A machine tool thermal error compensation method and system based on data analysis

Abstract

The present invention relates to the field of machine tool control technology, and in particular to a machine tool thermal error compensation method and system based on data analysis. A machine tool thermal error compensation system based on data analysis comprises: a data acquisition module, a machine tool thermal error analysis data construction module, a machine tool thermal error analysis module and a machine tool thermal error compensation module. The present invention determines the operating state of the machine tool with the assistance of multi-source data by collecting the axis temperature data set and vibration signal data of each axis of the machine tool, as well as the current data at the machine tool servo motor, the ambient temperature data around the machine tool and obtaining an infrared image at the machine tool. Based on the operating state of the machine tool, the weight distribution of features related to thermal errors is adjusted, highlighting features that are strongly correlated with thermal errors under different working conditions, improving the accuracy of thermal error analysis, and thus improving the effect of machine tool thermal error compensation.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tool control, and in particular to a machine tool thermal error compensation method and system based on data analysis. Background Art

[0002] The thermal error of machine tools is an important factor affecting machining accuracy. Traditional thermal error compensation methods usually establish a relationship model between temperature and thermal deformation based on temperature data collected by temperature sensors, and then predict and compensate for thermal errors. However, most of these methods assume that the machine tool is in a certain ideal stable state and only consider the impact of temperature changes on thermal errors, without considering the differences in machine tools under different working conditions (such as idling, light-load cutting, heavy-load cutting, etc.). Under different working conditions, the heat source distribution, heat transfer path and thermal deformation law inside the machine tool are significantly different, resulting in the thermal error model established based on a single working state being difficult to accurately reflect the actual situation. Therefore, the traditional thermal error compensation method that relies solely on temperature analysis often has poor compensation effect when the machine tool is in different working conditions, and it is difficult to meet the needs of high-precision machining. Summary of the Invention

[0003] The present invention collects the axis temperature data set and vibration signal data of each axis of the machine tool, as well as the current data of the machine tool servo motor, the ambient temperature data around the machine tool, and obtains an infrared image of the machine tool. With the assistance of multi-source data, the operating state of the machine tool is determined, and then the weight distribution of the features related to the thermal error is adjusted based on the operating state of the machine tool, highlighting the features that are strongly correlated with the thermal error under different working conditions, improving the accuracy of thermal error analysis, and thus improving the effect of thermal error compensation of the machine tool; and using infrared images to enhance the temperature change gradient characteristics of each axis, further improving the accuracy of thermal error analysis.

[0004] The present invention provides a machine tool thermal error compensation method based on data analysis, comprising:

[0005] At the monitoring time point, the axis temperature data set and vibration signal data of each axis of the machine tool are obtained, and the axis temperature data set includes several axis temperature data. At the same time, the current data of the servo motor of the machine tool, the ambient temperature data around the machine tool, and the infrared image of the machine tool are obtained. The machine tool thermal error analysis data is constructed based on the obtained axis temperature data set, vibration signal data, current data, and ambient temperature data. The machine tool thermal error analysis data is stored in a matrix form, and each line in the machine tool thermal error analysis data is a machine tool thermal error analysis vector corresponding to each axis of the machine tool.

[0006] The machine tool thermal error analysis data corresponding to the current monitoring time point and the machine tool thermal error analysis data corresponding to the previous N-1 monitoring time points are combined into a machine tool thermal error time series analysis data set, and the machine tool thermal error time series analysis data set and the infrared image are fed into a machine tool thermal error analysis model for processing, and a compensation value set is output; the control instructions of the machine tool are corrected based on the compensation value set to complete the thermal error compensation of the machine tool;

[0007] The machine tool thermal error analysis model includes a timing analysis layer, an infrared image analysis layer, a weight analysis layer, a feature adjustment layer, a feature enhancement layer and a compensation output layer. The timing analysis layer is established based on the LSTM model and is used to perform timing analysis and processing on the machine tool thermal error timing analysis data set to construct the machine tool thermal error timing analysis features; the infrared image analysis layer is used to perform image processing on the infrared image to construct a global temperature vector; the weight analysis layer is used to perform operating state analysis on the machine tool thermal error analysis data obtained at the current monitoring point to output the corresponding operating state vector, and then determine the weight distribution matrix based on the machine tool thermal error timing analysis features and the operating state vector, and adjust the weight of the machine tool thermal error timing analysis features based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis feature; the feature enhancement layer is used to perform feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector to construct an enhanced machine tool thermal error timing analysis feature; the compensation output layer is used to perform full connection operations on the enhanced machine tool thermal error timing analysis features to output the compensation amount for each axis.

[0008] Preferably, constructing machine tool thermal error analysis data based on the acquired shaft temperature data set, vibration signal data, current data and ambient temperature data specifically includes the following steps:

[0009] The time domain features and frequency domain features are extracted from the current data respectively, and the time domain features and frequency domain features corresponding to the current data are combined into a current feature vector;

[0010] For each axis, the mean of all axis temperature data in the axis temperature data set is calculated and recorded as the axis temperature data mean; all axis temperature data in the axis temperature data set are subjected to sliding average filtering, and then the axis temperature gradient value between adjacent axis temperature data is calculated; wavelet packet decomposition is performed on the vibration signal data to extract several characteristic frequency bands, and then the energy proportion of each characteristic frequency band is calculated, and the energy proportion of each characteristic frequency band is combined into a vibration energy vector; the mean of all axis temperature data, all axis temperature gradient values, vibration energy vector, current characteristic vector and ambient temperature data are combined to form a machine tool thermal error analysis vector;

[0011] All machine tool thermal error analysis vectors are spliced from top to bottom to form machine tool thermal error analysis data.

[0012] Preferably, the machine tool thermal error analysis data obtained at the current monitoring point is analyzed for operating status through the weight analysis layer to output the corresponding operating status vector, and then the weight distribution matrix is determined based on the machine tool thermal error timing analysis characteristics and the operating status vector, and the machine tool thermal error timing analysis characteristics are weighted based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis characteristics, which specifically includes the following steps:

[0013] The machine tool thermal error analysis data obtained at the current monitoring point is flattened by rows to obtain a machine tool thermal error analysis feature set. The machine tool thermal error analysis feature set is then sent to the operation status analysis network for processing. The operation status analysis network is established based on the random forest model to output the corresponding operation status. The corresponding operation status is then processed through word embedding to obtain the corresponding operation status vector;

[0014] Add the running state vector to the end of each row of the machine tool thermal error timing analysis feature and send it to the multi-layer perceptron for processing, and output the weight distribution matrix;

[0015] The Hadamard product of the thermal error timing analysis characteristics of the machine tool and the weight distribution matrix is calculated to obtain the reconstructed thermal error timing analysis characteristics of the machine tool.

[0016] Preferably, a feature enhancement operation is performed on the reconstructed machine tool thermal error timing analysis feature based on the global temperature vector through a feature enhancement layer to construct an enhanced machine tool thermal error timing analysis feature, specifically comprising the following steps:

[0017] The reconstructed machine tool thermal error timing analysis features are matrix multiplied with the value weight matrix and the key weight matrix to construct the corresponding machine tool thermal error analysis value matrix V and machine tool thermal error analysis key matrix K. The global temperature vector is matrix multiplied with the query weight matrix to construct the machine tool thermal error analysis query matrix Q. Feature enhancement is achieved through the corresponding operation of the following formula: H = softmax(QK T / D 0.5 )V, where H is the characteristic of enhanced machine tool thermal error timing analysis, T is the matrix transpose operation, and D is the dimension of the key matrix.

[0018] Preferably, training the operation status analysis network specifically includes the following steps:

[0019] A plurality of operation status analysis training samples are obtained, wherein the operation status analysis training samples are a feature set for thermal error analysis of machine tools, the operation status analysis training samples are labeled according to the operation status, and all labeled operation status analysis training samples are combined into an operation status analysis training set. The operation status analysis network is trained with the operation status analysis training set to determine whether a first training condition is met. If the first training condition is met, the trained operation status analysis network is output; otherwise, the operation status analysis network is continued to be trained with the operation status analysis training set.

[0020] Preferably, training the machine tool thermal error analysis model specifically includes the following steps:

[0021] Acquire several machine tool thermal error analysis training samples, which include a machine tool thermal error time series analysis data set and corresponding infrared images. Label the machine tool thermal error analysis training samples through a compensation amount set, and form a machine tool thermal error analysis training set with all labeled machine tool thermal error analysis training samples. Train a machine tool thermal error analysis model through the machine tool thermal error analysis training set to determine whether a second training condition is met. If so, output the trained machine tool thermal error analysis model; otherwise, continue to train the machine tool thermal error analysis model through the machine tool thermal error analysis training set.

[0022] The present invention also provides a machine tool thermal error compensation system based on data analysis, comprising:

[0023] The data acquisition module is used to obtain the axis temperature data set and vibration signal data of each axis of the machine tool at the monitoring time point. The axis temperature data set includes multiple axis temperature data; at the same time, it obtains the current data of the servo motor of the machine tool, the ambient temperature data around the machine tool, and the infrared image of the machine tool;

[0024] A machine tool thermal error analysis data construction module is used to construct machine tool thermal error analysis data based on the acquired axis temperature data set, vibration signal data, current data and ambient temperature data. The machine tool thermal error analysis data is stored in a matrix form, and each line in the machine tool thermal error analysis data is a machine tool thermal error analysis vector corresponding to each axis of the machine tool;

[0025] The machine tool thermal error analysis module is used to combine the machine tool thermal error analysis data corresponding to the current monitoring time point and the machine tool thermal error analysis data corresponding to the previous N-1 monitoring time points into a machine tool thermal error time series analysis data set, and send the machine tool thermal error time series analysis data set and infrared image to the machine tool thermal error analysis model for processing, and output a compensation value set; based on the compensation value set, the control instructions of the machine tool are corrected to complete the thermal error compensation of the machine tool;

[0026] A machine tool thermal error compensation module is used to correct the control instructions of the machine tool based on the compensation amount set to complete the thermal error compensation of the machine tool;

[0027] The machine tool thermal error analysis model includes a timing analysis layer, an infrared image analysis layer, a weight analysis layer, a feature adjustment layer, a feature enhancement layer and a compensation output layer. The timing analysis layer is established based on the LSTM model and is used to perform timing analysis and processing on the machine tool thermal error timing analysis data set to construct the machine tool thermal error timing analysis features; the infrared image analysis layer is used to perform image processing on the infrared image to construct a global temperature vector; the weight analysis layer is used to perform operating state analysis on the machine tool thermal error analysis data obtained at the current monitoring point to output the corresponding operating state vector, and then determine the weight distribution matrix based on the machine tool thermal error timing analysis features and the operating state vector, and adjust the weight of the machine tool thermal error timing analysis features based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis feature; the feature enhancement layer is used to perform feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector to construct an enhanced machine tool thermal error timing analysis feature; the compensation output layer is used to perform full connection operations on the enhanced machine tool thermal error timing analysis features to output the compensation amount for each axis.

[0028] The present invention has the following advantages:

[0029] The present invention collects the axis temperature data set and vibration signal data of each axis of the machine tool, as well as the current data of the machine tool servo motor, the ambient temperature data around the machine tool, and obtains an infrared image of the machine tool. With the assistance of multi-source data, the operating state of the machine tool is determined, and then the weight distribution of the features related to the thermal error is adjusted based on the operating state of the machine tool, highlighting the features that are strongly correlated with the thermal error under different working conditions, improving the accuracy of thermal error analysis, and thus improving the effect of thermal error compensation of the machine tool; and using infrared images to enhance the temperature change gradient characteristics of each axis, further improving the accuracy of thermal error analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic structural diagram of a machine tool thermal error compensation system based on data analysis adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0032] Embodiment 1, a method for compensating thermal errors of machine tools based on data analysis, comprising:

[0033] At the monitoring time point, the axis temperature data set and vibration signal data of each axis of the machine tool are obtained. The axis temperature data set includes several axis temperature data. It should be noted that a general machine tool has 2-3 axes for rotation and linear motion. These axes are also the main components for thermal error compensation. The axis temperature data set is collected by several fiber optic temperature sensors distributed on the axis, and the vibration signal data is collected by a vibration accelerometer set on the axis; at the same time, the current data at the servo motor of the machine tool is obtained by collecting through the Hall element, and the ambient temperature data around the machine tool and the infrared image of the machine tool are obtained. The ambient temperature data is obtained by the temperature sensors set around the machine tool, and the infrared image is obtained by the infrared thermal imager; the machine tool thermal error analysis data is constructed based on the obtained axis temperature data set, vibration signal data, current data and ambient temperature data, and the machine tool thermal error analysis data is stored in matrix form. Each line in the machine tool thermal error analysis data is a machine tool thermal error analysis vector corresponding to each axis of the machine tool;

[0034] The machine tool thermal error analysis data corresponding to the current monitoring time point and the machine tool thermal error analysis data corresponding to the previous N-1 monitoring time points are combined into a machine tool thermal error time series analysis data set, and the machine tool thermal error time series analysis data set and the infrared image are sent to the machine tool thermal error analysis model for processing, and a compensation amount set is output. The compensation amount set includes the compensation amount of each axis. The compensation amount here refers to the correction instruction issued to the corresponding axis of the machine tool to eliminate the thermal error, specifically a space vector value, including the action direction, correction value and action timing; the control instruction of the machine tool is corrected based on the compensation amount set to complete the thermal error compensation of the machine tool;

[0035] The machine tool thermal error analysis model includes a timing analysis layer, an infrared image analysis layer, a weight analysis layer, a feature adjustment layer, a feature enhancement layer and a compensation output layer. The timing analysis layer is established based on the LSTM model, which is used to perform timing analysis on the machine tool thermal error timing analysis data set to construct the machine tool thermal error timing analysis features. By performing timing analysis on the machine tool thermal error timing analysis data set, the temperature change, vibration change and current change of each axis of the machine tool can be analyzed. The temperature change can be used to analyze the temperature accumulation effect of the machine tool, thereby providing a reference for thermal error analysis. The vibration change and current change can be used to analyze the operating status of the machine tool; the infrared image analysis layer is established based on the convolutional neural network. The convolutional neural network here adopts the ResNet18 model to perform image processing on the infrared image to construct a global temperature vector. The global temperature vector can reflect the global temperature change at the machine tool, which is used to subsequently enhance the spatial relationship of each axis; the weight analysis layer is used to analyze the operating status of the machine tool thermal error analysis data obtained at the current monitoring point The analysis is performed to output the corresponding operating state vector, and then the weight distribution matrix is determined based on the machine tool thermal error timing analysis characteristics and the operating state vector, and the weight of the machine tool thermal error timing analysis characteristics is adjusted based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis feature. It should be noted that the operating state here refers to different working states of the machine tool, such as heavy cutting state and continuous milling state, etc. Under different operating states, the features related to thermal errors will have different weight coefficients. For example, under heavy cutting state, the temperature change feature weight corresponding to the screw shaft will have a higher reference value; the feature enhancement layer is used to perform feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector to construct an enhanced machine tool thermal error timing analysis feature. In the process of performing feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector, the temperature change gradient characteristics of each axis can be enhanced with the assistance of the global temperature change; the compensation output layer is used to perform a full connection operation on the enhanced machine tool thermal error timing analysis features to output the compensation amount of each axis;

[0036] This application collects the axis temperature data set and vibration signal data of each axis of the machine tool, as well as the current data at the machine tool servo motor, the ambient temperature data around the machine tool, and obtains an infrared image of the machine tool. With the assistance of multi-source data, the operating state of the machine tool is determined, and then the weight distribution of the features related to the thermal error is adjusted based on the operating state of the machine tool, highlighting the features that are strongly correlated with the thermal error under different working conditions, improving the accuracy of thermal error analysis, and thus improving the effect of thermal error compensation of the machine tool; and using infrared images to enhance the temperature change gradient characteristics of each axis, further improving the accuracy of thermal error analysis.

[0037] The machine tool thermal error analysis data is constructed based on the acquired axis temperature data set, vibration signal data, current data, and ambient temperature data. The specific steps include the following:

[0038] Time domain features and frequency domain features are extracted from the current data. The time domain features here can be peak value, standard value and fluctuation amplitude, and the frequency domain features can be spectrum amplitude and power spectrum density. The time domain features and frequency domain features corresponding to the current data are combined into a current feature vector.

[0039] For each axis, the mean of all axis temperature data in the axis temperature data set is calculated and recorded as the mean of axis temperature data; all axis temperature data in the axis temperature data set are subjected to sliding average filtering, and then the axis temperature gradient value between adjacent axis temperature data is calculated. The adjacent axis temperature data here refers to the corresponding optical fiber temperature sensors being adjacent in position, and the axis temperature gradient value is calculated as the difference between adjacent axis temperature data divided by the adjacent spacing, and the adjacent spacing refers to the distance between adjacent optical fiber temperature sensors; wavelet packet decomposition is performed on the vibration signal data to extract several characteristic frequency bands, and then the energy proportion of each characteristic frequency band is calculated, and the energy proportion of each characteristic frequency band is composed into a vibration energy vector; the mean of all axis temperature data, all axis temperature gradient values, vibration energy vector, current characteristic vector and ambient temperature data are combined into a machine tool thermal error analysis vector;

[0040] All machine tool thermal error analysis vectors are spliced from top to bottom to form machine tool thermal error analysis data;

[0041] The weight analysis layer is used to perform an operation state analysis on the machine tool thermal error analysis data obtained at the current monitoring point to output the corresponding operation state vector. The weight distribution matrix is then determined based on the machine tool thermal error timing analysis characteristics and the operation state vector. The weight of the machine tool thermal error timing analysis characteristics is then adjusted based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis characteristics. The specific steps include:

[0042] The machine tool thermal error analysis data obtained at the current monitoring point is flattened by rows to obtain a machine tool thermal error analysis feature set. The machine tool thermal error analysis feature set is then sent to the operation status analysis network for processing. The operation status analysis network is established based on the random forest model to output the corresponding operation status. The corresponding operation status is then processed through word embedding to obtain the corresponding operation status vector;

[0043] The operating state vector is added to the end of each row of the machine tool thermal error timing analysis feature and then fed into a multilayer perceptron for processing, outputting a weight distribution matrix. The multilayer perceptron here generally includes an input layer, a hidden layer, and an output layer. The number of neural nodes in the input layer and the output layer is consistent with the total number of axes of the machine tool. The number of neural nodes in the hidden layer is usually iteratively generated by a swarm optimization algorithm, and the parameters of the multilayer perceptron are determined by training the machine tool thermal error analysis model.

[0044] The Hadamard product of the thermal error timing analysis characteristics of the machine tool and the weight distribution matrix is calculated to obtain the reconstructed thermal error timing analysis characteristics of the machine tool;

[0045] The feature enhancement layer is used to enhance the reconstructed machine tool thermal error timing analysis feature based on the global temperature vector to construct an enhanced machine tool thermal error timing analysis feature. The specific steps include:

[0046] The reconstructed machine tool thermal error timing analysis features are matrix multiplied with the value weight matrix and the key weight matrix to construct the corresponding machine tool thermal error analysis value matrix V and machine tool thermal error analysis key matrix K. The global temperature vector is matrix multiplied with the query weight matrix to construct the machine tool thermal error analysis query matrix Q. Feature enhancement is achieved through the corresponding operation of the following formula: H = softmax(QK T / D 0.5 )V, where H is the enhanced machine tool thermal error timing analysis feature, T is the matrix transpose operation, and D is the dimension size of the key matrix; the value weight matrix, the key weight matrix, and the query weight matrix are set based on the self-attention mechanism in the Transformer model, and the parameters of the value weight matrix, the key weight matrix, and the query weight matrix are determined by training the machine tool thermal error analysis model;

[0047] Training the operation status analysis network includes the following steps:

[0048] Obtain several running status analysis training samples, which include a machine tool thermal error analysis feature set. The machine tool thermal error analysis feature set here is constructed by developers based on the actual shaft temperature data set, vibration signal data, current data, and ambient temperature data. Label the running status analysis training samples according to the running status. The labeling process here is determined by developers based on the experience of professional machine tool operators. All labeled running status analysis training samples are combined into a running status analysis training set. The running status analysis network is trained with the running status analysis training set to determine whether the first training condition is met. The first training condition here is generally that the number of training times reaches a certain number or the accuracy of the running status analysis network meets expectations. If the first training condition is met, the trained running status analysis network is output; otherwise, the running status analysis network continues to be trained with the running status analysis training set.

[0049] Training the machine tool thermal error analysis model includes the following steps:

[0050] Obtain several machine tool thermal error analysis training samples, which include a machine tool thermal error time series analysis data set and corresponding infrared images. The machine tool thermal error analysis training samples here include a machine tool thermal error time series analysis data set and corresponding infrared images, which are also obtained by developers based on actual running machine tools. The machine tool thermal error analysis training samples are labeled using a compensation amount set. The compensation amount set here is the optimal compensation method actually adopted by professional machine tool operators. All labeled machine tool thermal error analysis training samples are composed of a machine tool thermal error analysis training set. The machine tool thermal error analysis model is trained using the machine tool thermal error analysis training set to determine whether the second training condition is met. The second training condition here can also be that the number of training times reaches a certain number or the accuracy of the machine tool thermal error analysis model meets expectations. If the second training condition is met, the trained machine tool thermal error analysis model is output; otherwise, the machine tool thermal error analysis model continues to be trained using the machine tool thermal error analysis training set.

[0051] Example 2, a machine tool thermal error compensation system based on data analysis, see Figure 1 ,include:

[0052] The data acquisition module is used to obtain the axis temperature data set and vibration signal data of each axis of the machine tool at the monitoring time point. The axis temperature data set includes several axis temperature data. It should be noted that a general machine tool has 2-3 axes for rotation and linear motion. These axes are also the main components for thermal error compensation. The axis temperature data set is collected through several fiber optic temperature sensors distributed on the axis, and the vibration signal data is collected through the vibration accelerometer set on the axis; at the same time, the current data of the servo motor of the machine tool is obtained by collecting it through the Hall element, as well as the ambient temperature data around the machine tool and the infrared image of the machine tool. The ambient temperature data is obtained by the temperature sensors set around the machine tool, and the infrared image is obtained by the infrared thermal imager;

[0053] A machine tool thermal error analysis data construction module is used to construct machine tool thermal error analysis data based on the acquired axis temperature data set, vibration signal data, current data and ambient temperature data. The machine tool thermal error analysis data is stored in a matrix form, and each line in the machine tool thermal error analysis data is a machine tool thermal error analysis vector corresponding to each axis of the machine tool;

[0054] The machine tool thermal error analysis module is used to combine the machine tool thermal error analysis data corresponding to the current monitoring time point and the machine tool thermal error analysis data corresponding to the previous N-1 monitoring time points into a machine tool thermal error time series analysis data set, and send the machine tool thermal error time series analysis data set and infrared images to the machine tool thermal error analysis model for processing, and output a compensation amount set. The compensation amount set includes the compensation amount of each axis. The compensation amount here refers to the correction instruction issued to the corresponding axis of the machine tool to eliminate the thermal error, specifically a space vector value, including the action direction, correction value and action timing;

[0055] A machine tool thermal error compensation module is used to correct the control instructions of the machine tool based on the compensation amount set to complete the thermal error compensation of the machine tool;

[0056] The machine tool thermal error analysis model includes a timing analysis layer, an infrared image analysis layer, a weight analysis layer, a feature adjustment layer, a feature enhancement layer and a compensation output layer. The timing analysis layer is established based on the LSTM model, which is used to perform timing analysis on the machine tool thermal error timing analysis data set to construct the machine tool thermal error timing analysis features. By performing timing analysis on the machine tool thermal error timing analysis data set, the temperature change, vibration change and current change of each axis of the machine tool can be analyzed. The temperature change can be used to analyze the temperature accumulation effect of the machine tool, thereby providing a reference for thermal error analysis. The vibration change and current change can be used to analyze the operating status of the machine tool; the infrared image analysis layer is established based on the convolutional neural network. The convolutional neural network here adopts the ResNet18 model to perform image processing on the infrared image to construct a global temperature vector. The global temperature vector can reflect the global temperature change at the machine tool, which is used to subsequently enhance the spatial relationship of each axis; the weight analysis layer is used to analyze the operating status of the machine tool thermal error analysis data obtained at the current monitoring point The analysis is performed to output the corresponding operating state vector, and then the weight distribution matrix is determined based on the machine tool thermal error timing analysis characteristics and the operating state vector, and the weight of the machine tool thermal error timing analysis characteristics is adjusted based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis feature. It should be noted that the operating state here refers to the different working states of the machine tool, such as heavy cutting state and continuous milling state. Under different operating states, the features related to thermal error will have different weight coefficients. For example, in the heavy cutting state, the temperature change feature weight corresponding to the screw shaft will have a higher reference; the feature enhancement layer is used to perform feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector to construct an enhanced machine tool thermal error timing analysis feature. In the process of performing feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector, the temperature change gradient characteristics of each axis can be enhanced with the assistance of the global temperature change; the compensation output layer is used to perform a full connection operation on the enhanced machine tool thermal error timing analysis features to output the compensation amount of each axis.

[0057] It should be understood that those skilled in the art may make improvements or modifications based on the above description, and all such improvements and modifications shall fall within the scope of protection of the appended claims. Any portion of this specification not described in detail is prior art known to those skilled in the art.

Claims

1. A machine tool thermal error compensation method based on data analysis, characterized in that: include: At the monitoring time point, the axis temperature data set and vibration signal data of each axis of the machine tool are obtained, and the axis temperature data set includes several axis temperature data. At the same time, the current data of the servo motor of the machine tool, the ambient temperature data around the machine tool, and the infrared image of the machine tool are obtained. The machine tool thermal error analysis data is constructed based on the obtained axis temperature data set, vibration signal data, current data, and ambient temperature data. The machine tool thermal error analysis data is stored in a matrix form, and each line in the machine tool thermal error analysis data is a machine tool thermal error analysis vector corresponding to each axis of the machine tool. The machine tool thermal error analysis data corresponding to the current monitoring time point and the machine tool thermal error analysis data corresponding to the previous N-1 monitoring time points are combined into a machine tool thermal error time series analysis data set, and the machine tool thermal error time series analysis data set and the infrared image are fed into the machine tool thermal error analysis model for processing, and a compensation value set is output; The control instructions of the machine tool are modified based on the compensation amount set to complete the thermal error compensation of the machine tool; The machine tool thermal error analysis model includes a timing analysis layer, an infrared image analysis layer, a weight analysis layer, a feature adjustment layer, a feature enhancement layer, and a compensation output layer. The timing analysis layer is established based on the LSTM model and is used to perform timing analysis on the machine tool thermal error timing analysis dataset to construct the machine tool thermal error timing analysis features. The infrared image analysis layer is used to process the infrared image to construct the global temperature vector; The weight analysis layer is used to perform operation state analysis on the machine tool thermal error analysis data obtained at the current monitoring point to output the corresponding operation state vector. The weight distribution matrix is then determined based on the machine tool thermal error timing analysis characteristics and the operation state vector. The weight of the machine tool thermal error timing analysis characteristics is then adjusted based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis characteristics. The feature enhancement layer is used to perform feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector to construct enhanced machine tool thermal error timing analysis features; The compensation output layer is used to perform full connection operations on the enhanced machine tool thermal error timing analysis features to output the compensation amount of each axis.

2. The method for compensating machine tool thermal errors based on data analysis according to claim 1, characterized in that: The machine tool thermal error analysis data is constructed based on the acquired axis temperature data set, vibration signal data, current data, and ambient temperature data. The specific steps include the following: The time domain features and frequency domain features are extracted from the current data respectively, and the time domain features and frequency domain features corresponding to the current data are combined into a current feature vector; For each axis, the mean of all axis temperature data in the axis temperature data set is calculated and recorded as the axis temperature data mean; all axis temperature data in the axis temperature data set are subjected to sliding average filtering, and then the axis temperature gradient value between adjacent axis temperature data is calculated; wavelet packet decomposition is performed on the vibration signal data to extract several characteristic frequency bands, and then the energy proportion of each characteristic frequency band is calculated, and the energy proportion of each characteristic frequency band is combined into a vibration energy vector; the mean of all axis temperature data, all axis temperature gradient values, vibration energy vector, current characteristic vector and ambient temperature data are combined to form a machine tool thermal error analysis vector; All machine tool thermal error analysis vectors are spliced from top to bottom to form machine tool thermal error analysis data.

3. The method for compensating machine tool thermal errors based on data analysis according to claim 2, characterized in that: The weight analysis layer is used to perform an operation state analysis on the machine tool thermal error analysis data obtained at the current monitoring point to output the corresponding operation state vector. The weight distribution matrix is then determined based on the machine tool thermal error timing analysis characteristics and the operation state vector. The weight of the machine tool thermal error timing analysis characteristics is then adjusted based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis characteristics. The specific steps include: The machine tool thermal error analysis data obtained at the current monitoring point is flattened by rows to obtain a machine tool thermal error analysis feature set. The machine tool thermal error analysis feature set is then sent to the operation status analysis network for processing. The operation status analysis network is established based on the random forest model to output the corresponding operation status. The corresponding operation status is then processed through word embedding to obtain the corresponding operation status vector; Add the running state vector to the end of each row of the machine tool thermal error timing analysis feature and send it to the multi-layer perceptron for processing, and output the weight distribution matrix; The Hadamard product of the thermal error timing analysis characteristics of the machine tool and the weight distribution matrix is calculated to obtain the reconstructed thermal error timing analysis characteristics of the machine tool.

4. The method for compensating machine tool thermal errors based on data analysis according to claim 3, characterized in that: The feature enhancement layer is used to enhance the reconstructed machine tool thermal error timing analysis feature based on the global temperature vector to construct an enhanced machine tool thermal error timing analysis feature. The specific steps include: The reconstructed machine tool thermal error timing analysis features are matrix multiplied with the value weight matrix and the key weight matrix to construct the corresponding machine tool thermal error analysis value matrix V and machine tool thermal error analysis key matrix K. The global temperature vector is matrix multiplied with the query weight matrix to construct the machine tool thermal error analysis query matrix Q. Feature enhancement is achieved through the corresponding operation of the following formula: H = softmax(QK T / D 0.5 )V, where H is the characteristic of enhanced machine tool thermal error timing analysis, T is the matrix transpose operation, and D is the dimension of the key matrix.

5. The method for compensating machine tool thermal errors based on data analysis according to claim 4, characterized in that: Training the operation status analysis network includes the following steps: A plurality of operation status analysis training samples are obtained, wherein the operation status analysis training samples are a feature set for thermal error analysis of machine tools, the operation status analysis training samples are labeled according to the operation status, and all labeled operation status analysis training samples are combined into an operation status analysis training set. The operation status analysis network is trained with the operation status analysis training set to determine whether a first training condition is met. If the first training condition is met, the trained operation status analysis network is output; otherwise, the operation status analysis network is continued to be trained with the operation status analysis training set.

6. The method for compensating machine tool thermal errors based on data analysis according to claim 5, characterized in that: Training the machine tool thermal error analysis model includes the following steps: Acquire a plurality of machine tool thermal error analysis training samples, the machine tool thermal error analysis training samples including a machine tool thermal error time series analysis data set and corresponding infrared images, annotate the machine tool thermal error analysis training samples using a compensation value set, form a machine tool thermal error analysis training set with all annotated machine tool thermal error analysis training samples, train a machine tool thermal error analysis model using the machine tool thermal error analysis training set, determine whether a second training condition is met, and if so, output the trained machine tool thermal error analysis model; Otherwise, the machine tool thermal error analysis model is continued to be trained using the machine tool thermal error analysis training set.

7. A machine tool thermal error compensation system based on data analysis, characterized in that: The system applies a machine tool thermal error compensation method based on data analysis as described in any one of claims 1 to 6, comprising: The data acquisition module is used to obtain the axis temperature data set and vibration signal data of each axis of the machine tool at the monitoring time point. The axis temperature data set includes multiple axis temperature data; at the same time, it obtains the current data of the servo motor of the machine tool, the ambient temperature data around the machine tool, and the infrared image of the machine tool; A machine tool thermal error analysis data construction module is used to construct machine tool thermal error analysis data based on the acquired axis temperature data set, vibration signal data, current data and ambient temperature data. The machine tool thermal error analysis data is stored in a matrix form, and each line in the machine tool thermal error analysis data is a machine tool thermal error analysis vector corresponding to each axis of the machine tool; The machine tool thermal error analysis module is used to combine the machine tool thermal error analysis data corresponding to the current monitoring time point and the machine tool thermal error analysis data corresponding to the previous N-1 monitoring time points into a machine tool thermal error time series analysis data set, and send the machine tool thermal error time series analysis data set and infrared image to the machine tool thermal error analysis model for processing, and output a compensation value set; based on the compensation value set, the control instructions of the machine tool are corrected to complete the thermal error compensation of the machine tool; A machine tool thermal error compensation module is used to correct the control instructions of the machine tool based on the compensation amount set to complete the thermal error compensation of the machine tool; The machine tool thermal error analysis model includes a timing analysis layer, an infrared image analysis layer, a weight analysis layer, a feature adjustment layer, a feature enhancement layer and a compensation output layer. The timing analysis layer is established based on the LSTM model and is used to perform timing analysis and processing on the machine tool thermal error timing analysis data set to construct the machine tool thermal error timing analysis features; the infrared image analysis layer is used to perform image processing on the infrared image to construct a global temperature vector; the weight analysis layer is used to perform operating state analysis on the machine tool thermal error analysis data obtained at the current monitoring point to output the corresponding operating state vector, and then determine the weight distribution matrix based on the machine tool thermal error timing analysis features and the operating state vector, and adjust the weight of the machine tool thermal error timing analysis features based on the weight distribution matrix to construct a reconstructed machine tool thermal error timing analysis feature; the feature enhancement layer is used to perform feature enhancement operations on the reconstructed machine tool thermal error timing analysis features based on the global temperature vector to construct an enhanced machine tool thermal error timing analysis feature; the compensation output layer is used to perform full connection operations on the enhanced machine tool thermal error timing analysis features to output the compensation amount for each axis.

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