CNN-BIGRU-A-based gantry machine tool overall real-time thermal error compensation method

Abstract

The invention relates to the technical field of machine tools, and particularly discloses a gantry machine tool overall real-time thermal error compensation method based on CNN-BIGRU-A. The gantry machine tool overall real-time thermal error compensation method comprises the steps that historical temperature data and historical thermal deformation data of a main shaft and a transmission shaft are obtained; constructing a thermal error prediction model, and training the thermal error prediction model according to the historical temperature data and the historical thermal deformation data to obtain a trained thermal error prediction model; transmitting the current temperature data of the main shaft and the transmission shaft to the trained thermal error prediction model to output a current thermal error compensation value of the main shaft and a current thermal deformation calculation value of the transmission shaft; the machine tool numerical control system calculates the current thermal error compensation value of the target position point according to the coordinate of the target position point on the transmission shaft; and the machine tool numerical control system performs compensation according to the current thermal error compensation value of the main shaft and the current thermal error compensation value of the target position point on the transmission shaft. According to the invention, a high-frequency and high-synchronization compensation strategy can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tools, and more specifically, to an overall real-time thermal error compensation method for a gantry machine tool based on CNN-BIGRU-A. Background Art

[0002] Machine tools are key equipment in the manufacturing industry, and their processing accuracy directly affects product quality and production efficiency. However, due to the accumulation of heat generated by friction, cutting, and electrical equipment in the various parts of the machine tool during the processing, temperature changes and thermal deformations occur. These thermal deformations will cause changes in the geometric accuracy of the machine tool, thereby affecting the dimensional accuracy and shape accuracy of the processed parts. According to research, the thermal error of machine tools accounts for 60%-70% of the total error of machine tools, which is a key problem restricting the improvement of machine tool processing accuracy. Therefore, research on machine tool thermal error compensation has important practical significance.

[0003] At present, the most widely used method to reduce the thermal error of machine tools is the error model compensation method, that is, by analyzing, summarizing and screening the temperature of the key heat source of the machine tool, the mapping relationship between the thermal error of the machine tool and the temperature of the key heat source is studied to obtain the error model. Traditional error compensation methods such as kernel principal component analysis, multi-scale transformation method, SVM, BPNN, etc. are only applicable to specific data sets, and rely on relevant human experience when extracting features, resulting in low generalization performance of the error model, and it is difficult to meet the model robustness requirements under external environmental changes and multiple processing conditions. The prediction performance has restricted the development of thermal error compensation technology to a certain extent. On the other hand, traditional error compensation technology is mainly aimed at the spindle, and there is little research on the thermal error of the machine tool transmission shaft and the overall thermal error of the machine tool. Compensation for the overall error of the machine tool is also a major problem. Summary of the invention

[0004] The present invention aims to overcome the above-mentioned defects and provides an overall real-time thermal error compensation method for gantry machine tools based on CNN-BIGRU-A, which can realize a compensation strategy with high frequency and high synchronization.

[0005] As a first aspect of the present invention, a method for overall real-time thermal error compensation of a gantry machine tool based on CNN-BIGRU-A is provided, comprising the following steps:

[0006] Step S1: Acquire historical temperature data and historical thermal deformation data of a machine tool spindle, and simultaneously acquire historical temperature data and historical thermal deformation data of each measuring point of a machine tool transmission shaft;

[0007] Step S2: A thermal error prediction model is constructed by fusing a one-dimensional convolutional layer, a bidirectional gated neural network layer, an attention mechanism layer, and a fully connected layer; the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft are used as input feature samples, and the historical thermal deformation data of the machine tool spindle and the historical thermal deformation data of each measuring point of the machine tool transmission shaft are used as output feature samples, and the thermal error prediction model is trained according to the input feature samples and the output feature samples to obtain a trained thermal error prediction model;

[0008] Step S3: reading the current temperature data of the machine tool spindle, transmitting the current temperature data of the machine tool spindle to the trained thermal error prediction model for prediction, so as to output the current thermal error compensation value of the machine tool spindle, and then transmitting the current thermal error compensation value of the machine tool spindle to the machine tool numerical control system, and the machine tool numerical control system performs compensation according to the current thermal error compensation value of the machine tool spindle;

[0009] Step S4: reading the current temperature data of each measuring point of the machine tool transmission shaft, transmitting the current temperature data of each measuring point of the machine tool transmission shaft to the trained thermal error prediction model for prediction, so as to output the current thermal deformation calculation value of each measuring point of the machine tool transmission shaft; then fitting the coordinates of each measuring point of the machine tool transmission shaft and the current thermal deformation calculation value of each measuring point into a one-variable multi-time equation to obtain the fitting coefficient of the machine tool transmission shaft; finally, transmitting the fitting coefficient of the machine tool transmission shaft to the machine tool numerical control system, the machine tool numerical control system substitutes the coordinates of the target position point on the machine tool transmission shaft into the one-variable multi-time equation to calculate the current thermal error compensation value of the target position point on the machine tool transmission shaft, and the machine tool numerical control system performs compensation according to the current thermal error compensation value of the target position point on the machine tool transmission shaft.

[0010] Furthermore, the acquisition of historical temperature data and historical thermal deformation data of the machine tool spindle also includes:

[0011] After the machine is heated for a specified time, a variable speed experiment of the machine tool spindle is carried out. After each group of experiments is carried out at a fixed speed for a specified time, the machine is stopped to detect the historical temperature data of the machine tool spindle through a temperature sensor and the historical thermal deformation data of the machine tool spindle through a laser interferometer;

[0012] The historical thermal deformation data of the machine tool spindle includes the historical thermal deformation data of the machine tool spindle in the X, Y and Z directions.

[0013] Furthermore, the acquisition of historical temperature data and historical thermal deformation data of each measuring point of the machine tool transmission shaft also includes:

[0014] After the specified time of the hot machine, the fixed measuring point experiment of the machine tool transmission shaft is carried out, and each measuring point of the machine tool transmission shaft is set respectively. After each measuring point of the machine tool transmission shaft is moved into position, the machine is stopped to detect the historical temperature data of each measuring point of the machine tool transmission shaft through a temperature sensor and the historical thermal deformation data of each measuring point of the machine tool transmission shaft through a laser interferometer;

[0015] The historical thermal deformation data of each measuring point of the machine tool transmission shaft includes the historical thermal deformation data of each measuring point of the machine tool transmission shaft in the X, Y and Z directions.

[0016] Furthermore, it also includes:

[0017] After the machine is heated for 5 minutes, the fixed-point test of the machine tool transmission shaft is carried out;

[0018] The temperature and thermal deformation of the machine tool transmission shaft are measured every L / N interval, where L is the length of the machine tool transmission shaft, N is the number of measuring points on the machine tool transmission shaft, the feed speed of the machine tool transmission shaft is 1000mm / min, and the machine tool transmission shaft stops moving after each measuring point moves into place;

[0019] After the current measuring point of the machine tool transmission shaft moves into position, it stops moving, so as to detect the historical temperature data of the current measuring point of the machine tool transmission shaft through a temperature sensor and the historical thermal deformation data of the current measuring point of the machine tool transmission shaft through a laser interferometer; then the machine tool transmission shaft continues to move until the historical temperature data of subsequent measuring points of the machine tool transmission shaft and the historical thermal deformation data of subsequent measuring points are all detected.

[0020] Furthermore, the method of using the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft as input feature samples and using the historical thermal deformation data of the machine tool spindle and the historical thermal deformation data of each measuring point of the machine tool transmission shaft as output feature samples also includes:

[0021] Standardizing the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft to obtain a plurality of standardized historical temperature data;

[0022] The grey correlation analysis algorithm is used to calculate the correlation between each standardized historical temperature data and its corresponding historical thermal deformation data, and the historical temperature data with a correlation exceeding a preset threshold value is screened out from the plurality of standardized historical temperature data, and the historical temperature data with a correlation exceeding the preset threshold value is used as an input feature sample, and the historical thermal deformation data corresponding to the historical temperature data with a correlation exceeding the preset threshold value is used as an output feature sample; wherein the calculation formula of the correlation is as follows:

[0023]

[0024] Where: x0(k) is the historical thermal deformation data, [x1(k), x2(k), ..., x t (k)] is the standardized historical temperature data, and ρ is the gray resolution coefficient.

[0025] Furthermore, the fusion of the one-dimensional convolutional layer, the bidirectional gated neural network layer, the attention mechanism layer and the fully connected layer to construct the thermal error prediction model also includes:

[0026] The one-dimensional convolution layer and the bidirectional gated neural network layer are used to extract and reconstruct features; the attention mechanism layer is used to redistribute the weight parameters of each feature in the feature dimension in the form of dot product; the fully connected layer is used to map the dimension to the output dimension and generate the final prediction result;

[0027] Wherein, the one-dimensional convolution layer includes two convolution layers, a pooling layer and a normalization layer;

[0028] The bidirectional gated neural network layer uses two hidden layers and a bidirectional network design; the operating formula of the bidirectional gated neural network layer is as follows:

[0029] z t =f(W z ·[h t-1 ,x t ])

[0030] r t =f(W r ·[h t-1 ,x t ])

[0031]

[0032] Where: W z , W r , W h is the weight matrix; f() is the activation function Sigmoid, g() is the activation function Tanh; r t is the reset gate; t is the update gate; x t is the input data; h t-1 is the hidden layer state of the previous time step; is the candidate state of the hidden layer at the current time step; h t is the hidden layer output of the current time step;

[0033] The attention mechanism layer uses a feature attention mechanism based on dot products to reweight the network output. The operating formula of the attention mechanism layer is as follows:

[0034]

[0035] Among them, Q, K, and V represent the query matrix, key matrix, and value matrix respectively, and dk is the feature dimension of the key matrix and query matrix.

[0036] Further, the step of training the thermal error prediction model according to the input feature samples and the output feature samples to obtain a trained thermal error prediction model further includes:

[0037] Dividing the data set consisting of the input feature samples and the output feature samples into a training set, a validation set and a test set;

[0038] The thermal error prediction model is trained with the input feature samples and the output feature samples in the training set, the performance of the thermal error prediction model is verified with the input feature samples and the output feature samples in the validation set, and the performance index of the trained thermal error prediction model is evaluated with the input feature samples and the output feature samples in the test set; wherein the performance index includes the root mean square error RMSE and the mean absolute error MAE, and the calculation formula thereof is:

[0039]

[0040] Among them, y i is the true label of the test set data, f(x i ) is the predicted label of the test set data obtained by the thermal error prediction model.

[0041] Furthermore, the current thermal error compensation value of the machine tool spindle includes current thermal error compensation values ​​in the X, Y, and Z directions of the machine tool spindle.

[0042] Furthermore, the step S4 further includes:

[0043] Taking the X direction of the machine tool transmission axis as an example, read the current temperature data of N measuring points in the X direction of the machine tool transmission axis, and reduce the dimension of the current temperature data of the N measuring points to obtain the current temperature data of m measuring points, with a dimension of [n, m], where n is the number of temperature sensors, that is, the current temperature data of each measuring point is detected by n temperature sensors; average the current temperature data with a dimension of [n, m] in the first dimension to obtain the average current temperature data of the m measuring points, and convert the dimension to [1, m];

[0044] The average current temperature data of the m measuring points in the X direction of the machine tool transmission axis is transmitted to the trained thermal error prediction model for prediction, so as to output the current thermal deformation calculation values ​​[e1, e2, ..., e m ];

[0045] Assume that the coordinates of the m measuring points in the X direction of the machine tool transmission axis are [x1, x2, ..., x m ], the coordinates of the m measuring points [x1, x2, ..., x m ] and the current thermal deformation calculation values ​​of m measuring points [e1, e2, …, e m ] is fitted into a cubic equation and the fitting coefficient matrix [a, b, c, d] of the machine tool transmission shaft is obtained;

[0046] Finally, the fitting coefficient matrix [a, b, c, d] of the machine tool transmission shaft is transmitted to the machine tool numerical control system. The machine tool numerical control system substitutes the coordinates of the target position point on the machine tool transmission shaft into the univariate multi-time equation to calculate the current thermal error compensation value of the target position point on the machine tool transmission shaft. The machine tool numerical control system performs compensation based on the current thermal error compensation value of the target position point on the machine tool transmission shaft.

[0047] Furthermore, before step S1, the method further includes:

[0048] A three-dimensional model of the machine tool is constructed, and heat-sensitive areas of the machine tool spindle and the machine tool drive shaft are divided according to the heat conduction characteristics of the machine tool. Temperature collection points are selected in the heat-sensitive areas of the machine tool spindle and the machine tool drive shaft, and temperature sensors are installed at the temperature collection points. The historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool drive shaft are detected by the temperature sensors, and the detected historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool drive shaft are transmitted to a host computer.

[0049] The present invention provides a gantry machine tool overall real-time thermal error compensation method based on CNN-BIGRU-A, which has the following beneficial effects: first, sensors are installed in the thermal sensitive area of ​​the machine tool, and software and hardware strategies for data transmission such as adapters and serial acquisition cards are prepared; secondly, spindle speed change experiments and transmission shaft fixed measurement point experiments are carried out to collect temperature data and thermal deformation; then, the temperature characteristics are screened and reduced by using the correlation analysis of the error. Then, a thermal error prediction model is constructed by integrating algorithms such as 1DCNN, BIGRU, and attention mechanism, and the model is used to perform deep feature extraction on the temperature data. Finally, according to the development of thermal error compensation software, the spindle thermal error compensation value is directly transmitted to the machine tool numerical control system, the coordinates of each measurement point of the transmission shaft and the calculated value of thermal deformation of each measurement point are fitted into a univariate multi-time equation, the fitting coefficient is transmitted to the machine tool numerical control system, and the thermal error compensation value of the target position point on the machine tool transmission shaft is calculated according to the real-time coordinates of the target position point for real-time compensation, which can realize a high-frequency and high-synchronization compensation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention.

[0051] Figure 1 A flow chart of the overall real-time thermal error compensation method for gantry machine tools based on CNN-BIGRU-A provided by the present invention.

[0052] Figure 2 This is a graph of historical thermal deformation data in the three directions of X, Y and Z of the machine tool spindle collected in the machine tool spindle speed change experiment provided by the present invention.

[0053] Figure 3 This is a structural diagram of the thermal error prediction model provided by the present invention.

[0054] Figure 4(a)-Figure 4(c) This is a diagram showing the thermal error prediction effect of the existing MLR model in the main axis X, Y, and Z directions during the training phase.

[0055] Figure 5(a)-Figure 5(c) This is a diagram showing the thermal error prediction effect of the existing MLR model in the main axis X, Y, and Z directions during the testing phase.

[0056] Figure 6(a)-Figure 6(c) This is a diagram showing the thermal error prediction effect of the existing ANN model in the main axis X, Y, and Z directions during the training phase.

[0057] Figure 7(a)-Figure 7(c) This is a diagram showing the thermal error prediction effect of the existing ANN model in the X, Y, and Z directions of the main axis during the testing phase.

[0058] Figure 8(a)-Figure 8(c) This is a diagram showing the thermal error prediction effects of the CNN_BiGRU_A thermal error prediction model of the present invention in the main axis X, Y, and Z directions during the training phase.

[0059] Figure 9(a)-Figure 9(c) This is a diagram showing the thermal error prediction effects of the CNN_BiGRU_A thermal error prediction model of the present invention in the main axis X, Y, and Z directions during the test phase.

[0060] Figure 10(a)-Figure 10(f) This is a diagram of the thermal error compensation interface provided by the present invention. DETAILED DESCRIPTION

[0061] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of a gantry machine tool overall real-time thermal error compensation method based on CNN-BIGRU-A proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0062] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0063] In this embodiment, a real-time thermal error compensation method for a gantry machine tool based on CNN-BIGRU-A is provided. Figure 1 As shown, the overall real-time thermal error compensation method of the gantry machine tool based on CNN-BIGRU-A includes the following steps:

[0064] Step S1: Acquire historical temperature data and historical thermal deformation data of a machine tool spindle, and simultaneously acquire historical temperature data and historical thermal deformation data of each measuring point of a machine tool transmission shaft;

[0065] It should be noted that, before step S1, the following steps are also included:

[0066] A three-dimensional model of the machine tool is constructed, and heat-sensitive areas of the machine tool spindle and the machine tool drive shaft are divided according to the heat conduction characteristics of the machine tool. Temperature collection points are selected in the heat-sensitive areas of the machine tool spindle and the machine tool drive shaft, and temperature sensors are installed at the temperature collection points. The historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool drive shaft are detected by the temperature sensor, and the detected historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool drive shaft are transmitted to the host computer through an adapter, a serial port acquisition card, etc.

[0067] In an embodiment of the present invention, 39 temperature sensors are respectively installed in the heat-sensitive areas of the machine tool spindle and the machine tool transmission shaft, and the installation positions are the front and rear bearings of the spindle (2 temperature sensors), the coolant inlet and outlet (2 temperature sensors), the left and right bed (6 temperature sensors), the left and right grating scales in the X direction (12 temperature sensors), the left and right grating scales in the Y direction (7 temperature sensors), the left and right grating scales in the Z direction (8 temperature sensors), the environment and the workbench (2 temperature sensors), and they are transmitted to the host computer through three serial port acquisition cards with 10 channels, and then the host computer returns the predicted current thermal error compensation value to the CNC machine tool for real-time compensation.

[0068] Preferably, the acquisition of historical temperature data and historical thermal deformation data of the machine tool spindle also includes:

[0069] After the machine is heated for a specified time, a variable speed experiment of the machine tool spindle is carried out. After each group of experiments is carried out at a fixed speed for a specified time, the machine is stopped to detect the historical temperature data of the machine tool spindle through a temperature sensor and the historical thermal deformation data of the machine tool spindle through a laser interferometer;

[0070] The historical thermal deformation data of the machine tool spindle includes the historical thermal deformation data of the machine tool spindle in the X, Y, and Z directions. Figure 2 As shown, it can be seen that there is a strong correlation between the three.

[0071] In the embodiment of the present invention, the machine is heated for two hours and the machine tool spindle is subjected to a variable speed experiment with a speed gradient of 1000 r / min (1000 r / min, 2000 r / min, ..., 8000 r / min, ..., 2000 r / min, 1000 r / min). Each speed lasts for 5 minutes. After that, the machine is stopped and the temperature data is collected using a temperature sensor and the thermal deformation data is collected using a laser interferometer.

[0072] Preferably, the acquisition of historical temperature data and historical thermal deformation data of each measuring point of the machine tool transmission shaft further includes:

[0073] After the specified time of the hot machine, the fixed measuring point experiment of the machine tool transmission shaft is carried out, and the measuring points of the machine tool transmission shaft in the X, Y and Z directions are respectively set. After the measuring points of the machine tool transmission shaft are moved into place, the machine is stopped to detect the historical temperature data of the measuring points of the machine tool transmission shaft through the temperature sensor and the historical thermal deformation data of the measuring points of the machine tool transmission shaft through the laser interferometer;

[0074] The historical thermal deformation data of each measuring point of the machine tool transmission shaft includes the historical thermal deformation data of each measuring point of the machine tool transmission shaft in the X, Y and Z directions.

[0075] Specifically, it also includes:

[0076] After the machine is heated for 5 minutes, the fixed-point test of the machine tool transmission shaft is carried out;

[0077] Taking the X direction of the machine tool transmission shaft as an example, the temperature measurement and thermal deformation measurement are performed every L / N interval in the X direction of the machine tool transmission shaft, where L is the length of the machine tool transmission shaft, N is the number of measuring points in the X direction of the machine tool transmission shaft, and the feed speed of the machine tool transmission shaft is 1000mm / min. After each measuring point in the X direction of the machine tool transmission shaft moves to its position, it stops moving.

[0078] After the current measuring point in the X direction of the machine tool transmission axis moves to a position, the movement is stopped, so as to detect the historical temperature data of the current measuring point in the X direction of the machine tool transmission axis by a temperature sensor and the historical thermal deformation data of the current measuring point in the X direction of the machine tool transmission axis by a laser interferometer; then the machine tool transmission axis continues to move until the historical temperature data of subsequent measuring points in the X direction of the machine tool transmission axis and the historical thermal deformation data of subsequent measuring points are all detected;

[0079] The Y-direction thermal error experiment and Z-direction thermal error experiment of the machine tool transmission shaft are carried out with the same experimental idea as above.

[0080] Step S2: A thermal error prediction model is constructed by fusing a one-dimensional convolutional layer (1DCNN), a bidirectional gated neural network layer (BIGRU), an attention mechanism layer (Attention) and a fully connected layer; the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft are used as input feature samples, and the historical thermal deformation data of the machine tool spindle and the historical thermal deformation data of each measuring point of the machine tool transmission shaft are used as output feature samples, and the thermal error prediction model is trained according to the input feature samples and the output feature samples to obtain a trained thermal error prediction model;

[0081] Preferably, Figure 3 As shown, the thermal error prediction model is constructed by integrating a one-dimensional convolutional layer, a bidirectional gated neural network layer, an attention mechanism layer and a fully connected layer, and further includes:

[0082] The one-dimensional convolution layer and the bidirectional gated neural network layer are used to extract and reconstruct features; the attention mechanism layer is used to redistribute the weight parameters of each feature in the feature dimension in the form of dot product, so that the network can better focus on the information related to error compensation in the input data to make predictions more accurately; the fully connected layer is used to map the dimension to the output dimension and generate the final prediction result;

[0083] Wherein, the one-dimensional convolution layer includes two convolution layers, a pooling layer and a normalization layer;

[0084] 1DCNN is a variant of convolutional neural network (CNN) that is specifically designed to process one-dimensional sequence data, such as time series data, text data, etc. Unlike traditional two-dimensional CNNs in image processing, 1DCNN captures local patterns and features in the sequence by applying one-dimensional convolution operations on the input sequence. In 1DCNN, the convolution kernel is one-dimensional and slides along the length of the input sequence. Similar to two-dimensional CNN, 1DCNN can also extract features in the data by learning the weights of the convolution kernel. These features may include local patterns, trends, periodicity, etc. in the sequence. By stacking multiple convolutional layers and pooling layers, the network can gradually extract higher-level and more abstract features. By sliding the convolution kernel to perform element-by-element multiplication and accumulation operations on one-dimensional input data, the network is able to learn the convolution kernel parameters for different features, such as edge detection and texture detection. 1DCNN has several advantages when processing sequence data. First, it can automatically learn and extract features in the sequence without manually designing feature extractors. Second, 1DCNN can capture the relationship between local patterns and global structures, thereby better understanding sequence data. In addition, 1DCNN can also flexibly adapt to sequence data of different lengths and complexities by adjusting the size and number of convolution kernels. Assuming the input matrix size is w, the convolution kernel size is k, the stride is s, and the number of zero-filling layers is p, the size of the feature map generated after one-dimensional convolution is:

[0085]

[0086] The pooling layer, also known as downsampling, plays a role in reducing dimension and operation parameters, extracting important features, and maintaining translation invariance in convolutional neural networks. Similar to convolution, the pooling operation also uses a sliding pooling kernel to extract features. Each time it slides to an area, the maximum value in the area is taken as the pooling output. This method is called max pooling. In addition, the output mean can also be calculated, which is called mean pooling. After several layers of convolution and pooling operations, the feature map obtained will be expanded row by row and connected into a vector.

[0087] BatchNormalization is a commonly used technique in deep learning, which aims to accelerate the training process of the model and improve the generalization ability of the model. It normalizes the activation of the middle layer of the network on each small batch of data, so that the input distribution of each layer is stable and the internal covariate shift is reduced. Internal covariate shift refers to the change in the input distribution of each layer of the neural network, which makes it difficult to update the network parameters, thereby affecting the training effect of the model. The BatchNormalization layer is usually added after the convolutional layer or fully connected layer of the network as part of the network structure. During the training process, it reduces the internal covariate shift by normalizing each small batch of data, thereby accelerating the convergence of the model. The Dropout layer is a regularization technique used to avoid overfitting. It randomly discards a part of the neurons in the network, making the network an incomplete sub-network at each forward propagation, thereby reducing the dependency between neurons, improving the generalization ability of the deep learning model, and reducing the risk of overfitting.

[0088] The bidirectional gated neural network layer uses two hidden layers and a bidirectional network design; the GRU network is a variant of a recurrent neural network (RNN), which not only overcomes the gradient vanishing or gradient exploding problems of the traditional RNN network when dealing with long-term dependencies, but also effectively avoids the problems of the LSTM network, such as complex internal structure, long training time, and overfitting risk. The GRU network combines the input gate and the forget gate of the LSTM into an update gate to reduce the number of model parameters and improve the efficiency of model training. The operating formula of the bidirectional gated neural network layer is as follows:

[0089] z t =f(W z ·[h t-1 ,x t ])

[0090] r t =f(W r ·[h t-1 , x t ])

[0091]

[0092] Where: W z , W r , W h is the weight matrix; f() is the activation function Sigmoid, g() is the activation function Tanh; r t is the reset gate; t is the update gate; x t is the input data; ht-1 is the hidden layer state of the previous time step; is the candidate state of the hidden layer at the current time step; h t is the hidden layer output of the current time step;

[0093] Among them, the attention mechanism layer uses a feature attention mechanism based on dot product to reweight the network output. The model uses a convolutional neural network for feature extraction and is optimized through BatchNormalization and dropout layers to enhance the performance of the model.

[0094] The purpose of introducing the attention mechanism is to allow the neural network to focus on the relevant parts when processing the input data, so that it can automatically learn and selectively pay attention to the important information in the input, and improve the performance and generalization ability of the model. Common attention mechanisms include self-attention mechanism, channel attention mechanism and spatial attention mechanism. In this study, we adopted the self-attention mechanism. Depending on the application object, the self-attention mechanism can be divided into two types: based on the time step dimension and based on the output / hidden layer feature dimension. The self-attention mechanism based on the time step dimension aims to capture the relationship between different time steps in the sequence data, while the self-attention mechanism based on the output / hidden layer feature dimension focuses on capturing the correlation between features at different positions in the input sequence. In this study, we used a self-attention mechanism based on dot product. The operating formula of the attention mechanism layer is as follows:

[0095]

[0096] Among them, Q, K, and V represent the query matrix, key matrix, and value matrix, respectively, and their dimensions are (n, d k )、(m,d k )、(m,d v) , n is the number dimension of the query, m is the number of key values, d k is the feature dimension of the key matrix and query matrix, and dv is the feature dimension of the value;

[0097] The calculation process is:

[0098] ① Calculate the dot product of the query vector and the key vector to obtain the matching score matrix S;

[0099]

[0100] Among them, q i T is the transpose of the query vector, k j is the key vector.

[0101] ②Perform Softmax normalization on the score matrix to obtain the attention weight matrix A;

[0102]

[0103] Among them, S ij is the matching score matrix, d k The feature dimension for keys and queries.

[0104] ③Add the attention weight matrix A and the value matrix V to obtain the context tensor matrix C.

[0105]

[0106] Among them, A ij is the attention weight matrix, v j is a value matrix.

[0107] Preferably, the method of using the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft as input feature samples and using the historical thermal deformation data of the machine tool spindle and the historical thermal deformation data of each measuring point of the machine tool transmission shaft as output feature samples further includes:

[0108] Standardizing the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft to obtain a plurality of standardized historical temperature data; wherein the historical temperature data are all mapped to the interval [0,1];

[0109]

[0110] Where: x is the historical temperature data collected, x min is the minimum value of the historical temperature data collected, x max It is the maximum value of the historical temperature data collected.

[0111] The grey correlation analysis algorithm is used to calculate the correlation between each standardized historical temperature data and its corresponding historical thermal deformation data, and the historical temperature data with a correlation exceeding a preset threshold value is selected from the plurality of standardized historical temperature data to achieve the dimensionality reduction operation of the historical temperature data, so as to better capture the global and local features of the historical temperature data, achieve the purpose of reconstructing features, reducing model parameters and calculation amount, and improving model accuracy;

[0112] The historical temperature data (historical temperature data after dimensionality reduction) whose correlation exceeds the preset threshold is used as the input feature sample, and the historical thermal deformation data corresponding to the historical temperature data (historical temperature data after dimensionality reduction) whose correlation exceeds the preset threshold is used as the output feature sample; wherein, the calculation formula of the correlation is as follows:

[0113]

[0114] Where: x0(k) is the historical thermal deformation data, that is, the target sequence, [x1(k), x2(k), ..., x t (k)] is the standardized historical temperature data, that is, the comparison sequence, ρ is the gray resolution coefficient, the value range is [0,1], and it is generally taken as 0.5.

[0115] Preferably, the step of training the thermal error prediction model according to the input feature samples and the output feature samples to obtain a trained thermal error prediction model further includes:

[0116] Divide the data set consisting of the input feature samples and the output feature samples into a training set, a validation set, and a test set in a ratio of 6:2:2;

[0117] The thermal error prediction model is trained with the input feature samples and output feature samples in the training set, the performance of the thermal error prediction model is verified with the input feature samples and output feature samples in the validation set, the performance index of the trained thermal error prediction model is evaluated with the input feature samples and output feature samples in the test set, and LSTM, 1DCNN, GRU, and 1DCNN-BIGRU are used simultaneously to predict the thermal error of the machine tool and the performance is compared with 1DCNN_BIGRU_A to verify the superiority of the model; wherein the performance indexes include the root mean square error RMSE and the mean absolute error MAE, and the calculation formula thereof is:

[0118]

[0119] Among them, y i is the true label of the test set data, f(x i ) is the predicted label of the test set data obtained by the thermal error prediction model.

[0120] The CNN_BiGRU_A thermal error prediction model used in the present invention is compared with various network models to evaluate the performance of the model. Figure 4(a)-Figure 4(c) , Figure 5(a)-Figure 5(c) , Figure 6(a)-Figure 6(c) , Figure 7(a)-Figure 7(c) , Figure 8(a)-Figure 8(c) and Figure 9(a)-Figure 9(c) As shown, it can be seen that the thermal error prediction values ​​of the MLR model, ANN model and CNN_BiGRU_A thermal error prediction model on the training set are basically consistent with the true values, but the thermal error prediction values ​​of the MLR model and ANN model on the test set are significantly different from the true values, proving that their generalization capabilities are insufficient. The CNN_BiGRU_A thermal error prediction model used in the present invention shows an ideal effect in thermal error prediction on the test set.

[0121] Step S3: reading the current temperature data of the machine tool spindle, transmitting the current temperature data of the machine tool spindle to the trained thermal error prediction model for prediction, so as to output the current thermal error compensation value of the machine tool spindle, and then transmitting the current thermal error compensation value of the machine tool spindle to the machine tool numerical control system, and the machine tool numerical control system performs compensation according to the current thermal error compensation value of the machine tool spindle;

[0122] Preferably, the current thermal error compensation value of the machine tool spindle includes the current thermal error compensation values ​​of the machine tool spindle in the X, Y, and Z directions, with a dimension of [1,3]. The software directly transmits the current thermal error compensation values ​​of the machine tool spindle in the X, Y, and Z directions to the machine tool CNC system through the NC-Link protocol.

[0123] Step S4: reading the current temperature data of each measuring point of the machine tool transmission shaft, transmitting the current temperature data of each measuring point of the machine tool transmission shaft to the trained thermal error prediction model for prediction, so as to output the current thermal deformation calculation value of each measuring point of the machine tool transmission shaft; then fitting the coordinates of each measuring point of the machine tool transmission shaft and the current thermal deformation calculation value of each measuring point into a one-variable multi-time equation to obtain the fitting coefficient of the machine tool transmission shaft; finally, transmitting the fitting coefficient of the machine tool transmission shaft to the machine tool numerical control system, the machine tool numerical control system substitutes the coordinates of the target position point on the machine tool transmission shaft into the one-variable multi-time equation to calculate the current thermal error compensation value of the target position point on the machine tool transmission shaft, and the machine tool numerical control system performs compensation according to the current thermal error compensation value of the target position point on the machine tool transmission shaft.

[0124] Preferably, the step S4 further includes:

[0125] Taking the X direction of the machine tool transmission axis as an example, read the current temperature data of N measuring points in the X direction of the machine tool transmission axis, and reduce the dimension of the current temperature data of the N measuring points to obtain the current temperature data of m measuring points, with a dimension of [n, m], where n is the number of temperature sensors, that is, the current temperature data of each measuring point is detected by n temperature sensors; average the current temperature data with a dimension of [n, m] in the first dimension to obtain the average current temperature data of the m measuring points, and convert the dimension to [1, m];

[0126] The average current temperature data of the m measuring points in the X direction of the machine tool transmission axis is transmitted to the trained thermal error prediction model for prediction, so as to output the current thermal deformation calculation values ​​[e1, e2, ..., e m ];

[0127] Assume that the coordinates of the m measuring points in the X direction of the machine tool transmission axis are [x1, x2, ..., x m ], the coordinates of the m measuring points [x1, x2, ..., x m] and the current thermal deformation calculation values ​​of m measuring points [e1, e2, ..., e m ] is fitted into a cubic equation and the fitting coefficient matrix [a, b, c, d] of the machine tool transmission shaft is obtained;

[0128] Finally, the fitting coefficient matrix [a, b, c, d] of the machine tool transmission shaft is transmitted to the machine tool numerical control system. The machine tool numerical control system substitutes the coordinates of the target position point on the machine tool transmission shaft into the univariate multi-time equation to calculate the current thermal error compensation value of the target position point on the machine tool transmission shaft. The machine tool numerical control system performs compensation based on the current thermal error compensation value of the target position point on the machine tool transmission shaft.

[0129] In addition, the remaining functions of the software of the present invention also include temperature collection point setting, real-time display of temperature data, error compensation value display, data collection, etc.

[0130] As shown in Figure 10(a), the temperature sensor data window is used to display the real-time collected equipment operating temperature data; as shown in Figure 10(b), the operation status window is used to count and display the number of normal and abnormal collection times of the temperature sensor; as shown in Figure 10(c), the spindle thermal error prediction window is used to display the spindle thermal error compensation value predicted by the model. This window displays the thermal error compensation values ​​of the spindle in the three directions of X, Y, and Z in the same window. As shown in Figure 10(d), the transmission shaft X prediction error window is used to display the thermal error compensation value of the transmission shaft in the X direction predicted by the model. As shown in Figure 10(e), the transmission shaft Y prediction error window is used to display the thermal error compensation value of the transmission shaft in the Y direction predicted by the model. As shown in Figure 10(f), the transmission shaft Z prediction error window is used to display the thermal error compensation value of the transmission shaft in the Z direction predicted by the model.

[0131] The overall real-time thermal error compensation method of the gantry machine tool based on CNN-BIGRU-A proposed in the present invention first divides the heat sensitive area according to the heat conduction characteristics of the machine tool, installs temperature sensors in the heat sensitive area, and establishes the software and hardware strategy of data transmission; then, thermal error experiments of the main shaft and the transmission shaft are carried out respectively to collect the temperature signal and thermal error signal of each measuring point, and the thermal error-related temperature characteristics of the main shaft and the transmission shaft are screened out by using grey correlation analysis; then, a thermal error prediction model is constructed by integrating algorithms such as 1DCNN, BIGRU, and attention mechanism, and the model is used to perform deep feature extraction and error prediction on the temperature data; finally, according to the development of thermal error compensation software, the spindle error is directly transmitted to the machine tool CNC system, the transmission shaft measuring point error and the measuring point coordinates are fitted into a univariate multi-time equation, the fitting coefficient is transmitted to the CNC system, and real-time compensation is performed according to the real-time position calculation of the machine tool.

[0132] In view of the problem that machine tool thermal errors are difficult to compensate, the present invention proposes a gantry machine tool overall real-time thermal error compensation method based on CNN-BIGRU-A, and carries out relevant experimental verification according to the method. The effectiveness of the algorithm model in the present invention is proved through experimental verification.

[0133] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A real-time thermal error compensation method for gantry machine tools based on CNN-BIGRU-A, characterized in that: The steps include: Step S1: Acquire historical temperature data and historical thermal deformation data of a machine tool spindle, and simultaneously acquire historical temperature data and historical thermal deformation data of each measuring point of a machine tool transmission shaft; Step S2: A thermal error prediction model is constructed by fusing a one-dimensional convolutional layer, a bidirectional gated neural network layer, an attention mechanism layer, and a fully connected layer; the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft are used as input feature samples, and the historical thermal deformation data of the machine tool spindle and the historical thermal deformation data of each measuring point of the machine tool transmission shaft are used as output feature samples, and the thermal error prediction model is trained according to the input feature samples and the output feature samples to obtain a trained thermal error prediction model; Step S3: reading the current temperature data of the machine tool spindle, transmitting the current temperature data of the machine tool spindle to the trained thermal error prediction model for prediction, so as to output the current thermal error compensation value of the machine tool spindle, and then transmitting the current thermal error compensation value of the machine tool spindle to the machine tool numerical control system, and the machine tool numerical control system performs compensation according to the current thermal error compensation value of the machine tool spindle; Step S4: reading the current temperature data of each measuring point of the machine tool transmission shaft, transmitting the current temperature data of each measuring point of the machine tool transmission shaft to the trained thermal error prediction model for prediction, so as to output the current thermal deformation calculation value of each measuring point of the machine tool transmission shaft; Then, the coordinates of each measuring point of the machine tool transmission shaft and the current thermal deformation calculation value of each measuring point are fitted into a one-variable multi-time equation to obtain the fitting coefficient of the machine tool transmission shaft; finally, the fitting coefficient of the machine tool transmission shaft is transmitted to the machine tool numerical control system, and the machine tool numerical control system substitutes the coordinates of the target position point on the machine tool transmission shaft into the one-variable multi-time equation to calculate the current thermal error compensation value of the target position point on the machine tool transmission shaft, and the machine tool numerical control system performs compensation according to the current thermal error compensation value of the target position point on the machine tool transmission shaft.

2. The overall real-time thermal error compensation method of a gantry machine tool based on CNN-BIGRU-A according to claim 1 is characterized in that: The acquisition of historical temperature data and historical thermal deformation data of the machine tool spindle also includes: After the machine is heated for a specified time, a variable speed experiment of the machine tool spindle is carried out. After each group of experiments is carried out at a fixed speed for a specified time, the machine is stopped to detect the historical temperature data of the machine tool spindle through a temperature sensor and the historical thermal deformation data of the machine tool spindle through a laser interferometer; The historical thermal deformation data of the machine tool spindle includes the historical thermal deformation data of the machine tool spindle in the X, Y and Z directions.

3. The overall real-time thermal error compensation method of a gantry machine tool based on CNN-BIGRU-A according to claim 1 is characterized in that: The acquisition of historical temperature data and historical thermal deformation data of each measuring point of the machine tool transmission shaft also includes: After the specified time of the hot machine, the fixed measuring point experiment of the machine tool transmission shaft is carried out, and each measuring point of the machine tool transmission shaft is set respectively. After each measuring point of the machine tool transmission shaft is moved into position, the machine is stopped to detect the historical temperature data of each measuring point of the machine tool transmission shaft through a temperature sensor and the historical thermal deformation data of each measuring point of the machine tool transmission shaft through a laser interferometer; The historical thermal deformation data of each measuring point of the machine tool transmission shaft includes the historical thermal deformation data of each measuring point of the machine tool transmission shaft in the X, Y and Z directions.

4. The overall real-time thermal error compensation method of a gantry machine tool based on CNN-BIGRU-A according to claim 3 is characterized in that: Also includes: After the machine is heated for 5 minutes, the fixed-point test of the machine tool transmission shaft is carried out; The temperature and thermal deformation of the machine tool transmission shaft are measured every L / N interval, where L is the length of the machine tool transmission shaft, N is the number of measuring points on the machine tool transmission shaft, the feed speed of the machine tool transmission shaft is 1000mm / min, and the machine tool transmission shaft stops moving after each measuring point moves into place; After the current measuring point of the machine tool transmission shaft moves into position, it stops moving, so as to detect the historical temperature data of the current measuring point of the machine tool transmission shaft through a temperature sensor and the historical thermal deformation data of the current measuring point of the machine tool transmission shaft through a laser interferometer; then the machine tool transmission shaft continues to move until the historical temperature data of subsequent measuring points of the machine tool transmission shaft and the historical thermal deformation data of subsequent measuring points are all detected.

5. The overall real-time thermal error compensation method of a gantry machine tool based on CNN-BIGRU-A according to claim 1 is characterized in that: The method of using the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft as input feature samples and using the historical thermal deformation data of the machine tool spindle and the historical thermal deformation data of each measuring point of the machine tool transmission shaft as output feature samples also includes: Standardizing the historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool transmission shaft to obtain a plurality of standardized historical temperature data; The grey correlation analysis algorithm is used to calculate the correlation between each standardized historical temperature data and its corresponding historical thermal deformation data, and the historical temperature data with a correlation exceeding a preset threshold value is screened out from the plurality of standardized historical temperature data, and the historical temperature data with a correlation exceeding the preset threshold value is used as an input feature sample, and the historical thermal deformation data corresponding to the historical temperature data with a correlation exceeding the preset threshold value is used as an output feature sample; wherein the calculation formula of the correlation is as follows: Where: x0(k) is the historical thermal deformation data, [x1(k), x2(k), ..., x t (k)] is the standardized historical temperature data, and ρ is the gray resolution coefficient.

6. The overall real-time thermal error compensation method of a gantry machine tool based on CNN-BIGRU-A according to claim 1 is characterized in that: The thermal error prediction model is constructed by fusing a one-dimensional convolutional layer, a bidirectional gated neural network layer, an attention mechanism layer, and a fully connected layer, and further includes: The one-dimensional convolution layer and the bidirectional gated neural network layer are used to extract and reconstruct features; the attention mechanism layer is used to redistribute the weight parameters of each feature in the feature dimension in the form of dot product; the fully connected layer is used to map the dimension to the output dimension and generate the final prediction result; Wherein, the one-dimensional convolution layer includes two convolution layers, a pooling layer and a normalization layer; The bidirectional gated neural network layer uses two hidden layers and a bidirectional network design; the operating formula of the bidirectional gated neural network layer is as follows: z t =f(W z ·[h t-1 ,x t ]) r t =f(W r ·[h t-1 ,x t ]) Where: W z , W r , W h is the weight matrix; f() is the activation function Sigmoid, g() is the activation function Tanh; r t is the reset gate; t is the update gate; x t is the input data; h t-1 is the hidden layer state of the previous time step; is the candidate state of the hidden layer at the current time step; h t is the hidden layer output of the current time step; The attention mechanism layer uses a feature attention mechanism based on dot products to reweight the network output. The operating formula of the attention mechanism layer is as follows: Among them, Q, K, and V represent the query matrix, key matrix, and value matrix respectively. k is the characteristic dimension of the key matrix and query matrix.

7. The overall real-time thermal error compensation method of a gantry machine tool based on CNN-BIGRU-A according to claim 1 is characterized in that: The step of training the thermal error prediction model according to the input feature samples and the output feature samples to obtain a trained thermal error prediction model further includes: Dividing the data set consisting of the input feature samples and the output feature samples into a training set, a validation set and a test set; The thermal error prediction model is trained with the input feature samples and the output feature samples in the training set, the performance of the thermal error prediction model is verified with the input feature samples and the output feature samples in the validation set, and the performance index of the trained thermal error prediction model is evaluated with the input feature samples and the output feature samples in the test set; wherein the performance index includes the root mean square error RMSE and the mean absolute error MAE, and the calculation formula thereof is: Among them, y i is the true label of the test set data, f(x i ) is the predicted label of the test set data obtained by the thermal error prediction model.

8. The method for overall real-time thermal error compensation of a gantry machine tool based on CNN-BIGRU-A according to claim 1, characterized in that: The current thermal error compensation value of the machine tool spindle includes the current thermal error compensation values ​​of the machine tool spindle in the X, Y, and Z directions.

9. The overall real-time thermal error compensation method of a gantry machine tool based on CNN-BIGRU-A according to claim 1, characterized in that: The step S4 further includes: Taking the X direction of the machine tool transmission shaft as an example, the current temperature data of N measuring points in the X direction of the machine tool transmission shaft are read, and the current temperature data of the N measuring points are reduced in dimension to obtain the current temperature data of m measuring points, and the dimension is [n, m], where n is the number of temperature sensors, that is, the current temperature data of each measuring point is detected by n temperature sensors; the current temperature data of the dimension [n, m] is averaged in the first dimension to obtain the average current temperature data of the m measuring points, and the dimension is converted to [1, m]; The average current temperature data of the m measuring points in the X direction of the machine tool transmission axis is transmitted to the trained thermal error prediction model for prediction, so as to output the current thermal deformation calculation values ​​[e1, e2, ..., e m ]; Assume that the coordinates of the m measuring points in the X direction of the machine tool transmission axis are [x1, x2, ..., x m ], the coordinates of the m measuring points [x1, x2, ..., x m ] and the current thermal deformation calculation values ​​of m measuring points [e1, e2, ..., e m ] is fitted into a cubic equation and the fitting coefficient matrix [a, b, c, d] of the machine tool transmission shaft is obtained; Finally, the fitting coefficient matrix [a, b, c, d] of the machine tool transmission shaft is transmitted to the machine tool numerical control system, and the machine tool numerical control system substitutes the coordinates of the target position point on the machine tool transmission shaft into the univariate multi-time equation to calculate the current thermal error compensation value of the target position point on the machine tool transmission shaft. The machine tool numerical control system performs compensation based on the current thermal error compensation value of the target position point on the machine tool transmission shaft.

10. The method for overall real-time thermal error compensation of a gantry machine tool based on CNN-BIGRU-A according to claim 1, characterized in that: Before step S1, the method further includes: A three-dimensional model of the machine tool is constructed, and heat-sensitive areas of the machine tool spindle and the machine tool drive shaft are divided according to the heat conduction characteristics of the machine tool. Temperature collection points are selected in the heat-sensitive areas of the machine tool spindle and the machine tool drive shaft, and temperature sensors are installed at the temperature collection points. The historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool drive shaft are detected by the temperature sensors, and the detected historical temperature data of the machine tool spindle and the historical temperature data of each measuring point of the machine tool drive shaft are transmitted to a host computer.

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