Tool barrel accumulated residue state active control and tool wear inhibition method

Abstract

The present application relates to the technical field of machining, and discloses a tool barrel accumulated slag state active control and tool wear inhibition method, which is applied to a machining control system, and the system comprises a plurality of accumulated slag monitoring sensors and an active control module arranged on the inner wall of the tool barrel. The method is as follows: accumulated slag state data sets are generated by collecting vibration frequency spectrum data and temperature gradient data through the accumulated slag monitoring sensors; the active control module receives real-time data to construct accumulated slag state matrices; the spatio-temporal evolution characteristics of accumulated slag deposition are determined, including processing the accumulated slag state matrices, extracting features, modeling, predicting evolution, constructing the spatio-temporal evolution characteristics of the active control model and updating the data sets; the tool barrel cooling medium is dynamically regulated according to the evolution characteristics, including mapping section identification, control execution actions and dynamic allocation of the cooling medium. The method realizes accurate monitoring and active control of the tool barrel accumulated slag state, effectively inhibits tool wear, improves machining quality and efficiency, and prolongs the service life of the tool and equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical processing, specifically a tool barrel accumulation state active control and tool wear inhibition method. BACKGROUND

[0002] In the field of mechanical processing, the tool barrel as a key component, its accumulation state and tool wear have a significant impact on processing quality, efficiency and equipment life. The traditional tool barrel accumulation control and tool wear inhibition method has many limitations.

[0003] From the perspective of accumulation control, in the early mechanical processing, the monitoring and handling of tool barrel accumulation were relatively passive. The operator mainly relied on experience to judge the accumulation situation, such as observing abnormal vibration, sound during processing or regularly stopping to check the accumulation state of the inner wall of the tool barrel. This method is not only inefficient, but also cannot accurately grasp the real-time state of the accumulation, which easily leads to excessive deposition of accumulation and affects the normal operation of the tool barrel. With the development of technology, although some monitoring methods based on simple sensors have appeared, such as using pressure sensors or temperature sensors to monitor the physical parameters inside the tool barrel, these single type sensors can only obtain limited information and cannot fully reflect the complex state of the accumulation. For example, the pressure sensor can only monitor the pressure change, but cannot directly reflect the deposition position and growth trend of the accumulation; the temperature sensor can sense the temperature change, but has no effect on the vibration characteristics of the accumulation. In addition, the traditional accumulation control method often uses a fixed cooling medium regulation strategy, which cannot be dynamically adjusted according to the real-time evolution characteristics of the accumulation, resulting in low efficiency of the use of cooling medium and inability to effectively inhibit the formation and development of the accumulation.

[0004] In terms of tool wear inhibition, tool wear is an inevitable problem in mechanical processing, and unreasonable control of tool barrel accumulation will further exacerbate tool wear. The traditional tool wear inhibition method mainly focuses on the improvement of tool material and the optimization of tool structure, such as using tool materials with better wear resistance or designing more reasonable tool geometry. However, these methods, although to some extent, can prolong the service life of the tool, ignore the internal relationship between tool barrel accumulation and tool wear. The uneven deposition of accumulation in the tool barrel will cause the tool to bear uneven load and friction during processing, thereby accelerating the wear of the tool. At the same time, the traditional method lacks in-depth study of the correlation between the accumulation state and tool wear, and cannot effectively inhibit the tool wear by actively controlling the accumulation state.

[0005] With the development of the mechanical processing industry towards high precision, high efficiency and automation, higher requirements are put forward for the control of the accumulation state of the tool barrel and the inhibition of tool wear. A method is needed that can monitor the accumulation state in real time and comprehensively, and actively regulate and control according to the spatio-temporal evolution characteristics of the accumulation, in order to improve the processing quality and efficiency, and prolong the service life of the tool and equipment. There is a lack of an accumulation state active control and tool wear inhibition method for the tool barrel based on multi-sensor fusion and active control model in the prior art, which cannot accurately predict the spatio-temporal evolution characteristics of the accumulation deposition and dynamically regulate and control the cooling medium, and is difficult to meet the needs of modern mechanical processing. Therefore, it is urgent to develop a new method to solve these problems. SUMMARY

[0006] The purpose of the present application is to provide an accumulation state active control and tool wear inhibition method for the tool barrel to solve the problems raised in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solution: an accumulation state active control and tool wear inhibition method for the tool barrel, applied to a mechanical processing control system, the system comprising a plurality of accumulation monitoring sensors arranged on the inner wall of the tool barrel and an active control module connected to the accumulation monitoring sensors, the distance between any two adjacent accumulation monitoring sensors being a set distance, the method comprising:

[0008] Collecting vibration frequency spectrum data and temperature gradient data of the inner wall of the tool barrel through the accumulation monitoring sensors to generate an accumulation state data set;

[0009] Receiving real-time accumulation state data of the plurality of accumulation monitoring sensors through the active control module to construct an accumulation state matrix of the tool barrel;

[0010] Determining the spatio-temporal evolution characteristics of the accumulation deposition according to the accumulation state data set and the real-time data of each accumulation monitoring sensor, wherein determining the spatio-temporal evolution characteristics of the accumulation deposition comprises processing the accumulation state matrix, extracting deposition characteristics in combination with the accumulation state data set, predicting the accumulation trend according to the temperature gradient change and pressure distribution information, outputting the spatio-temporal evolution characteristics of the accumulation deposition through an active control model, and updating the accumulation state data set according to the spatio-temporal evolution characteristics;

[0011] According to the spatio-temporal evolution characteristics, dynamically regulating and controlling the cooling medium of the tool barrel.

[0012] Preferably, the determination of the spatio-temporal evolution characteristics of the accumulation deposition comprises:

[0013] Processing the accumulation state matrix to extract vibration frequency spectrum characteristics, temperature distribution characteristics and accumulation growth trend;

[0014] According to the vibration spectrum characteristics and the temperature distribution characteristics, a deposition intensity model is established for the slag state matrix, the inner wall of the cutter barrel is divided into a plurality of sub-sections and a section identifier is marked, the vibration spectrum of the sub-sections is matched with the slag state data set, and the section identifier is marked in the slag state data set;

[0015] According to the temperature gradient change and the slag growth trend, evolution of the slag deposition is predicted, and deposition prediction information of each sub-section is calculated;

[0016] An active control model is constructed, the deposition prediction information is taken as an input parameter of the active control model, spatial correlation modeling of the deposition prediction information is performed through the active control model, and a spatio-temporal evolution feature of the slag deposition is output;

[0017] According to the spatio-temporal evolution feature, the slag state data set is updated, and the spatio-temporal evolution feature of the slag deposition is obtained.

[0018] Preferably, the processing of the slag state matrix comprises:

[0019] The slag state matrix is standardized, a deposition hot spot section in the matrix is intercepted through a sliding window, noise filtering is performed on the hot spot section, and a slag growth trend is calculated through a feature decomposition algorithm;

[0020] The spatial correlation feature of the slag state matrix is calculated, the adhesion strength, the deposition stability coefficient and the slag blind area index between sections are calculated according to the spatial correlation feature, a feature fusion network is constructed, and the temperature distribution characteristics are calculated through the feature fusion network;

[0021] The time domain feature and the frequency domain feature collected by each slag monitoring sensor are extracted, a deposition feature vector of the sensor is calculated according to the phase difference between the time domain feature and the frequency domain feature, the slag monitoring sensors at different positions are matched according to the deposition feature vector, and a slag growth trend is calculated.

[0022] Preferably, the deposition intensity modeling of the slag state matrix comprises:

[0023] According to the vibration spectrum characteristics, vibration spectrum sampling points are extracted in each frame of data, the sampling points are associated and mapped with the temperature distribution characteristics, a deposition intensity atlas is generated, the deposition intensity atlases collected by a plurality of sensors are spatially aligned, and a deposition intensity distribution of the cutter barrel is calculated;

[0024] An adhesion threshold value is set, and the adhesion source is located according to the vibration frequency spectrum value of the multi-frame accumulated slag state matrix, the adhesion strength difference value is calculated, and if the adhesion strength difference value is greater than or equal to the adhesion threshold value, it indicates that there is an accumulated slag blind area in the section, the flow field model constraint compensation is performed on the current section, the deposition intensity distribution of the current section is iteratively corrected according to the corresponding fluid dynamics model of the current section, and the intensity compensation value of the blind area deposition is calculated according to the correction result.

[0025] The deposition intensity distribution is used to model the deposition intensity of the accumulated slag state matrix, and the deposition model of the cutter barrel is temperature-labeled through the temperature distribution characteristics.

[0026] Preferably, the temperature gradient change is calculated according to the positions of the accumulated slag monitoring sensors, and the temperature gradient change calculation process includes:

[0027] According to a plurality of sets of accumulated slag deposition data, a temperature change point is extracted, and the change point is mapped to a unified coordinate system according to the deployment position of the sensor, the change point is fitted through a spatial interpolation algorithm, and an accumulated slag field model of the cutter barrel is generated;

[0028] The flow path along the accumulated slag field model is sampled at equal intervals, the adhesion coefficient change rate, the temperature fluctuation index and the deposition change slope of the path are calculated according to the sampling results, and the deposition change parameter is calculated according to the adhesion coefficient change rate, the temperature fluctuation index and the deposition change slope;

[0029] According to the deployment parameters and the collection accuracy of the accumulated slag monitoring sensors, the time-space evolution characteristics of the accumulated slag growth in each frame of data are projected to the accumulated slag field model, the accumulated slag field model is partitioned along the flow direction according to the number of sensors, the evolution law of the accumulated slag growth in the partition is analyzed, and the deposition distribution characteristics are calculated according to the evolution law;

[0030] The temperature gradient change is calculated according to the deposition change parameter and the deposition distribution characteristics, and the calculation process of the temperature gradient change includes: based on the position range from the first accumulated slag monitoring sensor to the last accumulated slag monitoring sensor, selecting a spatial coordinate point in the sensor deployment direction, accumulating the product of the deposition field intensity characteristic weight value and the accumulated slag distribution characteristic weight value in the spatial resolution range, and superimposing the influence value of the sensor collection frequency on the deposition intensity change rate.

[0031] Preferably, the deposition prediction information of each sub-section is calculated, and the calculation process of the deposition prediction information of each sub-section includes:

[0032] The flow main path of the accumulated slag field model is taken as a reference line, the peak position of the accumulated slag growth in each frame of data is taken as a reference point, the deposition offset is calculated, and the deposition distribution curve is drawn according to the coordinates.

[0033] The growth rate and direction in the accumulated slag growth trend are corrected according to the temperature gradient change.

[0034] From the latest deposition distribution point, the distribution curve is drawn according to the growth rate and the correction result of the direction, and the next period deposition distribution point is generated until the distribution point covers the entire target section, and the deposition prediction information is generated.

[0035] Preferably, the active control model comprises:

[0036] An input layer for organizing the deposition prediction information into spatial distribution data and performing standardization processing;

[0037] A feature fusion layer for extracting the section correlation features of the deposition by processing the spatial distribution data, and constructing the dependency relationship between the physical units;

[0038] A medium regulation layer for integrating the correlation relationship of the accumulated slag deposition on the spatial unit, and generating a cooling medium regulation strategy.

[0039] Preferably, the space-time evolution features of the accumulated slag deposition comprise:

[0040] According to the space-time evolution features of the accumulated slag deposition output by the active control model, the identification of the sub-section is corresponded to the space-time evolution features;

[0041] The section data in the accumulated slag state data set is reorganized according to the space-time features, and a section distribution map sorted by the deposition intensity of the accumulated slag is generated;

[0042] According to the reorganized section distribution map, the optimized space-time evolution features of the accumulated slag deposition are output.

[0043] Preferably, the dynamic regulation of the cooling medium of the cutter barrel comprises:

[0044] The section identification is one-to-one mapped with the space-time evolution features of the accumulated slag deposition;

[0045] According to the space-time evolution features of the accumulated slag deposition, the execution action of the cooling medium is controlled, including flow pressure adjustment, cooling liquid proportioning and jet angle operation;

[0046] According to the spatial distribution of the accumulated slag deposition and the preset medium regulation strategy, the cooling medium is dynamically allocated to the corresponding physical section.

[0047] Preferably, the execution action of the cooling medium comprises:

[0048] When the deposition of the accumulated slag in the target section reaches the preset intensity threshold, the flow pressure increasing instruction of the adjacent nozzle is triggered;

[0049] According to the cooling liquid proportioning strategy, the additive components are dynamically combined to generate a jet angle vector;

[0050] Adjust a pressure parameter of the target section ejection unit based on the ejection angle vector.

[0051] Compared with the prior art, the present application has the following advantages:

[0052] In the aspect of slag accumulation state monitoring, the vibration frequency spectrum data and temperature gradient data are collected by the multiple slag accumulation monitoring sensors arranged on the inner wall of the cutter barrel to generate a slag accumulation state data set, which can comprehensively and real-timely obtain the slag accumulation related information of the inner wall of the cutter barrel. The layout of the multiple sensors and the set distance between adjacent sensors ensure the spatial coverage of the slag accumulation state of the inner wall of the cutter barrel, avoiding the limitation of single sensor monitoring, so that the distribution and change of slag accumulation at different positions can be captured. The vibration frequency spectrum data can reflect the change of the vibration characteristics of the cutter barrel caused by slag deposition, and the temperature gradient data can reflect the heat conduction characteristics in the slag deposition process, and the combination of the two provides a rich information basis for accurately judging the slag accumulation state.

[0053] In the aspect of slag accumulation state analysis and modeling, the active control module receives the real-time data of the multiple sensors to construct a cutter barrel slag accumulation state matrix, and performs a series of processing on it, including standardization processing, noise filtering, feature decomposition, etc., which can extract key information such as vibration frequency spectrum features, temperature distribution characteristics and slag accumulation growth trend. By dividing the inner wall of the cutter barrel into multiple sub-sections and identifying the sections, fine analysis of the slag accumulation state is realized. The spatial correlation feature calculation of the slag accumulation state matrix can analyze the adhesion strength, deposition stability coefficient and slag accumulation blind area index between sections, and construct a feature fusion network to calculate the temperature distribution characteristics, so that the analysis of the slag accumulation state is more comprehensive and in-depth. In the deposition intensity modeling process, the deposition intensity map is generated by the correlation mapping of the vibration frequency spectrum features and the temperature distribution characteristics, the adhesion source positioning and the flow field model constraint compensation are combined with the adhesion threshold value, which can accurately calculate the deposition intensity distribution and the strength compensation value of blind area deposition, and improve the accuracy and reliability of the slag accumulation state modeling.

[0054] In the aspect of slag deposition evolution prediction, the temperature gradient change is calculated according to the positions of the slag monitoring sensors, the slag field model is fitted and generated by the spatial interpolation algorithm, and the deposition change parameters are calculated by equidistant sampling along the flow path, and the temperature gradient change is calculated combined with the deposition distribution characteristics, which can comprehensively consider the changes of various physical parameters in the slag deposition process. Taking the main flow path of the slag field model as the reference line, the growth rate and direction of the slag accumulation growth trend are corrected combined with the temperature gradient change to generate deposition prediction information, which realizes the distribution prediction of the slag deposition in the next period and provides a reliable basis for active control.

[0055] In terms of the construction and application of active control models, the active control model includes an input layer, a feature fusion layer, and a medium regulation layer. It can process spatially distributed data from deposition prediction information, extract segmental correlation features, construct dependencies between physical units, and then generate cooling medium regulation strategies. Through this model, intelligent conversion from slag state data to cooling medium regulation strategies is achieved, enabling dynamic adjustment of cooling medium regulation based on the spatiotemporal evolution characteristics of slag deposition, thus improving the targeting and effectiveness of regulation.

[0056] In terms of dynamic control of the cooling medium, a one-to-one mapping is established between section markers and the spatiotemporal evolution characteristics of slag deposition. Based on these spatiotemporal evolution characteristics, the flow rate and pressure of the cooling medium, the coolant ratio, and the injection angle are controlled, and the cooling medium is dynamically allocated to the corresponding physical sections. When slag deposition reaches a preset intensity threshold in the target section, a flow rate and pressure boosting command is triggered for adjacent nozzles. Additive components are dynamically combined according to the coolant ratio strategy, and the pressure parameters of the injection unit in the target section are adjusted based on the injection angle vector, achieving precise control of the cooling medium. This dynamic control strategy can effectively and promptly suppress the formation and development of slag, reducing the wear effect of slag on the cutting tool. Attached Figure Description

[0057] Figure 1 This is a schematic diagram illustrating the working principle of the active control method for slag accumulation in the cutter barrel and the method for suppressing cutter wear described in this invention.

[0058] Figure 2 This is a schematic diagram illustrating the working principle of the slag state matrix processing method.

[0059] Figure 3 This is a diagram illustrating the working principle of the temperature gradient change calculation method.

[0060] Figure 4 This is a schematic diagram illustrating the working principle of the active control model. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figures 1-4 The present invention relates to a method for active control of slag accumulation in a tool barrel and suppression of tool wear, which is applied to a machining control system comprising multiple slag accumulation monitoring sensors disposed on the inner wall of the tool barrel and an active control module connected to the slag accumulation monitoring sensors, wherein adjacent slag accumulation monitoring sensors are spaced apart by a set distance, and specifically includes the following steps:

[0063] The vibration frequency spectrum data and temperature gradient data of the inner wall of the cutter barrel are collected by the slag accumulation monitoring sensor to generate a slag accumulation state data set. The slag accumulation monitoring sensor is deployed on the inner wall of the cutter barrel at a set interval, covering the key areas prone to slag accumulation, and synchronously collecting vibration and temperature data in real time to form an original data set containing spatial and temporal dimension information.

[0064] The active control module receives real-time slag accumulation state data of multiple slag accumulation monitoring sensors to construct a slag accumulation state matrix. The active control module maps multi-dimensional data to a matrix structure based on sensor position coordinates and collection time sequence, with the matrix rows and columns corresponding to spatial position and time sequence respectively, realizing the structured representation of slag accumulation state.

[0065] According to the slag accumulation state data set and real-time data of each slag accumulation monitoring sensor, the spatio-temporal evolution characteristics of slag deposition are determined. The specific process includes: processing the slag accumulation state matrix, extracting deposition characteristics in combination with the slag accumulation state data set, predicting slag accumulation trend according to temperature gradient change and pressure distribution information, outputting the spatio-temporal evolution characteristics of slag deposition through the active control model, and updating the slag accumulation state data set according to the spatio-temporal evolution characteristics.

[0066] According to the spatio-temporal evolution characteristics, the cutter barrel cooling medium is dynamically regulated. The active control module generates medium regulation instructions based on the spatio-temporal evolution characteristics to drive the cooling system to adjust parameters such as flow, pressure, ratio, and injection angle, realizing active intervention on slag deposition.

[0067] Embodiment 1:

[0068] The determination of the spatio-temporal evolution characteristics of slag deposition specifically includes processing of the slag accumulation state matrix, deposition intensity modeling, temperature gradient change calculation, deposition prediction information generation, and active control model construction, etc. Each link is related to each other, forming a complete slag evolution characteristic analysis process.

[0069] The processing of the accumulated slag state matrix is the basis for determining the evolution characteristics. The accumulated slag state matrix is constructed by the active control module according to the real-time data of multiple accumulated slag monitoring sensors. The rows and columns of the matrix correspond to the spatial positions of the sensors and the data acquisition time sequence, respectively. In the processing process, first, the matrix is standardized to eliminate the dimensional differences of the data, so that the vibration spectrum data and temperature gradient data of different sensors are comparable. After standardization, the sliding window technology is used to intercept the deposition hot spot section in the matrix. The size and moving step of the window are dynamically adjusted according to the processing conditions of the cutter barrel and the accumulated slag historical data to ensure that the rapidly changing areas of the accumulated slag can be accurately captured. Noise filtering is performed on the hot spot section. The median filter or Gaussian filter algorithm can be used to remove high-frequency noise and random interference and retain signal characteristics reflecting the nature of accumulated slag deposition. Subsequently, the processed matrix is reduced in dimension through a feature decomposition algorithm (such as principal component analysis PCA), and the principal component parameters that can represent the growth trend of the accumulated slag are extracted. These parameters include the accumulated slag thickness growth rate, deposition area expansion speed, etc., thereby realizing the quantitative description of the growth trend of the accumulated slag.

[0070] In the deposition intensity modeling link, the accumulated slag state matrix needs to be analyzed in combination with the vibration spectrum characteristics and temperature distribution characteristics. First, the inner wall of the cutter barrel is divided into multiple sub-sections. The division of the sub-sections is based on the deployment position of the sensors and the structural characteristics of the cutter barrel, such as being divided at equal intervals along the axial direction of the cutter barrel, and each sub-section corresponds to a unique section identifier. Through the vibration spectrum feature extraction algorithm, the characteristic frequency components related to the deposition of accumulated slag are identified from each frame of data. For example, when the thickness of the accumulated slag increases, the vibration energy of a specific frequency will change significantly. These sampling points corresponding to the characteristic frequencies are extracted. The sampling points are associated and mapped with the temperature distribution characteristics, such as high-temperature regions usually corresponding to positions where accumulated slag is easily adhered. By establishing a two-dimensional mapping relationship between vibration energy and temperature, a deposition intensity map is generated. The deposition intensity maps generated by multiple sensors are spatially aligned, that is, the local maps are spliced into a global deposition intensity distribution map according to the physical coordinates of the sensors, thereby intuitively reflecting the differences in the intensity of the accumulated slag in different regions of the inner wall of the cutter barrel.

[0071] In the deposition intensity modeling process, an adhesion threshold value is set to identify the blind zone of the deposit. The adhesion threshold value is determined by analyzing historical deposit data, which is usually a relative change threshold of the vibration spectrum value. When the vibration spectrum value of multiple frames of deposit state matrix changes beyond the threshold value, it indicates that there may be a deposit blind zone in the corresponding section that the sensor cannot directly monitor. At this time, the flow field model is used to constrain and compensate the deposition intensity of the section. The flow field model is based on the principle of fluid dynamics, and simulates the deposition and scouring process of the deposit under the action of the fluid by calculating the flow velocity and pressure distribution of the cooling liquid in the section. Through iterative correction of the measured data and the predicted deposition intensity, the intensity compensation value of the blind zone deposition is calculated to correct the estimation error of the deposition intensity distribution. Finally, combined with the deposition intensity distribution and temperature distribution characteristics, the deposit state matrix is labeled to form a deposition model containing thermal-mechanical coupling characteristics.

[0072] The calculation of temperature gradient change is a key link in deposit trend prediction. First, according to multiple sets of deposit deposition data, points with significant temperature changes (i.e. positions where the temperature gradient exceeds a set threshold) are extracted and mapped to a unified coordinate system, which is established as a cylindrical coordinate system with the cutter barrel axis as the reference. Through a spatial interpolation algorithm (such as Kriging interpolation or polynomial interpolation), the temperature change points are fitted to generate a continuous deposit field model, which reflects the corresponding relationship between the temperature field of the inner wall of the cutter barrel and the deposit distribution. Along the flow path (i.e. the direction of cooling liquid flow) of the deposit field model, equidistant sampling is performed, and the spacing of the sampling points is determined according to the model resolution and the evolution speed of the deposit, usually 1 / 2 to 1 times the spacing of the sensor. The adhesion coefficient change rate, temperature fluctuation index and deposition change slope are calculated for each sampling point, where the adhesion coefficient change rate reflects the change in the scouring ability of the cooling liquid to the deposit, the temperature fluctuation index represents the stability of the local thermal environment, and the deposition change slope is the rate of change of the deposit thickness with time. The three parameters are summed by weighting to generate a deposition change parameter, and the weight coefficients are determined according to the historical experience of the processing technology.

[0073] In addition, according to the deployment parameters (such as spacing, angle) and the collection accuracy of the sensor, the deposition growth evolution characteristics in each frame of data are projected into the deposition field model, and the model is divided into multiple analysis partitions along the flow direction, and the number of partitions matches the number of sensors. By statistically analyzing the mean, variance and other characteristics of the deposition growth in each partition, the deposition distribution characteristics are calculated, such as the spatial distribution range of the high deposition rate area. Finally, the calculation of the temperature gradient change comprehensively considers the deposition change parameters and the deposition distribution characteristics. The specific process is as follows: a series of spatial coordinate points are selected in the direction of sensor deployment. For each coordinate point, the product of the deposition field intensity characteristic weight value and the deposition distribution characteristic weight value in its spatial resolution range is calculated, and the weight value decays exponentially with the increase of spatial distance. At the same time, the influence value of the sensor collection frequency on the deposition intensity change rate is superimposed, which is calculated through the linear relationship model of the collection frequency and the deposition rate, thereby forming a complete temperature gradient change parameter.

[0074] In the deposition prediction information generation link, the flow main path (i.e. the direction of the main flow of the cooling liquid) of the deposition field model is taken as the reference line, and the peak position of the deposition growth in each frame of data is taken as the reference point. The deposition offset of the reference point relative to the reference line is calculated, which reflects the spatial distribution deviation of the deposition of the deposition. According to the offset and the coordinate data, the deposition distribution curve is drawn, and the horizontal coordinate is the spatial position and the vertical coordinate is the deposition intensity. The growth rate and direction in the deposition growth trend are corrected using the temperature gradient change parameter, for example, in the high temperature area, the deposition growth rate may accelerate, and the parameters of the prediction model need to be adjusted accordingly. Starting from the latest deposition distribution point, the distribution curve is drawn according to the corrected growth rate and direction, and the deposition distribution point of the next period is generated point by point until the entire target section is covered, forming a complete deposition prediction information, which contains the deposition intensity prediction value and the spatial distribution form of each sub-section in the future period.

[0075] The construction of the active control model is the core of realizing the analysis of the evolution characteristics of the accumulated slag. The model includes an input layer, a feature fusion layer, and a medium regulation layer. The input layer receives the deposition prediction information and organizes it into a spatial distribution data matrix, with each element of the matrix corresponding to a predicted parameter (such as accumulated slag intensity, growth rate) of a sub-section. At the same time, the data is standardized to meet the input requirements of the model. The feature fusion layer uses a graph neural network (GNN) or a convolutional neural network (CNN) structure to process spatial distribution data and extract the correlation characteristics between sub-sections, such as the degree of mutual influence of adjacent section accumulated slag deposition, and to construct a dependency model between physical units. The output of the feature fusion layer is a high-dimensional vector containing spatial correlation characteristics, which represents the overall evolution trend of the accumulated slag deposition on the inner wall of the cutter barrel. The medium regulation layer integrates the correlation of each spatial unit based on the output of the feature fusion layer to generate cooling medium regulation strategies, such as increased cooling fluid flow instructions or jet angle adjustment schemes for high deposition risk sections.

[0076] Finally, according to the spatio-temporal evolution characteristics output by the active control model, the sub-section identification is matched with the evolution characteristics (such as deposition intensity, spatial distribution), and the section data in the accumulated slag state data set is reorganized according to the spatio-temporal characteristics to generate a section distribution map sorted by deposition intensity. By analyzing the reorganized distribution map, the optimized accumulated slag deposition evolution characteristics are extracted, which will serve as the basis for dynamic regulation of the cooling medium to achieve active control of the accumulated slag state in the cutter barrel.

[0077] Example 2:

[0078] The processing of the accumulated slag state matrix includes key steps such as standardization, hot section extraction, noise filtering, feature decomposition, spatial correlation analysis, feature fusion network construction, and multi-sensor feature matching.

[0079] The standardization of the accumulated slag state matrix is the core step of data preprocessing. After receiving real-time data from multiple accumulated slag monitoring sensors, the active control module constructs a two-dimensional matrix containing vibration spectrum data and temperature gradient data. The rows of the matrix correspond to the spatial positions of the sensors, and the columns correspond to the time series. Since the measurement data of different types of sensors have different dimensions (such as the frequency value of the vibration spectrum and the Celsius temperature), the matrix needs to be standardized to eliminate the influence of dimensional differences on subsequent analysis. The standardization method uses Z-score standardization, which calculates the difference between each data point and the mean value and divides it by the standard deviation, so that the processed data conforms to the standard normal distribution with a mean of 0 and a standard deviation of 1. Through standardization, the data of different physical quantities is comparable, facilitating subsequent feature extraction and model analysis.

[0080] After the standardization, the sliding window technique is used to intercept the deposition hotspot section in the matrix. The size of the sliding window is determined according to the time span of the rapid growth of the historical deposition data, for example, set to 10-20 sampling periods, and the window moving step is 1-5 sampling periods, to ensure the continuity and integrity of the hotspot section. The determination of the deposition hotspot section is based on the mutation of vibration spectrum energy or the significant change of temperature gradient. When the average energy of the data in the window exceeds the preset threshold (such as 1.5 times of the historical average), the window is determined as a hotspot section. The intercepted hotspot section is filtered for noise, and the median filtering algorithm is used to remove impulse noise, or the Gaussian filtering algorithm is used to smooth the data curve, and the true signal characteristics reflecting the deposition of the deposition are retained. After noise filtering, the matrix of the hotspot section is processed by dimension reduction through a feature decomposition algorithm (such as principal component analysis PCA), and the first k principal components (k is determined according to the cumulative variance contribution rate, usually the number of principal components with a cumulative contribution rate of more than 85%) are extracted. The coefficient vector of the principal component reflects the contribution degree of each original variable (vibration frequency, temperature, etc.) to the growth trend of the deposition, so as to realize the quantitative description of the growth trend of the deposition, such as the time series change of the principal component score can represent the growth rate of the deposition thickness.

[0081] The spatial correlation features of the deposition state matrix are calculated to analyze the deposition correlation between different sub-sections. The spatial correlation features are obtained by calculating the cross-correlation coefficient matrix of the data of each sub-section. The cross-correlation coefficient reflects the similarity of the data of two sections in the time sequence, and the larger the coefficient value (close to 1) indicates that the correlation of the deposition between the sections is stronger. Based on the cross-correlation coefficient matrix, the adhesion strength, deposition stability coefficient and deposition blind area index between the sections are further calculated. The adhesion strength is calculated by the peak frequency component energy of the cross-correlation coefficient of adjacent sections, which represents the possibility of the deposition adhesion between adjacent sections; the deposition stability coefficient is calculated by the ratio of the standard deviation to the mean value of the section data, which reflects the fluctuation degree of the deposition state of the section, and the smaller the coefficient indicates that the deposition is more stable; the deposition blind area index is calculated by the energy entropy of the section data, and the larger the entropy value indicates that the deposition state of the section is more complex, which may exist the situation of insufficient sensor coverage or monitoring blind area.

[0082] After obtaining the spatial correlation features, a feature fusion network is constructed to calculate the temperature distribution characteristics. The feature fusion network adopts a fully connected neural network structure, the input layer is the spatial correlation features (such as cross-correlation coefficient, adhesion strength, stability coefficient, etc.) and the temperature raw data, the hidden layer realizes the nonlinear transformation of the features through a nonlinear activation function (such as ReLU), and the output layer is the fused temperature distribution characteristic parameters, including the mean, variance, gradient, etc. of the temperature field. The feature fusion network is trained and optimized by historical data to ensure that the output temperature distribution characteristics can accurately reflect the coupling relationship between the deposition and the temperature field. For example, in the section where the deposition grows rapidly, the temperature distribution characteristics may present the characteristics of local high temperature and significant gradient change, and through the feature fusion network, these characteristics can be associated with the spatial correlation features, improving the accuracy of temperature field analysis.

[0083] In addition, for the data collected by each deposition monitoring sensor, its time domain features and frequency domain features are extracted. The time domain features include mean, variance, peak value, kurtosis, etc. statistical quantities, reflecting the overall characteristics of the vibration signal in the time dimension; the frequency domain features are obtained by converting the time domain signal to the frequency domain signal through fast Fourier transform (FFT), and extracting the frequency spectrum energy distribution, characteristic frequency (such as the inherent frequency of the deposition), etc. According to the phase difference between the time domain features and the frequency domain features, the deposition feature vector of the sensor is calculated. The phase difference analysis is used to identify the time delay relationship between different frequency components in the vibration signal and the deposition, for example, the high frequency vibration may lag behind the increase of the deposition thickness, and through the phase difference calculation, the time sequence correlation between the frequency components and the deposition state can be established. The deposition feature vector is composed of the normalized time domain features, frequency domain features and phase difference parameters, and each element of the vector corresponds to a feature dimension.

[0084] Based on the deposition feature vector, the feature matching is performed on the deposition monitoring sensors at different positions to identify the sensor group with similar deposition state. The feature matching adopts the Euclidean distance or cosine similarity measurement method to calculate the distance or similarity between the deposition feature vectors of any two sensors, the sensors with a distance less than a set threshold (such as 0.5) or a similarity greater than a set threshold (such as 0.8) are divided into the same group, indicating that the deposition states of the sections where these sensors are located are similar. Through feature matching, the sensors can be divided into several groups, each group corresponding to a typical deposition mode, thereby realizing the group analysis of the deposition growth trend. For example, the sensors in the same group may all be in the high flow cooling area of the cutter barrel, and the deposition growth trend in this area is greatly affected by the fluid scouring. Through group analysis, the deposition evolution law of this area can be extracted, providing a basis for subsequent zoning regulation.

[0085] On the basis of feature matching, the space distribution and data characteristics of sensors in the group are combined to calculate the growth trend of the slag. For each group, the least squares method is used to fit the curve of the change of the slag thickness with time, and the slope of the curve is the growth rate of the slag corresponding to the group. At the same time, by analyzing the spatial position distribution of the sensors in the group, the main direction of the growth of the slag is determined, for example, the expansion trend along the axial or circumferential direction of the cutter barrel. In addition, considering the time synchronization of the data collected by the sensors, the growth rate and direction of the sensors in the group are weighted and averaged, and the weight is determined according to the collection accuracy of the sensors and the importance of the spatial position (such as the sensors near the cutting area of the cutter have higher weight). Finally, the growth trend parameters of the slag of the group are obtained, including the growth rate, the expansion direction, the spatial distribution range, etc.

[0086] In the whole process of slag state matrix processing, each link is closely connected: the standardization processing lays the data foundation for subsequent feature extraction, the sliding window and noise filtering ensure that the analysis focuses on the key area, the feature decomposition realizes data dimension reduction and trend quantization, the spatial correlation analysis reveals the deposition correlation between sections, the feature fusion network improves the accuracy of temperature field analysis, and the multi-sensor feature matching and group analysis realize the fine description of the growth trend of the slag in different regions.

[0087] Example 3:

[0088] The deposition intensity modeling of the slag state matrix specifically includes the correlation mapping of vibration spectrum characteristics and temperature distribution characteristics, deposition intensity map generation, spatial alignment processing, adhesion threshold setting, flow field model constraint compensation, and temperature labeling, etc. Through multi-dimensional data fusion and physical model driving, the deposition intensity of the inner wall of the cutter barrel is accurately modeled and blind area compensation is realized.

[0089] The correlation mapping based on vibration spectrum feature extraction and temperature distribution characteristics is the basis of deposition intensity modeling. The vibration spectrum data collected by the slag monitoring sensor in real time contains characteristic frequency components reflecting the thickness and adhesion state of the slag, for example, when the deposition of the slag causes the local mass increase of the inner wall of the cutter barrel, the low-frequency component energy in the vibration spectrum will be significantly enhanced. In each frame of data, the sampling points of the vibration spectrum are extracted by the peak detection algorithm, and usually the top 5% of the energy frequency points are selected as the characteristic frequencies. The vibration energy values corresponding to these frequency points are directly related to the deposition intensity. At the same time, the temperature distribution characteristics reflect the differences in the thermal environment of different regions of the inner wall of the cutter barrel. The high-temperature region may exacerbate the adhesion of the slag due to material thermal expansion, and the low-temperature region may cause the slag to fall off due to the flushing of the cooling liquid. By establishing a two-dimensional mapping relationship between the vibration spectrum sampling points and the temperature values, for example, using a polynomial fitting or a neural network model, the vibration energy values and the temperature values are converted into a deposition intensity index, which can be quantified as a dimensionless deposition intensity value (range 0-1). The larger the value, the higher the deposition intensity of the slag.

[0090] A deposition intensity map is generated and spatially aligned. For each deposit monitoring sensor, the associated mapping of the deposition intensity indicators is arranged in time sequence according to its deployment position (e.g. circumferential angle of the tool cylinder, axial coordinate), forming a one-dimensional deposition intensity map corresponding to the sensor. The horizontal axis of the map is time, and the vertical axis is the deposition intensity value. To achieve global integration of multi-sensor data, the deposition intensity maps of each sensor need to be spatially aligned. The core of spatial alignment is to convert the local coordinate system of different sensors into the global cylindrical coordinate system of the tool cylinder (with the tool cylinder axis as the z-axis, the circumferential angle as the θ-axis, and the radial distance as the r-axis). Through the coordinate transformation matrix, the deposition intensity map of each sensor is mapped to the corresponding position in the global coordinate system, and then the bilinear interpolation algorithm is used to fill in the blank areas between adjacent sensors, finally generating a two-dimensional deposition intensity distribution map covering the entire circumference of the tool cylinder inner wall. This distribution map directly displays the spatial distribution characteristics of the deposition intensity of the deposits, for example, the deposition intensity near the cutting area of the tool may be significantly higher than that in other areas.

[0091] In the deposition intensity modeling process, an adhesion threshold value is set to identify the deposit blind area. The adhesion threshold value is determined by analyzing the vibration frequency spectrum variation range of historical deposition data, and is usually set to 2-3 times the standard deviation of the vibration energy fluctuation range under normal working conditions. When the difference between the vibration frequency spectrum values of multiple frames of deposit state matrix (i.e. the difference between the vibration energy of the current frame and the previous frame) exceeds the adhesion threshold value, it indicates that there may be a blind area where the deposits grow rapidly and are not directly monitored by the sensor. At this time, the beamforming algorithm is used to locate the adhesion source and determine the spatial position of the blind area. The beamforming algorithm estimates the azimuth angle of the signal source by calculating the phase difference of the signals of each sensor, thereby achieving preliminary positioning of the deposit blind area.

[0092] For the identified deposit blind area, flow field model constraint compensation is needed. The flow field model is based on the Navier-Stokes equation of fluid dynamics, combined with the geometric shape of the tool cylinder inner wall and the cooling liquid flow parameters (such as flow rate, pressure), and solves the fluid velocity field and pressure distribution of the section through the finite element method. The input parameters of the flow field model include the density, viscosity, inlet flow rate of the cooling liquid, and the rotational speed of the tool cylinder, etc. By comparing the predicted deposition intensity of the model with the measured value of the sensor, an iterative correction mechanism is established: if the error between the predicted value and the measured value exceeds the preset threshold (such as 10%), adjust the boundary conditions (such as wall roughness) or fluid parameters in the model, recalculate the deposition intensity distribution, until the error converges. Through iterative correction, the intensity compensation value of the blind area deposition is calculated, which is used to correct the estimated value of the blind area region in the deposition intensity distribution map, improving the modeling accuracy.

[0093] After the blind area compensation is completed, the deposition intensity modeling is performed on the deposition state matrix according to the deposition intensity distribution. The essence of the deposition intensity modeling is to replace each element (corresponding to a spatial point on the inner wall of the tool cylinder) in the deposition state matrix with a corresponding deposition intensity value to form a deposition intensity matrix. The matrix not only contains the current deposition intensity of each spatial point, but also retains the historical information of the evolution of the deposition through the time sequence. At the same time, the deposition model is temperature-labeled through the temperature distribution characteristics, that is, the temperature field information is superimposed in the deposition intensity matrix. Specifically, the deposition intensity value and the temperature value of each spatial point form a key-value pair and are stored in the matrix, so that the deposition model has the characteristics of thermal-mechanical coupling. For example, the deposition intensity value of a certain spatial point is 0.7 and the temperature value is 50°C, which is labeled as (0.7, 50°C) for subsequent analysis of the interaction between the deposition and the temperature field.

[0094] In the whole process of deposition intensity modeling, the correlation mapping of vibration spectrum characteristics and temperature distribution characteristics is the core of data-driven, and through the coupling analysis of characteristic frequency energy and temperature, a quantitative index of deposition is established; the spatial alignment processing realizes the global integration of multi-sensor data, solving the contradiction between local monitoring and global modeling; the adhesion threshold setting and flow field model constraint compensation are aimed at the deposition blind area problem, and the robustness of modeling is improved through physical model driving; the temperature labeling part injects the thermal environment dimension into the deposition model, making it closer to the actual processing conditions. Each step supports each other, forming a complete technical chain from data acquisition, feature extraction, spatial modeling to blind area compensation.

[0095] In addition, the results of the deposition intensity modeling can be further used for deposition trend prediction and cooling medium regulation. For example, by analyzing the time sequence change of the deposition intensity matrix, the expansion direction and rate of the deposition can be predicted; based on the deposition intensity distribution, the active control module can develop differentiated cooling strategies to increase the flow of cooling liquid or adjust the injection angle in the high deposition intensity section, thereby achieving active inhibition of the deposition. At the same time, the temperature labeling information of the deposition intensity model can guide the adjustment of the cooling liquid ratio, for example, using cooling liquid additives with better heat dissipation performance in high temperature sections to reduce the possibility of deposition adhesion.

[0096] Example 4:

[0097] The calculation of the temperature gradient change according to the deposition monitoring sensor position includes steps such as temperature change point extraction, deposition field model construction, flow path sampling analysis, partition evolution rule analysis, and temperature gradient change comprehensive calculation. Through spatial interpolation, parameter calculation, and feature projection, the quantitative analysis of the correlation characteristics of the temperature field on the inner wall of the tool cylinder and the deposition is realized.

[0098] Temperature change points are extracted based on multiple sets of slag deposition data. Slag monitoring sensors are deployed at predetermined intervals on the inner wall of the cutter barrel to collect temperature gradient data in real time. When the rate of temperature change at a certain location exceeds a preset threshold (e.g., 0.5℃ per minute), it is identified as a temperature change point. These points reflect abrupt changes in local thermal state caused by slag deposition; for example, a temperature jump may occur in the slag adhesion area due to frictional heating. Based on the sensor deployment positions (axial coordinate z, circumferential angle θ), the temperature change points are mapped to a unified cylindrical coordinate system, forming a discrete set of temperature feature points.

[0099] A slag accumulation field model of the cutterhead is generated by fitting temperature change points using a spatial interpolation algorithm. The interpolation algorithm employed is Kriging interpolation, which is based on regionalized variable theory and estimates the temperature value at unknown locations by calculating the spatial autocorrelation function of temperature change points. The Kriging interpolation formula is:

[0100]

[0101] in, The temperature value at the location to be estimated. For the first Temperature values ​​at points of temperature change The weighting coefficients are determined by the semi-variogram. This represents the number of temperature change points involved in the interpolation. The algorithm generates a continuous temperature field distribution surface, the morphology of which reflects the influence of slag deposition on heat conduction; for example, areas with thicker slag deposits may form high-temperature zones.

[0102] Equal-interval sampling was performed along the flow path of the slag field model. The flow path was defined as the mainstream direction of the coolant on the inner wall of the cutter barrel (usually axial). The sampling interval was set according to the model resolution and the slag evolution rate, for example, one sample point was collected every 10 mm. For each sampling point, three key parameters were calculated: the rate of change of adhesion coefficient, the temperature fluctuation index, and the deposition slope. The rate of change of adhesion coefficient reflects the change in the scouring ability of the coolant on the slag surface, and is obtained by calculating the ratio of the difference in adhesion coefficient between adjacent sampling points to the axial distance through a fluid dynamics model; the temperature fluctuation index is the ratio of the standard deviation to the mean of the temperature value at the sampling point, characterizing the thermal environment stability; the deposition slope is obtained by fitting the curve of deposition intensity change over time, reflecting the slag growth rate.

[0103] Calculate sedimentary change parameters based on the above three parameters. The formula is:

[0104]

[0105] in, The rate of change of the adhesion coefficient. This is the temperature fluctuation index. to deposit the changing slope, to weight coefficient (determined by historical data training, meet ). The deposition change parameter comprehensively represents the dynamic characteristics of the deposition of the accumulated slag, and the larger the value is, the more active the evolution of the accumulated slag is.

[0106] According to the deployment parameters (such as spacing, angle) and collection accuracy of the accumulated slag monitoring sensor, the accumulated slag growth evolution characteristics (such as deposition intensity, growth rate) in each frame of data are projected to the accumulated slag field model. The projection process is realized by coordinate transformation, which maps the sensor measurement values to the corresponding spatial positions of the model. Along the flow direction (axial direction) of the accumulated slag field model, the model is divided into multiple analysis partitions according to the number of sensors, and each partition corresponds to the monitoring range of one or more sensors. By statistical analysis of the mean, variance, skewness and other characteristics of the accumulated slag growth in the analysis partition, the deposition distribution characteristics are extracted, such as the axial position range of the high deposition rate partition, the spatial distribution form of the deposition intensity, etc.

[0107] The calculation of the temperature gradient change integrates the deposition change parameter and the deposition distribution characteristic. The specific process is: a series of spatial coordinate points are selected in the deployment direction (axial direction) of the sensor For each coordinate point, the product of the deposition field intensity characteristic weight value and the accumulated slag distribution characteristic weight value is calculated in the spatial resolution range (such as the cylindrical region with as the center and a radius of 5mm), wherein is obtained by normalizing the deposition intensity value, is calculated by the probability density function of the deposition distribution. The cumulative product of all coordinate points is added to the influence value of the deposition intensity change rate on the sampling frequency of the sensor (computed by the linear relationship model between the sampling frequency and the deposition rate, is the proportional coefficient), and finally the temperature gradient change value is obtained, and the calculation formula is:

[0108]

[0109] The formula quantifies the comprehensive influence of spatial deposition characteristics and time sampling frequency on the temperature gradient, reflecting the spatiotemporal coupling effect of the deposition of the accumulated slag.

[0110] During the whole calculation process of temperature gradient, the slag accumulation field model is used as a data carrier to integrate the spatial distribution information of temperature field and deposition field; the flow path sampling and partition analysis realize the multi-scale decomposition of the dynamic characteristics of slag accumulation evolution; the introduction of weight coefficient and frequency influence value ensures the adaptability of the calculation results to the actual working conditions.

[0111] Example 5:

[0112] The dynamic regulation of the cooling medium of the tool cylinder includes section identification and evolution characteristic mapping, cooling medium execution action control, dynamic allocation strategy formulation, and key parameter adjustment, etc. Through the spatiotemporal characteristic driving and physical unit linkage, the precise intervention of the slag deposition of the tool cylinder is realized.

[0113] The section identification and slag deposition evolution characteristic mapping are the basis of dynamic regulation. After determining the spatiotemporal evolution characteristics of slag deposition, the active control module divides the inner wall of the tool cylinder into sub-sections matching the sensor deployment density (such as dividing into a section every 20 mm along the axial direction and a sector every 30° in the circumferential direction), and each sub-section is assigned a unique section identification (such as “Z1-θ1” representing the first section in the axial direction and the first sector in the circumferential direction). Through the characteristic matching algorithm, the evolution characteristics of each sub-section such as deposition intensity, growth trend, and temperature distribution are established in one-to-one correspondence with the section identification, forming a “section identification-evolution characteristic” mapping table. The mapping table is updated in real time to ensure that the control instructions can accurately point to the slag risk area. For example, when it is monitored that the deposition intensity of section “Z3-θ2” exceeds the threshold value 0.6 in the continuous 5 frames of data, the mapping table immediately marks this section as a high-risk area, triggering the cooling medium regulation process.

[0114] The execution action control of the cooling medium is the core link of dynamic regulation, which specifically includes flow pressure adjustment, cooling liquid ratio, and spray angle operation. The flow pressure adjustment is realized through a proportional electromagnetic valve, and the active control module dynamically adjusts the cooling liquid flow according to the deposition intensity of the section: when the deposition intensity of a section is in the range of 0.3-0.6, the flow is increased by 20%; when it exceeds 0.6, the flow is increased by 50%, and the auxiliary nozzle of the adjacent section is started to cooperate with the flushing. The pressure adjustment range is set according to the rated parameters of the cooling system, for example, when the basic pressure is 0.5 MPa, the pressure of the high-risk section can be increased to 0.8-1.0 MPa to enhance the flushing force.

[0115] The cooling liquid proportioning strategy is based on a pre-set multi-component additive scheme. The additives include lubricating type (such as fatty acid ester), heat dissipation type (such as ethylene glycol), and anti-adhesion type (such as polytetrafluoroethylene micro powder), and the proportioning ratio of each additive is determined by a neural network model trained by historical data. For example, when the zone temperature exceeds 60°C and the deposition intensity is greater than 0.5, the model automatically selects a proportioning scheme with 60% heat dissipation type additive and 30% anti-adhesion type additive, dynamically mixes the components through a multi-channel metering pump, and generates a cooling liquid that adapts to the current thermal-mechanical environment. The generation of the spray angle vector is based on the spatial coordinates of the zone and the deposition morphology, and the optimal spray direction of the nozzle is determined through geometric calculation: taking the center of the zone as the target, calculating the angle between the nozzle axis and the target, combining the deposition offset direction (such as a 15° clockwise offset along the circumference), generating a three-dimensional spray angle vector containing pitch and azimuth, and driving the servo motor to adjust the nozzle posture.

[0116] The dynamic allocation of cooling medium is based on the spatial distribution of accumulated sediment deposition and the pre-set control strategy. The active control module first divides the deposition intensity distribution map into high (>0.6), medium (0.3-0.6), and low (<0.3) risk level areas, and formulates differentiated allocation strategies for different levels: high-risk areas are preferentially allocated 80% of the cooling liquid flow, and all components are activated; medium-risk areas are allocated 15% of the flow, and basic cooling liquid (without additives) is used; low-risk areas are allocated 5% of the flow, and only basic cooling function is maintained. Dynamic allocation is achieved through a multi-way electromagnetic valve group, with each electromagnetic valve corresponding to a physical zone or nozzle group. The module sends switching instructions based on the real-time mapping table to ensure that the cooling liquid flows accurately to high-demand areas. For example, when the zone "Z5-θ4" is marked as high-risk, the corresponding electromagnetic valve V5-4 is immediately fully opened, while the electromagnetic valves of low-risk zones are closed, achieving dynamic reallocation of flow.

[0117] When controlling the cooling medium to perform actions, the pre-set intensity threshold is a key condition for triggering differentiated control. The threshold is determined by the wear resistance of the tool cylinder material and the processing process requirements, for example, when the deposition intensity reaches 70% of the critical adhesion strength of the material (usually set to 0.5-0.7), the flow pressure boost instruction of the adjacent nozzle is triggered. The boost process uses closed-loop control: the pressure sensor feeds back the nozzle outlet pressure in real time, and if the deviation between the actual pressure and the target pressure (such as 1.0 MPa) exceeds 5%, the active control module automatically adjusts the proportional valve opening until the pressure stabilizes. For example, when the deposition intensity of the zone "Z2-θ3" reaches 0.6, the adjacent nozzle groups N2-3 and N2-4 simultaneously enter the boost mode, and the pressure gradually increases from 0.5 MPa to 0.9 MPa until the deposition intensity drops below the threshold.

[0118] The dynamic combination of cooling liquid ratio and the adjustment of spray angle are the key means to inhibit the accumulation of slag. The selection and mixing ratio of additive components are driven by real-time temperature field and deposition intensity data. For example, in the section where the temperature gradient changes sharply (>2℃ / mm), the proportion of anti-adhesion additive is automatically increased to 40% to reduce the adhesion of slag to the surface of the tool cylinder. The adjustment frequency of the spray angle vector matches the evolution speed of the slag. When the deposition offset increases by 5mm, the spray angle is recalculated and the nozzle is driven to rotate to ensure that the spray direction always points to the front area of the growing slag. For example, if the deposition distribution curve of a certain section is offset by 10mm along the axial direction, the pitch angle of the spray angle is adjusted by 5° to cover the new deposition area.

[0119] During the whole dynamic regulation process, the real-time mapping of section identification and evolution characteristics ensures the spatial accuracy of control instructions. The multi-parameter coordinated adjustment of cooling medium flow, ratio, and angle realizes the multi-dimensional inhibition of slag deposition. The preset threshold and closed-loop control mechanism guarantee the stability and reliability of the regulation process. Through the above steps, the active control module can dynamically generate and execute the optimal regulation strategy according to the spatio-temporal changes of slag deposition, effectively inhibit the tool wear caused by excessive deposition of slag, and improve the continuity and machining precision of the machining system.

[0120] It should be noted that, in this text, relational terms such as first and second are used merely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprise", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0121] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for actively controlling the slag accumulation state of a tool barrel and suppressing tool wear, applied to a machining control system, the system comprising multiple slag accumulation monitoring sensors disposed on the inner wall of the tool barrel and an active control module connected to the slag accumulation monitoring sensors, wherein adjacent slag accumulation monitoring sensors are spaced apart by a predetermined distance, characterized in that, The method includes: The vibration spectrum data and temperature gradient data of the inner wall of the cutter barrel are collected by the slag monitoring sensor to generate a slag status dataset. The active control module receives real-time slag status data from multiple slag monitoring sensors and constructs a slag status matrix for the cutter barrel. Based on the slag state dataset and real-time data from each slag monitoring sensor, the spatiotemporal evolution characteristics of slag deposition are determined. The determination of the spatiotemporal evolution characteristics of slag deposition includes: processing the slag state matrix, extracting deposition features by combining the slag state dataset, predicting slag trends based on temperature gradient changes and pressure distribution information, outputting the spatiotemporal evolution characteristics of slag deposition through an active control model, and updating the slag state dataset based on the spatiotemporal evolution characteristics. Based on the spatiotemporal evolution characteristics, the cooling medium of the blade barrel is dynamically controlled; The determination of the spatiotemporal evolution characteristics of slag deposition includes: The slag state matrix is ​​processed to extract vibration spectrum features, temperature distribution characteristics, and slag growth trend. Based on the vibration spectrum features and temperature distribution characteristics, deposition intensity modeling is performed on the slag state matrix. The inner wall of the cutter barrel is divided into multiple sub-segments and labeled with segment identifiers. The vibration spectrum of each sub-segment is correlated and matched with the slag state dataset, and the segment identifiers are labeled in the slag state dataset. Temperature gradient changes are calculated based on the location of slag monitoring sensors. The evolution of slag deposition is predicted based on the temperature gradient changes and the slag growth trend, and deposition prediction information for each sub-segment is calculated. An active control model is constructed, using the deposition prediction information as input parameters. The active control model performs spatial correlation modeling on the deposition prediction information, outputting the spatiotemporal evolution characteristics of slag deposition. The slag state dataset is updated based on the spatiotemporal evolution characteristics to obtain the spatiotemporal evolution characteristics of slag deposition. The processing of the slag state matrix includes: The slag state matrix is ​​standardized, and hotspot segments in the matrix are extracted using a sliding window. Noise filtering is applied to these hotspot segments, and the slag growth trend is calculated using a feature decomposition algorithm. The spatial correlation characteristics of the slag state matrix are calculated, and the adhesion strength, deposition stability coefficient, and slag blind zone index between segments are calculated based on these characteristics. A feature fusion network is constructed, and the temperature distribution characteristics are calculated using this network. The time-domain and frequency-domain features collected by each slag monitoring sensor are extracted, and the deposition feature vector of the sensor is calculated based on the phase difference between the time-domain and frequency-domain features. Feature matching is performed on slag monitoring sensors at different locations based on the deposition feature vector, and the slag growth trend is calculated.

2. The method for actively controlling the slag accumulation state of the tool barrel and suppressing tool wear according to claim 1, characterized in that, The deposition intensity modeling of the slag state matrix includes: Based on the vibration spectrum characteristics, vibration spectrum sampling points are extracted in each frame of data. The sampling points are correlated and mapped with the temperature distribution characteristics to generate a deposition intensity map. The deposition intensity maps collected by multiple sensors are spatially aligned to calculate the deposition intensity distribution of the cutter barrel. An adhesion threshold is set, and the adhesion source is located based on the vibration spectrum value of the multi-frame slag state matrix. The adhesion intensity difference is calculated. If the adhesion intensity difference is greater than or equal to the adhesion threshold, it indicates that there is a slag blind zone in this section. The flow field model constraint compensation is performed on the current section. The deposition intensity distribution of the current section is iteratively corrected according to the fluid dynamics model corresponding to the current section. The intensity compensation value of the blind zone deposition is calculated based on the correction result. Based on the deposition intensity distribution, the deposition intensity model of the slag state matrix is ​​performed, and the temperature of the deposition model of the cutter barrel is labeled using the temperature distribution characteristics.

3. The method for actively controlling the slag accumulation state of the tool barrel and suppressing tool wear according to claim 2, characterized in that, The calculation of temperature gradient change based on the location of the slag monitoring sensor includes: Based on multiple sets of slag deposition data, temperature change points are extracted, and these points are mapped to a unified coordinate system according to the sensor deployment location. A spatial interpolation algorithm is then used to fit the change points to generate a slag field model of the cutter barrel. Equal-interval sampling is performed along the flow path of the slag field model. Based on the sampling results, the rate of change of adhesion coefficient, temperature fluctuation index, and deposition change slope of the path are calculated. Based on the rate of change of adhesion coefficient, temperature fluctuation index, and deposition change slope, deposition change parameters are calculated. Based on the deployment parameters and acquisition accuracy of the sediment monitoring sensors, the spatiotemporal evolution characteristics of sediment growth in each frame of data are projected onto the sediment field model. The sediment field model is divided into zones according to the number of sensors along the flow direction. The evolution law of sediment growth within the zone is analyzed, and the deposition distribution characteristics are calculated based on the evolution law. Based on the depositional change parameters and the depositional distribution characteristics, the temperature gradient change is calculated. The calculation process of the temperature gradient change includes: selecting spatial coordinate points in the sensor deployment direction based on the position range from the first sediment monitoring sensor to the last sediment monitoring sensor, accumulating the product of the sedimentation field strength characteristic weight value and the sedimentation distribution characteristic weight value within the spatial resolution range, and superimposing the influence value of the sensor acquisition frequency on the depositional intensity change rate.

4. The method for actively controlling the slag accumulation state of the tool barrel and suppressing tool wear according to claim 3, characterized in that, The calculation of deposition prediction information for each sub-segment includes: Using the main flow diameter of the slag field model as the baseline, and taking the peak position of slag growth in each frame of data as the reference point, the deposition offset is calculated, and the deposition distribution curve is plotted according to coordinates. Based on the temperature gradient change, the growth rate and direction of the slag growth trend are corrected; Starting from the most recent sediment distribution point, the distribution curve is continued to be plotted based on the correction results of the growth rate and direction, generating the sediment distribution point for the next period, until the distribution point covers the entire target area, generating sediment prediction information.

5. The method for actively controlling the slag accumulation state of the tool barrel and suppressing tool wear according to claim 1, characterized in that, The active control model includes: The input layer is used to organize the deposition prediction information into spatially distributed data and perform standardization processing; The feature fusion layer is used to extract the segment association features of the deposition by processing spatial distribution data and to build the dependency relationship between physical units; The medium regulation layer is used to integrate the correlation of slag deposition on the spatial unit and generate a cooling medium regulation strategy.

6. The method for actively controlling the slag accumulation state of the tool barrel and suppressing tool wear according to claim 1, characterized in that, The acquisition of the spatiotemporal evolution characteristics of slag deposition includes: Based on the spatiotemporal evolution characteristics of sludge deposition output by the active control model, the sub-segment identifiers are mapped to the spatiotemporal evolution characteristics. The segment data in the slag state dataset are reorganized according to spatiotemporal characteristics to generate a segment distribution map sorted by slag deposition intensity; Based on the reconstructed section distribution map, the optimized sedimentary evolution characteristics are output.

7. The method for actively controlling the slag accumulation state of the tool barrel and suppressing tool wear according to claim 1, characterized in that, The dynamic control of the cooling medium of the cutter barrel includes: Map the segment identifiers one-to-one with the segments of the spatiotemporal evolution characteristics of sludge deposition; Based on the spatiotemporal evolution characteristics of slag deposition, the actions of the cooling medium are controlled, including flow and pressure adjustment, coolant ratio and injection angle operation. Based on the spatial distribution of slag deposition and the preset medium control strategy, the cooling medium is dynamically allocated to the corresponding physical sections.

8. The method for actively controlling the slag accumulation state of the tool barrel and suppressing tool wear according to claim 7, characterized in that, The actions for controlling the cooling medium include: When the slag deposits in the target section reach a preset intensity threshold, a flow rate boosting command is triggered for the adjacent nozzles. The additive components are dynamically combined according to the coolant mixing strategy to generate the injection angle vector. The pressure parameters of the injection unit in the target section are adjusted based on the injection angle vector.

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