Material selection method, system, equipment and medium for hard alloy tool life prediction based on working condition parameters

Abstract

This invention discloses a material selection method, system, equipment, and medium for predicting the life of cemented carbide cutting tools based on operating parameters. It relates to the field of cemented carbide cutting tool manufacturing technology. The method includes: collecting cutting force distribution and temperature gradient data using a stress sensing array and a temperature sensing array in the tool tip region; constructing stress-temperature-wear synergistic effect data; calculating fatigue damage characteristic values ​​based on the synergistic effect data to obtain wear prediction correction coefficients; compensating the tool tip stress field using the correction coefficients to output life prediction data; designing a carbide phase gradient distribution structure based on the synergistic effect data and fatigue damage characteristic values; adjusting the carbide grain orientation angle in conjunction with the wear prediction correction coefficients and life prediction data; and outputting optimal material structure parameters to achieve optimized selection of cemented carbide cutting tool materials. This invention improves the accuracy of tool life prediction and realizes dynamic optimization of material structure.

Description

Technical Field

[0001] This invention relates to the field of cemented carbide tool manufacturing technology, specifically to a material selection method, system, equipment, and medium for predicting the life of cemented carbide tools based on working condition parameters. Background Technology

[0002] Predicting tool life using carbide cutting tools is challenging due to the dynamic changes in cutting temperature and force during machining. Relying primarily on single-factor cutting force or temperature monitoring for tool life prediction fails to capture the synergistic effects of stress, temperature, and wear during the cutting process, resulting in low prediction accuracy.

[0003] Traditional methods for selecting cemented carbide cutting tool materials primarily rely on experience and static test data, failing to adequately consider the dynamic response characteristics of the tool material under actual machining conditions. Especially in complex conditions such as high-speed cutting and machining of difficult-to-machine materials, the lack of dynamic optimization methods for tool material structural parameters leads to generally low tool life.

[0004] In existing technologies, the distribution structure and grain orientation angle of carbide phases are often designed using fixed schemes, which cannot be dynamically adjusted according to actual cutting conditions. This static design method is difficult to adapt to the dynamic changes in stress and temperature fields during cutting, thus limiting further improvements in tool performance. Summary of the Invention

[0005] The purpose of this invention is to provide a material selection method, system, equipment, and medium for predicting the life of cemented carbide cutting tools based on working condition parameters. By establishing a stress-temperature-wear synergistic model, the dynamic optimization design of the carbide phase gradient distribution structure is realized, thereby improving the accuracy of tool life prediction and service life.

[0006] The material selection method for predicting the life of cemented carbide tools based on working condition parameters provided in this embodiment of the invention includes the following steps:

[0007] Collect cutting force data, cutting temperature data, and tool wear data of carbide tools during the cutting process;

[0008] The cutting force distribution at multiple points is acquired in real time using a stress sensing array in the tool tip region. Based on the cutting temperature gradient collected by the temperature sensing array, the three-dimensional distribution of the stress field at the tool tip is calculated, and the synergistic effect data of stress, temperature and wear in the tool tip region is constructed.

[0009] Fatigue damage characteristic values ​​are calculated based on stress-temperature-wear synergistic data, and wear prediction correction coefficients are obtained by fitting fatigue damage characteristic values ​​with tool wear data.

[0010] The three-dimensional distribution of the tool tip stress field is compensated by the wear prediction correction coefficient, and the tool life prediction data is output.

[0011] The carbide phase gradient distribution structure is designed based on the stress-temperature-wear synergistic data and fatigue damage characteristic values. The carbide grain orientation angle is determined using the wear prediction correction coefficient. At the same time, the carbide grain orientation angle is adjusted based on the tool life prediction data. The optimal material structure parameters are output, and the cemented carbide tool material is selected based on the optimal material structure parameters.

[0012] Furthermore, data on cutting force, cutting temperature, and tool wear of carbide tools during the machining process are collected, including:

[0013] Collect cutting force data, perform differential calculation on the cutting force data to obtain the stress change rate, identify the stress change location based on the stress change rate, calculate the time difference between adjacent stress change locations, and determine the propagation speed and direction of the stress wave.

[0014] The triggering time is calculated based on the propagation speed and direction of the stress wave, and cutting temperature data are collected sequentially according to the triggering time;

[0015] The cutting temperature data is segmented according to the direction of stress wave propagation. The temperature value corresponding to the stress change location is marked in each segment to establish the correspondence between cutting force and cutting temperature.

[0016] The detection location and time are determined based on the corresponding data of cutting force and cutting temperature. Tool wear data are collected, and tool cutting force data, cutting temperature data, and tool wear data are output.

[0017] Furthermore, the cutting force distribution at multiple points is acquired in real time using a stress sensing array in the tool tip region. Based on the cutting temperature gradient collected by the temperature sensing array, the three-dimensional distribution of the stress field at the tool tip is calculated, and data on the synergistic effect of stress, temperature, and wear in the tool tip region are constructed, including:

[0018] The stress sensor array in the tool tip region is used to acquire multi-point cutting force distribution data in real time, and the temperature sensor array is used to collect cutting temperature gradient data. The stress gradient field and temperature gradient field are calculated by the difference between adjacent measurement points, and the location of abrupt change points is identified.

[0019] Establish the propagation paths of stress gradient and temperature gradient based on the location of the abrupt change point, calculate the amplitude ratio and phase difference of stress gradient and temperature gradient along the propagation path, and determine the stress-temperature coupling region by the variation law of amplitude ratio and phase difference.

[0020] Within the stress-temperature coupling region, the direction of stress energy distribution is determined by the directionality of the temperature gradient, and the distribution weights of the stress gradient in the radial, circumferential, and axial directions are calculated. These distribution weights are then used to determine the three-dimensional components of the tool tip stress field.

[0021] The three-dimensional components are compensated and corrected by temperature gradient, and the three-dimensional distribution of the stress field at the tool tip is obtained by superposition calculation based on the corrected stress components.

[0022] By combining the three-dimensional distribution of the stress field at the tool tip with tool wear data, we can construct data on the synergistic effects of stress, temperature, and wear in the tool tip region.

[0023] Furthermore, fatigue damage characteristic values ​​are calculated based on stress-temperature-wear synergistic data. These fatigue damage characteristic values ​​are then fitted with tool wear data to obtain wear prediction correction coefficients, including:

[0024] The stress-temperature-wear synergistic data in the tool tip region are segmented along the cutting direction. Temperature peaks and stress peaks are identified in each segment. The locations of the peaks are paired in spatial order to establish a stress-temperature propagation sequence.

[0025] Based on the stress-temperature propagation sequence, the propagation delay between adjacent peaks is extracted. The action period is divided based on the propagation delay. The rate of change of temperature field and the cumulative stress value in each period are calculated to construct the fatigue damage function.

[0026] The distribution of heat concentration areas is determined based on the rate of change of the temperature field. The temperature gradient of the heat concentration areas is used as a weighting factor and combined with the fatigue damage function to calculate the fatigue damage characteristic value.

[0027] The fatigue damage characteristic values ​​are divided into regions according to the distribution pattern of heat concentration areas, and a corresponding relationship is established with the tool wear data of each region to obtain the wear prediction function.

[0028] The stress-temperature coupling strength of each heat concentration region is calculated based on the wear prediction function. The coefficients of the prediction function are then weighted and corrected using the coupling strength to obtain the wear prediction correction coefficient.

[0029] Furthermore, the three-dimensional distribution of the tool tip stress field is compensated using a wear prediction correction coefficient, and the output tool life prediction data includes:

[0030] The wear prediction correction coefficient is decomposed into radial, circumferential and axial correction components along the heat concentration region. Time-frequency analysis is performed on the correction components to calculate the time-series correlation intensity in each direction and determine the dominant direction of the tool tip stress field distortion.

[0031] A spatial reference coordinate system is established based on the dominant direction. The radial, circumferential and axial deviations and delays are calculated using time-series correlation strength to construct a compensation matrix for the tool tip stress field.

[0032] The initial compensation value is calculated based on the compensation matrix and the three-dimensional distribution of the blade tip stress field. The temperature field compensation coefficient is calculated using the time-series correlation intensity. The temperature field compensation coefficient is then superimposed with the initial compensation value to obtain the corrected three-dimensional distribution.

[0033] The stress gradient change rate is calculated based on the corrected three-dimensional distribution state. The damage evolution rate is adjusted using the stress gradient change rate. The damage accumulation curve is calculated based on the adjusted damage evolution rate, and the fatigue critical state is determined to obtain the tool wear threshold.

[0034] By combining the tool wear threshold with the corrected three-dimensional distribution state, tool life prediction data is output.

[0035] Furthermore, based on stress-temperature-wear synergistic data and fatigue damage characteristic values, a carbide phase gradient distribution structure is designed, and the carbide grain orientation angle is determined using wear prediction correction coefficients, including:

[0036] The stress-temperature-wear synergistic data and fatigue damage characteristic values ​​are transformed into an energy distribution field. The phase characteristics of the energy distribution field are calculated, and the direction of maximum energy gradient is obtained based on the phase characteristics. The spatial distribution function of the carbide phase is constructed based on the direction of maximum energy gradient.

[0037] The energy transfer path is analyzed along the spatial distribution function of the carbide phase, the energy phase difference between adjacent regions is calculated, and the volume fraction of the carbide phase in the thermally concentrated region is determined using the energy phase difference.

[0038] Adjust the carbide phase distribution density in the stress concentration region according to the energy phase difference change law to form a carbide phase gradient distribution structure;

[0039] Based on the analysis of the stress field phase characteristics of the carbide phase gradient distribution structure, the stress field phase characteristics are coupled with the energy phase difference to calculate the crack propagation driving force, and the force direction of the carbide grains is determined by the crack propagation driving force.

[0040] The crack propagation resistance coefficient is calculated by utilizing the stress direction and phase characteristics of carbide grains. The resistance coefficient is then combined with the wear prediction correction coefficient to determine the carbide grain orientation angle.

[0041] Furthermore, based on tool life prediction data, the carbide grain orientation angle is adjusted to output optimal material structure parameters. Based on these optimal material structure parameters, cemented carbide tool materials are selected, including:

[0042] Wavelet decomposition is performed on the tool life prediction data to obtain the time spectrum. The phase characteristics of the time spectrum are calculated, and the phase characteristics are mapped to the cutting area to obtain the phase distribution of the stress field and temperature field. The stress-temperature coupling strength is calculated using the phase distribution of the stress field and temperature field.

[0043] An energy transfer function is constructed based on the stress-temperature coupling strength. The energy transfer function is decomposed into radial and circumferential components. A phase compensation matrix is ​​constructed using the radial and circumferential components. The orientation angle of the carbide grains is adjusted through the phase compensation matrix.

[0044] The energy phase difference is calculated using the adjusted carbide grain orientation angle. The stress field distortion region is identified based on the energy phase difference. The spatial distribution of the stress field distortion region is combined with the carbide grain orientation angle to calculate the material structure stability boundary.

[0045] The optimal carbide grain orientation angle is determined based on the material structure stability boundary. The optimal carbide grain orientation angle is combined with the stress field distortion region distribution to output the optimal material structure parameters.

[0046] Based on the optimal material structure parameters, selection criteria are established, and cemented carbide tool materials are selected using these criteria to obtain the optimal material selection scheme.

[0047] This invention provides a material selection system for predicting the life of cemented carbide cutting tools based on working condition parameters. The system includes:

[0048] The data acquisition module is used to collect cutting force data, cutting temperature data, and tool wear data of carbide tools during the cutting process;

[0049] The synergistic effect data construction module is used to acquire multi-point cutting force distribution in real time using the stress sensing array in the tool tip area, calculate the three-dimensional distribution state of the tool tip stress field based on the cutting temperature gradient collected by the temperature sensing array, and construct the synergistic effect data of stress-temperature-wear in the tool tip area.

[0050] The wear prediction and correction module is used to calculate fatigue damage characteristic values ​​based on stress-temperature-wear synergistic data, and fit the fatigue damage characteristic values ​​with tool wear data to obtain wear prediction and correction coefficients.

[0051] The tool life prediction module is used to compensate for the three-dimensional distribution of the tool tip stress field using the wear prediction correction coefficient and output tool life prediction data.

[0052] The material selection module is used to design the carbide phase gradient distribution structure based on stress-temperature-wear synergistic data and fatigue damage characteristic values, determine the carbide grain orientation angle using wear prediction correction coefficient, adjust the carbide grain orientation angle based on tool life prediction data, output the optimal material structure parameters, and select cemented carbide tool materials based on the optimal material structure parameters.

[0053] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0054] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps in any of the aforementioned methods.

[0055] This invention utilizes a stress-sensing array and a temperature-sensing array in the tool tip region to collect multi-dimensional working condition data in real time, establishing a stress-temperature-wear synergistic model to improve the accuracy of tool life prediction. By combining fatigue damage eigenvalues ​​with wear prediction correction coefficients, precise compensation for the three-dimensional distribution of the tool tip stress field is achieved, making the life prediction results more reliable. Based on the stress-temperature-wear synergistic data, a carbide phase gradient distribution structure is designed, and the carbide grain orientation angle is dynamically adjusted through the wear prediction correction coefficient, allowing the material structure to be optimized according to changes in cutting conditions, significantly improving tool life. Simultaneously, this method systematically designs optimal material structure parameters, providing a scientific basis for the selection of cemented carbide tool materials. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart illustrating the material selection method for predicting the life of cemented carbide cutting tools based on working condition parameters, as provided in an embodiment of the present invention.

[0058] Figure 2 This is a flowchart illustrating the tool life prediction and cemented carbide material optimization selection according to an embodiment of the present invention.

[0059] Figure 3 This is a schematic diagram of the material selection system for predicting the life of cemented carbide cutting tools based on working condition parameters, provided in an embodiment of the present invention. Detailed Implementation

[0060] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements.

[0061] The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0062] like Figure 1 As shown, Figure 1 This is a flowchart of a material selection method for predicting the life of cemented carbide cutting tools based on working condition parameters, provided in an embodiment of the present invention. The method includes the following steps:

[0063] Collect cutting force data, cutting temperature data, and tool wear data of carbide tools during the cutting process;

[0064] The cutting force distribution at multiple points is acquired in real time using a stress sensing array in the tool tip region. Based on the cutting temperature gradient collected by the temperature sensing array, the three-dimensional distribution of the stress field at the tool tip is calculated, and the synergistic effect data of stress, temperature and wear in the tool tip region is constructed.

[0065] Fatigue damage characteristic values ​​are calculated based on stress-temperature-wear synergistic data, and wear prediction correction coefficients are obtained by fitting fatigue damage characteristic values ​​with tool wear data.

[0066] The three-dimensional distribution of the tool tip stress field is compensated by the wear prediction correction coefficient, and the tool life prediction data is output.

[0067] The carbide phase gradient distribution structure is designed based on the stress-temperature-wear synergistic data and fatigue damage characteristic values. The carbide grain orientation angle is determined using the wear prediction correction coefficient. At the same time, the carbide grain orientation angle is adjusted based on the tool life prediction data. The optimal material structure parameters are output, and the cemented carbide tool material is selected based on the optimal material structure parameters.

[0068] In one optional embodiment, collecting cutting force data, cutting temperature data, and tool wear data of the carbide tool during the cutting process includes:

[0069] Collect cutting force data, perform differential calculation on the cutting force data to obtain the stress change rate, identify the stress change location based on the stress change rate, calculate the time difference between adjacent stress change locations, and determine the propagation speed and direction of the stress wave.

[0070] The triggering time is calculated based on the propagation speed and direction of the stress wave, and cutting temperature data are collected sequentially according to the triggering time;

[0071] The cutting temperature data is segmented according to the direction of stress wave propagation. The temperature value corresponding to the stress change location is marked in each segment to establish the correspondence between cutting force and cutting temperature.

[0072] The detection location and time are determined based on the corresponding data of cutting force and cutting temperature. Tool wear data are collected, and tool cutting force data, cutting temperature data, and tool wear data are output.

[0073] During the cutting process, cutting force data is acquired by installing strain gauge force sensors on the cutting tool. Three mutually perpendicular strain gauge force sensors are installed on the tool shank to measure tangential force, radial force, and axial force, respectively. Each force sensor consists of a full-bridge circuit composed of four strain gauges. When the tool is subjected to force and deforms, the resistance value of the strain gauges changes, generating a voltage signal proportional to the cutting force. After the voltage signal is amplified, it is digitized by a 16-bit analog-to-digital converter at a sampling frequency of 10kHz. The acquired cutting force data is differentially calculated to obtain the stress change rate. The specific implementation of the differential calculation is as follows: for any point i in the time series, the difference in force value between its adjacent points is calculated and divided by the corresponding time interval, i.e., (F[i+1]-F[i-1]) / (t[i+1]-t[i-1]), where F represents the force value and t represents the time.

[0074] Based on the calculated stress change rate, the location of stress abrupt changes is identified. The method for identifying stress abrupt changes involves comparing the stress change rate with a preset threshold; when the absolute value of the change rate exceeds this threshold, it is determined to be a stress abrupt change point. This threshold is set to 1.5 times the maximum stress change rate under normal cutting conditions. For different combinations of carbide tools and workpiece materials, this threshold is determined through 20 pre-cutting tests. After identifying the stress abrupt change location, the time difference Δt between adjacent stress abrupt change locations is calculated. Using three force sensors installed at different positions on the tool, the time it takes for the stress wave to reach each sensor is measured. Combined with the known distance Δs between the sensors, the propagation speed of the stress wave is calculated as v = Δs / Δt. The propagation direction of the stress wave is determined by comparing the order in which the stress abrupt change is recorded by the three force sensors; the line connecting the sensor where the stress wave first arrives and the sensor where it last arrives represents the propagation direction.

[0075] Based on the propagation velocity *v* and direction of the stress wave, the triggering time of the stress wave arriving at each temperature measurement point is calculated. The triggering time is calculated as follows: t 触发 =t 起始 +s / v, where t 起始Let s be the time of the stress wave at the starting point, and s be the distance from the starting point to the temperature measurement point. The distance s is measured using a coordinate system pre-marked on the tool with an accuracy of 0.01 mm. Temperature sensors are activated sequentially according to their trigger times to acquire cutting temperature data. K-type thermocouples with a diameter of 0.1 mm are used as temperature sensors, embedded at positions 1 mm, 3 mm, and 5 mm from the tool tip, respectively. The weak voltage signals output by the thermocouples are processed by cold junction compensation and signal conditioning circuitry, and then sampled at a frequency of 1 kHz using a 16-bit analog-to-digital converter.

[0076] The collected cutting temperature data was segmented according to the direction of stress wave propagation. The specific segmentation method was as follows: based on the stress wave propagation path, the temperature data was divided into three segments, corresponding to the start, middle, and end segments of stress wave propagation. Within each data segment, the temperature value corresponding to the moment of stress abrupt change was identified. The moment of stress abrupt change was compared with the temperature acquisition time, and the closest temperature acquisition time was found. If the time difference between the two exceeded half the sampling period, the temperature value at that moment was calculated using linear interpolation. Through this step, a time-correspondence table between cutting force and cutting temperature was established, containing the moment of stress abrupt change, the corresponding temperature, the stress value, and location information.

[0077] Based on the established data on the relationship between cutting force and cutting temperature, the key locations and optimal times for tool wear detection are determined. In the cutting force-temperature correlation table, the three points with the highest temperature values ​​and the three points with the largest stress change rates are identified. The weighted average of the spatial coordinates of these six points is taken as the detection location. The weight allocation is 0.6 for temperature and 0.4 for stress. Wear detection times are 30 seconds, 60 seconds, 120 seconds, 240 seconds, 480 seconds of continuous machining, and when the workpiece surface roughness exceeds the requirements. Tool wear data is collected using a 200x tool microscope, measuring the wear width on the rake face and flank face. The rake face wear measurement point is located 0.5 mm from the tool tip, with the measurement direction perpendicular to the cutting edge. The flank face wear measurement points are located at 1 / 4, 1 / 2, and 3 / 4 of the cutting edge, respectively, and the average value is taken as the flank face wear amount. Finally, the tool cutting force data, cutting temperature data, and tool wear data are output.

[0078] Through multi-parameter collaborative detection and analysis, comprehensive monitoring and accurate assessment of the usage status of cemented carbide cutting tools are achieved. Stress wave propagation analysis enables precise temporal sequencing of temperature acquisition, improving the accuracy of the correlation between cutting force and temperature data. Multi-parameter fusion technology enhances the reliability and accuracy of tool life prediction, effectively reducing production interruptions and quality problems caused by unexpected tool failure. It can intelligently recommend suitable tool materials and usage parameters based on different cutting conditions, improving machining efficiency and economy, extending tool life, and reducing production costs.

[0079] In one optional embodiment, a multi-point cutting force distribution is acquired in real time using a tool tip region stress sensing array. Based on the cutting temperature gradient collected by a temperature sensing array, the three-dimensional distribution of the tool tip stress field is calculated, and data on the synergistic effect of stress, temperature, and wear in the tool tip region is constructed, including:

[0080] The stress sensor array in the tool tip region is used to acquire multi-point cutting force distribution data in real time, and the temperature sensor array is used to collect cutting temperature gradient data. The stress gradient field and temperature gradient field are calculated by the difference between adjacent measurement points, and the location of abrupt change points is identified.

[0081] Establish the propagation paths of stress gradient and temperature gradient based on the location of the abrupt change point, calculate the amplitude ratio and phase difference of stress gradient and temperature gradient along the propagation path, and determine the stress-temperature coupling region by the variation law of amplitude ratio and phase difference.

[0082] Within the stress-temperature coupling region, the direction of stress energy distribution is determined by the directionality of the temperature gradient, and the distribution weights of the stress gradient in the radial, circumferential, and axial directions are calculated. These distribution weights are then used to determine the three-dimensional components of the tool tip stress field.

[0083] The three-dimensional components are compensated and corrected by temperature gradient, and the three-dimensional distribution of the stress field at the tool tip is obtained by superposition calculation based on the corrected stress components.

[0084] By combining the three-dimensional distribution of the stress field at the tool tip with tool wear data, we can construct data on the synergistic effects of stress, temperature, and wear in the tool tip region.

[0085] A stress sensing array is deployed in the tool tip region to acquire multi-point cutting force distribution data in real time. Specifically, a sensing array is composed of four miniature piezoelectric stress sensors, arranged in a diamond pattern at a distance of 0.5 mm from the tool tip, with a sensor spacing of 0.8 mm. Each sensor has a sampling frequency of 20 kHz, a sensitivity of 0.1 N, and can measure stress components in three directions. The voltage signals output by the sensors are processed by a preamplifier and filter before being digitized by a 16-bit high-speed analog-to-digital converter. Simultaneously, a temperature sensing array acquires cutting temperature gradient data. The temperature sensing array consists of six K-type miniature thermocouples, each with a diameter of 0.05 mm, installed at distances of 0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm from the tool tip, respectively. The thermocouple outputs are processed by cold junction compensation and signal conditioning circuitry before being acquired by a 16-bit analog-to-digital converter at a sampling frequency of 10 kHz.

[0086] The stress gradient field and temperature gradient field are calculated by the difference between data from adjacent measuring points. The stress gradient is calculated using the spatial center difference method, whereby for a spatial location p, the stress gradient is equal to the difference in stress values ​​between two adjacent points divided by the distance between those two points. The acquired raw stress data is low-pass filtered with a cutoff frequency of 2kHz to remove high-frequency noise. The stress difference between adjacent measuring points is calculated and divided by the actual distance between the measuring points to obtain the stress gradient in that direction. The stress gradient data in six directions are processed to construct the stress gradient field in the tool tip region. The temperature data is processed in the same way to obtain the temperature gradient field. Points in the stress gradient field and temperature gradient field where the rate of change of gradient value exceeds a preset threshold are identified as abrupt change points. The threshold for identifying abrupt change points is three times the rate of change of gradient under normal cutting conditions, and was determined through 15 pre-cutting tests.

[0087] Based on the identified abrupt change points, propagation paths for stress and temperature gradients are established. The propagation paths are determined by connecting adjacent abrupt change points in the time series to form the spatiotemporal trajectory of gradient propagation. The amplitude ratio and phase difference of the stress and temperature gradients are calculated along the propagation paths. By analyzing the variation patterns of the amplitude ratio and phase difference along the propagation paths, the stress-temperature coupling region is determined. Optionally, continuous regions where the rate of change of the amplitude ratio is less than 20% and the rate of change of the phase difference is less than 15% are identified as coupling regions.

[0088] Within a defined stress-temperature coupling region, the direction of stress energy distribution is determined using the directionality of the temperature gradient. The temperature gradient direction is determined by the gradient vector direction at each point in the gradient field. Averaging the temperature gradient vectors within the coupling region yields the dominant heat flow direction. Based on the angle between the heat flow direction and the tool coordinate system, the distribution weights of the stress gradient in the radial, circumferential, and axial directions are calculated. The distribution weights are calculated by dividing the projected length of the heat flow direction vector on the three coordinate axes by the total vector length, resulting in weight coefficients for each direction. These distribution weights are then used to determine the three-dimensional components of the tool tip stress field.

[0089] Temperature gradients are used to compensate for and correct the three-dimensional stress components. Based on the variation of the elastic modulus of cemented carbide at different temperatures, a table of relationships between temperature and stress correction coefficients is established. Correction coefficients are obtained from the table based on the measured temperature gradients to compensate for the stress components. The corrected stress value equals the original stress value multiplied by the temperature correction coefficient. The temperature correction coefficient decreases with increasing temperature, reflecting the characteristic of cemented carbide's elastic modulus decreasing with increasing temperature. The corrected stress components are then superimposed to obtain the three-dimensional distribution of the tool tip stress field. The superposition calculation uses a vector synthesis method, where stress components in each direction are synthesized according to vector addition rules to obtain the composite stress field.

[0090] By combining the three-dimensional distribution of the tool tip stress field with tool wear data, a synergistic data model of stress-temperature-wear in the tool tip region is constructed. Tool wear data is collected using an industrial microscope at different stages of the cutting process, measuring the wear width on the rake and flank faces, as well as the change in the tool tip fillet radius. The wear data is correlated with the corresponding stress and temperature field data to establish a correlation table. Based on this correlation table, the wear characteristics of carbide tools under different stress-temperature combinations are analyzed, and key characteristic parameters are extracted, including the wear rate in the maximum stress region, the highest temperature region, and the region where the stress-temperature gradient coincides.

[0091] Based on the extracted feature parameters, a method for predicting the life of cemented carbide tools is established. The prediction method employs a feature parameter weighted scoring mechanism, with the weights of each parameter determined through regression analysis. Based on the scoring results, tool life levels are classified, and the expected remaining life is calculated. The predicted tool life results serve as a basis for material selection, evaluating the performance of cemented carbide materials under different cutting conditions. The material selection method selects the most suitable material combination from a cemented carbide material library based on the predicted stress-temperature distribution characteristics and the properties of the material being machined. When stress dominates, high-toughness cemented carbide is preferred; when temperature dominates, high-heat-resistant cemented carbide is preferred; and when stress and temperature are balanced, cemented carbide with a balanced overall performance is selected.

[0092] This embodiment achieves accurate characterization of the stress-temperature coupling effect in the tool tip region through the coordinated monitoring of stress and temperature sensing arrays. Temperature gradient directionality analysis optimizes the calculation of stress energy distribution in three directions, improving the accuracy of the three-dimensional stress field distribution. The tool life prediction method based on stress-temperature-wear synergistic data significantly improves prediction accuracy. It can intelligently recommend the most suitable cemented carbide material according to the characteristics of different cutting conditions, extending tool life, reducing production costs, and improving machining efficiency and quality.

[0093] In one optional embodiment, fatigue damage characteristic values ​​are calculated based on stress-temperature-wear synergistic data, and the wear prediction correction coefficients are obtained by fitting the fatigue damage characteristic values ​​with tool wear data, including:

[0094] The stress-temperature-wear synergistic data in the tool tip region are segmented along the cutting direction. Temperature peaks and stress peaks are identified in each segment. The locations of the peaks are paired in spatial order to establish a stress-temperature propagation sequence.

[0095] Based on the stress-temperature propagation sequence, the propagation delay between adjacent peaks is extracted. The action period is divided based on the propagation delay. The rate of change of temperature field and the cumulative stress value in each period are calculated to construct the fatigue damage function.

[0096] The distribution of heat concentration areas is determined based on the rate of change of the temperature field. The temperature gradient of the heat concentration areas is used as a weighting factor and combined with the fatigue damage function to calculate the fatigue damage characteristic value.

[0097] The fatigue damage characteristic values ​​are divided into regions according to the distribution pattern of heat concentration areas, and a corresponding relationship is established with the tool wear data of each region to obtain the wear prediction function.

[0098] The stress-temperature coupling strength of each heat concentration region is calculated based on the wear prediction function. The coefficients of the prediction function are then weighted and corrected using the coupling strength to obtain the wear prediction correction coefficient.

[0099] First, the stress-temperature-wear synergistic data in the tool tip region were segmented along the cutting direction. By controlling the start and end points of the cut, a 10mm section of the workpiece surface was machined each time, with data continuously collected during the machining process. A data acquisition system was used to synchronously record cutting force, cutting temperature, and machining time at a sampling frequency of 10kHz. Temperature peaks and stress peaks were identified in each data segment. Temperature peak identification was implemented as follows: for the collected temperature data sequence, a window with a fixed width of 50 sampling points was set. The window was slid point by point; when the temperature value at the center of the window was greater than all other points within the window and greater than 1.5 times the average temperature of the window, that point was marked as the temperature peak. Stress peak identification used a similar method, but the window width was set to 30 sampling points, and the judgment threshold was 1.8 times the average value of the window. To eliminate noise interference, only valid peaks with a spacing greater than 20 sampling points between adjacent peaks were retained. The identified temperature peaks and stress peaks were then paired according to spatial order. The peak point is located in the tool coordinate system. For any temperature peak point, the stress peak points within a 2mm range are searched. If multiple stress peak points exist, the one closest in time is selected as the pairing point. By connecting these paired peak points, a stress-temperature propagation sequence is established.

[0100] Based on the established stress-temperature propagation sequence, the propagation delay between adjacent peaks is extracted. The timestamps of paired peak points are directly read, and the difference between the stress peak time and the temperature peak time is calculated. For each pair of peak points, their spatial coordinates and corresponding timestamps are recorded. The propagation velocity is calculated by measuring the spatial distance and time difference between adjacent peak points. The action period is divided based on the propagation delay. The fluctuation pattern of the propagation delay is identified; when the propagation delay exhibits periodic changes, each complete change process is defined as an action period. For carbide cutting tools, the action period typically corresponds to the complete entry and exit process of a cutting tooth. The rate of change of the temperature field within each period is calculated. Within each action period, the highest and lowest temperature points are identified, the temperature difference is calculated, and then divided by the time interval to obtain the rate of change of temperature for that period. Simultaneously, the cumulative stress value is calculated by summing the stress values ​​at all sampling times within the action period to obtain the total cumulative stress. A fatigue damage function is constructed using the rate of change of the temperature field and the cumulative stress value.

[0101] The distribution of heat concentration regions is determined based on the rate of change of the temperature field. The temperature rate of change data is mapped onto the tool coordinate system to form a temperature rate of change distribution map. Continuous regions with a rate of change exceeding 1.8 times the average value are identified and marked as heat concentration regions. For ease of calculation and representation, irregularly shaped heat concentration regions are simplified to minimum bounding rectangles. Within each heat concentration rectangle, nine uniformly distributed measuring points are arranged, and the temperature gradient at each point is calculated. For each measuring point, the temperature difference between it and four adjacent measuring points in the four directions is calculated, divided by the corresponding spatial distance to obtain the temperature gradient components in the four directions. The root mean square of these four components is taken as the temperature gradient value at that point. The calculated temperature gradients are used as weighting factors and combined with the fatigue damage function to calculate the fatigue damage characteristic value. The temperature gradient value is normalized by dividing by the maximum value of the temperature gradients at all measuring points, and then multiplied by the fatigue damage function value at the corresponding measuring point to obtain the weighted fatigue damage characteristic value.

[0102] Fatigue damage characteristic values ​​were partitioned according to the distribution pattern of heat concentration areas. Based on the tool geometry, the tool tip region was divided into three main regions: the rake face region, the flank face region, and the transition region. The rake face region is located within 0-0.8 mm from the cutting edge, the flank face region is located within 0-0.5 mm below the cutting edge, and the transition region is the arc region connecting the rake and flank faces. Within each region, fatigue damage characteristic values ​​were correlated with measured tool wear data. Wear data was acquired using a 200x tool microscope with a measurement accuracy of 0.001 mm. After 30, 60, 90, and 120 minutes of use of the carbide tool, the wear amount at five measuring points in each of the three regions was measured. The microscope was focused on the measuring points, and the height difference was measured and recorded as a reference surface. A wear prediction function was established based on the correspondence between wear data and fatigue damage characteristic values. The fatigue damage characteristic values ​​were divided into three intervals: 0-0.3, 0.3-0.7, and 0.7-1.0. Within each interval, the linear relationship between the wear amount and the fatigue damage characteristic value was fitted using the least squares method, resulting in three linear equations. Independent prediction functions were established for the rake face region, flank face region, and transition region to reflect the wear characteristics of different areas.

[0103] The stress-temperature coupling strength in each heat concentration region is calculated based on the wear prediction function. The spatial overlap and time response relationship between the stress and temperature fields are measured. The spatial overlap is calculated by comparing the values ​​of the stress and temperature gradient fields at the same coordinate points and calculating their correlation coefficient. The time response relationship is obtained through time lag analysis of the stress peak and temperature peak; the shorter the time lag, the stronger the coupling strength. The stress-temperature coupling strength value is obtained by weighted averaging of the spatial overlap and time response relationship, with a weight ratio of 6:4. The coefficients of the prediction function are then weighted and corrected based on the coupling strength to obtain the wear prediction correction coefficients.

[0104] This invention achieves accurate prediction of carbide tool life through precise analysis of the synergistic effects of stress, temperature, and wear in the tool tip region. The identification of heat concentration areas and the construction of fatigue damage functions make the wear prediction more closely reflect the complex working conditions in actual cutting processes. The stress-temperature coupling strength calculation and correction coefficient application methods improve the adaptability of the prediction model under different cutting conditions. The tool material selection method based on zonal prediction enables optimized material selection for specific working conditions, avoiding the blindness of traditional experience-based material selection. This significantly extends the service life of carbide tools, improves machining efficiency and economy, and reduces production costs.

[0105] In one optional embodiment, the three-dimensional distribution of the tool tip stress field is compensated using a wear prediction correction coefficient, and the output tool life prediction data includes:

[0106] The wear prediction correction coefficient is decomposed into radial, circumferential and axial correction components along the heat concentration region. Time-frequency analysis is performed on the correction components to calculate the time-series correlation intensity in each direction and determine the dominant direction of the tool tip stress field distortion.

[0107] A spatial reference coordinate system is established based on the dominant direction. The radial, circumferential and axial deviations and delays are calculated using time-series correlation strength to construct a compensation matrix for the tool tip stress field.

[0108] The initial compensation value is calculated based on the compensation matrix and the three-dimensional distribution of the blade tip stress field. The temperature field compensation coefficient is calculated using the time-series correlation intensity. The temperature field compensation coefficient is then superimposed with the initial compensation value to obtain the corrected three-dimensional distribution.

[0109] The stress gradient change rate is calculated based on the corrected three-dimensional distribution state. The damage evolution rate is adjusted using the stress gradient change rate. The damage accumulation curve is calculated based on the adjusted damage evolution rate, and the fatigue critical state is determined to obtain the tool wear threshold.

[0110] By combining the tool wear threshold with the corrected three-dimensional distribution state, tool life prediction data is output.

[0111] The wear prediction correction coefficient is decomposed into radial, circumferential, and axial correction components along the heat concentration region. During this decomposition, the geometric center of the heat concentration region is first determined, and a local polar coordinate system is established with this center as the origin. The radial component points towards the center of the heat concentration region, the circumferential component is perpendicular to the radial direction and along the cutting direction, and the axial component is perpendicular to the radial-circumferential plane. The component decomposition method involves arranging a measurement grid in the heat concentration region with a grid density of 9 measurement points per square millimeter. Stress and temperature are measured at each measurement point, and the local wear prediction correction coefficient is calculated. Then, based on the positional relationship of the measurement points relative to the center point, the correction coefficient is projected onto the radial, circumferential, and axial directions. Time-frequency analysis is performed on the decomposed correction components to calculate the temporal correlation strength in each direction. Specifically, the time-frequency analysis involves sampling the correction component data in each direction at a sampling frequency 10 times the cutting speed for a sampling duration of 30 seconds of continuous cutting. A sliding window is applied to the sampled data, with a window width of 10% of the number of sampling points and a window overlap rate of 50%. Within each window, four feature parameters are extracted: mean, standard deviation, peak value, and trough value. The rate of change of corresponding feature parameters between adjacent windows is defined as the temporal variation intensity. The temporal correlation intensity is obtained by weighting the temporal variation intensities of the four feature parameters, with weight ratios of 3 for the mean, 2 for the standard deviation, 3 for the peak value, and 2 for the trough value. By comparing the temporal correlation intensities in three directions, the dominant direction of the tool tip stress field distortion is determined. The dominant direction is determined by the direction with the highest temporal correlation intensity.

[0112] A spatial reference coordinate system is established based on the dominant direction. The system uses the geometric center of the heat concentration region as the origin, the dominant direction as the first coordinate axis, the direction perpendicular to the dominant direction and within the cutting plane as the second coordinate axis, and the direction perpendicular to the cutting plane as the third coordinate axis. The radial, circumferential, and axial deviations and delays are calculated using time-series correlation strength. The deviation is calculated by dividing the time-series correlation strength of the non-dominant direction by the time-series correlation strength of the dominant direction to obtain the relative deviation ratio. The delay is calculated by measuring the time difference between the peak occurrence time of the time-series variation in the non-dominant direction and the peak occurrence time in the dominant direction, expressed in milliseconds. A compensation matrix for the tool tip stress field is constructed. This compensation matrix is ​​a third-order square matrix, with diagonal elements representing the self-compensation coefficients in the three directions and off-diagonal elements representing cross-compensation coefficients.

[0113] The preliminary compensation value is calculated based on the compensation matrix and the three-dimensional distribution of the stress field at the cutting edge. The three-dimensional distribution refers to the distribution data of the stress field in the cutting edge region under a spatial reference coordinate system. This data is acquired in real time by a stress sensor array, with the number of acquisition points in a 5×5×3 grid over the cutting edge region. The preliminary compensation value is calculated by multiplying the compensation matrix by the three-dimensional distribution data of the stress field to obtain stress compensation values ​​in three directions. The temperature field compensation coefficient is calculated using time-series correlation strength. The temperature field compensation coefficient is calculated by normalizing the time-series correlation strengths in the three directions to form a weight vector, which is then multiplied by the temperature field sensitivity parameter. The temperature field sensitivity parameter is a coefficient reflecting the degree of influence of temperature changes on stress, determined through thermo-coupling experiments, with a value range of 0.5 to 1.5. The temperature field compensation coefficient is then superimposed with the preliminary compensation value to obtain the corrected three-dimensional distribution. The superposition method involves multiplying the corresponding elements of the temperature field compensation coefficient and the preliminary compensation value to obtain the final corrected three-dimensional distribution value of the stress field.

[0114] The stress gradient change rate is calculated based on the corrected three-dimensional distribution. The method involves calculating the stress difference between each point in the three-dimensional mesh and its six adjacent points, forming six stress gradient components. The time derivative of each stress gradient component is calculated, representing the change per unit time, to obtain the stress gradient change rate. This rate is then multiplied by the base damage rate (the material damage rate under standard cutting conditions), and the adjusted damage evolution rate is integrated over time to obtain the cumulative damage over time curve. The integration uses the trapezoidal rule, with a time step of 1 / 20 of the cutting cycle. The fatigue critical state is then determined, yielding the tool wear threshold. The fatigue critical state is determined when the cumulative damage reaches 85% of the material's fatigue limit. The material fatigue limit is the critical stress value determined by the stress-life curve of cemented carbide materials. The tool wear threshold refers to the wear amount corresponding to reaching the fatigue critical state, measured in millimeters.

[0115] The tool wear threshold is combined with the corrected three-dimensional stress distribution to output tool life prediction data. The combination method calculates the cutting time required to reach the wear threshold based on the corrected three-dimensional stress field distribution. The calculation process involves dividing the wear threshold by the wear rate at a feature point to obtain the expected service time. The feature point is selected based on the point with the largest stress gradient change rate. The output tool life prediction data includes the expected service time, wear threshold, and reliability score. The reliability score is based on the coefficient of variation of time-series correlation strength and historical prediction error data, comprehensively evaluating the credibility of the prediction results and categorizing them into high, medium, and low levels.

[0116] For carbide cutting tool material selection, material formulation recommendations are provided based on the predicted results. The dominant load-bearing directions of the tool are determined based on the time-dependent strength ratios in three directions. Then, suitable carbide component ratios are selected based on the stress levels and temperature field distribution in each direction. When radial loads are dominant, a high-strength formulation is recommended, increasing the hard phase content to over 92%. When circumferential loads are dominant, a tough formulation is recommended, increasing the cobalt content to over 10%. When axial loads are dominant, a composite formulation is recommended, adding composite hard phases such as TiC and TaC. Material selection also considers temperature field distribution characteristics. When the highest temperature region is concentrated at the tool tip, a material with high heat resistance is selected; when the temperature distribution is relatively uniform, a material formulation with good thermal conductivity is selected.

[0117] This invention accurately identifies the dominant direction of tool stress field distortion by decomposing the wear prediction correction coefficient into three directional components and performing time-frequency analysis, thus solving the problem of traditional prediction methods neglecting spatial distribution differences. The establishment of a spatial reference coordinate system and the construction of the compensation matrix enable precise correction of the stress field distribution, effectively eliminating prediction biases caused by material inhomogeneity. The introduction of a temperature field compensation coefficient considers the comprehensive impact of temperature-stress coupling on tool wear, improving prediction accuracy under extreme cutting conditions. The calculation method for the stress gradient change rate reveals the accelerating effect of internal stress inhomogeneity on fatigue damage, making wear prediction more consistent with actual working conditions.

[0118] In one optional embodiment, the carbide phase gradient distribution structure is designed based on stress-temperature-wear synergistic data and fatigue damage characteristic values, and the carbide grain orientation angle is determined using a wear prediction correction coefficient, including:

[0119] The stress-temperature-wear synergistic data and fatigue damage characteristic values ​​are transformed into an energy distribution field. The phase characteristics of the energy distribution field are calculated, and the direction of maximum energy gradient is obtained based on the phase characteristics. The spatial distribution function of the carbide phase is constructed based on the direction of maximum energy gradient.

[0120] The energy transfer path is analyzed along the spatial distribution function of the carbide phase, the energy phase difference between adjacent regions is calculated, and the volume fraction of the carbide phase in the thermally concentrated region is determined using the energy phase difference.

[0121] Adjust the carbide phase distribution density in the stress concentration region according to the energy phase difference change law to form a carbide phase gradient distribution structure;

[0122] Based on the analysis of the stress field phase characteristics of the carbide phase gradient distribution structure, the stress field phase characteristics are coupled with the energy phase difference to calculate the crack propagation driving force, and the force direction of the carbide grains is determined by the crack propagation driving force.

[0123] The crack propagation resistance coefficient is calculated by utilizing the stress direction and phase characteristics of carbide grains. The resistance coefficient is then combined with the wear prediction correction coefficient to determine the carbide grain orientation angle.

[0124] The stress-temperature-wear synergistic data and fatigue damage characteristics were converted into an energy distribution field. An array of measuring points was arranged on the tool surface with a spacing of 0.2 mm to collect stress and temperature data. Stress data was acquired using a piezoelectric stress sensor with a measurement range of 0-2000 MPa and an accuracy of ±5 MPa; temperature data was acquired using an infrared thermal imager with a measurement range of 20-1200℃ and an accuracy of ±2℃. The collected stress and strain values ​​were multiplied by the unit volume to obtain the elastic strain energy; the temperature value was multiplied by the material's specific heat capacity, density, and volume to obtain the thermal energy. The elastic strain energy and thermal energy were added together to obtain the total energy value. A three-dimensional mesh was constructed centered on the measuring points, and the energy value of each measuring point was interpolated and extended into the mesh to form a continuous energy distribution field. The phase characteristics of the energy distribution field were calculated.

[0125] The direction of maximum energy gradient is determined based on phase characteristics. Specifically, the gradient of energy value in eight surrounding directions is calculated at each measuring point, and the direction with the largest gradient is taken as the local direction of maximum energy gradient. The local directions of maximum energy gradient at all measuring points are statistically analyzed, and the direction with the highest frequency of occurrence is taken as the overall direction of maximum energy gradient. A spatial distribution function of the carbide phase is constructed based on the direction of maximum energy gradient. A cross-section is plotted along the direction of maximum energy gradient, and the energy value variation curve along the cross-section is measured. The variation curve is normalized to obtain the relative energy distribution function. The range of the relative energy distribution function is mapped to 0-1, representing the spatial distribution probability of the carbide phase.

[0126] Energy transfer paths were analyzed along the spatial distribution function of the carbide phase. During tool cutting, a high-speed camera recorded the temperature changes on the tool surface at a frame rate of 5000 frames / s. By comparing consecutive frame images, the movement trajectory of the temperature peak point was tracked to obtain the thermal energy transfer path. Simultaneously, stress sensor data was recorded, and the propagation path of the stress wave was tracked to obtain the mechanical energy transfer path. The thermal and mechanical energy transfer paths were superimposed to form a comprehensive energy transfer path. The energy phase difference between adjacent regions was calculated. The calculation method involved selecting equidistant measuring points along the energy transfer path, with a spacing of 0.5 mm between the measuring points. The time of occurrence of the energy peak was recorded at each measuring point, and the difference between the peak times of adjacent measuring points was the phase difference. The volume fraction of the carbide phase in the heat concentration region was determined using the energy phase difference. A correspondence between the phase difference and the volume fraction of the carbide phase was established.

[0127] The carbide phase distribution density in stress concentration regions is adjusted based on the energy phase difference variation law. The adjustment method involves calculating the spatial gradient of the energy phase difference, i.e., the change in phase difference per unit distance. Regions with a large phase difference gradient indicate drastic changes in energy transfer impedance, requiring increased gradient variation of the carbide phase. Regions with a small phase difference gradient indicate more uniform energy transfer, allowing for a uniformly distributed carbide phase. This forms a gradient distribution structure of the carbide phase. Based on the adjusted carbide phase distribution density, the composition ratio of the cemented carbide material is designed. In high-density regions, the content of hard phases such as WC and TiC is increased; in gradient variation regions, a composition gradient design is adopted; and in low-density regions, the content of binder phases such as Co is increased. The gradient distribution structure of the carbide phase is achieved by controlling the composition ratio of different regions through powder metallurgy processes.

[0128] Stress field phase characteristics are analyzed based on the gradient distribution structure of carbide phases. The method involves arranging an array of stress sensors on the gradient structure material to measure stress values ​​at various points during the cutting process. Time-domain analysis is performed on the stress data to extract the propagation characteristics of stress waves. The spatial distribution of stress wave propagation velocity forms the stress field phase characteristics. The crack propagation driving force is calculated by coupling the stress field phase characteristics with the energy phase difference. The stress field phase characteristics are expressed as the reciprocal of the stress wave propagation velocity, multiplied by the energy phase difference, and then multiplied by the material fracture toughness parameter to obtain the crack propagation driving force. The direction of force on carbide grains is determined using the crack propagation driving force. The directional component of the crack propagation driving force is analyzed; the direction of maximum driving force is perpendicular to the optimal orientation plane of the carbide grains.

[0129] The crack propagation resistance coefficient is calculated using the stress direction and phase characteristics of carbide grains. The calculation method converts the angle between the stress direction of the carbide grain and the energy transfer path into a resistance coefficient. The resistance coefficient is maximum when the stress direction is perpendicular to the energy transfer path and minimum when they are parallel. The resistance coefficient is combined with a wear prediction correction coefficient to determine the carbide grain orientation angle. A weighted average of the resistance coefficient and correction coefficient (3:7) is then applied. The weighted average reflects the optimal orientation angle of the carbide grains. The optimal orientation angle typically ranges from 15° to 75°, with the specific value determined by the operating parameters. Once the carbide grain orientation angle is determined, the grain growth direction is controlled through directional solidification or heat treatment processes to optimize tool performance.

[0130] This invention solves the problem of premature failure in traditional cemented carbide cutting tools due to unreasonable carbide phase distribution by using energy phase difference analysis, achieving precise control of the carbide phase gradient distribution. The construction technology of the carbide phase spatial distribution function establishes a direct link between the microstructure design and macroscopic performance of the tool material, improving the targeting of material design. The carbide grain orientation angle optimization technology maximizes the performance of the tool under specific working conditions, significantly extending the service life of cemented carbide tools, improving cutting efficiency, and reducing production costs.

[0131] like Figure 2 The diagram shows the flowchart for tool life prediction and cemented carbide material optimization in this embodiment.

[0132] In one optional embodiment, the carbide grain orientation angle is adjusted based on tool life prediction data to output optimal material structure parameters. The selection of cemented carbide tool materials based on these optimal material structure parameters includes:

[0133] Wavelet decomposition is performed on the tool life prediction data to obtain the time spectrum. The phase characteristics of the time spectrum are calculated, and the phase characteristics are mapped to the cutting area to obtain the phase distribution of the stress field and temperature field. The stress-temperature coupling strength is calculated using the phase distribution of the stress field and temperature field.

[0134] An energy transfer function is constructed based on the stress-temperature coupling strength. The energy transfer function is decomposed into radial and circumferential components. A phase compensation matrix is ​​constructed using the radial and circumferential components. The orientation angle of the carbide grains is adjusted through the phase compensation matrix.

[0135] The energy phase difference is calculated using the adjusted carbide grain orientation angle. The stress field distortion region is identified based on the energy phase difference. The spatial distribution of the stress field distortion region is combined with the carbide grain orientation angle to calculate the material structure stability boundary.

[0136] The optimal carbide grain orientation angle is determined based on the material structure stability boundary. The optimal carbide grain orientation angle is combined with the stress field distortion region distribution to output the optimal material structure parameters.

[0137] Based on the optimal material structure parameters, selection criteria are established, and cemented carbide tool materials are selected using these criteria to obtain the optimal material selection scheme.

[0138] Wavelet decomposition was performed on tool life prediction data to obtain the time-frequency spectrum. Multi-scale wavelet basis functions were used for wavelet decomposition, with a decomposition depth of 5 levels, decomposing the life prediction data into sub-signals of different frequency bands. The Daubechies wavelet was chosen as the wavelet basis function due to its good time-frequency localization characteristics, making it suitable for analyzing non-stationary signals. Data acquisition was conducted using a high-precision sensor array, including stress and temperature sensors, with a sampling frequency 10 times the cutting frequency. The acquired data included parameters such as tool surface stress distribution, temperature distribution, and cutting force variation. The acquired data underwent preprocessing to remove noise and outliers, retaining only valid signal components. Preprocessing methods included median filtering and wavelet thresholding denoising, with a filtering window width of 5% of the number of sampling points. The phase characteristics of the time-frequency spectrum were calculated. Hilbert transform was applied to the sub-signals to obtain analytic signals; the phase derivative of the analytic signal is the instantaneous frequency. The phase characteristics were mapped onto the cutting region to obtain the phase distribution of the stress and temperature fields. The mapping method establishes a correspondence between the time-frequency domain and the spatial domain. Based on the sensor's placement and measurement time, the phase information in the time-frequency spectrum is mapped onto the spatial coordinates of the tool surface. The mapping resolution is 0.2 mm, covering the entire cutting area. The stress-temperature coupling strength is calculated using the phase distributions of the stress and temperature fields. Cross-correlation analysis is performed on the phase distributions of the stress and temperature fields to calculate the cross-correlation coefficients. The larger the cross-correlation coefficient, the higher the coupling degree between the stress and temperature fields. The spatial distribution of the cross-correlation coefficients forms a stress-temperature coupling strength distribution map.

[0139] An energy transfer function is constructed based on the stress-temperature coupling strength. The stress-temperature coupling strength is used as a weight to weight and average the stress field energy and temperature field energy to obtain a comprehensive energy field. The spatial distribution characteristics of the comprehensive energy field form the energy transfer function. The energy transfer function is decomposed into radial and circumferential components. A polar coordinate system is established with the tool tip as the origin. The energy transfer function is represented in the polar coordinate system and projected along the radial and circumferential directions to obtain the radial and circumferential components, respectively. The radial component reflects the energy transfer characteristics into the tool interior, while the circumferential component reflects the energy transfer characteristics along the cutting surface. A phase compensation matrix is ​​constructed using the radial and circumferential components. The phase difference between the radial and circumferential components is calculated to form a two-dimensional phase difference matrix. The element values ​​of the phase difference matrix represent the phase delay of energy transfer at different locations, and the matrix dimension is the same as the number of points in the spatial grid. The carbide grain orientation angle is adjusted using the phase compensation matrix. The phase compensation matrix is ​​superimposed with the initial carbide grain orientation angle using a weighted sum, with weighting coefficients of 0.7 and 0.3. The initial carbide grain orientation angle is set according to the grain orientation of conventional cemented carbide materials, typically 45 degrees. The adjusted orientation angle ranges from 15 to 75 degrees, reflecting the optimal orientation of carbide grains at different locations.

[0140] The energy phase difference is calculated using the adjusted carbide grain orientation angle. The orientation angle is converted into energy transfer impedance, which is proportional to the sine of the orientation angle. Energy transfers slowly in high-impedance regions, resulting in phase lag; it transfers quickly in low-impedance regions, resulting in phase lead. Impedance differences between adjacent regions lead to the energy phase difference. Stress field distortion regions are identified based on the energy phase difference. The spatial gradient of the energy phase difference is calculated; regions with gradient values ​​greater than a threshold are defined as stress field distortion regions. The threshold is set to 1.5 times the average gradient value. Distortion regions typically appear at material phase interfaces or abrupt structural changes. The spatial distribution of stress field distortion regions is combined with the carbide grain orientation angle to calculate the material's structural stability boundary. A local coordinate system is established in the distortion region to analyze the gradient distribution of the orientation angle. When the orientation angle gradient is less than a critical value, it is defined as a structurally stable region; when the gradient is greater than the critical value, it is defined as a structurally unstable region. The critical value is determined through material fatigue experiments, with a typical value of 15 degrees / mm. The boundary between the structurally stable and unstable regions forms the material's structural stability boundary.

[0141] The optimal carbide grain orientation angle is determined based on the material structure stability boundary. Within the stability boundary, the orientation angle with the smallest energy phase difference is selected as the optimal orientation angle. The optimal orientation angle ensures the most uniform energy transfer, reduces stress concentration, and improves tool life. The optimal carbide grain orientation angle is combined with the stress field distortion region distribution to output the optimal material structure parameters. Based on the optimal orientation angle and distortion region distribution, parameters such as carbide phase volume fraction, carbide grain size, and binder phase distribution are determined. Increasing the binder phase content in the distortion region improves toughness; increasing the hard phase content in the undistorted region improves hardness and wear resistance. Selection criteria are established based on the optimal material structure parameters. The structural parameters are converted into material performance indicators, including hardness, bending strength, fracture toughness, and thermal stability. The conversion relationship is determined through a material database and performance testing. The indicator weights are automatically adjusted according to the working condition parameters; hardness and wear resistance have higher weights when cutting hard materials, while fracture toughness has a higher weight when cutting tough materials. The selection criteria are used to select cemented carbide tool materials, resulting in the optimal material selection scheme. The comprehensive performance score of the candidate materials is calculated, and the material with the highest score is the optimal selection. The overall performance score is a weighted sum of each indicator and its corresponding weight. The material selection results include tungsten carbide grain size, cobalt content, type and content of additives, and preparation process parameters.

[0142] This invention obtains the time-frequency spectrum and calculates phase characteristics through wavelet decomposition, achieving accurate characterization of the dynamic changes in stress and temperature fields during cutting, overcoming the limitations of traditional methods that only focus on amplitude and ignore phase information. The radial and circumferential component decomposition technique of the energy transfer function enables precise description of the tool's energy transfer characteristics in different directions, achieving directional optimization of material structure design. The method of adjusting the carbide grain orientation angle using a phase compensation matrix solves the problem of early failure caused by unreasonable orientation angles in traditional cemented carbide materials, significantly improving tool life.

[0143] like Figure 3 As shown, Figure 3 This is a schematic diagram of a material selection system for predicting the life of cemented carbide tools based on working condition parameters, provided in an embodiment of the present invention. The system includes:

[0144] The data acquisition module is used to collect cutting force data, cutting temperature data, and tool wear data of carbide tools during the cutting process;

[0145] The synergistic effect data construction module is used to acquire multi-point cutting force distribution in real time using the stress sensing array in the tool tip area, calculate the three-dimensional distribution state of the tool tip stress field based on the cutting temperature gradient collected by the temperature sensing array, and construct the synergistic effect data of stress-temperature-wear in the tool tip area.

[0146] The wear prediction and correction module is used to calculate fatigue damage characteristic values ​​based on stress-temperature-wear synergistic data, and fit the fatigue damage characteristic values ​​with tool wear data to obtain wear prediction and correction coefficients.

[0147] The tool life prediction module is used to compensate for the three-dimensional distribution of the tool tip stress field using the wear prediction correction coefficient and output tool life prediction data.

[0148] The material selection module is used to design the carbide phase gradient distribution structure based on stress-temperature-wear synergistic data and fatigue damage characteristic values, determine the carbide grain orientation angle using wear prediction correction coefficient, adjust the carbide grain orientation angle based on tool life prediction data, output the optimal material structure parameters, and select cemented carbide tool materials based on the optimal material structure parameters.

[0149] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0150] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0151] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A material selection method for predicting the life of cemented carbide cutting tools based on working condition parameters, characterized in that, Includes the following steps: Collect cutting force data, cutting temperature data, and tool wear data of carbide tools during the cutting process; The cutting force distribution at multiple points is acquired in real time using a stress sensing array in the tool tip region. Based on the cutting temperature gradient collected by the temperature sensing array, the three-dimensional distribution of the stress field at the tool tip is calculated, and the synergistic effect data of stress, temperature and wear in the tool tip region is constructed. Fatigue damage characteristic values ​​are calculated based on stress-temperature-wear synergistic data, and wear prediction correction coefficients are obtained by fitting fatigue damage characteristic values ​​with tool wear data. The three-dimensional distribution of the tool tip stress field is compensated by the wear prediction correction coefficient, and the tool life prediction data is output. The carbide phase gradient distribution structure is designed based on the stress-temperature-wear synergistic data and fatigue damage characteristic values. The carbide grain orientation angle is determined using the wear prediction correction coefficient. At the same time, the carbide grain orientation angle is adjusted based on the tool life prediction data. The optimal material structure parameters are output, and the cemented carbide tool material is selected based on the optimal material structure parameters. The cutting force distribution at multiple points is acquired in real time using a stress sensing array in the tool tip region. Based on the cutting temperature gradient collected by a temperature sensing array, the three-dimensional distribution of the stress field at the tool tip is calculated, and the synergistic effect data of stress, temperature, and wear in the tool tip region is constructed, including: The stress sensor array in the tool tip region is used to acquire multi-point cutting force distribution data in real time, and the temperature sensor array is used to collect cutting temperature gradient data. The stress gradient field and temperature gradient field are calculated by the difference between adjacent measurement points, and the location of abrupt change points is identified. Establish the propagation paths of stress gradient and temperature gradient based on the location of the abrupt change point, calculate the amplitude ratio and phase difference of stress gradient and temperature gradient along the propagation path, and determine the stress-temperature coupling region by the variation law of amplitude ratio and phase difference. Within the stress-temperature coupling region, the direction of stress energy distribution is determined by the directionality of the temperature gradient, and the distribution weights of the stress gradient in the radial, circumferential, and axial directions are calculated. These distribution weights are then used to determine the three-dimensional components of the tool tip stress field. The three-dimensional components are compensated and corrected by temperature gradient, and the three-dimensional distribution of the stress field at the tool tip is obtained by superposition calculation based on the corrected stress components. By combining the three-dimensional distribution of the stress field at the tool tip with tool wear data, we can construct data on the synergistic effects of stress, temperature, and wear in the tool tip region.

2. The material selection method for predicting the life of cemented carbide tools based on working condition parameters according to claim 1, characterized in that, The data collected on cutting force, cutting temperature, and tool wear of carbide cutting tools during the machining process include: Collect cutting force data, perform differential calculation on the cutting force data to obtain the stress change rate, identify the stress change location based on the stress change rate, calculate the time difference between adjacent stress change locations, and determine the propagation speed and direction of the stress wave. The triggering time is calculated based on the propagation speed and direction of the stress wave, and cutting temperature data are collected sequentially according to the triggering time; The cutting temperature data is segmented according to the direction of stress wave propagation. The temperature value corresponding to the stress change location is marked in each segment to establish the correspondence between cutting force and cutting temperature. The detection location and time are determined based on the corresponding data of cutting force and cutting temperature. Tool wear data are collected, and tool cutting force data, cutting temperature data, and tool wear data are output.

3. The material selection method for predicting the life of cemented carbide tools based on working condition parameters according to claim 1, characterized in that, Fatigue damage characteristic values ​​are calculated based on stress-temperature-wear synergistic data. Wear prediction correction coefficients are obtained by fitting these fatigue damage characteristic values ​​with tool wear data, including: The stress-temperature-wear synergistic data in the tool tip region are segmented along the cutting direction. Temperature peaks and stress peaks are identified in each segment. The locations of the peaks are paired in spatial order to establish a stress-temperature propagation sequence. Based on the stress-temperature propagation sequence, the propagation delay between adjacent peaks is extracted. The action period is divided based on the propagation delay. The rate of change of temperature field and the cumulative stress value in each period are calculated to construct the fatigue damage function. The distribution of heat concentration areas is determined based on the rate of change of the temperature field. The temperature gradient of the heat concentration areas is used as a weighting factor and combined with the fatigue damage function to calculate the fatigue damage characteristic value. The fatigue damage characteristic values ​​are divided into regions according to the distribution pattern of heat concentration areas, and a corresponding relationship is established with the tool wear data of each region to obtain the wear prediction function. The stress-temperature coupling strength of each heat concentration region is calculated based on the wear prediction function. The coefficients of the prediction function are then weighted and corrected using the coupling strength to obtain the wear prediction correction coefficient.

4. The material selection method for predicting the life of cemented carbide tools based on working condition parameters according to claim 1, characterized in that, The three-dimensional distribution of the tool tip stress field is compensated using a wear prediction correction coefficient, and the output tool life prediction data includes: The wear prediction correction coefficient is decomposed into radial, circumferential and axial correction components along the heat concentration region. Time-frequency analysis is performed on the correction components to calculate the time-series correlation intensity in each direction and determine the dominant direction of the tool tip stress field distortion. A spatial reference coordinate system is established based on the dominant direction. The radial, circumferential and axial deviations and delays are calculated using time-series correlation strength to construct a compensation matrix for the tool tip stress field. The initial compensation value is calculated based on the compensation matrix and the three-dimensional distribution of the blade tip stress field. The temperature field compensation coefficient is calculated using the time-series correlation intensity. The temperature field compensation coefficient is then superimposed with the initial compensation value to obtain the corrected three-dimensional distribution. The stress gradient change rate is calculated based on the corrected three-dimensional distribution state. The damage evolution rate is adjusted using the stress gradient change rate. The damage accumulation curve is calculated based on the adjusted damage evolution rate, and the fatigue critical state is determined to obtain the tool wear threshold. By combining the tool wear threshold with the corrected three-dimensional distribution state, tool life prediction data is output.

5. The material selection method for predicting the life of cemented carbide tools based on working condition parameters according to claim 1, characterized in that, Based on the stress-temperature-wear synergistic data and fatigue damage characteristic values, a carbide phase gradient distribution structure was designed. The carbide grain orientation angle was determined using the wear prediction correction coefficient, including: The stress-temperature-wear synergistic data and fatigue damage characteristic values ​​are transformed into an energy distribution field. The phase characteristics of the energy distribution field are calculated, and the direction of maximum energy gradient is obtained based on the phase characteristics. The spatial distribution function of the carbide phase is constructed based on the direction of maximum energy gradient. The energy transfer path is analyzed along the spatial distribution function of the carbide phase, the energy phase difference between adjacent regions is calculated, and the volume fraction of the carbide phase in the thermally concentrated region is determined using the energy phase difference. Adjust the carbide phase distribution density in the stress concentration region according to the energy phase difference change law to form a carbide phase gradient distribution structure; Based on the analysis of the stress field phase characteristics of the carbide phase gradient distribution structure, the stress field phase characteristics are coupled with the energy phase difference to calculate the crack propagation driving force, and the force direction of the carbide grains is determined by the crack propagation driving force. The crack propagation resistance coefficient is calculated by utilizing the stress direction and phase characteristics of carbide grains. The resistance coefficient is then combined with the wear prediction correction coefficient to determine the carbide grain orientation angle.

6. The material selection method for predicting the life of cemented carbide tools based on working condition parameters according to claim 1, characterized in that, Based on tool life prediction data, the carbide grain orientation angle is adjusted to output optimal material structure parameters. Carbide tool materials are then selected based on these optimal material structure parameters, including: Wavelet decomposition is performed on the tool life prediction data to obtain the time spectrum. The phase characteristics of the time spectrum are calculated, and the phase characteristics are mapped to the cutting area to obtain the phase distribution of the stress field and temperature field. The stress-temperature coupling strength is calculated using the phase distribution of the stress field and temperature field. An energy transfer function is constructed based on the stress-temperature coupling strength. The energy transfer function is decomposed into radial and circumferential components. A phase compensation matrix is ​​constructed using the radial and circumferential components. The orientation angle of the carbide grains is adjusted through the phase compensation matrix. The energy phase difference is calculated using the adjusted carbide grain orientation angle. The stress field distortion region is identified based on the energy phase difference. The spatial distribution of the stress field distortion region is combined with the carbide grain orientation angle to calculate the material structure stability boundary. The optimal carbide grain orientation angle is determined based on the material structure stability boundary. The optimal carbide grain orientation angle is combined with the stress field distortion region distribution to output the optimal material structure parameters. Based on the optimal material structure parameters, selection criteria are established, and cemented carbide tool materials are selected using these criteria to obtain the optimal material selection scheme.

7. A material selection system for predicting the life of cemented carbide cutting tools based on working condition parameters, used to implement the method described in any one of claims 1-6, characterized in that, The system includes: The data acquisition module is used to collect cutting force data, cutting temperature data, and tool wear data of carbide tools during the cutting process; The synergistic effect data construction module is used to acquire multi-point cutting force distribution in real time using the stress sensing array in the tool tip area, calculate the three-dimensional distribution state of the tool tip stress field based on the cutting temperature gradient collected by the temperature sensing array, and construct the synergistic effect data of stress-temperature-wear in the tool tip area. The wear prediction and correction module is used to calculate fatigue damage characteristic values ​​based on stress-temperature-wear synergistic data, and fit the fatigue damage characteristic values ​​with tool wear data to obtain wear prediction and correction coefficients. The tool life prediction module is used to compensate for the three-dimensional distribution of the tool tip stress field using the wear prediction correction coefficient and output tool life prediction data. The material selection module is used to design the carbide phase gradient distribution structure based on stress-temperature-wear synergistic data and fatigue damage characteristic values, determine the carbide grain orientation angle using wear prediction correction coefficient, adjust the carbide grain orientation angle based on tool life prediction data, output the optimal material structure parameters, and select cemented carbide tool materials based on the optimal material structure parameters.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

Images