CN115099266A

CN115099266A - Hard vehicle surface white layer prediction method based on gradient lifting decision tree

Info

Publication number: CN115099266A
Application number: CN202210612991.3A
Authority: CN
Inventors: 朱欢欢; 李厚佳; 迟玉伦; 张梦梦; 周立波; 辜庭皓; 曹斌
Original assignee: SHANGHAI TECHNICIAN SCHOOL; Shanghai University of Engineering Science
Current assignee: SHANGHAI TECHNICIAN SCHOOL; Shanghai University of Engineering Science
Priority date: 2022-05-31
Filing date: 2022-05-31
Publication date: 2022-09-23

Abstract

The invention relates to a hard vehicle surface white layer prediction method based on a gradient lifting decision tree, which comprises the following steps: signal data acquisition: collecting dynamic cutting signal data in the hard turning process; characteristic extraction and analysis: performing feature extraction and analysis on the dynamic cutting signal data to obtain a main feature quantity for identifying a white layer; building and training a prediction model: constructing a hard turning surface white layer prediction model based on a gradient lifting decision tree, wherein the main characteristic quantity is used as the input of the gradient lifting decision tree, and the gradient lifting decision tree inputs a prediction result; online prediction: and carrying out signal data acquisition, feature extraction and analysis on line, inputting the main feature quantity into a trained hard turning surface white layer prediction model, and obtaining a prediction result. Compared with the prior art, the method can realize the online prediction of the white layer on the surface of the hard vehicle, and has higher accuracy.

Description

Hard vehicle surface white layer prediction method based on gradient lifting decision tree

Technical Field

The invention relates to a hard vehicle surface white layer prediction method, in particular to a hard vehicle surface white layer prediction method based on a gradient lifting decision tree.

Background

With the continuous development of numerical control machine tools and cutter materials, hard turning has the advantages of high processing efficiency, small pollution, high flexibility and the like, has become a development trend by taking hard cutting as a finish machining mode of hardened steel, and is widely applied to the fields of bearings, grinding tools, machine tools, automobile manufacturing industry and the like. In the hard turning process, the surface of a high-hardness material workpiece is easy to generate a white layer phenomenon, the hard turning white layer phenomenon is that the surface of the workpiece is subjected to phase change due to the plastic deformation of the workpiece and high temperature generated in the cutting process, and then a layer of deteriorated layer is formed on the processed surface, and the stress field, the temperature field and the tissue change are mutually coupled in the hard cutting process.

As shown in fig. 1 (a), the hard turning white layer forming process forms three cutting deformation regions under the action of the tool on the chip, which are respectively: the shear plane deformation zone (first deformation zone), the friction zone between the front tool face and the chip (second deformation zone), and the friction zone between the rear tool face and the machined surface (third deformation zone), wherein under the shearing action of the first deformation zone and the plowing action of the third deformation zone, the end of the workpiece generates mechanical stress, and the distribution of the shearing and plowing action loads is shown as (b) in fig. 1. In the cutting process, the shearing area of the workpiece is severely deformed, and the friction between the cutter and the workpiece does work to generate heat, as shown in (c) in fig. 1, so that three heating areas are generated during cutting: heat generated by plastic deformation of shearing surface,The frictional heat generated between the front tool face and the chips and the frictional heat generated between the rear tool face and the workpiece are far away from the surface of the machined workpiece, and the generated frictional heat is negligible. After the cutting heat is generated, the surface temperature of the workpiece can be sharply increased along with heat conduction and dispersion, so that the microstructure transformation of the surface of the workpiece occurs. The transformation process of the hard cutting structure is shown in fig. 1 (d), the temperature of the surface of the workpiece is rapidly increased along with the increase of the cutting time, and when the temperature is rapidly increased to the austenite temperature (namely, A) _c1 Wire), the surface structure begins to undergo austenite transformation. The temperature rising process of the surface of the hard cutting workpiece is a rapid temperature rising process, so that the transformation of austenite can be rapidly completed by the surface structure. Over time, the machined surface temperature of the workpiece begins to drop rapidly due to heat conduction and heat dissipation, at which time the cooling rate is much greater than the martensite transformation rate, when the machined surface temperature of the workpiece is below the martensite temperature (i.e., M) _s Wire), martensitic transformation occurs to form a final microstructure, i.e., a white layer phenomenon.

Although the hard turning white layer can enhance the corrosion resistance and the wear resistance of materials, the formation of the white layer causes the uniformity of the surface structure of a workpiece to be poor, cracks are more easily formed, and the quality of the processed surface of the workpiece is seriously affected.

The white layer belongs to a microstructure, usually exists on the surface of metal or in a sub-surface layer area, and presents a bright white structure under an optical microscope after metallographic corrosion, so that the quality and performance of the produced workpiece are greatly damaged. In order to ensure the quality of a processed product, the white layer phenomenon on the surface of a processed workpiece is avoided, and in actual processing, an operator is difficult to directly observe whether the white layer phenomenon appears on the surface of the processed workpiece through naked eyes, and the white layer phenomenon on the surface of the processed workpiece is often detected by adopting a mode of preparing a white layer test piece. However, due to the complex process of preparing the white-layer test piece, time and labor are consumed, a great deal of manpower and material resources of a factory can be seriously wasted, the production efficiency of the factory is greatly influenced, the full detection of each product part cannot be carried out, and the production yield of the processed product is difficult to ensure.

Therefore, in order to effectively and fully detect the white layer phenomenon of each part in the hard turning in real time, a more effective detection means is needed to realize the real-time online prediction of the white layer phenomenon on the surface of the workpiece in the hard turning.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a hard vehicle surface white layer prediction method based on a gradient lifting decision tree.

The purpose of the invention can be realized by the following technical scheme:

a hard vehicle surface white layer prediction method based on a gradient boosting decision tree comprises the following steps:

signal data acquisition: collecting dynamic cutting signal data in the hard turning process;

characteristic extraction and analysis: carrying out feature extraction and analysis on the dynamic cutting signal data to obtain a main feature quantity for identifying a white layer;

building and training a prediction model: constructing a hard turning surface white layer prediction model based on a gradient lifting decision tree, wherein the main characteristic quantity is used as the input of the gradient lifting decision tree, and the gradient lifting decision tree inputs a prediction result;

online prediction: and carrying out signal data acquisition, feature extraction and analysis on line, inputting the main feature quantity into a trained hard turning surface white layer prediction model, and obtaining a prediction result.

Preferably, the dynamic cutting signal data comprises any one or a combination of a plurality of dimensional data of an acoustic emission signal, a power signal and a vibration signal.

Preferably, the feature extraction and analysis comprises:

performing time domain feature extraction and wavelet packet energy feature extraction on the dynamic cutting signal data, and taking the extracted parameters as feature parameters for identifying the white layer;

and analyzing the influence degree of the characteristic parameters by using a characteristic importance analysis method, and extracting a main characteristic quantity for identifying the white layer by using a main component analysis method.

Preferably, the time-domain feature extraction includes performing time-domain feature extraction on the acoustic emission signal and the power signal respectively.

Preferably, the time-domain characteristics include peak value, root-mean-square, form factor, peak factor, pulse factor, margin factor, maximum value, average value, and minimum value.

Preferably, the wavelet packet energy feature extraction includes respectively performing wavelet packet decomposition on the acoustic emission signal and the vibration signal.

Preferably, when the prediction model is constructed and trained, different models are obtained according to dimension training of input data of the hard turning surface white layer prediction model, the prediction performance of the models is evaluated, and the models with the best performance are selected as online prediction models.

Preferably, the predicted performance of the evaluation model comprises:

testing the model by using a test set, and constructing a confusion matrix based on a test result, wherein the confusion matrix comprises four basic indexes which are respectively: the number TP of samples correctly predicted as a white layer, the number FP of samples incorrectly predicted as a white layer for non-white layer samples, the number FN of samples incorrectly predicted as a non-white layer for white layer samples, and the number TN of samples correctly predicted as a non-white layer;

and (3) visually evaluating the performance of the model by using a confusion matrix, wherein the model comprises an accuracy A, an accuracy P, a recall R, F1 point value, a sensitivity TPR, a specificity FPR, an ROC curve and an AUC value, and specifically:

the ROC curve is a curve drawn by taking the sensitivity TPR as an ordinate and the specificity FPR as an abscissa, and the AUC value is the area under the ROC curve.

Preferably, the higher the accuracy A, accuracy P, recall R, F1 score values and AUC values, the better the model performance.

Preferably, the model parameters are adjusted and optimized by using a grid search method in the process of predicting the model training.

Compared with the prior art, the invention has the following advantages:

(1) the invention provides a method for online prediction of a white layer on the surface of a hard turning workpiece based on a gradient lifting decision tree by combining various sensor technologies such as acoustic emission, three-dimensional vibration, power and the like, and has important significance for online intelligent prediction of a white layer phenomenon in a hard turning process.

(2) In order to evaluate the prediction performance of the white layer of the model, the invention provides a set of evaluation method based on the confusion matrix to ensure the prediction performance of the gradient-enhanced decision tree model, and the prediction effect of the model is ensured by utilizing the accuracy, classification precision, recall ratio, F1 value, roc curve and Auc value, so that the data dimension of the input prediction model can be adjusted according to the requirement, and the training of model parameters is carried out, thereby ensuring the accuracy of the subsequent on-line prediction.

(3) The structure of the model evaluated by the predictive performance evaluation method provided by the invention shows that compared with the characteristics of power, vibration signals and the like, the acoustic emission signal characteristics are more sensitive to the turning white layer phenomenon and are important characteristic parameters for predicting the white layer phenomenon in the turning process, experiments further prove that the classification accuracy, classification precision, recall rate, F1 value and Auc value of the model containing the acoustic emission signal characteristics are greatly improved, and an effective solution is provided for accurately predicting the turning white layer phenomenon.

(4) According to the method, a large amount of experimental researches are carried out on the prediction of the hard car white layer phenomenon by utilizing the gradient lifting decision tree model, and the results show that through comparison and analysis with other model algorithms (SVM algorithm and xgboost algorithm): the gradient lifting decision tree model established by the invention has higher accuracy in predicting the white layer phenomenon in the hard driving process, can more effectively identify the white layer phenomenon generated in the hard driving process, and has important significance in realizing the online prediction of the white layer phenomenon in the hard driving process.

Drawings

FIG. 1 is a schematic view of a hard white layer formation analysis of the present invention;

FIG. 2 is a flowchart illustrating an overall scheme of a hard vehicle surface white layer prediction method based on a gradient boosting decision tree according to the present invention;

FIG. 3 is a schematic flow chart of a gradient boosting decision tree according to the present invention;

FIG. 4 is a flowchart illustrating a method for predicting a white layer on a hard vehicle surface based on a gradient boosting decision tree according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of the ROC curve coordinate and its evaluation index of the present invention;

FIG. 6 is a preprocessed signal from various sensors in an embodiment of the invention;

FIG. 7 is a time domain feature comparison diagram in accordance with an embodiment of the present invention;

FIG. 8 is a diagram of energy ratios of frequency bands according to an embodiment of the present invention;

FIG. 9 is a diagram illustrating the relative importance of top ten features in an embodiment of the invention;

FIG. 10 is a diagram illustrating parameter optimization and selection of a gradient boosting decision tree according to an embodiment of the present invention;

FIG. 11 is a graph comparing ROC curves and AUC values for models containing and not containing acoustic emission signals according to embodiments of the present invention;

FIG. 12 is a graph comparing ROC curves and AUC values of different models in examples of the present invention.

Detailed Description

The invention is described in detail below with reference to the figures and the specific embodiments. Note that the following description of the embodiment is merely an example of the nature, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiment.

Examples

As shown in fig. 2, the invention relates to a hard vehicle surface white layer prediction method based on a gradient boosting decision tree, which comprises the following steps:

constructing and training a prediction model: constructing a hard turning surface white layer prediction model based on a gradient lifting decision tree, wherein the main characteristic quantity is used as the input of the gradient lifting decision tree, and the gradient lifting decision tree inputs a prediction result;

online prediction: and (4) carrying out signal data acquisition, feature extraction and analysis on line, inputting the main feature quantity into a trained hard turning surface white layer prediction model, and obtaining a prediction result.

Wherein, the dynamic cutting signal data comprises any one dimension data or a combination of a plurality of dimension data in the acoustic emission signal, the power signal and the vibration signal. When the prediction model is built and trained, different models are obtained according to dimension training of input data of the hard turning surface white layer prediction model, the prediction performance of the models is evaluated, and the models with the best performance are selected as online prediction models. Experimental verification shows that the prediction model constructed by taking the data of the acoustic emission signal, the power signal and the vibration signal as the input of the prediction model has the best performance and the highest prediction accuracy.

The method provided by the invention can be used for monitoring the hard turning process on line by utilizing the acoustic emission, power and vibration sensors and can achieve the purpose of predicting the white layer of the hard turning surface on line in real time by depending on an intelligent algorithm model. The gradient lifting decision tree has better performance in the aspects of processing model overfitting problems, data missing problems and model prediction efficiency, and is more favorable for achieving the purpose of predicting the white layer on the surface of a machined workpiece on line.

As can be seen from fig. 2, the method of the present invention mainly includes: signal data acquisition, feature extraction and analysis, prediction model construction and training and prediction result analysis. The following is specifically described:

firstly, signal data acquisition

And acquiring various normal (without white layer) and abnormal (with white layer) sensor signal data in the hard turning process by using a power sensor, an acoustic emission sensor and a vibration sensor, and using the acquired data as sample data for training and testing a subsequent model.

Second, feature extraction and analysis

The characteristic extraction and analysis comprises the following steps:

and analyzing the influence degree of the characteristic parameters by using a characteristic importance analysis method, and extracting main characteristic quantity for identifying the white layer by using a main component analysis method.

The time domain feature extraction comprises time domain feature extraction on the acoustic emission signal and the power signal respectively, wherein the time domain features comprise a peak value, a root mean square, a form factor, a peak value factor, a pulse factor, a margin factor, a maximum value, an average value and a minimum value. The wavelet packet energy characteristic extraction comprises the step of respectively carrying out wavelet packet decomposition on the acoustic emission signal and the vibration signal.

Thirdly, building and training a prediction model

The gradient lifting decision tree model is used as an integrated algorithm based on a tree, can be selected and sequenced according to the feature importance degree during construction, can realize high prediction accuracy of the model under relatively less parameter adjusting time, and is suitable for predicting a white layer of a hard turning surface. Meanwhile, in order to evaluate the effect of the white layer recognition of the hard turning surface, the invention provides a set of evaluation method for evaluating the predictive performance of the gradient boosting decision tree model based on the confusion matrix so as to ensure that the model has excellent white layer recognition capability and can more accurately predict the white layer phenomenon generated in the hard turning process.

1. Gradient Boost Decision Tree (GBDT)

As shown in fig. 3, the gradient boosting decision tree includes a plurality of decision trees, and the final prediction model is generated by combining the results of all the decision trees. Each decision tree is constructed to reduce the residual error of the previous model, and the final residual error is close to the zero point in the gradient direction through a continuous iteration mode.

Fig. 4 is a flowchart of an algorithm for performing statistical classification by applying a gradient boosting decision tree algorithm to a sliced white layer signal sample. Firstly, acoustic emission signals, time domain characteristics of power signals and vibration signals and wavelet packet energy characteristic parameters are respectively extracted and normalized. And then, taking the characteristic parameters as input samples of the model and continuously training and learning, and finally, obtaining the hard car white layer prediction result as the output of the model. In the model training and learning process, firstly, the obtained characteristic parameters are input into the 1 st gradient lifting decision tree to obtain the estimation of the model on a training sample, and then, the model residual error is calculated based on the obtained sample estimation result. And then, repeatedly training the 2 nd model according to the process based on the input information of the original sample and the residual error until M models are trained, and finally obtaining the prediction result of the hard car white layer.

For a white layer training data set containing samples, T { (x) ₁ ,y ₁ ),...,(x _N ,y _N ) The specific algorithm flow is as follows:

(1) the learner is first initialized, i.e.:

in the formula (f) ₀ (x) For an initial decision tree with only one root node, c is a constant that minimizes the loss function, L (y) _i C) is a loss function for calculating the difference between the target value and the calculated value, wherein y _i Is the ith training data.

In order to further improve the performance of the model and reduce the residual value, a log-likelihood function is introduced as a loss function to reduce the residual loss of the sample, and the expression is as follows:

L(y,f(x))＝log(1+exp(-yf(x))) (2)

(2) assuming that the number of iterations M is 1, 2.. times, M, then for each sample i is 1, 2.. times, N, the negative gradient, i.e., the residual, of the ith training sample is calculated, with:

using the residual value as the true value of the new sample, and according to the sample and the negative gradient direction (x, r) _mi ) (i ═ 1, 2.., N) is calculated and fitted to the residual values, resulting in a decision tree T consisting of J leaf nodes _m The corresponding leaf node region is R _mj (J ═ 1, 2.. times, J), then the best fit value for each leaf node is:

updating the strong learner, then:

wherein, I is an indicative function of the ith training sample in the jth leaf node region, and:

(3) after M iterations, the final learning obtained is:

different from other models, the gradient lifting decision tree model can realize the identification and the sorting of characteristic parameters according to the influence degree on the prediction result, not only can shorten the calculation time and accelerate the training speed, but also can improve the prediction precision of the model, and the specific method is as follows:

for a single decision tree T, the importance can be obtained by calculating the number of times that the variable is selected as the decision tree splitting variable in the iteration process, as shown in equation (7).

Wherein J-1 is the number of non-leaf nodes, v _t Is a feature associated with a non-leaf node t,

the node is a reduced value after being split in a square error mode, and the larger the value is, the higher the influence degree of the characteristic parameter on the prediction result is, the more important the influence degree is;

for sets of decision trees

The global importance of a feature variable can be measured by the average of its importance in a single decision tree, as shown in equation (8).

Where M is the number of decision trees,

is the importance of the feature parameter k in the mth decision tree, and the sum of the importance of all feature parameters is 1.

And optimizing the model parameters by utilizing a grid search method in the process of the prediction model training. In addition, in the construction process of the model, in order to evaluate the reliability of the model and ensure that the model has excellent white layer recognition capability, the invention provides a set of evaluation method for evaluating the prediction performance of the gradient boosting decision tree model based on the confusion matrix to ensure the prediction performance of the model.

2. Evaluation method

In order to evaluate the prediction performance of the white layer of the model, the invention provides a set of evaluation method based on the confusion matrix to ensure the prediction performance of the gradient boosting decision tree model, and the prediction effect of the model is ensured by utilizing the accuracy, the classification precision, the recall rate, the F1 value, the roc curve and the Auc value.

TABLE 1 confusion matrix

As shown in table 1, the confusion matrix is a two-dimensional cross table with real values as row variables and predicted values as column variables, and divides the real categories and prediction categories of samples into four basic indexes (TP, FP, FN, TN, respectively, where TP represents the number of samples that are correctly predicted as a white layer, FP represents the number of samples that are incorrectly predicted as a white layer, FN represents the number of samples that are incorrectly predicted as a non-white layer, and TN represents the number of samples that are correctly predicted as a non-white layer) to represent the distribution of various classification results, so that the confusion degree of the classification model can be effectively measured. After the model is constructed, the characteristics of the model performance are evaluated by using the confusion matrix visualization, and the prediction classification condition of the model on the white layer sample is effectively distinguished. In addition, in order to avoid the condition that the white layer sample of the hard turning surface is judged as an abnormal sample by mistake, the accuracy (A), the accuracy (P), the recall rate (R) and the F1 value are selected to evaluate the effectiveness of the prediction model.

(1) Accuracy (A)

The accuracy rate represents the proportion of the number of samples predicted to be correct by the model to the total number of samples in the test set, and the formula is shown as (9).

(2) Rate of accuracy (P)

The accuracy rate represents the proportion of the number of white layer samples predicted correctly by the model to the number of white layer samples in the prediction category, and the formula is shown as (10).

(3) Recall rate (R)

The recall ratio represents the proportion of the number of samples predicted to be correct by the model to the number of samples of the white layer in the actual category, and the formula is shown as (11).

(4) F1 score value

The F1 point value is the harmonic mean of the precision rate and the recall rate, and the formula is shown as (12).

(5) ROC curve and AUC values

The ROC (receiver operating characteristic) curve reflects the correlation between the sensitivity and specificity of the model, and is a curve for measuring the predictive performance of the model. It uses the discrimination threshold value for distinguishing the positive sample from the negative sample as the critical point, and after calculating the corresponding sensitivity and specificity at each critical point, draws the ROC curve with the sensitivity TPR as the ordinate and the specificity FPR as the abscissa, and the schematic diagram is shown in fig. 5 (a). The sensitivity is the percentage of the positive sample which is correctly predicted, the higher the sensitivity is, the higher the probability that the positive sample is correctly recognized is indicated, the specificity is the percentage of the negative sample which is correctly predicted, and the higher the specificity is, the higher the probability that the negative sample is correctly recognized is indicated. The expression is shown in formula (13) and formula (14).

As can be seen from fig. 5 (a), the prediction accuracy of the ROC curve is determined by the position of the curve, and when the critical point is located on the reference line AB, the sensitivity and specificity of the model are half each, and the prediction result is meaningless. When the critical point is located on a straight line AC or CB, namely the specificity of the model is 1 or the sensitivity of the model is 1, the model is indicated to have the highest prediction precision, and the generalization performance of the model is the best. When the critical point is located on the curve ADB, it indicates that there is an overlapping region between the negative sample and the positive sample, i.e. there is a case of missing judgment or erroneous judgment in the model, as shown in (b) in fig. 5. In conjunction with the above analysis, it can be seen that the closer the ROC curve is to the upper left corner (point C), the better the prediction performance of the model. In order to quantify the prediction performance of the model, the AUC value (area under the ROC curve) is adopted to intuitively calculate the prediction accuracy of the model, namely the larger the AUC value is, the higher the prediction accuracy of the model is.

To verify the validity of the above model, a hard cutting test was performed. The test was performed on actual hard car bearing products at the factory. The experiment selects a horizontal numerical control lathe RFCX26, the power of a main shaft of the lathe is 15kw, and the highest rotating speed can reach 4500 r/min; selecting a ring workpiece of the quenched bearing steel as a processing test piece, wherein the workpiece is made of GCr15, and the average hardness of the workpiece is HRC 60. The machine tool is provided with an automatic rotary tool turret, 12 cutters can be placed on the tool turret at most, the number of the actually used cutters in the test is five, and the actually used cutters are respectively a first cutter for boring, a second cutter for lathing a sealing groove, a third cutter for finely lathing the bottom surface of a raceway, a fourth cutter for roughly lathing the raceway and a fifth cutter for finely lathing the raceway; the fifth tool finish turning raceway is used as the last procedure to most easily generate a white layer, so that the turning tool adopts a Polycrystalline Cubic Boron Nitride (PCBN) blade with negative chamfers to perform finish cutting on a workpiece, the negative chamfers of the blade are 0.1mm, the angle is 15 degrees, and the white layer generation belongs to a microstructure, so that the process online prediction is performed on the white layer easily generated in the fifth finish turning machining process.

The factory workshop where this experiment was located is full-automatic production line, 24 hours incessant production, and single product part process time is 92.28s, and the experiment has lasted 3 days totally, therefore produces 2808 of processing product part in the experimentation. In order to facilitate the processing of workpieces, firstly, a blank prepared in advance is fixed on a special spindle of a machine tool spindle so as to reduce errors caused by impact; then, after the cutting test is finished, taking down the machined workpiece from the machine tool, and measuring the external dimension of the workpiece on a dimension measuring instrument according to the requirements of an inspection instruction after alcohol cleaning; and finally, obtaining the machined surface of the workpiece meeting the requirements, and carrying out white layer analysis research on the machined surface.

In order to effectively identify the white layer phenomenon in the hard turning process of the part, the experiment carries out online prediction and identification on the machining process of the part based on various sensor signals such as acoustic emission signals, three-way vibration signals, power signals and the like. The acoustic emission sensor adopts Fuji AE303S, the resonant frequency of the sensor is 30 +/-20% kHz, the sensitivity can reach 75 +/-5 dB, and the acoustic emission sensor is fixed on a tool rest shaft to monitor acoustic emission signals in the hard turning process; the test adopts a three-way vibration sensor with the model number of 8396, the sensitivity of 5000mV/g and the measurement range of +/-2 g, and the three-way vibration sensor is fixed on a tool rest shaft and is used for monitoring vibration signals in three directions in the cutting process; the U2044XA power sensor of moral science and technology is adopted in this experiment, and this sensor has super fast real-time measurement speed, can reach 50000 readings/second, and measurement time can be saved to inside zeroing and automatic calibration function, reduces and measures the uncertainty, and power sensor installs in lathe electrical cabinet for monitor machine tool spindle motor's power changes.

The power signal, the vibration signal and the acoustic emission signal acquired by the power sensor, the vibration sensor and the acoustic emission sensor after being respectively amplified by the signal amplifier are processed by a computer after being converted by A/D, D/A of a data acquisition card through the installation and debugging of the various sensors, and the acquired signal data are displayed on an interface of a test terminal in real time. The signal acquisition card is in a PCI 8735 model, has 32 analog quantity inputs and 16 digital quantity inputs, and can realize multi-channel synchronous data acquisition.

In order to establish the relationship between the white layer phenomenon of the hard turning workpiece and the monitoring signals of the various sensors, a microstructure test specimen preparation is required to be carried out on the machined workpiece, and firstly, a wire cutting machine is used for cutting a sample of the machined workpiece; embedding the cut workpiece by using a metallographic test embedding machine, and respectively taking four points A, B, C, D on the embedded blocks for subsequent experimental tests; then, grinding and polishing on a grinding and polishing machine by using water grinding abrasive paper until the surface of the sample forms a mirror surface; and finally, corroding the surface of the sample by using a nitric acid ethanol solution with the volume fraction of 4%, washing and drying the sample by using clear water when the surface color of the sample shows a bronze color, and observing the microstructure of the white layer by using a microscope.

Sensor signal data can be lost or damaged due to a plurality of uncertain factors existing on site in the actual processing process of a factory, so that the prediction accuracy of a model has deviation, and therefore, the original signal data needs to be preprocessed before various sensor signal data are analyzed, so that the robustness of the model is improved. Selecting a mean value interpolation method subject to data distribution for the case of signal data loss; and for the signal data damage condition, deleting damaged sensor data. FIG. 6 is raw signals of various sensors under normal conditions obtained after data preprocessing, wherein (a) in FIG. 6 shows power signals collected by power sensors during hard turning; FIG. 6 (b) shows acoustic emission signals collected by an acoustic emission sensor during hard turning; fig. 6 (c), (d), and (e) show three vibration signals in three directions perpendicular to each other collected by a three-way vibration sensor during the hard turning process, respectively.

The sensor data signal contains a large amount of useful information, which integrates tool state, cutting parameters, machine tool system and cutting vibration information, so that every small change of the sensor data signal reflects the change of the cutting state of the machine tool, and all researches on the sensor data signal are based on the collected original signals. On this basis, the feature extraction needs to be performed on the acquired original signal to obtain useful information.

1) Time domain feature extraction: and respectively extracting time domain characteristics including a peak value, a root mean square, a form factor, a peak value factor, a pulse factor, a margin factor, a maximum value, an average value and a minimum value from the collected power signal and the sound emission signal. Fig. 7 is a graph comparing time domain characteristics of a power signal and an acoustic emission signal with and without a white layer. As can be seen from fig. 7, regardless of the power signal or the acoustic emission signal, the time domain characteristics in the presence of the white layer and the time domain characteristics in the absence of the white layer show a significant difference, so that the time domain characteristics of the power signal and the acoustic emission signal can be used as characteristic parameters for distinguishing the presence or absence of the white layer on the surface of the workpiece.

2) Wavelet packet energy feature extraction: in the test, 3-layer wavelet packet decomposition is performed on the collected vibration signal and acoustic emission signal respectively, as shown in fig. 8, the energy ratio of each frequency band is shown, in fig. 8, (a) the energy ratio of each frequency band is shown when no white layer exists, and (b) the energy ratio of each frequency band is shown when a white layer exists, wherein the corresponding white layer pictures are white layer micrographs corresponding to A, B, C, D points sampled after sample inlaying respectively, compared with the vibration signal Y and X directions, the energy ratio of each frequency band when the vibration signal Z direction and the acoustic emission signal have a white layer and the energy ratio of each frequency band is different from that of each frequency band when no white layer exists, different operating states can cause different energy distribution situations of the signals in each frequency band, and after a white layer phenomenon occurs on the surface of a workpiece, energy in some frequency bands can be increased, and energy in other frequency bands can be reduced. Therefore, the energy ratio of the vibration signal Z direction and each frequency band of the acoustic emission signal can be used as a characteristic parameter to effectively identify whether the white layer phenomenon appears on the surface of the workpiece.

3) Signal feature importance analysis: in the embodiment, 34 feature quantities are extracted, including 18 time domain features and 16 energy features, which are respectively 9 time domain features extracted from the power signal, 8 energy features extracted from the vibration signal in the Z direction, and 9 time domain features extracted from the acoustic emission signal and 8 energy features, but these features are easily correlated with each other, and in order to investigate the influence degree of the extracted different features on the white layer phenomenon in the turning process, the extracted features are subjected to importance analysis. The relative feature importance of the top ten ranked features is shown in fig. 9, where a higher value of relative importance indicates that the feature is more sensitive to the lathe work white layer phenomenon.

The top ten ranked features shown in FIG. 9 are the acoustic emission signal mean (AETD6), the acoustic emission signal margin factor (AETD14), the acoustic emission signal minimum (AETD7), the acoustic emission signal peak factor (AETD0), the acoustic emission signal maximum (AETD5), the acoustic emission signal root mean square (AETD9), the acoustic emission signal eighth band energy (AEENergy7), the acoustic emission signal first band energy (AEENergy0), the Power signal peak factor (Power0), the Power signal mean (Power6), respectively, from which results can be derived, among the 10 previous important features, 8 features relate to acoustic emission signal features, which show that compared with features such as power and vibration signals, the related features of the acoustic emission signals are more sensitive to the turning white layer phenomenon, and provide an effective solution for accurately predicting the turning white layer phenomenon, therefore, the online prediction of the white layer phenomenon in the turning process based on the acoustic emission signal and the related sensor signal has important theoretical and practical values.

According to the characteristic parameters obtained after the data processing, the algorithm prediction model of the white layer phenomenon in the turning process based on the gradient lifting decision tree is characterized in that firstly, in order to improve the prediction accuracy and generalization capability of the model, the GBDT model is optimized by using grid search; then, carrying out experimental evaluation and comparative analysis on the parameter set with or without AE signal characteristics by using a GBDT model, and verifying the importance of the characteristics of acoustic emission signals (AE signals) on the prediction of a white layer phenomenon in the turning process; finally, through the generalization performance comparative analysis with other algorithms, the GBDT model is further verified to have better performance in the aspects of prediction precision and model interpretation capability, and the white layer phenomenon in the turning process is more effectively identified.

The GBDT-based model provided by the invention relates to the determination of a plurality of parameters, the values of the parameters have great influence on the prediction performance of the model, and the quantity of the parameters also influences the parameter optimization efficiency, so the parameter optimization of the model has important significance for the establishment of the model, and in order to detect the influence of 6 parameters, such as the learning rate of a model algorithm, the quantity of trees, the down-sampling proportion of training samples, the maximum depth of a decision tree, the minimum number of samples required by split nodes and the maximum feature number, on the GBDT model performance, the parameters in the model are optimized by adopting a grid search method, and the influence degree of the value of each parameter on the model prediction performance can be intuitively seen from a graph 10. The learning rate (learning _ rate) and the maximum iteration number (n _ estimators) increase with the value, and the model prediction accuracy rate shows a trend of descending first and then ascending. In order to improve the reliability of the model, the embodiment selects the learning rate (learning _ rate) to be 0.05 and the maximum number of iterations (n _ estimators) to be 30, so that not only is the model prevented from being over-fitted, but also the generalization capability of the model can be improved. The maximum depth (max _ depth) has stable prediction accuracy in the value range [8,12], so that the optimal value is searched in the interval, namely the value of the maximum depth is 8. When the value of subsample (subsample) is 0.6, the value of the minimum sample number (min _ samples _ split) required by the subdivision of the internal node is 6, and the value of the maximum feature number (max _ features) is 10, the prediction accuracy of the model is the highest. As can be known from the GBDT model trained based on the parameters, the accuracy of the model before optimization is 81.25%, the accuracy of the model after optimization is 90%, and the comparison of model prediction performance evaluation before and after parameter adjustment shows that the model after optimization obtains higher accuracy compared with the model before optimization, which shows that the model after grid optimization parameter adjustment has higher prediction accuracy and stronger generalization capability.

In order to verify the importance of the characteristics of acoustic emission signals (AE signals), the characteristic parameters after data processing are divided into two parts, namely a characteristic data set containing the acoustic emission signals and a characteristic data set without the acoustic emission signals, Principal Component Analysis (PCA) is respectively carried out on the two part characteristic data sets, the characteristic parameters with the cumulative contribution rate of more than 95% are selected for GBDT model construction, the experimental results are shown in table 2 and figure 11, AE in table 2 represents the acoustic emission signals, Power represents Power signals, and Vibrate represents vibration signals. As can be seen from table 2 and fig. 11, compared with the model without the acoustic emission signal feature, the classification accuracy of the model with the acoustic emission signal feature is improved from 0.55 to 0.9, the classification accuracy is improved from 0.57 to 0.85, the recall rate is improved from 0.73 to 1, the F1 value is improved from 0.64 to 0.92, and the Auc value is improved from 0.52 to 0.89, so that the validity of the feature importance analysis is verified, and the acoustic emission signal (AE signal) feature is also verified to have higher correlation in predicting the turning white layer phenomenon. Therefore, the acoustic emission signal (AE signal) characteristics can effectively improve the accuracy of GBDT algorithm prediction, and the method has an important effect on the prediction of the turning white layer phenomenon.

TABLE 2 influence of Acoustic emission signals (AE signals) on the model

In addition, 2808 pieces of collected data information of hard vehicle product parts are acquired based on experiments in the embodiment, a gradient lifting decision tree (GBDT) algorithm is compared with xgb and a support vector machine in generalization performance, an ROC curve is drawn, and a corresponding area (Auc value) under the ROC curve is calculated. The calculation results are shown in table 3 and fig. 12, the highest accuracy of GBDT is 0.9, and both SVM algorithm (accuracy 0.8) and xgb algorithm (accuracy 0.85) are smaller than this value; the F1 score of the algorithm is 0.92, which is greater than that of the SVM algorithm (F1 is 0.85) and the xgb algorithm (F1 is 0.87); in the ROC curve graph, the ROC curve of the model provided by the invention wraps the ROC curve of the SVM algorithm and the ROC curve of the xgb algorithm, namely the ROC curve of the model is closer to the upper left corner, which shows that the generalization performance of the model provided by the invention is better. The Auc value of the algorithm is 0.89, the Auc value of the SVM algorithm is 0.78, the Auc value of the algorithm xgb is 0.84, the change of the Auc value is consistent with the change of an ROC curve, various evaluation indexes are integrated, and the selected model GBDT has stronger generalization capability and higher prediction accuracy.

TABLE 3 prediction accuracy of different models

In summary, the hard-turning workpiece surface white layer prediction model is constructed based on the gradient lifting decision tree algorithm, in order to evaluate the prediction performance of the model white layer, the invention provides a set of evaluation method based on the confusion matrix to ensure the prediction performance of the gradient lifting decision tree model, and the accuracy, classification precision, recall rate, F1 value, roc curve and Auc value are used to ensure the prediction effect of the model, so as to screen the optimal prediction model (i.e. determine the dimensionality of the model input signal).

The result of extracting the experimental signal features shows that: compared with characteristics such as power and vibration signals, the acoustic emission signal characteristics are more sensitive to the turning white layer phenomenon and are important characteristic parameters for predicting the white layer phenomenon in the turning process, experiments further prove that the classification accuracy, the classification precision, the recall rate, the F1 value and the Auc value of the model containing the acoustic emission signal characteristics are greatly improved, and an effective solution is provided for accurately predicting the turning white layer phenomenon. Through comparison analysis with other model algorithms (SVM algorithm and xgboost algorithm), the results show that: the gradient lifting decision tree model established by the invention has higher accuracy in predicting the white layer phenomenon in the hard driving process, can more effectively identify the white layer phenomenon generated in the hard driving process, and has important significance in realizing the online prediction of the white layer phenomenon in the hard driving process.

The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.

Claims

1. A hard vehicle surface white layer prediction method based on a gradient lifting decision tree is characterized by comprising the following steps:

constructing and training a prediction model: constructing a hard turning surface white layer prediction model based on a gradient lifting decision tree, wherein the main characteristic quantity is used as the input of the gradient lifting decision tree, and the prediction result is input by the gradient lifting decision tree;

2. The hard surface white layer prediction method based on the gradient boosting decision tree as claimed in claim 1, wherein the dynamic cut signal data comprises any one or a combination of a plurality of dimensional data of acoustic emission signal, power signal and vibration signal.

3. The method according to claim 2, wherein the feature extraction and analysis comprises:

performing time domain feature extraction and wavelet packet energy feature extraction on the dynamic cutting signal data, and taking the extracted parameters as feature parameters for identifying a white layer;

4. The hard surface white layer prediction method based on the gradient boosting decision tree as claimed in claim 3, wherein the time domain feature extraction comprises time domain feature extraction for the acoustic emission signal and the power signal respectively.

5. The method according to claim 4, wherein the time domain features comprise peak value, root mean square, form factor, peak value factor, impulse factor, margin factor, maximum value, average value, and minimum value.

6. The hard vehicle surface white layer prediction method based on the gradient boosting decision tree as claimed in claim 3, wherein the wavelet packet energy feature extraction comprises respectively performing wavelet packet decomposition on the acoustic emission signal and the vibration signal.

7. The hard turning surface white layer prediction method based on the gradient lifting decision tree as claimed in claim 2, characterized in that during the construction and training of the prediction model, different models are obtained according to the dimension training of the input data of the hard turning surface white layer prediction model, the prediction performance of the model is evaluated, and the model with the best performance is selected as the model for online prediction.

8. The method according to claim 7, wherein the evaluating the prediction performance of the model comprises:

and (3) visually evaluating the performance of the model by using a confusion matrix, wherein the model performance comprises an accuracy A, an accuracy P, a recall R, F1 point value, a sensitivity TPR, a specificity FPR, a ROC curve and an AUC value, and specifically:

9. The method of claim 8, wherein the higher the accuracy A, accuracy P, recall R, F1 score value and AUC value, the better the model performance.

10. The hard vehicle surface white layer prediction method based on the gradient boosting decision tree as claimed in claim 1, wherein model parameters are optimized by using a grid search method in the prediction model training process.