WO2024000720A1 - 基于切削力成分解耦的变工况刀具磨损监测方法及系统 - Google Patents

基于切削力成分解耦的变工况刀具磨损监测方法及系统 Download PDF

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WO2024000720A1
WO2024000720A1 PCT/CN2022/108646 CN2022108646W WO2024000720A1 WO 2024000720 A1 WO2024000720 A1 WO 2024000720A1 CN 2022108646 W CN2022108646 W CN 2022108646W WO 2024000720 A1 WO2024000720 A1 WO 2024000720A1
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tool
cutting force
cutting
force
tool wear
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PCT/CN2022/108646
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French (fr)
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张俊
白乐乐
唐宇阳
张会杰
赵万华
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西安交通大学
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Priority to US18/486,671 priority Critical patent/US20240036543A1/en
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/404—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control arrangements for compensation, e.g. for backlash, overshoot, tool offset, tool wear, temperature, machine construction errors, load, inertia
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
    • G05B19/4065—Monitoring tool breakage, life or condition
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
    • B23Q17/0957—Detection of tool breakage
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
    • B23Q17/0971—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining by measuring mechanical vibrations of parts of the machine
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
    • B23Q17/098—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining by measuring noise
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
    • B23Q17/099—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining by measuring features of the machined workpiece
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0995—Tool life management
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/20—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring workpiece characteristics, e.g. contour, dimension, hardness
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/182—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by the machine tool function, e.g. thread cutting, cam making, tool direction control
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
    • G05B19/4069—Simulating machining process on screen
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q2717/00—Arrangements for indicating or measuring
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/37—Measurements
    • G05B2219/37252—Life of tool, service life, decay, wear estimation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/37—Measurements
    • G05B2219/37256—Wear, tool wear
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/41—Servomotor, servo controller till figures
    • G05B2219/41376—Tool wear, flank and crater, estimation from cutting force
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • State data during the cutting process of machine tools can be collected through various sensors (vibration, current, displacement, cutting force and other sensors). These state data more or less contain a large amount of sensitive information reflecting tool wear degradation. Although these status data contain a large amount of useful information related to tool wear, they also contain monitoring signal fluctuation information caused by changes in working condition parameters. Tool health status and cutting working condition parameters exhibit highly coupling characteristics to sensor signals, and information reflecting changes in tool wear status is submerged in the amplitude fluctuations of working condition parameter changes. This is the same problem as mentioned above for any type of sensor.
  • Modern signal processing and decomposition methods can decompose complex signal components into different frequency bands, and fault diagnosis and analysis can be achieved by extracting statistical features from the frequency band where the fault is located.
  • Application No. 202111308074.8 provides a tool wear prediction method based on multi-sensor feature fusion. It uses force, vibration, and acoustic emission sensors to monitor and collect various signal data, and analyzes and extracts features in the time domain, frequency domain, and time-frequency domain to predict tool wear. monitor.
  • the tool wear information can be separated to a certain extent using methods such as wavelet packet decomposition and reconstruction, empirical mode decomposition, second-generation wavelet decomposition, and variational mode decomposition, it is difficult to effectively separate most of the information.
  • fault signal resulting in a low signal-to-noise ratio of the extracted fault features.
  • the wear characteristics extracted by this method will still be interfered by working condition parameters.
  • Application publication number CN107877262A discloses a method for monitoring CNC machine tool tool wear based on deep learning, which can quickly and accurately identify the wear status of various tools under different processing conditions.
  • Application publication number CN107877262A discloses a tool wear prediction method based on a deep convolutional residual shrinkage network. Based on the preprocessing of collected machine tool vibration, cutting force and motor current signals, a residual shrinkage unit is constructed to extract the deep layers of the input signal. Features and filter noise interference; build a deep convolutional residual shrinkage network tool wear prediction model.
  • the milling force coefficient is related to the tool geometry and working conditions, and is used by researchers to study tool wear monitoring issues. Assuming that the workpiece material does not change, the tool remains unchanged, and the cooling conditions do not change, the cutting force coefficient is related to the tool geometric parameters. The cutting force coefficient is identified with the help of the milling force model. The edge force coefficient, which reflects the plowing effect, is most related to tool wear and is less affected by cutting parameters.
  • 202111403696.9 discloses a mechanism data fusion-driven tool wear status monitoring method under variable working conditions, and proposes to use indirectly measured cutting forces to identify milling force coefficients in real time to implement tool wear monitoring under variable working conditions.
  • this method requires identifying the milling force coefficient by changing the feed per tooth, which has certain requirements for cutting conditions.
  • the indirect method of obtaining cutting forces in multi-axis simultaneous cutting makes the identification of milling force coefficients more complicated.
  • the tool wear failure threshold is often evaluated based on the tool flank wear zone width, but the tool wear process is complex and difficult to measure with a single tool parameter.
  • the purpose of the present invention is to provide a variable working condition tool wear monitoring method and system based on cutting force component decoupling, so as to solve the problem of indirect monitoring of tool wear status in the manufacturing workshop.
  • the tool wear monitoring method under variable working conditions based on decoupling of cutting force components includes the following steps:
  • Step S1 Obtain the tool location point file according to the structural characteristics of the part
  • Step S2 Input the tool position point data into the cutting processing physical simulation model to extract and calculate the tool-workpiece engagement area TWE;
  • Step S3 Obtain the cross-point frequency response function FRF between the machine tool tip point and the sensor installation position through the hammering method
  • Step S4 Convert the surface roughness Ra requirement of a certain process of the processed part into a surface position error parameter, and calculate the maximum allowable tool tip excitation force of the tool at each position of the part during the cutting process; calculate based on the machine tool accuracy factor because the tool The maximum cutting force limit allowed due to wear;
  • Step S5 Obtain the spindle vibration data of the CNC machine tool cutting process, and establish a data set with marked tool name information based on the acquired data;
  • Step S8 Convert the measured cutting force and simulated cutting force data fragments into the frequency domain through fast Fourier transform, calculate the sum of spectral energy in the frequency range of the two types of cutting force, and use the measured cutting force spectrum The energy sum is subtracted from the simulated cutting force spectrum energy sum to obtain the spectrum energy sum reflecting tool wear information;
  • Step S10 Based on the maximum allowable excitation force at each position during the cutting process of the part obtained in step S4, by making a difference with the simulated cutting force in step S7, the maximum allowable theoretical cutting force increased due to tool wear at each position is obtained. ;
  • Step S11 Compare the cutting force increased due to actual tool wear in step S8 with the maximum allowable theoretical cutting force increased due to tool wear in step S10. If the actual tool wear is greater than the theoretically allowed tool wear, perform tool change. , otherwise continue to monitor the tool wear status until tool wear failure.
  • part surface roughness Ra is:
  • step 5 specifically includes collecting spindle vibration and displacement signals during the machine tool cutting process through a three-way acceleration sensor and an eddy current displacement sensor, and collecting spindle speed, feed speed, number of tool teeth, tool name, and spindle X/Y/Z coordinates. Data; associate sensor data with process instruction data through tool names to form a data set with tagged tool name information.
  • Step S8.1 When intercepting the cutting force frequency band, ensure that the data segment length is greater than or equal to a multiple of the data sampling frequency fs to ensure that the data has sufficient frequency resolution;
  • step 10 is:
  • variable working condition tool wear monitoring system based on cutting force component decoupling includes:
  • the measured cutting force and simulated cutting force data will be Fragment, convert the two types of cutting force data into the frequency domain through fast Fourier transform, calculate the sum of spectrum energy in the frequency band of the two types of cutting force, and subtract the sum of spectrum energy of the measured cutting force spectrum from the sum of the spectrum energy of the simulated cutting force, we get The sum of spectral energy reflecting tool wear information;
  • the cutting force increment obtained by completely different from traditional signal processing methods is used to diagnose the tool degradation state, without interference from cutting parameters.
  • the statistical monitoring index constructed based on wear cutting force shows a nonlinear monotonic increasing trend, which is in good agreement with the tool wear degradation process curve.
  • the method of demodulating wear cutting force based on frequency domain means is different from the time domain calculation cross-correlation function method, and is basically not interfered by factors such as tool eccentricity.
  • the present invention calculates the spectrum energy sum of the measured cutting force and the simulated cutting force respectively, and calculates the ratio of the spectrum energy sum of the measured cutting force and the simulated cutting force spectrum energy sum to obtain a robust monitoring index reflecting the tool wear status.
  • the physical meaning of this indicator is very clear, and the size of the indicator indicates the degree of tool degradation.
  • the measured cutting force and the simulated cutting force are basically equal, and the cutting force ratio index is close to 1; as the degree of tool wear increases, the measured cutting force gradually deviates from the simulated cutting force because it contains tool wear and vibration components.
  • the force ratio indicator gradually deviates from 1. Therefore, by setting appropriate monitoring thresholds, tool wear monitoring under variable load conditions can be achieved.
  • Figure 2 is a schematic diagram of tool wear cutting force increment demodulation and separation.
  • Figure 3 is a schematic diagram of the structure of the cutting force ratio index.
  • Figure 4 is a schematic diagram of dynamic evaluation of tool failure threshold.
  • level does not mean that the component is required to be absolutely horizontal, but may be slightly tilted.
  • horizontal only means that its direction is more horizontal than “vertical”. It does not mean that the structure must be completely horizontal, but can be slightly tilted.
  • the terms “setting”, “installation”, “connecting” and “connecting” should be understood in a broad sense.
  • they can It can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, or it can be an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be an internal connection between two components.
  • the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
  • the present invention proposes a variable working condition tool wear monitoring method based on cutting force component decoupling.
  • the implementation of the tool wear status monitoring method specifically includes the following steps.
  • Step S1 As shown in Figure 1, before the parts are processed, the parts are first 3D modeled through UG NX software, and then reasonable process parameters and process steps are selected according to the processing conditions and quality requirements. Generate the G code of the part processing process, complete the CNC programming link, and obtain the CLS tool position file.
  • Step S2 As shown in Figure 1, define cutting parameter information such as parts and tools in cutting process simulation software such as MechPro, import the tool position point file obtained in step S1, and perform offline simulation to generate cutting forces and tool vibrations during the cutting process. , material removal rate and other physical and geometric information. At the same time, the tool-workpiece engagement area information TWE is obtained through simulation to facilitate subsequent real-time simulation calculation of cutting force.
  • cutting parameter information such as parts and tools in cutting process simulation software such as MechPro
  • Step S3 Use the hammering method to obtain the cross-point frequency response function FRF of the machine tool tool tip point and the sensor installation position through the LMS Test.Lab system.
  • Step S4 Convert the surface roughness Ra requirement of a certain process of the part to be processed into a surface position error parameter, thereby calculating the maximum allowable tool tip point excitation force of the tool at each position of the part during the cutting process.
  • the calculation expression of part surface roughness Ra is:
  • F x and F y represent the maximum allowable tool tip point excitation force of the tool at each position of the part.
  • Step S5 Tool wear factor is only one of the key factors in forming machining accuracy.
  • the safety factor is considered based on the geometric error and dynamic accuracy of the machine tool, and the machine tool accuracy factor is formed based on expert experience to calculate the maximum cutting force limit allowed due to tool wear.
  • the calculation expression is:
  • ⁇ represents the error correction coefficient that takes into account the geometric accuracy and dynamic accuracy performance of the machine tool.
  • Step S6 As shown in Figure 1, collect spindle vibration and displacement signals during the machine tool cutting process through three-way acceleration sensors, eddy current displacement sensors, etc., and collect spindle speed, feed speed, number of tool teeth, and tool through Siemens edge computing module Name, spindle X/Y/Z coordinate data, etc.
  • Step S7 Associate the sensor data with the process instruction data through the tool name to form a data set with tagged tool name information.
  • Step S8 Remove the trend term from the marked spindle vibration data, process it with low-pass filtering, and use it as the input signal of the indirect measurement model of milling force to estimate the cutting force of the tool in real time, so as to decouple and separate the cutting force components of tool wear.
  • Step S9 As shown in Figure 1, input online data such as spindle speed and feed speed into the real-time simulation model of milling force, and simulate milling force online by combining the tool-workpiece engagement area (TWE) and milling force coefficient to simulate milling. Because the force does not consider the effect of tool wear, it can be used as a measured cutting force for sharp tools.
  • TWE tool-workpiece engagement area
  • Step S10 As shown in Figure 2, use the measured cutting force and simulated cutting force data segments to convert the two types of cutting force data into the frequency domain through Fast Fourier Transform (FFT), and calculate the frequency range of the two types of cutting force within the frequency range.
  • FFT Fast Fourier Transform
  • Spectral energy sum The spectrum energy sum reflecting the tool wear information is obtained by subtracting the spectrum energy sum of the simulated cutting force from the measured cutting force spectrum energy sum.
  • the step S10 includes:
  • Step S10.1 When intercepting the cutting force frequency band, ensure that the data segment length is greater than or equal to the multiple of the data sampling frequency fs to achieve sufficient frequency resolution.
  • Step S10.2 When calculating the energy sum of the cutting force spectrum, the frequency components within the effective frequency band range can be selected. The high-frequency components can be ignored and not taken into account. For example, the frequency range ranges from 1 times the cutting frequency to 10 times the frequency.
  • Step S10.3 When the cutting force components of tool wear are decoupled and separated, the residual difference between the measured cutting force and the simulated cutting force is directly calculated through the difference method.
  • the expression is:
  • ⁇ F i-mea (j ⁇ ) represents the energy sum of the measured cutting force spectrum
  • ⁇ F i-pre (j ⁇ ) represents the spectrum energy sum of the simulated cutting force
  • ⁇ F i-wear (j ⁇ ) represents the spectrum energy sum of the residual cutting force.
  • Step S11 As shown in Figure 2, on the basis of measured cutting force, simulated cutting force spectrum energy and sum, the cutting force ratio index is obtained through calculation, and the cutting force ratio index is used to monitor the degradation state of the tool under time-varying cutting conditions. .
  • K i-MFR (j ⁇ ) ⁇ F i-mea (j ⁇ )/ ⁇ F i-pre (j ⁇ ) (4)
  • K i-MFR (j ⁇ ) represents the milling force ratio index
  • i represents the three directions of X, Y, and Z.
  • Step S12 As shown in Figure 4, based on the maximum allowable excitation force at each position during the part cutting process obtained in step S5, by making a difference with the simulated cutting force in step S9, the maximum allowable force at each position can be obtained.
  • the theoretical cutting force increases due to tool wear.
  • Step S13 Compare the actual cutting force increased due to tool wear in step S10 with the maximum allowable theoretical cutting force increased due to tool wear in step S12. If the actual tool wear degree is greater than the theoretically allowed tool wear degree, tool replacement is performed, otherwise the tool wear status continues to be monitored until tool wear failure.
  • Step S14 By implementing the above method, the cutting force ratio index change curve of the three end mills during the wear evolution process under time-varying working conditions is obtained, as shown in Figure 5.
  • the cutting conditions parameters experienced by the end mill during its life cycle are shown in Table 1 below:
  • the system includes:
  • M1 Multi-source data collection and preprocessing module
  • the data sources of CNC machine tool cutting process mainly include external acceleration sensor data and machine tool CNC system data.
  • Three-way acceleration sensors and eddy current displacement sensors collect spindle X/Y/Z vibration and displacement response data; machine tool CNC system data is collected based on Siemens edge computing modules, including cutting usage data (spindle speed, feed speed), machine tool spindle X/ Y/Z coordinate data, tool name, program name, current program line, and number of tool teeth. Tool names and program names are used to mark and match monitoring data and tools.
  • the data acquisition system hardware includes a CNC machining center equipped with Siemens edge computing modules, a three-way acceleration sensor, an eddy current displacement sensor, an integrated data acquisition and computing machine, and LabVIEW data acquisition and status monitoring software.
  • the CNC code is obtained, and then the tool position point file for part processing is generated.
  • the calculated tool-workpiece meshing area, real-time collected spindle speed, feed speed data, cutting force coefficient, etc. are used as the input of the cutting force model, and the instantaneous cutting force is used as the model output to realize online simulation of the cutting force.
  • Real-time simulation of cutting forces takes calculation efficiency into consideration, and the tool-workpiece engagement area (TWE) can be calculated through cutting process simulation software such as MechPro.
  • the expression of cutting force element is:
  • Indirect estimation of cutting force requires the use of machine tool frequency response and tool vibration displacement data.
  • the frequency response of machine tools is mainly obtained through the hammering method. Hammer tapping experiments are conducted along the feed direction of the spindle and perpendicular to the feed direction. Multiple sets of acceleration frequency response functions are obtained, and the frequency response functions in each direction are averaged. It is worth obtaining the tool tip point-sensor installation position cross-point acceleration frequency response function.
  • the acceleration data is integrated twice to obtain the tool displacement data, the tool tip displacement is subjected to fast Fourier transform (FFT), and the machine tool cross-point frequency response function is interpolated, and the ratio of the tool tip displacement to the frequency response function is calculated.
  • FFT fast Fourier transform
  • the cutting force spectrum can be obtained, and the estimated cutting force spectrum is calculated through the inverse Fourier transform (IFFT) to obtain the true dynamic cutting force of the cutting process.
  • IFFT inverse Fourier transform
  • the cutting force decoupling and separation module aims to extract and separate the cutting force components increased due to tool wear from the measured cutting force components.
  • the obtained measured and simulated cutting forces are intercepted into associated data segments in the form of a sliding window, and then the time domain cutting forces are transformed into the frequency domain.
  • the cutting force spectrum in the characteristic frequency band of the cutting force spectrum is selected to calculate the amplitude energy sum.
  • Subtracting the simulated cutting force spectrum energy sum from the measured cutting force spectrum energy sum, the frequency domain energy sum of the cutting force increased due to tool wear can be obtained.
  • the cutting force ratio index can be obtained by measuring the energy sum of the cutting force spectrum in the frequency domain and calculating the ratio of the energy sum of the simulated cutting force spectrum.
  • the cutting force ratio index can effectively reflect the degree of tool wear without being interfered by cutting parameters. When cutting with a new tool, the cutting force ratio index fluctuates around 1; as the degree of tool wear increases, the cutting force ratio index gradually deviates from 1.
  • the cutting force ratio expression is:
  • the maximum cutting force allowed by the tool to complete the surface accuracy of the part under forced vibration can be solved.
  • the cutting force generated by the actual cutting process of the tool is actually a linear superposition of the simulated cutting force and the cutting force component increased due to tool wear.
  • the maximum wear degree allowed by the current tool to complete the part processing can be solved, that is, through decoupled wear cutting Maximum allowable value of force. Therefore, by constraining the cutting force threshold of tool wear in real time, the failure threshold can be dynamically adjusted according to the roughing and finishing requirements of part processing.
  • the cutting condition definition database is used to store relevant data for implementing the tool wear status monitoring method proposed by the present invention.
  • the cutting force real-time simulation module it is necessary to store the special name of each tool with the same specification, so that the monitoring system can match the corresponding tool parameters according to the read tool name, and match the corresponding milling force coefficient according to the tool-workpiece relationship.
  • the tool failure threshold dynamic adjustment module the surface roughness Ra requirements of parts under different working steps need to be stored. The surface roughness of the parts in this process and working step can be matched based on the program name, current program line and other information.
  • the cutting force value is increased according to the maximum tool wear allowed by the surface roughness Ra, and it is determined in real time whether the current tool is in a wear failure state.

Abstract

一种基于切削力成分解耦的变工况刀具磨损监测方法及系统，根据刀位点文件离线计算刀具-工件啮合区域TWE；离线计算刀具在零件各位置处所允许的切削力。采集数控机床主轴振动数据，根据获取的系统内部数据建立具有标记刀具名称信息的数据集；基于振动信号实时估计刀具的切削力；计算实际因刀具磨损而增加的切削力和在零件精度约束下最大允许的因刀具磨损而增加的理论切削力。将实测与理论磨损切削力实时作比较，若实际刀具磨损程度大于理论允许的刀具磨损程度则执行换刀，否则继续监测刀具状态直到刀具磨损失效。可实现变负载下的刀具磨损监测，基于零件精度约束实时判定刀具是否磨损失效，可最大限度利用刀具使用寿命。

Description

基于切削力成分解耦的变工况刀具磨损监测方法及系统 技术领域
本发明属于数控机床切削加工过程状态监测与诊断控制领域，特别涉及基于切削力成分解耦的变工况刀具磨损监测方法及系统。
背景技术
不管在学术界还是工程应用领域，切削加工过程中刀具磨损状态间接监测技术一直是多年来智能数控机床研究的热点。在工程应用方面，ARTIS等商业化的刀具状态监测系统，基于带宽监测策略(动态阈值策略)实现了大批量加工场合下的刀具异常监测。带宽监测方法利用的是包含切削工艺贡献成分与刀具健康状态的耦合信息。通过先在线学习包含工况参数信息的监测指标，在此基础上设置异常监测的最大允许波动区间，该方法避开了切削参数对监测指标的干扰影响。然而，由于切削加工过程具有一定的随机性，这种监测方法对失效上、下阈值设置非常敏感，在刀具磨损监测方面很容易产生误报或漏报现象。频繁误报会影响加工效率，漏报则可能会造成零件加工不合格。
在真实数控切削加工过程中，考虑到加工效率等因素，往往存在一把刀具粗加工、精加工一体完成，刀具在磨损退化过程中，可能经历的切削参数组合差别较大。粗加工使用较为饱和的切削用量尽可能保证切削效率，精加工为保证零件的加工精度选取的切削参数较小。此外，在槽腔拐角、环形薄壁、T型薄壁等处经常会降低进给速度来尽可能保证切削过程保持稳定。因在，在真实加工过程中切削参数是时变的，这会导致传感器监测到的数据中反映刀具磨损退化的信息会被切削工况所调制，为刀具磨损状态准确监测带来极大干扰。
通过各类传感器(振动、电流、位移、切削力等传感器)可采集机床切削加工过程中的状态数据，这些状态数据或多或少都包含大量反映刀具磨损退化 的敏感信息。尽管这些状态数据中蕴含大量与刀具磨损相关的有用信息，同时也含有工况参数变化引起的监测信号波动信息。刀具健康状态与切削工况参数对传感器信号呈现高度耦合特性，反映刀具磨损状态变化的信息淹没在工况参数变化的幅值波动中。这对于任何类型的传感器都存在上述问题。
因此，从信息成分复杂的传感器原始信号中提炼(提纯)反映刀具磨损成分是解决时变切削工况下刀具磨损问题的关键。变切削参数刀具状态监测问题本质上是各类因素叠加的变负载问题。上述问题是变工况故障诊断与状态监测领域普遍共性难题。
现代信号处理与分解方法可将复杂的信号成分分解到不同的频带，通过对故障所在频带提取统计特征，可实现故障诊断与分析。申请号202011566473.X公开了一种基于功率和振动信号的模型融合刀具磨损监测方法及系统，通过提取传统的各类时频域指标并进行特征融合与降维实现刀具磨损监测。申请号202111308074.8提供一种基于多传感器特征融合的刀具磨损预测方法，利用力、振动、声发射传感器监测和采集各类信号数据，在时域、频域和时频域内分析并提取特征进行刀具磨损监测。然而，基于小波包分解与重构、经验模态分解、二代小波分解、变分模态分解等方法尽管在一定程度上可以分离出刀具磨损信息，但这种方法很难有效分离出大部分故障信号，导致提取的故障特征信噪比低。通过该方法提取的磨损特征还是会受到工况参数的干扰。
近些年逐渐兴起的深度学习方法在特征提取方面表现出很强的优势，可从传感器原始信号中自适应提取虚拟特征来表征刀具磨损程度。申请公布号CN107877262A公开了一种基于深度学习的数控机床刀具磨损监测方法，快速准确的识别出不同加工条件下的各种刀具的磨损状态。申请公布号CN107877262A公开一种基于深度卷积残差收缩网络的刀具磨损预测方法，在对采集机床振动、切削力以及电机电流信号预处理基础上，构建残差收缩单元，提取输入信号的深层次特征并过滤噪声干扰；构建深度卷积残差收缩网络刀具磨损预测模型。然而，这种特征提取方法，一方面需要大量的模型训练数据， 在实际工业生产中样本数据获取困难。另外，深度学习模型可解释性与泛化能力不足。当切削工况发生改变时，模型的识别准确度会急剧下降。这种方法在解决时变工况下的刀具磨损状态监测问题方面还需要进一步深入研究。
将切削加工动力学模型与实时数据相结合研究变切削参数下刀具磨损监测问题也是一种可行的方法。切削力模型中，铣削力系数与刀具几何形状、工况条件相关，被学者用来研究刀具磨损监测问题。假定工件材料不改变，刀具不变，冷却条件等不改变，则切削力系数与刀具几何参数相关。切削力系数是借助铣削力模型辨识而来，反映犁耕效应的刃口力系数与刀具磨损最相关，受到切削参数的影响较小。申请号202111403696.9公开了机理□数据融合驱动的变工况刀具磨损状态监测方法，提出利用间接测量的切削力来实时辨识铣削力系数实现变工况刀具磨损监测。然而，这种方法需要通过改变每齿进给量来辨识铣削力系数，对切削工况有一定的要求。此外，在多轴联动切削加工间接获取切削力方法使得铣削力系数的辨识更加复杂。
综上可知，现有刀具磨损监测方法还有如下不足：
(1)没有从传感器信号组成成分角度，研究刀具磨损状态、切削参数分别对传感器监测信号的贡献成分。没有从复杂的传感器原始信号中全面解耦分离出刀具磨损的贡献出成分，在此基础上提取时频域统计特征实现变工况下的刀具磨损监测。
(2)现有方法提出的监测指标由于数量等级的不一致，使得不同刀具磨损失效阈值设置比较复杂。生产现场往往有粗加工、精加工等场合，再加上切削钛合金、铝合金等不同材料导致刀具类别非常多，为刀具磨损监测阈值带来困难。
(3)在刀具磨损失效阈值设置上，没有将零件加工精度与刀具磨损监测相结合。在刀具磨损失效阈值经常以刀具后刀面磨损带宽度为评估依据，但刀具磨损过程复杂，很难用单一的刀具参数来衡量。
发明内容
本发明的目的在于提供基于切削力成分解耦的变工况刀具磨损监测方法及系统，以解决制造车间刀具磨损状态间接监测问题。
为实现上述目的，本发明采用以下技术方案：
基于切削力成分解耦的变工况刀具磨损监测方法，包括以下步骤：
步骤S1：根据零件结构特征，获取刀位点文件；
步骤S2：将刀位点数据输入到切削加工物理仿真模型中提取计算刀具-工件啮合区域TWE；
步骤S3：通过锤击法，获取机床刀尖点与传感器安装位置的跨点频响函数FRF；
步骤S4：根据被加工零件某工序的表面粗糙度Ra要求转化成表面位置误差参数，计算切削加工过程中刀具在零件各位置处最大允许的刀尖点激励力；根据机床精度因子计算得到因为刀具磨损而允许的最大切削力极限值；
步骤S5：获取数控机床切削加工过程主轴振动数据，根据获取的数据建立具有标记刀具名称信息的数据集；
步骤S6：对标记好的主轴振动数据进行去除趋势项，低通滤波处理，作为铣削力间接测量模型的输入信号实时估计刀具的切削力，用于刀具磨损切削力成分的解耦分离；
步骤S7：将主轴数据输入到铣削力实时仿真模型中，结合刀具-工件啮合区域TWE与铣削力系数在线仿真铣削力，作为锋利刀具的测量切削力；
步骤S8：将测量切削力、仿真切削力数据片段，通过快速傅里叶变换将两类切削力数据转换至频域，分别计算两类切削力频段区间内的频谱能量和，利用测量切削力频谱能量和减去仿真切削力频谱能量和，得到反映刀具磨损信息的频谱能量和；
步骤S9：在测量切削力、仿真切削力频谱能量和基础上，通过作商得到切削力比值指标，通过切削力比值指标来监测刀具在时变切削工况下的退化状态；
步骤S10：基于步骤S4中得到零件切削加工过程中各个位置处最大允许的 激励力，通过与步骤S7中的仿真切削力做差，得到各个位置处最大允许的因为刀具磨损而增加的理论切削力；
步骤S11：将步骤S8中的实际刀具磨损而增加的切削力与步骤S10中最大允许的因为刀具磨损而增加的理论切削力作比较，若实际刀具磨损程度大于理论允许的刀具磨损程度则执行换刀，否则继续监测刀具磨损状态直到刀具磨损失效。
进一步的，零件表面粗糙度Ra计算表达式为：
Figure PCTCN2022108646-appb-000001
其中，F x,F y表示刀具在零件各位置处最大允许的刀尖点激励力。
进一步的，因为刀具磨损而允许的最大切削力极限值；计算表达为：
F MT-i＝F i/δ     (2)
其中，δ表示考虑机床几何精度、动态精度性能的误差修正系数。
进一步的，步骤5具体包括通过三向加速度传感器、电涡流位移传感器采集机床切削加工过程中主轴振动、位移信号，采集主轴转速、进给速度、刀具齿数、刀具名称、主轴X/Y/Z坐标数据；通过刀具名称将传感器数据与工艺指令数据进行关联，形成具有标记刀具名称信息的数据集。
进一步的，步骤7中，将主轴转速、进给速度在线数据输入到铣削力实时仿真模型中。
进一步的，步骤8具体包括：
步骤S8.1：在截取切削力频段时，确保数据片段长度大于或等于数据采样频率fs的倍频，保证数据具有足够的频率分辨率；
步骤S8.2：在计算切削力频谱能量和时，可选择有效频段区间内的频率成分，高频成分可忽略不考虑在内，如频段区间从切削频率1倍频至10倍频；
步骤S8.3：在刀具磨损切削力成分解耦分离时，直接通过做差法计算测量切削力与仿真切削力之间的残差；表达式为：
Figure PCTCN2022108646-appb-000002
其中，∑F i-mea(jω)表示测量切削力频谱能量和；∑F i-pre(jω)表示仿真切削力频谱能量和；ΔF i-wear(jω)表示切削力残差频谱能量和。
进一步的，切削力比值表达式：
K i-MFR(jω)＝∑F i-mea(jω)/∑F i-pre(jω)  (4)
其中，K i-MFR(jω)表示铣削力比值指标，i表示X,Y,Z三个方向。
进一步的，步骤10中的表达式为：
Figure PCTCN2022108646-appb-000003
进一步的，基于切削力成分解耦的变工况刀具磨损监测系统，包括：
切削力实时仿真模块：根据零件结构特征，获取刀位点文件；将刀位点数据输入到切削加工物理仿真模型中提取计算刀具-工件啮合区域TWE；
切削力间接估计模块：通过锤击法，获取机床刀尖点与传感器安装位置的跨点频响函数FRF；根据被加工零件某工序的表面粗糙度Ra要求转化成表面位置误差参数，从而计算切削加工过程中刀具在零件各位置处最大允许的刀尖点激励力；根据机床精度因子计算得到因为刀具磨损而允许的最大切削力极限值；获取数控机床切削加工过程主轴振动数据，根据获取的数据建立具有标记刀具名称信息的数据集；对标记好的主轴振动数据进行去除趋势项，低通滤波处理，作为铣削力间接测量模型的输入信号实时估计刀具的切削力，用于刀具磨损切削力成分的解耦分离；将主轴数据输入到铣削力实时仿真模型中，结合刀具-工件啮合区域TWE与铣削力系数在线仿真铣削力，作为锋利刀具的测量切削力；将测量切削力、仿真切削力数据片段，通过快速傅里叶变换将两类切削力数据转换至频域，分别计算两类切削力频段区间内的频谱能量和，利用测量切削力频谱能量和减去仿真切削力频谱能量和，得到反映刀具磨损信息的频 谱能量和；
切削力比值指标构造模块：在测量切削力、仿真切削力频谱能量和基础上，通过作商得到切削力比值指标，通过切削力比值指标来监测刀具在时变切削工况下的退化状态；基于得到零件切削加工过程中各个位置处最大允许的激励力，通过与仿真切削力做差，得到各个位置处最大允许的因为刀具磨损而增加的理论切削力；将实际刀具磨损而增加的切削力与最大允许的因为刀具磨损而增加的理论切削力作比较，若实际刀具磨损程度大于理论允许的刀具磨损程度则执行换刀，否则继续监测刀具磨损状态直到刀具磨损失效。
与现有技术相比，本发明有以下技术效果：
本发明提出一种可适用于变切削参数条件下的刀具磨损状态监测方法。通过实时采集机床数控系统工艺指令数据作为瞬时铣削力模型输入，然后可得到不考虑刀具磨损等影响的仿真预测切削力。通过安装于主轴侧壁的三向加速度传感器等实时采集主轴振动响应信号，结合机床频响估计瞬时切削力。进一步将测量切削力与仿真切削力变换至频域，通过分别计算特征频段的频谱幅值能量和，通过计算测量切削力与仿真切削力残差，即可解耦分离出反映刀具磨损的切削力成分。利用完全不同于传统信号处理手段获取的切削力增量诊断刀具退化状态，不受切削参数干扰。基于磨损切削力构造的统计监测指标呈现非线性单调递增趋势，与刀具磨损退化过程曲线具有很好的吻合度。基于频域手段解调磨损切削力方法不同于时域计算互相关函数方法，基本不受刀具偏心等因素的干扰。
本发明在测量切削力与仿真切削力分别计算频谱能量和的基础上，通过计算测量切削力频谱能量和与仿真切削力频谱能量和的比值，即可得到反映刀具磨损状态的鲁棒监测指标。该指标物理意义非常明确，指标的大小表示刀具的退化程度。新刀切削时，测量切削力与仿真切削力基本相等，切削力比值指标接近1；随着刀具磨损程度增加，测量切削力因为包含刀具磨损与振动成分，其值大小逐渐偏离仿真切削力，切削力比值指标逐渐偏离1。因此，通过设置 合适的监测阈值，即可实现变负载情况下的刀具磨损监测。
本发明多齿铣刀与工件相互作用会产生周期性激励力，这种作用在工艺系统的受迫振动是影响零件表面粗糙度的关键因素之一。通过零件表面粗糙度Ra与机床刀尖点频响即可计算刀尖点允许的最大切削力。通过计算最大允许切削力与所选取切削参数下的仿真切削力参差，即可分离得到允许刀具磨损而增加的切削力最大值。因此，通过实时评估磨损切削力最大值即可作为评估刀具能否继续加工的参考标准。这种刀具失效阈值动态方法，可通过零件切削精度动态调整，在精加工判定为失效的刀具，在粗加工却能继续参与切削，充分利用了刀具的寿命，又能兼顾零件加工精度。
附图说明
为了更清楚的说明本发明实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本发明的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为仿真切削力与测量切削力获取示意图。
图2为刀具磨损切削力增量解调分离示意图。
图3为切削力比值指标构造示意图。
图4为刀具失效阈值动态评估示意图。
图5中(a)到(d)为切削力比值指标表征刀具磨损退化过程。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本发明实施例的组件可以以各种不同的配置来布置和设计。
因此，以下对在附图中提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的选定实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。
在本发明实施例的描述中，需要说明的是，若出现术语“上”、“下”、“水平”、“内”等指示的方位或位置关系为基于附图所示的方位或位置关系，或者是该发明产品使用时惯常摆放的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一”、“第二”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
此外，若出现术语“水平”，并不表示要求部件绝对水平，而是可以稍微倾斜。如“水平”仅仅是指其方向相对“竖直”而言更加水平，并不是表示该结构一定要完全水平，而是可以稍微倾斜。
在本发明实施例的描述中，还需要说明的是，除非另有明确的规定和限定，若出现术语“设置”、“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。
下面结合附图对本发明做进一步详细描述：
本发明为解决刀具磨损退化过程中时变切削参数对刀具磨损监测的干扰影响，提出一种基于切削力成分解耦的变工况刀具磨损监测方法。所述刀具磨损状态监测方法实施具体包括如下步骤。
步骤S1：如图1所示，在零件加工之前，首先通过UG NX软件对零件进行3D建模，进而根据加工条件及质量要求选定合理的工艺参数及工艺步骤。生成零件加工过程G代码，完成数控编程环节，得到CLS刀位文件。
步骤S2：如图1所示，在MechPro等切削过程工艺仿真软件中定义零件、刀具等切削参数信息，导入步骤S1中获取的刀位点文件，离线仿真生成切削加工过程中切削力、刀具振动、材料去除率等物理与几何信息。同时仿真得到刀具-工件啮合区域信息TWE便于后续切削力实时仿真计算。
步骤S3：通过锤击法，通过LMS Test.Lab系统获取机床刀尖点与传感器安装位置的跨点频响函数FRF。
步骤S4：根据被加工零件某工序的表面粗糙度Ra要求转化成表面位置误差参数，从而计算切削加工过程中刀具在零件各位置处最大允许的刀尖点激励力。零件表面粗糙度Ra计算表达式为：
Figure PCTCN2022108646-appb-000004
其中，F x,F y表示刀具在零件各位置处最大允许的刀尖点激励力。
步骤S5：刀具磨损因素只是形成加工精度的关键因素之一。在结合机床几何误差、动态精度基础上考虑安全系数，根据专家经验形成机床精度因子计算得到因为刀具磨损而允许的最大切削力极限值。计算表达为：
F MT-i＝F i/δ     (2)
其中，δ表示考虑机床几何精度、动态精度性能的误差修正系数。
步骤S6：如图1所示，通过三向加速度传感器、电涡流位移传感器等采集机床切削加工过程中主轴振动、位移信号，通过Siemens公司边缘计算模块采集主轴转速、进给速度、刀具齿数、刀具名称、主轴X/Y/Z坐标数据等。
步骤S7：通过刀具名称将传感器数据与工艺指令数据进行关联，形成具有标记刀具名称信息的数据集。
步骤S8：对标记好的主轴振动数据进行去除趋势项，低通滤波处理，作为铣削力间接测量模型的输入信号实时估计刀具的切削力，从而用于刀具磨损切 削力成分的解耦分离。
步骤S9：如图1所示，将主轴转速、进给速度等数在线据输入到铣削力实时仿真模型中，结合刀具-工件啮合区域(TWE)与铣削力系数等在线仿真铣削力，仿真铣削力因为不考虑刀具磨损效应，故而可作为锋利刀具的测量切削力。
步骤S10：如图2所示，将测量切削力、仿真切削力数据片段，通过快速傅里叶变换(FFT)将两类切削力数据转换至频域，分别计算两类切削力频段区间内的频谱能量和。利用测量切削力频谱能量和减去仿真切削力频谱能量和，得到反映刀具磨损信息的频谱能量和。
优选的，所述步骤S10包括：
步骤S10.1：在截取切削力频段时，确保数据片段长度大于或等于数据采样频率fs的倍频，使足够的频率分辨率。
步骤S10.2：在计算切削力频谱能量和时，可选择有效频段区间内的频率成分，高频成分可忽略不考虑在内，如频段区间从切削频率1倍频至10倍频。
步骤S10.3：在刀具磨损切削力成分解耦分离时，直接通过做差法计算测量切削力与仿真切削力之间的残差。表达式为：
Figure PCTCN2022108646-appb-000005
其中，∑F i-mea(jω)表示测量切削力频谱能量和；∑F i-pre(jω)表示仿真切削力频谱能量和；ΔF i-wear(jω)表示切削力残差频谱能量和。
步骤S11：如图2所示，在测量切削力、仿真切削力频谱能量和基础上，通过作商得到切削力比值指标，通过切削力比值指标来监测刀具在时变切削工况下的退化状态。
K i-MFR(jω)＝∑F i-mea(jω)/∑F i-pre(jω)   (4)
其中，K i-MFR(jω)表示铣削力比值指标，i表示X,Y,Z三个方向。
步骤S12：如图4所示，基于步骤S5中得到零件切削加工过程中各个位置处最大允许的激励力，通过与步骤S9中的仿真切削力做差，即可得到各个位置 处最大允许的因为刀具磨损而增加的理论切削力。
Figure PCTCN2022108646-appb-000006
步骤S13：将步骤S10中的实际刀具磨损而增加的切削力与步骤S12中最大允许的因为刀具磨损而增加的理论切削力作比较。若实际刀具磨损程度大于理论允许的刀具磨损程度则执行换刀，否则继续监测刀具磨损状态直到刀具磨损失效。
步骤S14：通过将上述方法进行实施，得到3把立铣刀在时变工况下磨损演化过程中的切削力比值指标变化曲线如图5所示。立铣刀生命周期内经历的切削工况参数如下表1所示：
表1切削工况参数组合
Figure PCTCN2022108646-appb-000007
基于切削力成分解耦的变工况刀具磨损监测系统，所述系统包括：
M1：多源数据采集与预处理模块：
数控机床切削加工过程数据来源主要包括外置加速度传感器数据与机床数控系统数据。三向加速度传感器、电涡流位移传感器采集主轴X/Y/Z振动、位移响应数据；基于西门子边缘计算模块采集机床数控系统数据，包括切削用量数据(主轴转速、进给速度)、机床主轴X/Y/Z坐标数据、刀具名称、程序名称、当前程序行、刀具齿数。刀具名称、程序名称用于标记与匹配监测数据与刀具。数据采集系统硬件包括具有具备西门子边缘计算模块的数控加工中心、三向加速度传感器、电涡流位移传感器、数据采集与计算一体机、LabVIEW数据采集 与状态监测软件。
M2：切削力实时仿真模块：
基于UG NX、MasterCAM等CAM软件得到数控代码，进而生成零件加工的刀位点文件。将刀位点数据输入到切削加工物理仿真模型中快速提取计算刀具-工件啮合区域(Tool–workpiece-engagement,TWE)。将计算好的刀具-工件啮合区域、实时采集的主轴转速、进给速度数据、切削力系数等作为切削力模型输入，瞬时切削力作为模型输出，即可实现切削力的在线仿真。切削力实时仿真考虑到计算效率，可通过MechPro等切削过程仿真软件计算刀具-工件啮合区域(TWE)。切削力微元的表达式为：
Figure PCTCN2022108646-appb-000008
Figure PCTCN2022108646-appb-000009
M3：切削力间接估计模块：
切削力间接估计需要用到机床频响与刀具振动位移数据数据。机床频响获取主要通过锤击法获取，分别沿主轴进给方向与垂直于进给方向进行力锤敲击实验，获得多组加速度频响函数，将每个方向上的频响函数进行求平均值得到刀尖点-传感器安装位置跨点加速度频响函数。将加速度数据经二次积分得到刀具位移数据，将刀尖点位移进行快速傅里叶变换(FFT)，并对机床跨点频响函数进行插值处理，通过计算刀尖点位移与频响函数比值即可得到切削力频谱，将估计的切削力频谱经过逆傅里叶变换(IFFT)计算得到切削过程的真实动态切削力。切削力间接估计表达式为：
Figure PCTCN2022108646-appb-000010
M4：切削力频域解耦分离模块
切削力解耦分离模块，目的是从测量切削力成分中提取分离出因为刀具磨 损而增加的切削力成分。将获取的测量与仿真切削力通过滑窗形式截取关联的数据片段，然后将时域切削力变换至频域。考虑对高频噪音干扰影响，选取切削力频谱特征频段区间的切削力频谱计算幅值能量和。将测量切削力频谱能量和减去仿真切削力频谱能量和，即可得到因刀具磨损而增加的切削力频域能量和。
M5：切削力比值指标构造模块
对频域测量切削力频谱能量和与仿真切削力频谱能量和计算比值，即可得到切削力比值指标。切削力比值指标能够有效反映刀具磨损程度而不受切削参数干扰。新刀切削时，切削力比值指标在1上下波动；随着刀具磨损程度的增加，该切削力比值指标逐渐偏离1。切削力比值表达式为：
M6：刀具失效阈值动态调整模块
根据零件表面粗糙度Ra值，结合机床刀尖点频响函数即可求解在受迫振动下刀具完成零件表面精度所允许的最大切削力。刀具实际切削加工产生的切削力实际上是仿真切削力与刀具磨损而增加的切削力成分的线性叠加。通过计算所用切削参数下的仿真切削力，结合受迫振动下刀具完成零件表面精度Ra所允许的最大切削力，可求解得到当前刀具完成零件加工允许的最大磨损程度，即通过解耦的磨损切削力最大允许值。因此，通过实时约束刀具磨损切削力阈值，即可根据零件加工粗加工、精加工要求动态调整失效阈值。强迫振动形成的表面误差及切削合力表达式为：
Figure PCTCN2022108646-appb-000011
Figure PCTCN2022108646-appb-000012
M7：切削工况定义数据库
切削工况定义数据库，用于存储实现本发明提出的刀具磨损状态监测方法 的相关数据。切削力实时仿真模块中，需要存储每把相同规格刀具的专用名称，使得监测系统能够根据读取的刀具名称匹配对应刀具参数，根据刀具-工件关系，匹配对应的铣削力系数。在刀具失效阈值动态调整模块中，需要存储不同工步下零件的表面粗糙度Ra要求，可根据程序名称、当前程序行等信息匹配到该工序、工步零件表面粗糙度。根据表面粗糙度Ra允许的最大刀具磨损而增加切削力值，实时判定当前刀具是否处于磨损失效状态。
以上仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (9)

  1. 基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，包括以下步骤：
    步骤S1：根据零件结构特征，获取刀位点文件；
    步骤S2：将刀位点数据输入到切削加工物理仿真模型中提取计算刀具-工件啮合区域TWE；
    步骤S3：通过锤击法，获取机床刀尖点与传感器安装位置的跨点频响函数FRF；
    步骤S4：根据被加工零件某工序的表面粗糙度Ra要求转化成表面位置误差参数SLE，计算切削加工过程中刀具在零件各位置处最大允许的刀尖点激励力；根据机床精度因子计算得到因为刀具磨损而允许的最大切削力极限值；
    步骤S5：获取数控机床切削加工过程主轴振动数据，根据获取的数据建立具有标记刀具名称信息的数据集；
    步骤S6：对标记好的主轴振动数据进行去除趋势项，低通滤波处理，作为铣削力间接测量模型的输入信号实时估计刀具的切削力，用于刀具磨损切削力成分的解耦分离；
    步骤S7：将机床切参数据实时输入到铣削力实时仿真模型中，结合刀具-工件啮合区域TWE与铣削力系数在线仿真铣削力，作为锋利刀具的测量切削力；
    步骤S8：将测量切削力、仿真切削力数据片段，通过快速傅里叶变换将两类切削力数据转换至频域，分别计算两类切削力频段区间内的频谱能量和，利用测量切削力频谱能量和减去仿真切削力频谱能量和，得到反映刀具磨损信息的频谱能量和；
    步骤S9：在测量切削力、仿真切削力频谱能量和基础上，通过作商得到切削力比值指标，通过切削力比值指标来监测刀具在时变切削工况下的退化状态；
    步骤S10：基于步骤S4中得到零件切削加工过程中各个位置处最大允许的激励力，通过与步骤S7中的仿真切削力做差，得到各个位置处最大允许的因为 刀具磨损而增加的理论切削力；
    步骤S11：将步骤S8中的实际刀具磨损而增加的切削力与步骤S10中最大允许的因为刀具磨损而增加的理论切削力作比较，若实际刀具磨损程度大于理论允许的刀具磨损程度则执行换刀，否则继续监测刀具磨损状态直到刀具磨损失效。
  2. 根据权利要求1所述的基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，零件表面粗糙度Ra计算表达式为：
    Figure PCTCN2022108646-appb-100001
    其中，F x,F y表示刀具在零件各位置处最大允许的刀尖点激励力，X,Y表示刀具刀尖点位移，H ij表示机床频响。。
  3. 根据权利要求1所述的基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，因为刀具磨损而允许的最大切削力极限值；计算表达为：
    F MT-i＝F i/δ  (2)
    其中，δ表示考虑机床几何精度、动态精度性能的误差修正系数。
  4. 根据权利要求1所述的基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，步骤5具体包括通过三向加速度传感器、电涡流位移传感器采集机床切削加工过程中主轴振动、位移信号，采集主轴转速、进给速度、刀具齿数、刀具名称、主轴X/Y/Z坐标数据；通过刀具名称将传感器数据与工艺指令数据进行关联，形成具有标记刀具名称信息的数据集。
  5. 根据权利要求1所述的基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，步骤7中，将主轴转速、进给速度在线数据输入到铣削力实时仿真模型中。
  6. 根据权利要求1所述的基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，步骤8具体包括：
    步骤S8.1：在截取切削力频段时，确保数据片段长度大于或等于数据采样 频率fs的倍频，使足够的频率分辨率；
    步骤S8.2：在计算切削力频谱能量和时，可选择有效频段区间内的频率成分，高频成分可忽略不考虑在内，如频段区间从切削频率1倍频至10倍频；
    步骤S8.3：在刀具磨损切削力成分解耦分离时，直接通过做差法计算测量切削力与仿真切削力之间的残差；表达式为：
    Figure PCTCN2022108646-appb-100002
    其中，∑F i-mea(jω)表示测量切削力频谱能量和；∑F i-pre(jω)表示仿真切削力频谱能量和；ΔF i-wear(jω)表示切削力残差频谱能量和。
  7. 根据权利要求1所述的基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，切削力比值表达式：
    K i-MFR(jω)＝∑F i-mea(jω)/∑F i-pre(jω)  (4)
    其中，K i-MFR(jω)表示铣削力比值指标，i表示X,Y,Z三个方向。
  8. 根据权利要求1所述的基于切削力成分解耦的变工况刀具磨损监测方法，其特征在于，步骤10中的表达式为：
    Figure PCTCN2022108646-appb-100003
  9. 基于切削力成分解耦的变工况刀具磨损监测系统，其特征在于，包括：
    切削力实时仿真模块：根据零件结构特征，获取刀位点文件；将刀位点数据输入到切削加工物理仿真模型中提取计算刀具-工件啮合区域TWE；
    切削力间接估计模块：通过锤击法，获取机床刀尖点与传感器安装位置的跨点频响函数FRF；根据被加工零件某工序的表面粗糙度Ra要求转化成表面位置误差参数，从而计算切削加工过程中刀具在零件各位置处最大允许的刀尖点激励力；根据机床精度因子计算得到因为刀具磨损而允许的最大切削力极限值；获取数控机床切削加工过程主轴振动数据，根据获取的数据建立具有标记 刀具名称信息的数据集；对标记好的主轴振动数据进行去除趋势项，低通滤波处理，作为铣削力间接测量模型的输入信号实时估计刀具的切削力，用于刀具磨损切削力成分的解耦分离；将主轴数据输入到铣削力实时仿真模型中，结合刀具-工件啮合区域TWE与铣削力系数在线仿真铣削力，作为锋利刀具的测量切削力；将测量切削力、仿真切削力数据片段，通过快速傅里叶变换将两类切削力数据转换至频域，分别计算两类切削力频段区间内的频谱能量和，利用测量切削力频谱能量和减去仿真切削力频谱能量和，得到反映刀具磨损信息的频谱能量和；
    切削力比值指标构造模块：在测量切削力、仿真切削力频谱能量和基础上，通过作商得到切削力比值指标，通过切削力比值指标来监测刀具在时变切削工况下的退化状态；基于得到零件切削加工过程中各个位置处最大允许的激励力，通过与仿真切削力做差，得到各个位置处最大允许的因为刀具磨损而增加的理论切削力；将实际刀具磨损而增加的切削力与最大允许的因为刀具磨损而增加的理论切削力作比较，若实际刀具磨损程度大于理论允许的刀具磨损程度则执行换刀，否则继续监测刀具磨损状态直到刀具磨损失效。
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