CN110716500A - Method and system for determining segmented modeling points of temperature sensitive interval - Google Patents
Method and system for determining segmented modeling points of temperature sensitive interval Download PDFInfo
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Abstract
The invention discloses a method and a system for determining a temperature sensitive interval segmented modeling point, which are used for obtaining M batches of machine tool temperature value increment measurement values and thermal deformation measurement values which are sorted from low to high according to environmental temperature in a whole year range; correspondingly establishing a thermal error prediction model of each batch of machine tool according to the screened data of each batch; establishing an MxM thermal deformation prediction matrix and a prediction residual standard deviation matrix; drawing a predicted residual standard deviation change trend graph according to the predicted residual standard deviation matrix: finding a temperature sensitive interval in the predicted residual standard deviation change trend graph, wherein the temperature sensitive interval refers to an environment temperature interval corresponding to the predicted residual standard deviation jumping; and selecting a temperature sensitive interval segmentation point in the temperature sensitive interval. The system comprises a data acquisition system and a computer configured with a temperature sensitive interval sectional modeling point calculation program. The method can effectively determine the segmented modeling points of the temperature sensitive interval, and has intuition and good universality.
Description
Technical Field
The invention relates to the technical field of modeling of a thermal error prediction model of a machine tool, in particular to a method and a system for determining segmented modeling points of a temperature sensitive interval.
Background
With the rapid development of precision and ultra-precision machining technology, higher requirements are put forward on the machining precision and reliability of numerical control machines and machining centers. In the actual processing and running process of the numerical control machine tool, as a process system is influenced by factors such as friction heat, cutting heat, environmental temperature and the like, the parts of the machine tool expand to generate thermal deformation. This thermal deformation changes the relative position of the machine tool components, causing the tool to move away from the desired cutting point, resulting in a reduction in the machine tool machining accuracy, and such errors caused by thermal deformation are referred to as thermal errors. The thermal error is the largest error source of the numerical control machine tool, and the proportion of the thermal error is increased along with the improvement of the precision of the machine tool; particularly, in precision machining, the thermal error accounts for 40% -70% of the total error of the machine tool, so that the reduction of the thermal error has important significance for improving the machining precision of the precision machine tool.
The numerical control machine tool thermal error prediction method mainly comprises the selection of temperature sensitive points and the application of a mathematical modeling algorithm; most of the related papers and patents are also centered around the selection of temperature sensitive points and the application of mathematical modeling algorithms. In 2011, mianmao et al studied the thermal error time series modeling technique in the precision machine tool, and considered the influence of the past value of the studied sequence on the model, so that the modeling precision is high (refer to the literature, "research on thermal error time series modeling technique of precision data machine tool", from 2011 national precision engineering academy).
In 2013, Miao Enming et al performed correlation research on Temperature sensitive points by using fuzzy clustering and combining with a gray correlation method, firstly classified all Temperature variables according to correlation strength by using fuzzy clustering analysis, then calculated the correlation degree between the Temperature variables and the thermal deformation in each class by using a gray correlation analysis method, determined the sensitive point variables in each class, and finally combined the sensitive point variables in each class for thermal error modeling (see the literature, "Temperature-sensing selection of thermal error model of CNC mapping center", from Journal of Advanced Manufacturing Technology ").
The invention discloses a Chinese patent CN201410097157.0 'selection optimization method for a numerical control machine thermal error compensation modeling temperature measuring point combination', which excludes a part of temperature measuring point positions according to a main factor strategy, and filters the rest temperature measuring point positions of a sensor by using a weight of an established thermal error BP neural network model.
Chinese patent No. CN201610256595.6 discloses a method and system for predicting thermal error of a numerically-controlled machine tool based on an unbiased estimation splitting model, which screens out temperature variables brought into a thermal error compensation model by a linear correlation coefficient method, and establishes a thermal error compensation model of the machine tool by using the unbiased estimation splitting model.
The latest experimental research finds that the prediction precision jump interval influenced by the ambient temperature exists in the thermal error compensation model of the machine tool, which causes the prediction performance of the model to fluctuate, and greatly reduces the prediction precision and the prediction robustness of the model. Therefore, how to determine the environmental temperature interval which can cause the prediction performance to generate the waveguide, namely the temperature sensitive interval, is the key for accurately establishing the thermal error prediction model, but no better method exists at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the method for determining the temperature-sensitive interval segmented modeling point, which can effectively determine the temperature-sensitive interval segmented modeling point and has intuition and good universality.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for determining temperature sensitive interval segment modeling points, comprising the steps of:
step 1: acquiring a temperature value increment measurement value of M batches of machine tool main heat sources and a thermal deformation measurement value of a machine tool main shaft which are sequenced from low to high according to the environmental temperature in the whole year range;
step 2: screening the temperature value increment measured value and the thermal deformation measured value of each batch to be respectively used as a temperature variable and a thermal error dependent variable, and correspondingly establishing a thermal error prediction model of each batch of the machine tool according to the screened data of each batch;
and step 3: obtaining the predicted values of the thermal deformation amount of the thermal error model of each batch of the machine tool to the temperature variables of M batches respectively, thereby obtaining an MxM thermal deformation amount prediction matrix;
and 4, step 4: obtaining a prediction residual standard deviation matrix with the size of M multiplied by M according to the difference between the predicted value and the measured value of the thermal deformation amount;
and 5: drawing a predicted residual standard deviation change trend graph according to the predicted residual standard deviation matrix: the horizontal axis represents the batch, and the vertical axes at the two ends of the horizontal axis respectively represent the predicted residual standard deviation and the temperature; finding a temperature sensitive interval in the predicted residual standard deviation change trend graph, wherein the temperature sensitive interval refers to an environment temperature interval corresponding to the predicted residual standard deviation jumping;
step 6: and selecting a temperature sensitive interval segmentation point in the temperature sensitive interval.
Further, the predicted residual standard deviation is calculated as follows:
in the formula, SDvkRepresenting the predicted residual standard deviation of the thermal error prediction model of the machine tool of the v batch on the measured value of the thermal deformation amount of the k batch, wherein v is 1, 2. M is the number of the acquired data batches, the value of M is to ensure that the union set of the environmental temperature changes when the M batches of data are acquired can basically cover the annual temperature change range, and M is not less than 1; skjqA qth sampled measurement value representing a j-axis direction in the k-th lot of measured values of the amount of thermal deformation, q being 1, 2.
Is represented by the formulakjqAnd (3) predicting the thermal deformation amount of the corresponding machine tool thermal error prediction model of the v batch on the temperature variable of the k batch.
Further, the temperature sensitive interval segmentation point is selected as follows:
verifying the probability distribution type of the array formed by the prediction precision of each batch of data by the prediction model, and acquiring the corresponding standard deviation sigma; the prediction accuracy of each batch of prediction models on M batches of data refers to predictionResidual standard deviation SDvk(ii) a Marking potential temperature sensitive interval segmentation points of each batch of data through a 3 sigma principle; and when all M batches of data finish marking the potential temperature sensitive interval segmentation points in sequence, selecting a threshold value according to engineering requirements to obtain a final temperature sensitive interval segmentation point.
The invention also provides a system for determining the temperature sensitive interval segmented modeling point, which comprises a data acquisition system and a computer, wherein the computer is internally provided with a temperature sensitive interval segmented modeling point calculation program and carries out the steps according to the steps 1 to 6; the data acquisition system comprises an infrared thermal imager, a temperature sensor group and an eddy current displacement sensor; the infrared thermal imager is used for making a thermal imaging picture for the machine tool to obtain the temperature color picture characteristic of the machine tool; according to the temperature color chart characteristics displayed by the thermal imager, the heat source area of the machine tool can be marked manually; the temperature difference sensor group is respectively arranged at a heat source area of the manual marking machine tool and an environment where the machine tool is arranged; the temperature sensor is arranged at a heat source area of the manual marking machine tool and used for acquiring the temperature of the corresponding heat source area of the machine tool; the temperature sensor is arranged in the environment where the machine tool is located and used for measuring the change of the environmental temperature; and the eddy current displacement sensor is arranged in the X direction, the Y direction and/or the Z direction of the machine tool spindle and is used for acquiring the thermal deformation of the machine tool spindle relative to the workbench.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention covers the environmental temperature in the whole year range when acquiring the temperature sensitive interval, and adapts to the actual production requirement. Meanwhile, the invention takes the real measured value as the basis, which is the basis for ensuring the objectivity, and objectively reflects the influence of the environmental temperature change on the prediction performance by predicting the residual standard deviation matrix, thereby scientifically determining the real and effective temperature sensitive interval.
2. The method creatively adopts the prediction residual standard deviation change trend graph to intuitively find the temperature sensitive interval, and is convenient and quick.
3. In the range of the potential temperature sensitive interval segmentation points, the final temperature sensitive interval segmentation points can be determined according to engineering requirements, and the method has strong universality.
4. The system of the invention combines the hardness and hardness, does not need complex and expensive instruments and equipment, reduces the cost and is easy to popularize.
Drawings
FIG. 1 is a predicted residual standard deviation trend chart plotted according to a predicted residual standard deviation matrix;
FIG. 2 is a block diagram of a process for selecting a segmentation point for a temperature sensitive interval.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and preferred embodiments.
First, data acquisition
The data acquisition system comprises an infrared thermal imager, a temperature sensor group and an eddy current displacement sensor;
the infrared thermal imager is used for making a thermal imaging picture for the machine tool to obtain the temperature color picture characteristic of the machine tool; according to the temperature color chart characteristics displayed by the thermal imager, the heat source area of the machine tool can be marked manually;
the temperature sensor group is respectively arranged at a heat source area of the manual marking machine tool and an environment where the machine tool is arranged; the temperature sensor is arranged in a main heat source area (a main shaft, a motor, a lead screw bearing and the like) of the manual marking machine tool and is used for acquiring the temperature of the corresponding machine tool heat source area; the temperature sensor is arranged in the environment where the machine tool is located and used for measuring the change of the environmental temperature;
and the eddy current displacement sensor is arranged in the X direction, the Y direction and/or the Z direction of the machine tool spindle and is used for acquiring the thermal deformation of the machine tool spindle relative to the workbench.
Arranging N temperature sensors at a heat source of the machine tool to acquire the temperature at the heat source of the machine tool, wherein N is not less than the number of heat source areas. A temperature sensor is arranged for measuring the temperature change condition of the environment where the machine tool is located; namely, N +1 temperature sensors are arranged in total, and the temperature values of a machine tool heat source and the environment are periodically sampled at intervals; obtaining M batches of sampling data in the whole year range according to the sequence of the environmental temperature from low to high, and obtaining MThe value is to ensure that the union set of the environmental temperature changes when M batches of data are obtained can basically cover the annual temperature change range, and M is not less than 1; the temperature difference increment obtained by sampling N +1 temperature sensors in each batch is used as a temperature variable delta Tvi1, 2.·, M; 1, 2., N +1, namely obtaining a temperature variable array with the size of M x (N +1), wherein the number of the temperature variables is N + 1;
one or more eddy current displacement sensors are arranged in the X direction, the Y direction and/or the Z direction of the machine tool spindle, and the thermal deformation S of the machine tool spindle is periodically carried out in batches according to the sequence of low ambient temperature to high ambient temperature in the whole year rangevjAnd performing interval sampling.
Secondly, acquiring a temperature sensitive interval
Screening out the temperature variable x brought into the thermal error compensation prediction model in each batch of datav1,xv2,...,xvmThe screening method of the temperature variable refers to Chinese invention patents: "a numerical control machine tool thermal error prediction method and system based on unbiased estimation split model" (patent number: CN201610256595.6) linear correlation coefficient method; using the temperature variable after screening as the temperature variable and the thermal deformation S of the main shaft of the machine toolvjEstablishing a machine tool thermal error compensation prediction model for the thermal error dependent variable by utilizing multivariate linear regression analysis;
the temperature variable x is obtained by screening M batches of data in the obtained thermal error compensation prediction model of each batch of the machine toolv1,xv2,...,xvmSubstituting the independent variable into a model to obtain a thermal deformation predicted value matrix with the size of M multiplied by M, and obtaining a prediction residual standard deviation matrix with the size of M multiplied by M according to the difference state of the thermal deformation predicted value and the thermal deformation measured value, wherein the calculation formula of the prediction residual standard deviation is shown as the formula (1):
in the formula, SDvkRepresenting the predicted residual standard deviation of the thermal error prediction model of the machine tool of the v batch on the measured value of the thermal deformation amount of the k batch, wherein v is 1, 2. SkjqA q-th sampling measurement value representing a j-axis direction in the k-th batch of thermal deformation measurement values, wherein q is 1, 2.
Is represented by the formulakjqAnd (3) predicting the thermal deformation amount of the corresponding machine tool thermal error prediction model of the v batch on the temperature variable of the k batch.
In the obtained prediction residual standard deviation data matrix with the size of M multiplied by M, the rows of the matrix represent a machine tool thermal error compensation prediction model established by the v-th batch of data, the columns represent the prediction residual standard deviations of the prediction model for other batches of data, the matrix data are drawn into images according to the rows, and the environmental temperature change data when the corresponding batches of data are obtained are brought on, so that a prediction residual standard deviation change trend graph which is established by the M batches of data and changes along with the environmental temperature is obtained, and a temperature sensitive interval is found in the prediction residual standard deviation change trend graph, wherein the temperature sensitive interval refers to a temperature interval corresponding to the jump of the prediction residual standard deviation, and as shown in figure 1, corresponding curves between the batch 10 and the batch 12 are crossed, namely jump, and the corresponding temperature interval is 21-29 ℃.
Thirdly, selecting a segmentation point of a temperature sensitive interval
As shown in fig. 2, the temperature sensitive interval segment point selection process is as follows:
selecting kth batch of data, wherein K is 1.., and M carries out K-S test of a single sample to judge whether the kth batch of data accords with normal distribution;
if the standard deviation sigma is in accordance with the normal distribution, carrying out confidence interval parameter test on the variance of a single normal population to obtain the standard deviation sigma, and if the standard deviation sigma is in accordance with other distributions, carrying out confidence interval parameter test on the variance of a single other distribution population to obtain the standard deviation sigma;
marking the segmentation points of the potential temperature sensitive interval of the batch of data by a 3 sigma principle: the probability of the jump occurring at each temperature point in the temperature sensitive interval is calculated by using the 3 sigma principle, for example, the temperature point with the probability greater than 70% can be used as the segmentation point of the potential temperature sensitive interval.
And when all M batches of data finish marking the potential temperature sensitive interval segmentation points in sequence, selecting a threshold value according to engineering requirements to obtain a final temperature sensitive interval segmentation modeling point. If the accuracy requirement is high, the temperature point with the jump probability of 90% can be selected, and if the accuracy requirement is low, the temperature point with the jump probability of 60% can be selected.
Analysis of four examples
In order to verify whether the selection of the temperature sensitive interval segmented modeling point is proper, a machine tool thermal error compensation segmented prediction model which is self-developed by the inventor is adopted to carry out the research of a thermal error prediction method aiming at the Z direction of a spindle of a Leaderway-V450 type numerical control machine tool, and the research is compared with a prediction model in the existing calculation.
And respectively taking a batch of temperature value increment measurement values and thermal deformation measurement values on two sides of a segmentation point of a temperature sensitive interval, and respectively establishing a corresponding machine tool thermal error prediction model by adopting a multiple linear regression analysis method, thereby obtaining a machine tool thermal error compensation segmentation prediction model.
Respectively carrying out thermal deformation S on M batches of data by adopting machine tool thermal error compensation segmented prediction modelvjPredicting to obtain predicted values of the thermal deformation of the M batches, and obtaining the prediction performance of the segmented model according to the difference state of the predicted values of the thermal deformation of the M batches and the measured values of the thermal deformation of the M batches, wherein the difference comparison comprises the comparison of prediction residual standard deviations, and the calculation formula of the prediction residual standard deviations is shown as a formula (1); the prediction performance comprises prediction precision and the discrete degree of the prediction precision; the prediction residual standard deviation mean value SDM of the segmented model to other batches of data is used for representing the prediction precision of the model, the smaller the value of the prediction residual standard deviation mean value SDM is, the higher the prediction precision of the model is, and the calculation formula of the SDM is shown as a formula (2); the standard deviation SDD of the residual standard deviation is predicted for other batches of data by the segmented model and is used for representing the discrete degree of the prediction precision of the model, the smaller the value of the standard deviation SDD is, the higher the robustness of the model is, and the calculation formula of the SDD is shown in a formula (3).
The formula for SDM is as follows:
the formula for SDD is as follows:
in the formula, SDkAnd (3) representing the predicted residual standard deviation of the machine tool thermal error compensation segmented prediction model on the k-th batch thermal deformation measurement value.
In the example, an infrared thermal imager Ti200 is used for performing thermal imaging on a Leaderway-V450 type numerical control machine tool which idles for one hour at the rotating speed of 4000rpm, heat source areas are judged and marked, and temperature sensors T1-T9 are arranged in each heat source area in the Z direction of a main shaft of the machine tool, and a temperature sensor T10 is arranged for measuring the ambient temperature. The amount of thermal deformation is measured according to the international standard ISO230-3:2007IDT (machine tool inspection general rule part 3: determination of thermal effects). The experimental conditions were: a total of 18 batches of experiments were performed over the entire year without air conditioning at different speeds, and the experimental data are shown in table 1 below (some data are omitted for space reasons).
Table 118 batch numerical control machine tool spindle Z thermal deformation experiment data record
Screening out the temperature variable x brought into the thermal error compensation prediction model in each batch of datav1,xv2,...,xvmThe screening method of the temperature variable refers to Chinese invention patents: the following table 2 shows the temperature variable conditions after screening 18 batches of data obtained by a linear correlation coefficient method in a numerical control machine tool thermal error prediction method and system (patent number: CN201610256595.6) based on an unbiased estimation splitting model.
TABLE 218 temperature variable conditions after batch data screening
Using the screened temperature variable as an independent variable and the thermal deformation S of the machine tool spindlevjThe machine thermal error compensation prediction models established for the thermal error dependent variable using 18 batches of data obtained by multiple linear regression analysis are described below as P-M1-P-M18 (some data are omitted for reasons of space).
P-M1：y＝0.61+2.10x1+2.07x2
P-M18：y＝0.66+3.16x1+2.91x2
And (3) solving a predicted value of the thermal deformation amount of each batch of the prediction model to 18 batches of data, obtaining a predicted residual standard deviation according to the difference state of the predicted value and the measured value of the thermal deformation amount, drawing a data matrix of the predicted residual standard deviation into an image according to rows, and carrying out acquisition of environmental temperature change data when the corresponding batch of data is obtained, so as to obtain a prediction precision change graph of the model established by the 18 batches of data, which is influenced by the environmental temperature, and thus, the prediction model established by the M batches of data has a jump interval influenced by the environmental temperature. As shown in fig. 1, a jump interval of the prediction model affected by the ambient temperature, which is referred to as a temperature sensitive interval for short, can be found according to fig. 1.
In the embodiment, 24 ℃ is selected as a temperature sensitive interval segmented modeling point, a segmented machine tool thermal error compensation prediction model is obtained by adopting a segmented modeling means, the segmented modeling means is that a batch of data is respectively taken from two sides of the temperature sensitive interval segmented modeling point for modeling, the modeling means is the same as the establishment of P-M1-P-M18 models, and the obtained segmented model is marked as P-M0.
The SDM ═ 4.60 μm and SDD ═ 1.40 μm were obtained by formula (2) and formula (3).
In the embodiment, the effectiveness and superiority of the temperature-sensitive interval segmented modeling technology disclosed by the invention for solving the problem of prediction performance fluctuation caused by the influence of the environment temperature on a machine tool thermal error compensation model of a strong-coupling temperature field are verified. The comparison of the prediction accuracy and the prediction robustness of the P-M1-P-M18 model which is not established by the temperature sensitive interval segmented modeling technology and the P-M0 model which is established by the temperature sensitive interval segmented modeling technology is shown in the following table 3.
TABLE 3 comparison table of two models for predicting performance
As can be seen from the table 3, compared with the machine tool thermal deformation compensation model which is not established by the temperature sensitive interval segmented modeling technology, the prediction precision of the machine tool thermal deformation compensation model which is established by the temperature sensitive interval segmented modeling technology is improved by 50.5%, and the prediction robustness is improved by 69.9%. Therefore, the temperature sensitive interval segmented modeling point determined by the method for determining the temperature sensitive interval segmented modeling point is scientific and reasonable, and the prediction precision and the robustness are improved.
Claims (6)
1. A method for determining temperature sensitive interval segment modeling points, comprising the steps of:
step 1: acquiring a temperature value increment measurement value of M batches of machine tool main heat sources and a thermal deformation measurement value of a machine tool main shaft which are sequenced from low to high according to the environmental temperature in the whole year range;
step 2: screening the temperature value increment measured value and the thermal deformation measured value of each batch to be respectively used as a temperature variable and a thermal error dependent variable, and correspondingly establishing a thermal error prediction model of each batch of the machine tool according to the screened data of each batch;
and step 3: obtaining the predicted values of the thermal deformation amount of the thermal error model of each batch of the machine tool to the temperature variables of M batches respectively, thereby obtaining an MxM thermal deformation amount prediction matrix;
and 4, step 4: obtaining a prediction residual standard deviation matrix with the size of M multiplied by M according to the difference between the predicted value and the measured value of the thermal deformation amount;
and 5: drawing a predicted residual standard deviation change trend graph according to the predicted residual standard deviation matrix: the horizontal axis represents the batch, and the vertical axes at the two ends of the horizontal axis respectively represent the predicted residual standard deviation and the temperature; finding a temperature sensitive interval in the predicted residual standard deviation change trend graph, wherein the temperature sensitive interval refers to an environment temperature interval corresponding to the predicted residual standard deviation jumping;
step 6: and selecting a temperature sensitive interval segmentation point in the temperature sensitive interval.
2. The method for determining the temperature-sensitive interval block modeling point as claimed in claim 1, wherein the step 2 uses a linear correlation coefficient method to screen the temperature variable from the temperature measurement values.
3. The method for determining the segmented modeling point of the temperature sensitive interval according to claim 1, wherein the calculation formula of the predicted residual standard deviation is as follows:
in the formula, SDvkRepresenting the predicted residual standard deviation of the thermal error prediction model of the machine tool of the v batch on the measured value of the thermal deformation amount of the k batch, wherein v is 1, 2. M is the number of the acquired data batches, the value of M is to ensure that the union set of the environmental temperature changes when the M batches of data are acquired can basically cover the annual temperature change range, and M is not less than 1; skjqA qth sampled measurement value representing a j-axis direction in the k-th lot of measured values of the amount of thermal deformation, q being 1, 2.
Is represented by the formulakjqAnd (3) predicting the thermal deformation amount of the corresponding machine tool thermal error prediction model of the v batch on the temperature variable of the k batch.
4. The method for determining temperature-sensitive interval segment modeling points as claimed in claim 1, wherein temperature-sensitive interval segment points are selected as follows:
verifying the probability distribution type of the array formed by the prediction precision of each batch of data by the prediction model, and acquiring the corresponding standard deviation sigma; the prediction precision of each batch of prediction models on M batches of data refers to the prediction residual standard deviation SDvk(ii) a Marking potential temperature sensitive interval segmentation points of each batch of data through a 3 sigma principle; and when all M batches of data finish marking the potential temperature sensitive interval segmentation points in sequence, selecting a threshold value according to engineering requirements to obtain a final temperature sensitive interval segmentation point.
5. The method for determining temperature-sensitive interval segment modeling points as set forth in claim 4, wherein the standard deviation is obtained as follows: selecting kth batch of data, wherein K is 1.., and M carries out K-S test of a single sample to judge whether the kth batch of data accords with normal distribution; if the standard deviation sigma is in accordance with the normal distribution, carrying out confidence interval parameter test on the variance of a single normal population to obtain the standard deviation sigma, and if the standard deviation sigma is in accordance with other distributions, carrying out confidence interval parameter test on the variance of a single other distribution population to obtain the standard deviation sigma.
6. A system for determining temperature-sensitive interval segmented modeling points is characterized by comprising a data acquisition system and a computer, wherein the computer is internally provided with a temperature-sensitive interval segmented modeling point calculation program and carries out the steps from step 1 to step 6 in claim 1; the data acquisition system comprises an infrared thermal imager, a temperature sensor group and an eddy current displacement sensor; the infrared thermal imager is used for making a thermal imaging picture for the machine tool to obtain the temperature color picture characteristic of the machine tool; according to the temperature color chart characteristics displayed by the thermal imager, the heat source area of the machine tool can be marked manually; the temperature difference sensor group is respectively arranged at a heat source area of the manual marking machine tool and an environment where the machine tool is arranged; the temperature sensor is arranged at a heat source area of the manual marking machine tool and used for acquiring the temperature of the corresponding heat source area of the machine tool; the temperature sensor is arranged in the environment where the machine tool is located and used for measuring the change of the environmental temperature; and the eddy current displacement sensor is arranged in the X direction, the Y direction and/or the Z direction of the machine tool spindle and is used for acquiring the thermal deformation of the machine tool spindle relative to the workbench.
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