CN115392292A - Cutter abrasion on-line monitoring method based on attention circulation neural network - Google Patents
Cutter abrasion on-line monitoring method based on attention circulation neural network Download PDFInfo
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- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
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Abstract
The invention provides a cutter abrasion on-line monitoring method based on an attention circulation neural network, which comprises the following steps: collecting tool wear data in the machining process of a machine tool, and making an original data set; preprocessing an original data set, including invalid data elimination, wavelet packet transformation and feature extraction; inputting the preprocessed experimental data set into a cutter wear monitoring model based on an attention circulation neural network for iterative training, and storing the model when the model monitoring error is within a specified range; and (3) acquiring a wear signal in the machining process of the cutter on line, preprocessing the wear signal by data, inputting the wear signal into a trained model, and carrying out on-line monitoring. The invention integrates the attention mechanism and the cyclic neural network in a cross way, constructs the cutter abrasion on-line monitoring method based on the attention cyclic neural network, can accurately monitor the cutter abrasion value, and has good generalization capability, self-adaptive capability and good robustness.
Description
Technical Field
The application relates to the technical field of machine tool cutter abrasion online monitoring, in particular to a cutter abrasion online monitoring method based on an attention circulation neural network.
Background
With the increase of the demand of industries such as aerospace, automobiles, precision machinery and the like on high-precision parts, the processing technology plays a key role in the modern manufacturing industry. The automation degree in the modern manufacturing industry is continuously improved, and under the drive of industrial big data, intelligent manufacturing becomes an important trend of future development. The intelligent manufacturing requirement is used for monitoring the production process in real time, and whether the cutter is in a healthy working state or not in the machining process of a product has an important influence on the machining quality of a workpiece. When the abrasion of the cutter exceeds the failure standard, if the cutter cannot be replaced in time, the surface quality of the machined workpiece can hardly meet the machining precision requirement. However, if the tool is replaced prematurely, it is wasteful and productivity is reduced. Therefore, the wear degree of the cutter is accurately monitored, and high processing efficiency and product precision can be brought to production and processing. Statistically, in 2018, month 4, the value of scrapped knives is up to $ 2 billion in the U.S. alone. Furthermore, approximately 20% of the machine tool down time is caused by tool failure. The German W.Koening statistical data shows that the intelligent cutter state monitoring system is introduced in the numerical control machining process, the production efficiency can be improved by 10-60%, and the downtime caused by cutter faults can be reduced by 75%. Therefore, the method has important significance in realizing accurate tool wear monitoring.
Disclosure of Invention
Aiming at the defects of the prior art in the field of online monitoring and research of cutter wear, the invention provides an online cutter wear monitoring method based on an attention-cycle neural network, which comprises the steps of preprocessing a data set and online monitoring of cutter wear, and the method can extract the characteristics of the cutter wear data and extract the data in a hierarchical manner in a grading and subsection manner, so that the real signals in the cutter wear process are completely stored; the tool wear signal data is a sequence with a time sequence relation, the model uses a recurrent neural network, and the network can fully consider the characteristics of historical information data and give weight when training; the model introduces an attention mechanism that can capture data features at the details at various times, which allows features of the real wear signal to be mined and noted in minute quantities. The model prediction accuracy is high, the generalization and self-adaption capabilities are good, the tool residual life prediction model provided by the text is strong in robustness, high in prediction accuracy rate and high in learning speed, and has good popularization and application values in milling.
In order to achieve the purpose, the invention provides an attention circulation neural network-based tool wear online monitoring method, which comprises the following steps of:
s1, collecting tool wear data in the machining process of a machine tool, and making an original data set: continuously acquiring signal data of a cutter in a cutting process at equal time intervals, recording processing parameter information of feeding speed and sampling frequency, and sorting the data to serve as an original data set;
s2, preprocessing a tool abrasion original data set, including invalid data elimination and wavelet packet transformation noise reduction:
s21, primarily rejecting invalid data, namely determining the position of a rejected data point by using a third quartile method, arranging data recorded by a sensor from small to large according to numerical values in the process of feeding and retracting, equally dividing the data into four parts, wherein a corresponding third truncation point is the position of the rejected data point;
s22, denoising detail invalid data, and extracting time and frequency characteristics of signal data by adopting wavelet transform, wherein a wavelet packet transform calculation formula is as follows:
where ψ (-) represents a wavelet function and has:
g k representing a wavelet function space W j Has an arbitrary function therein and has
k is an arbitrary integer, f (t) is space W j Any function of the number of the functions in (b),
is a scale function, alpha is a scale factor, tau is a displacement, t is a current time, a k Dt is the scaling factor, dt is the sign of the integral;
the scale function calculation formula is as follows:
wherein h is k Is the space V formed by the wavelet function in the real number domain calculation process j Any function in, k is any integer;
s3, inputting the preprocessed tool wear data set into a tool wear monitoring model based on an attention circulation neural network for iterative training;
s31, establishing a cutter wear monitoring model, wherein the cutter wear monitoring model comprises an attention mechanism feature extraction module and a gate control cyclic neural network monitoring module;
s32, training a cutter wear monitoring model: acquiring a preprocessed experimental data set, wherein the data set comprises wear data of a cutter cutting process and a wear value corresponding to the wear data, dividing the whole offline data set into a training set and a verification set according to a proportion, transmitting the training set and the verification set into a cutter wear monitoring model based on an attention circulation neural network for training, completing the training process by sequentially passing the data through an attention mechanism feature extraction module and a circulation neural network monitoring module, and outputting a training result and monitoring accuracy;
s33, storing and memorizing the t moment of the recurrent neural network monitoring module t Comprises the following steps:
S t ＝σ(UX t +WS t-1 )
wherein the content of the first and second substances,σ denotes softmax activation function, U, W denotes weight, X t Indicating the true input at the current moment, S t-1 A memory representing a previous time;
s34, outputting O to the recurrent neural network monitoring module at the t moment t Comprises the following steps:
O t ＝VS t +C
wherein, V represents the weight, C represents the output of the previous state moment;
s35, for the output moment y (t) predicted by the recurrent neural network monitoring module model, the following steps are carried out:
y(t)＝σ(O t )；
s4, judging whether the training result meets the requirement: calculating the error between the monitoring value and the actual value of the model, and when the experimental error is smaller than the threshold value specified by the current machining working condition, storing the trained cutter wear monitoring model and applying the model to online cutter wear detection analysis; based on the calculation of the tool wear monitoring model error of the attention gated recurrent neural network, the root mean square error RMSE and the average absolute error MAE are respectively selected as model evaluation indexes and the following parameters are provided:
where RMSE denotes the root mean square error calculation, y m Represents the predicted value of the m-th tool wear,
representing the true wear value of the mth cutter, and M represents the number of data in the verification set;
analyzing the accuracy of the tool prediction result, wherein the average absolute error MAE is as follows:
the range of the root mean square error RMSE and the range of the average absolute error MAE are both [0, + ∞ ], and when the tool wear monitoring value is matched with the tool wear true value, the RMSE and the MAE are both 0; when the numerical values of the RMSE and the MAE are larger, the network structure of the model needs to be adjusted to obtain small difference data;
s5, acquiring a wear signal in the machining process of the cutter on line, and carrying out on-line monitoring: and (3) loading the tool wear monitoring model stored in the step (S3), sequentially carrying out invalid data elimination and wavelet packet transformation on the sensor signal data acquired on line in the step (S2), and then transmitting the data into the stored model for wear monitoring to obtain a tool wear monitoring value.
Further, in step S31, the feature extraction module of the gravity mechanism multiplies the input data vector by a matrix to obtain 3 sub-vectors, which are Q vector, K vector and V vector respectively.
Further, the Q vector, the K vector and the V vector which are transmitted into the multi-head attention machine are subjected to linear transformation through a linear layer, 3 sub-vectors are transmitted into the scaling dot product attention machine, and multi-head calculation is achieved through multiple times of calculation.
Further, in the step S3, the gravity mechanism network performs feature extraction on the denoised experimental data, inputs the input data features into the gated recurrent neural network model, establishes a nonlinear mapping relationship between the tool wear degradation feature value and the tool wear value through the RNN layer, and outputs a tool wear monitoring value.
Preferably, the threshold value specified by the current machining condition in step S4 is calculated according to the current model monitoring accuracy, the machining parameter, and the precision required by machining.
Preferably, the tool wear monitoring model in step S31 is output through a customized full connection layer, and the customized full connection layer includes two liner connection layers, a ReLU activation function layer, and a Sigmoid activation function layer, and specifically includes the following steps:
s311, processing the data through a line connection layer and a ReLU activation function layer;
s312, transmitting the data to a second liner connection layer;
and S313, processing and outputting through a Sigmoid activation function layer.
Compared with the prior art, the invention has the beneficial effects that:
before monitoring cutter wear, the method eliminates invalid data and reduces noise through wavelet transformation, so that the integrity of experimental data is stronger; the method has the advantages of high monitoring accuracy on the cutter abrasion, small deviation of results, low discrete degree, good model fitting performance, and strong generalization performance and robustness; the invention provides a more convenient mode for using and maintaining the numerical control machine tool, and an operator can enter a tool changing link in advance through the tool wear monitoring method, thereby improving the machining efficiency of the machine tool.
Drawings
FIG. 1 is a schematic flow chart of the steps of a cutter wear online monitoring method based on an attention-cycle neural network provided by the invention;
FIG. 2 is a schematic diagram of invalid data in tool cutting signal data according to an embodiment of the present invention;
FIGS. 3a and 3b are schematic diagrams of color and black and white, respectively, of invalid data during a feed and a retract operation of an embodiment of the present invention processed by a third quartile method;
FIGS. 4a and 4b are schematic diagrams of an X-direction cutting force signal and an energy spectrum, respectively, according to an embodiment of the present invention;
fig. 5a and 5b are schematic diagrams illustrating visualization of X-direction cutting force raw signal data and noise reduction signal data, respectively, according to an embodiment of the present invention;
FIG. 6 is a structural schematic of a multi-head attention mechanism according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an RNN network according to an embodiment of the present invention;
FIGS. 8a and 8b are color and black-and-white graphs of tool wear monitor values versus actual values, respectively, for an embodiment of the present invention, using a C1 data set as an example;
FIGS. 9a and 9b are color and black-and-white graphs of tool wear monitor values versus actual values, using a C4 data set as an example, in accordance with an embodiment of the present invention;
fig. 10a and 10b are color and black plots of tool wear monitor values versus actual values for an embodiment of the present invention using a C6 data set as an example.
Detailed Description
The present application will be described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and are not to be construed as limiting the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The invention provides a cutter abrasion on-line monitoring method based on an attention circulation neural network, which comprises the following steps:
s1, acquiring tool wear data in the machining process of a machine tool, and making an original data set: continuously acquiring signal data of a cutter in a cutting process at equal time intervals, recording processing parameter information of feeding speed and sampling frequency, and sorting the data to serve as an original data set;
s2, preprocessing a tool abrasion original data set, including invalid data elimination and wavelet packet transformation noise reduction:
s21, primarily rejecting invalid data, namely determining the position of a rejected data point by using a third quartile method, arranging data recorded by a sensor from small to large according to numerical values in the process of feeding and retracting, equally dividing the data into four parts, wherein a corresponding third truncation point is the position of the rejected data point;
s22, denoising detail invalid data, and extracting time and frequency characteristics of signal data by adopting wavelet transform, wherein a wavelet packet transform calculation formula is as follows:
where ψ (-) represents a wavelet function and has:
g k representing a wavelet function space W j Has an arbitrary function therein and has
k is an arbitrary integer, f (t) is the space W j Any function of the number of the functions in (b),
is a scale function, alpha is a scale factor, tau is a displacement, t is a current time, a k Dt is the scaling factor, dt is the sign of the integral;
the scale function calculation formula is as follows:
wherein h is k Is the space V formed by the wavelet function in the real number domain calculation process j Any function in, k is any integer;
s3, inputting the preprocessed tool wear data set into a tool wear monitoring model based on an attention circulation neural network for iterative training;
s31, establishing a cutter wear monitoring model, wherein the cutter wear monitoring model comprises an attention mechanism feature extraction module and a gate control cyclic neural network monitoring module;
s32, training a cutter wear monitoring model: acquiring a preprocessed experimental data set, wherein the data set comprises wear data of a cutter cutting process and a wear value corresponding to the wear data, dividing the whole offline data set into a training set and a verification set according to the proportion of 8:2, transmitting the training set and the verification set into a cutter wear monitoring model based on an attention circulation neural network for training, and completing the training process by sequentially passing the data through an attention mechanism feature extraction module and a circulation neural network monitoring module, and outputting a training result and monitoring accuracy;
s33, storing and memorizing S for t moment of the recurrent neural network monitoring module t Comprises the following steps:
S t ＝σ(UX t +WS t-1 )
wherein, sigma represents softmax activation function, U, W represents weight, X t Indicating the true input at the current moment, S t-1 A memory representing a previous time;
s34, outputting O to the recurrent neural network monitoring module at the t moment t Comprises the following steps:
O t ＝VS t +C
wherein, V represents the weight, C represents the output of the previous state moment;
s35, for the output moment y (t) predicted by the recurrent neural network monitoring module model, the following steps are carried out:
y(t)＝σ(O t )；
s4, judging whether the training result meets the requirement: calculating the error between the monitoring value and the actual value of the model, and when the experimental error is smaller than the threshold value specified by the current machining working condition, storing the trained cutter wear monitoring model and applying the model to online cutter wear detection analysis; based on calculation of the cutter wear monitoring model error of the attention gated recurrent neural network, the root mean square error RMSE and the average absolute error MAE are respectively selected as model evaluation indexes, and the method comprises the following steps:
where RMSE denotes the root mean square error calculation, y m Represents the predicted value of the m-th tool wear,
representing the true wear value of the mth cutter, and M represents the number of data in the verification set;
analyzing the accuracy of the tool prediction result, wherein the average absolute error MAE is as follows:
the range of the root mean square error RMSE and the range of the average absolute error MAE are both [0, + ∞ ], and when the tool wear monitoring value is matched with the tool wear true value, the RMSE and the MAE are both 0; when the numerical values of the RMSE and the MAE are larger, the network structure of the model needs to be adjusted to obtain small difference data;
s5, acquiring a wear signal in the machining process of the cutter on line, and carrying out on-line monitoring: and (4) loading the tool wear monitoring model stored in the step (S3), sequentially carrying out invalid data elimination and wavelet packet transformation on the sensor signal data acquired on line in the step (S2), and then transmitting the data into the stored model for wear monitoring to obtain a tool wear monitoring value.
The following fig. 1 shows an attention-cycle neural network-based tool wear online monitoring method disclosed by the invention, which comprises the following steps:
s1, acquiring tool wear data in the machining process of a machine tool, and making an original data set: analyzing a tool wear data set milled by the PHM2010 of the public tool wear data set, wherein the data set comprises wear signal data of each feed process in a complete life cycle (C1, C2, C3, C4, C5 and C6) of 6 cutters and flank wear values (VB values) of 3 cutters (C1, C4 and C6) corresponding to the wear signal data; the complete life cycle of each cutter is 315 times of cutting feed; the data recorded in each cutting process comprises three-dimensional data of cutting force signals (x, y, z), three-dimensional data of vibration signals (x, y, z) and one-dimensional data of sound emission signals, seven dimensions are provided, the data recorded in each cutting process is about 20 ten thousand groups, and the processing parameters of the milling process experimental data set used are shown in the table 1.
| Cutting parameters | Main shaft rotating speed (r/min) | Feed speed (mm/min) | Milling Width (Y) (mm) | Depth of cut (Z) (mm) |
| Numerical value | 10400 | 1555 | 0.125 | 0.200 |
TABLE 1
S2, preprocessing a tool abrasion original data set, including invalid data elimination and wavelet packet transformation noise reduction:
s21, as shown in fig. 2, the amount of data recorded in each feed process is enormous, and invalid data is generated in the feed and retract processes. And (3) primarily removing invalid data, determining positions of removed data points by using a third quartile method, sequentially arranging data recorded by a sensor from small to large in the process of feeding and retracting, equally dividing the data into four parts, and taking a corresponding third truncation point as the position of the removed data point. And finding a feed rejection data point by a third quartile method, as shown in fig. 3a and 3b, wherein a blue line segment represents data which can be used for subsequent feature extraction, a red line segment represents invalid data generated by feed and retreat, and the data is rejected.
S22, denoising detail invalid data, and extracting time and frequency characteristics of signal data by adopting wavelet transform, wherein a wavelet packet transform calculation formula is as follows:
where ψ (-) represents a wavelet function and has:
g k representing a wavelet function space W j Within an arbitrary function and have
k is an arbitrary integer, f (t) is the space W j Any function of the number of the functions in (b),
is a scale function, alpha is a scale factor, tau is a displacement, t is a current time, a k Dt is the scaling factor, dt is the sign of the integral;
the scale function calculation formula is as follows:
wherein h is k Is the space V formed by the wavelet function in the real number domain calculation process j And k is any integer.
The X-direction cutting force signal data of the 10 th pass of the first tool C1 is subjected to energy spectrum analysis as shown in fig. 4a and 4 b. The method adopts 5-layer wavelet transformation, the basic function of the wavelet adopts 'sym 8', and the data is subjected to wavelet transformation according to the energy spectrum analysis of the signal data so as to remove non-signal data such as noise, wherein original cutting force signals and noise reduction signals in the x-axis direction are shown in figures 5a and 5 b.
S3, inputting the preprocessed tool wear data set into a tool wear monitoring model based on an attention circulation neural network for iterative training:
s31, establishing a cutter wear monitoring model, wherein the cutter wear monitoring model consists of an attention mechanism characteristic extraction module and a gate control cyclic neural network monitoring module; for an input data vector, the mechanism multiplies the data vector by a matrix to obtain 3 sub-vectors which are respectively a Q (Query) vector, a K (Key) vector and a V (Value) vector; then, the vector Q, the vector K and the vector V are subjected to linear transformation through a linear layer, 3 subvectors are transmitted into a scaling dot product attention mechanism, and i is called as a head in the attention mechanism after i times of calculation; and finally, connecting the results after the calculation of the scaling dot product attention mechanism, and performing linear transformation on the spliced result through a linear layer to obtain a final result. The tool wear monitoring model is output through a self-defined full connection layer, the self-defined full connection layer comprises two line connection layers, a ReLU activation function layer and a Sigmoid activation function layer, and the tool wear monitoring model specifically comprises the following steps:
s311, processing the data through a line connection layer and a ReLU activation function layer;
s312, transmitting the data to a second liner connection layer;
and S313, processing and outputting through a Sigmoid activation function layer.
The network structure of the multi-head attention mechanism is shown in fig. 6.
S32, training a cutter wear monitoring model: the method comprises the steps of obtaining a preprocessed experimental data set, dividing the whole offline data set into a training set and a verification set according to the proportion of 8:2, transmitting the training set and the verification set into a cutter abrasion monitoring model based on an attention circulation neural network for training, enabling the data to sequentially pass through an attention mechanism feature extraction module and a circulation neural network monitoring module, completing the training process, and outputting a training result and monitoring accuracy.
S33, storing and memorizing the t moment of the recurrent neural network monitoring module t Comprises the following steps:
S t ＝σ(UX t +WS t-1 )
wherein, sigma represents softmax activation function, U, W represents weight, X t Indicating the true input at the current moment, S t-1 A memory representing a previous time;
s34, outputting O to the recurrent neural network monitoring module at the t moment t Comprises the following steps:
O t ＝VS t +C
wherein, V represents the weight, C represents the output of the previous state moment;
s35, for the output moment y (t) predicted by the recurrent neural network monitoring module model, the following steps are carried out:
y(t)＝σ(O t )；
the recurrent neural network structure is shown in fig. 7.
The step S3 is an important point of the present invention, and is mainly embodied in that the attention mechanism network performs feature extraction on the denoised experimental data, inputs the currently input data features into the gated recurrent neural network model, establishes a nonlinear mapping relationship between the tool wear degradation feature value and the tool wear value through the RNN layer, and outputs the tool wear monitoring value.
S4, judging whether the training result meets the requirement: calculating the error between the monitoring value and the actual value of the model, and when the experimental error is smaller than the threshold value specified by the current machining working condition, storing the trained cutter wear monitoring model and applying the trained cutter wear monitoring model to online cutter wear detection analysis, wherein the threshold value is calculated according to the monitoring accuracy of the current model, the machining parameters and the precision required by machining; based on calculation of the cutter wear monitoring model error of the attention gated recurrent neural network, the root mean square error RMSE and the average absolute error MAE are respectively selected as model evaluation indexes, and the method comprises the following steps:
where RMSE denotes the root mean square error calculation, y m Represents the predicted value of the m-th tool wear,
representing the true wear value of the mth cutter, and M represents the number of data in the verification set;
analyzing the accuracy of the tool prediction result, wherein the average absolute error MAE is as follows:
the range of the root mean square error RMSE and the range of the average absolute error MAE are both [0, + ∞ ], and when the tool wear monitoring value is completely matched with the tool wear true value, the RMSE and the MAE are both 0; when the numerical values of the RMSE and the MAE are larger, the training precision of the model is poor, and the network structure of the model needs to be adjusted to obtain smaller difference data;
s5, acquiring a wear signal in the cutter machining process on line, and carrying out on-line monitoring: and (3) loading the tool wear monitoring model stored in the step (S3), sequentially carrying out invalid data elimination and wavelet packet transformation on the sensor signal data acquired on line in the step (S2), and then transmitting the data into the stored model for wear monitoring to obtain a tool wear monitoring value.
Table 2 shows the analysis of the prediction accuracy by the method, wherein RMSE/MAE represents the root mean square error function and the average absolute error function, respectively, and the value of the loss function is generally used to evaluate the accuracy of the model in the deep learning field. Fig. 8a and 8b, fig. 9a and 9b, and fig. 10a and 10b represent graphs of monitored values and true values obtained by monitoring through the above method with data C1, C4, and C6 as experimental data, respectively, table 2 shows the accuracy of the calculation results, and the last column average represents the average accuracy of three experiments. By analyzing the results of the data in the table, the calculation result of the invention can be well applied to the monitoring calculation of the actual cutter abrasion.
| No. | C1 | C4 | C6 | Average |
| RMSE | 1.771 | 3.125 | 2.305 | 2.400 |
| MAE | 0.588 | 0.944 | 0.860 | 0.797 |
TABLE 2
In conclusion, the monitoring results in this case prove that the attention-cycle-neural-network-based tool wear online monitoring method has a good monitoring effect.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made thereto without departing from the spirit and scope of the invention and it is intended to cover in the claims the invention any modifications and equivalents.
Claims (6)
1. A cutter abrasion on-line monitoring method based on attention circulation neural network is characterized by comprising the following steps:
s1, acquiring tool wear data in the machining process of a machine tool, and making an original data set: continuously acquiring signal data of a cutter in a cutting process at equal time intervals, recording processing parameter information of feeding speed and sampling frequency, and sorting the data to serve as an original data set;
s2, preprocessing a tool abrasion original data set, including invalid data elimination and wavelet packet transformation noise reduction:
s21, primarily rejecting invalid data, namely determining the position of a rejected data point by using a third quartile method, arranging data recorded by a sensor from small to large according to numerical values in the process of feeding and retracting, equally dividing the data into four parts, wherein a corresponding third truncation point is the position of the rejected data point;
s22, denoising detail invalid data, and extracting time and frequency characteristics of signal data by adopting wavelet transform, wherein a wavelet packet transform calculation formula is as follows:
where ψ (-) represents a wavelet function and has:
g k representing a wavelet function space W j Has an arbitrary function therein and has
k is an arbitrary integer, f (t) is the space W j Any function of the number of the functions in (b),
is a scale function, alpha is a scale factor, tau is a displacement, t is a current time, a k Is a scaling factor;
the scale function calculation formula is as follows:
wherein h is k Is the space V formed by the wavelet function in the real number domain calculation process j Any function in (c), k is any integer;
s3, inputting the preprocessed tool wear data set into a tool wear monitoring model based on an attention circulation neural network for iterative training;
s31, establishing a cutter wear monitoring model, wherein the cutter wear monitoring model comprises an attention mechanism feature extraction module and a gate control cyclic neural network monitoring module;
s32, training a cutter wear monitoring model: acquiring a preprocessed experimental data set, wherein the data set comprises wear data of a cutter cutting process and a wear value corresponding to the wear data, dividing the whole offline data set into a training set and a verification set in proportion, transmitting the training set and the verification set into a cutter wear monitoring model based on an attention circulation neural network for training, completing the training process by sequentially passing the data through an attention mechanism feature extraction module and a circulation neural network monitoring module, and outputting a training result and monitoring accuracy;
s33, storing and memorizing the t moment of the recurrent neural network monitoring module t Comprises the following steps:
S t ＝σ(UX t +WS t-1 )
wherein, sigma represents softmax activation function, U, W represents weight, X t Indicating the true input at the current moment, S t-1 A memory representing a previous time;
s34, outputting O to the recurrent neural network monitoring module at the t moment t Comprises the following steps:
O t ＝VS t +C
wherein, V represents the weight, C represents the output of the previous state moment;
s35, for the output moment y (t) predicted by the recurrent neural network monitoring module model, the following steps are carried out:
y(t)＝σ(O t )；
s4, judging whether the training result meets the requirement: calculating the error between the monitoring value and the actual value of the model, and when the experimental error is smaller than the threshold value specified by the current machining working condition, storing the trained cutter wear monitoring model and applying the model to online cutter wear detection analysis; based on calculation of the cutter wear monitoring model error of the attention gated recurrent neural network, the root mean square error RMSE and the average absolute error MAE are respectively selected as model evaluation indexes, and the method comprises the following steps:
where RMSE denotes the root mean square error calculation, y m The m-th predicted value of the tool wear is shown,
representing the true wear value of the mth cutter, and M represents the number of data in the verification set;
analyzing the accuracy of the tool prediction result, wherein the average absolute error MAE is as follows:
the range of the root mean square error RMSE and the range of the average absolute error MAE are both [0, + ∞ ], and when the tool wear monitoring value is matched with the tool wear true value, the RMSE and the MAE are both 0; when the RMSE and MAE values are larger, the network structure of the model needs to be adjusted to obtain small difference data;
s5, acquiring a wear signal in the machining process of the cutter on line, and carrying out on-line monitoring: and (4) loading the tool wear monitoring model stored in the step (S3), sequentially carrying out invalid data elimination and wavelet packet transformation on the sensor signal data acquired on line in the step (S2), and then transmitting the data into the stored model for wear monitoring to obtain a tool wear monitoring value.
2. The on-line tool wear monitoring method based on attention cycle neural network as claimed in claim 1, wherein said step S31 is implemented by an attention machine mechanism feature extraction module, which multiplies the input data vector by matrix to obtain 3 sub-vectors, i.e. Q vector, K vector and V vector.
3. The on-line tool wear monitoring method based on the attention-cycle neural network as claimed in claim 2, wherein the Q vector, the K vector and the V vector transmitted into the multi-head attention mechanism are subjected to linear transformation through a linear layer, 3 sub-vectors are transmitted into the scaling point integral attention mechanism, and multi-head calculation is realized through multiple calculations.
4. The on-line tool wear monitoring method based on the attention-cycle neural network as claimed in claim 1, wherein in step S3, the attention-cycle neural network performs feature extraction on the denoised experimental data, inputs the input data features into a gated-cycle neural network model, establishes a nonlinear mapping relationship between a tool wear degradation feature value and a tool wear value through an RNN layer, and outputs a tool wear monitoring value.
5. The method for on-line monitoring tool wear based on attention cycle neural network as claimed in claim 1, wherein the threshold value specified by the current machining condition in step S4 is calculated according to the current model monitoring accuracy, the machining parameter and the precision required by machining.
6. The tool wear online monitoring method based on the attention cycle neural network as claimed in claim 1, wherein the tool wear monitoring model in step S31 is output through a customized full-link layer, the customized full-link layer includes two line link layers, a ReLU activation function layer and a Sigmoid activation function layer, and specifically includes the following steps:
s311, processing the data through a line connection layer and a ReLU activation function layer;
s312, transmitting the data to a second liner connection layer;
and S313, processing and outputting through a Sigmoid activation function layer.
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