic of the thermal error of the machine tool, thereby improving the accuracy and the robustness of the prediction of the thermal error of the machine tool. The model can effectively predict the thermal deformation error in any environment and any working condition. The comprehensive model is beneficial to solving the problem that the traditional model is poor in robustness along with seasonal temperature change.

Drawings

FIG. 1 is a flow chart of the method of the present invention

FIG. 2 is a schematic layout diagram of an XK2650 temperature sensor, wherein FIG. 2 (a) is a schematic layout diagram of a front side, and FIG. 3 (a) is a schematic layout diagram of a back side;

fig. 3 is a schematic diagram comparing the environmental temperature prediction effects of the XK2650 planer type boring and milling machine in different seasons, wherein fig. 3 (a), 2 (B), 2 (C) and 2 (d) are temperature prediction effect diagrams in spring, summer, autumn and winter respectively, in which a is the measured temperature, B is the predicted temperature, and C is the prediction residual error;

FIG. 4 is a schematic diagram showing the comparison of the prediction effect of the X thermal variation error of the main shaft of the XK2650 planer type boring and milling machine under different constant rotation speeds and different seasons;

wherein, fig. 4 (a) and 4 (b) are schematic diagrams comparing the predicted effects under 300rpm conditions in spring and summer, fig. 4 (c) and 4 (d) are schematic diagrams comparing the predicted effects under 600rpm conditions in summer and autumn, and fig. 4 (e) and 4 (f) are schematic diagrams comparing the predicted effects under 900rpm conditions in autumn and winter;

FIG. 5 is a schematic diagram showing the comparison of the prediction effect of the X thermal variation error of the main shaft of the XK2650 planer boring and milling machine under the variable main shaft rotation speed spectrum and different season environmental conditions, wherein the method provided by the invention is an ETCP method, and the reference comparison method is a multivariate linear analysis (MRA for short) prediction method;

wherein, fig. 5 (a), 4 (b), 4 (c), 4 (d) are schematic diagrams comparing the measured thermal error under the rotation speed spectrum condition in spring, summer, autumn, winter and the thermal error prediction effect of the two methods.

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 invention relates to a technical idea.

The thermal deformation error of the machine tool is decomposed into superposition influenced by an external environment and an internal heat source, X represents a specified thermal deformation direction to be measured, and can be an X coordinate, a y coordinate and a z coordinate in a machine tool coordinate system.

ΔL_X＝ΔL_in+ΔL_ext（1）

Wherein Δ L_inDenotes the thermal deformation, Δ L, caused by the influence of an internal heat source_extIndicating deformation caused by an external heat source.

The modeling method of the thermal error caused by the external heat source comprises the following steps:

the deformation caused by the external heat source alone is linear with the variation of the response temperature tb (x) of the machine tool under the influence of the ambient temperature, i.e.:

ΔL_ext＝αL_X(t_b(x₂)-t_b(x₁))（2）

wherein L is_XAlpha represents the thermal expansion coefficient of the machine tool body for the nominal size of the thermal deformation direction to be measured of the machine tool, the response temperature of the surface of the machine tool is obtained not directly but by establishing a temperature response model of the machine tool body based on the lumped heat capacity principle and solving and calculating the model.

Furthermore, the environment temperature of the machine tool is obtained through analysis model prediction, and the actually measured discrete temperature is replaced by the continuously time-varying predicted temperature. Obtaining t by analytic model_e(x) Then, the response temperature t of the machine tool body can be obtained_b(x) Further, the solution of the equation (2) is obtained, that is, the thermal error caused by the external heat source can be obtained.

The thermal error modeling method of the influence of the internal heat source comprises the following steps: and determining an optimal temperature measuring point through temperature measuring point selection, grouping and optimization of the machine tool body, and establishing a multi-element linear regression model between the thermal deformation influenced by the internal heat source and the optimal measuring point.

And finally, superposing the models acted by the internal heat source and the ambient temperature to form a comprehensive prediction model.

Second, technical scheme

Based on the technical idea, the invention provides a heavy machine tool thermal error modeling method considering the environmental temperature, which is shown in fig. 1 and comprises the following specific steps:

1) ambient temperature prediction

And combining time sequence analysis and a Fourier series decomposition method, expressing the actually measured ambient temperature in a Fourier trigonometric series form, and acquiring information such as a time sequence, a fluctuation frequency, a fluctuation amplitude, a phase and the like of the ambient temperature. And measuring the updated temperature data and the current time signal in real time as input to realize real-time prediction of the temperature, and replacing the actually measured temperature to be used for thermal error response prediction modeling.

1.1) ambient temperature Fourier series decomposition

The ambient temperature expansion is in the form of a fourier series, i.e.:

wherein x represents time in minutes, which is determined by a time reference starting point and a current time; fundamental frequency omega₀=2π/T₀，T₀Is a period, pi is a circumferential ratio, A₀Is the mean term of temperature, A_nIs the amplitude of the temperature wave of each order, n =1,2, …, phi_nIs a phase angle, the physical meaning of which is a lag time of each orderBelow pair A₀,A_n,φ_nAdjusting and replacing various parameters;

1.2) mean term A₀

Mean value term A₀Is the average of the period over which the current temperature point is located, here by the running average of the historical temperatures

Instead, it is derived from the formula:

<math> <mrow> <msub> <mi>A</mi> <mn>0</mn> </msub> <mo>=</mo> <msubsup> <mi>A</mi> <mn>0</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>t</mi> <mrow> <mo>[</mo> <mi>x</mi> <mo>]</mo> <mo>-</mo> <mi>N</mi> <mo>+</mo> </mrow> </msub> <msub> <mi>t</mi> <mrow> <mo>[</mo> <mi>x</mi> <mo>]</mo> <mo>-</mo> <mrow> <mo>(</mo> <mi>N</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </msub> <mo>+</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>+</mo> <msub> <mi>t</mi> <mrow> <mo>[</mo> <mi>x</mi> <mo>]</mo> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> <mi>N</mi> </mfrac> <mo>[</mo> <mi>x</mi> <mo>]</mo> <mo>&GreaterEqual;</mo> <mi>N</mi> </mrow> </math>

wherein, [ x ]]Is to take integer of x at any time, N is the number of sampling points in one period, t_[x]Is the measured temperature at time x, the ambient temperature is slowly varying information, and the time x is considered]≤x＜[x]The ambient temperature during +1 is equal to t_[x](ii) a In the moving average method, historical data of N sampling points in at least one period is required to predict the current temperature; new measured value t_[x]-1Introduction of old t_[x]-NExit, then predicted mean A₀Can be updated online. The moving average method reserves all fluctuation information below the fundamental frequency;

1.3) amplitude term A_n

A_nThe amplitude term of each order of temperature fluctuation after decomposition is the maximum value T of fluctuation in the considered period_maxWith the average value A₀The difference is proportional, i.e.:

wherein:

T_{\max} (x) = \underset{[x] - N < u < [x] - 1}{\max (t_{[u]})}

β_nthe weight is the contribution component of the temperature waves with different frequency components to the total temperature change, and the weight represents the inherent characteristics of the workshop; (T)_max(x)-A₀ ⁽¹⁾(x) Is variable over different observation periods, but the observed value is determined for a known ambient temperature history;

1.4) phase

Phase position

Expressed as:

where alpha is_nA lag time coefficient representing a seasonal change, representing an inherent thermal characteristic of a particular plant, which is a particular value; phi is a₀n is the initial phase of each order frequency component of the temperature wave;

1.5) Total temperature prediction model

In summary, the analytical model for the prediction of the ambient temperature at any time x can be expressed as:

in the formula, the mean value A₀ ⁽¹⁾And maximum value T_max(x) Calculated from historical measurements over at least one period, and updated in real time, the fundamental frequency ω₀It is known that beta in the analytical model can be identified from measured temperature data using the nonlinear least squares principle_n,φ_0nAnd alpha_nAnd the parameters are equal, so that the online prediction of the environment temperature is realized.

2) Analytic modeling of transient temperature response of machine tool body

For a heavy numerical control machine tool meeting the lumped heat capacity condition, the surface of the machine tool exchanges heat with the ambient temperature according to the Newton's law of cooling, and then the heat balance differential equation is as follows:

in the formula: tau is_rIs a constant of time, and is,

wherein

Rho-density of the heat conductor, kg/m³；

c is specific heat capacity, J/(kg. k);

h-surface Heat transfer coefficient, unit W/(m 2)^．k)；

V-volume of heat conductor, m³；

A-heat transfer area of heat conductor and fluid, m²；

Solving the equations (3) and (4) to obtain the response temperature t of the machine tool_b(x)：

Wherein

Is the lagging phase angle between the machine tool surface temperature response and the ambient temperature.

From equation (5), it can be seen that the temperature response of the machine tool is composed of three parts: mean term, exponential decay term, harmonic response term. Time constant τ_rFor a constant value, the exponential decay term will approach 0 when time x is long enough. Starting from the machine tool being put into the shop, the slowly changing ambient temperature starts to affect the machine tool, indicating that the time of influence x is sufficiently long, so the exponential decay term can be considered to be 0. Equation (5) can thus be expressed as:

3) transient thermal deformation error model of heavy machine tool caused by external heat source

Combining the equations (2) and (7), the thermal deformation in the designated direction caused by the external heat source at any two times x1 and x2 can be calculated as:

for a heavy numerically controlled machine tool placed in a given workshop, the parameters α, τ are taken into account when considering the thermal deformation error in a given direction_rAnd ω are both known parameters and can be calculated as, α_nLX and

the parameter is a fixed parameter, shows the inherent thermal characteristic of the environment where the machine tool is located, and can be obtained through system identification. The thermal deformation of the machine tool is therefore dependent on the measured ambient temperature andtime is two variables.

4) Prediction model for transient thermal deformation error of heavy machine tool caused by internal heat source

The thermal deformation error DeltaL caused by the external heat source can be obtained by the formula (8)_extPredicted value of (d), and the overall thermal deformation error Δ L of the machine tool_XCan be obtained through measurement, so that the corresponding thermal deformation error Delta L caused by the internal heat source can be calculated and obtained through the formula (1)_in。

For machine tool thermal deformation error Delta L caused by internal heat source_inThe selected temperature distribution point can be determined through thermal error testing, temperature variable grouping, distribution point optimization and the like. The temperature of the measuring point determined after optimization is taken as input, and the delta L is calculated by the formula (1)_inAs output, a multiple linear regression prediction model is established based on the least square principle, and the model form is as follows:

wherein, Δ t_i(i =1,2, … m) temperature difference measured for the determined point after optimization, k_iIs a model coefficient, C is a constant, k_iAnd C is obtained by a least squares regression method.

5) Thermal error prediction comprehensive model of heavy machine tool under combined action of internal and external heat sources

The thermal deformation prediction model in the specified direction of the machine tool is obtained by combining the formula (1), the formula (8) and the formula (9):

third, example

The invention is explained in detail by a predictive modeling implementation process of thermal deformation error of an XK2650 planer boring and milling machine under the combined action of an internal heat source and an external heat source in combination with the attached drawings. FIG. 2 is a schematic diagram of a machine tool sensor unknown site, and Table 1 defines the layout of the sensor.

TABLE 1 temperature sensor layout definition

1. Ambient temperature prediction and effect verification

Predicting the environmental temperature of the workshop:

\begin{matrix} t_{e} (x) = A_{0}^{(1)} (x) + 0.73 (T_{\max} (x) - A_{0}^{(1)} (x)) \sin (0.0436 x - 1.75 + 0.012 A_{0}^{(1)} (x)) \\ - 0.23 (T_{\max} (x) - A_{0}^{(1)} (x)) \sin (0.0872 x + 0.45 - 0.008 A_{0}^{(1)} (x)) \\ - 0.04 (T_{\max} (x) - A_{0}^{(1)} (x)) \sin (0.131 x + 0.77 - 0.007 A_{0}^{(1)} (x)) - - - (11) \end{matrix}

and respectively selecting data of 30 days in four seasons to compare the predicted effects of the identified model formula (3). The comparison shows that the analytic model can well predict and reproduce the current temperature in the temperature range of 0-40 ℃ in different seasons, the residual between the predicted value and the measured value is less than 1 ℃, and the prediction model is a variable coefficient analytic model and can be conveniently applied to time lag and nonlinear thermal deformation response analysis of a machine tool. The comparative effect is shown in fig. 2.

2. Modeling of thermal deformation errors caused by ambient temperature

The environmental temperature and machine tool X thermal offset error data obtained by measurement for 30 days are selected, parameters of the formula (8) are identified based on a nonlinear least square principle and the combination formula (11), and partial parameter models are obtained and shown in the table (2).

Table 2 identified thermal error prediction model partial parameters

3. Integrated model identification

The total thermal error of the machine tool is the additive effect of the combined action of the internal heat source and the external heat source, and the deformation caused by the internal heat source can be considered to be the total deformation minus the deformation caused by the external heat source. For deformation caused by an internal heat source, a linear relation between the optimal temperature distribution point and the thermal deformation of the machine tool is established based on the most linear strategy through a large number of distribution points, variable grouping and distribution point optimization of the temperature sensor. The thermal deformation error prediction model caused by the internal heat source of the XK2650 planer type boring and milling machine established in the embodiment is as follows:

ΔL_in＝0.039Δt₃+0.009Δt₂₃-0.007Δt₁₆+0.01(12)

wherein t is₃、t₂₃、t₁₆Milling planer with separate indicationThe cross beam, the front end of the main shaft and the ambient temperature change.

The comprehensive prediction model of the thermal error of the machine tool obtained by integrating the formula (11), the table (2) and the formula (12) is as follows:

4. comparison of predicted effects of integrated models

In the embodiment of the invention, the prediction effect of the comprehensive model (ETCP model for short) of the invention is compared with the traditional multiple linear regression model (MRA model), and the comparison is carried out under different experimental conditions, including seasonal temperature change, spindle rotating speed change and the like. Firstly, establishing a multiple regression thermal error prediction model of a machine tool:

ΔL′＝0.048Δt₂₄-0.025Δt₅+0.026Δt₂₃+0.042Δt₁₅-0.027（14）

wherein t is₂₄、t₅、t₂₃、t₁₅Respectively representing the ambient temperature, the cross beam, the front end of the main shaft and the side surface of the ram.

The measurement is carried out in different seasons under different constant rotating speed conditions and different variable rotating speed experimental conditions, the prediction is carried out through the formula (13) and the formula (14), the effect is shown in the attached figures 4 and 5, and the statistical analysis and comparison of residual error data are shown in the attached tables 1 and 2. As can be seen from the attached drawings and the attached table, the model residual error provided by the method is smaller and the model prediction precision is higher along with the change of the main shaft rotating speed under the conditions of different seasons.

Table 3 comparison of residual statistical analysis of data corresponding to fig. 4

Table 4 comparison of residual statistical analysis of data corresponding to fig. 5

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

1. A heavy machine tool thermal deformation prediction method considering environmental temperature is characterized by comprising the following steps: predicting machine tool thermal deformation delta L caused by machine tool internal heat source_inAnd predicting the thermal deformation amount DeltaL of the machine tool caused by the external heat source of the machine tool_extThe amount of thermal deformation Δ L of the machine tool caused by internal and external heat sources_in、ΔL_extSuperposing to obtain the final thermal deformation of the machine tool;

real-time prediction of ambient temperature t of machine tool_e(x) And determining the surface temperature t of the machine tool according to the heat exchange balance relationship between the environment temperature and the surface temperature of the machine tool_b(x) X represents a time variable; calculating the thermal deformation amount delta L of the machine tool caused by the external heat source from the time x1 to the time x2_ext＝αL_X(t_b(x₂)-t_b(x₁) Alpha is the coefficient of thermal expansion of the machine tool material, L_XThe nominal size of the machine tool in the direction to be measured;

2. The heavy machine tool thermal deformation detection method according to claim 1,

the ambient temperature of the machine toolDegree t_e(x) The prediction formula of (c) is:

Wherein,

ω₀is the fundamental frequency;

φ_0ninitial phases of temperature waves of different frequency components;

τ_ris a time constant;

omega is the fundamental frequency of the temperature wave;

the phase angles of the respective orders are lags between the machine tool surface temperature response and the ambient temperature.