Dynamic regulation and control method and system for drilling machining tool of machine tool

Abstract

The invention discloses a dynamic regulation and control method and system for a drilling tool of a machine tool, relates to the field of mechanical automation, and solves the problems that the tool monitoring cost is high, the number of misinformation and missing information is large, and the chip removal efficiency is low. Constructing a cutting impedance dynamic model, inputting spindle motor current, motor voltage, spindle rotating speed, motor internal resistance, motor operation frequency and time information into the cutting impedance dynamic model to obtain cutting impedance, and determining an impedance break variable according to the cutting impedance; determining a current phase difference offset and a reference phase difference, and calculating a cutter phase distortion index according to the offset and the reference phase difference; the cutter phase distortion index and the impedance break variable are fused to generate a comprehensive health index; compared with a traditional method, the method has the advantages that the detection precision is high, the false alarm rate is low, the response speed is high, the hardware cost is reduced, and the service life of a tool is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of mechanical automation control, and more particularly to a method and system for dynamically controlling a drilling tool of a machine tool. Background Art

[0002] As high-end equipment manufacturing develops towards precision and intelligence, the state monitoring and dynamic control technology of machine tool drilling processes has become the core support for improving processing quality and ensuring equipment safety. In the fields of aerospace, precision molds, etc., deep-hole processing of complex materials has put forward higher requirements for the perception of tool health status. Its millisecond-level dynamic response and micron-level anomaly detection have become the key bottlenecks restricting processing efficiency and yield. In traditional processing, quality accidents caused by tool wear, tool breakage, and chip blockage occur frequently, resulting in a high proportion of unplanned tool replacement costs each year. Constructing a tool health assessment system through multi-source data fusion and realizing online dynamic optimization of processing parameters has become an important technical path to break through process bottlenecks and build an autonomous and controllable processing system in the field of intelligent machine tools.

[0003] Existing tool monitoring technologies have the following main limitations: (1) Some existing technologies detect tool breakage by circuit disconnection or use probe contact detection, which relies on physical signal threshold judgment and is difficult to capture the progressive wear state of the tool; (2) Some existing technologies need to intercept the stable stage signal based on axial force spectrum analysis, or rely on self-learning historical data, and cannot achieve millisecond-level real-time control; (3) Some existing technologies rely on fixed displacement algorithms or phase difference detection for chip breaking control, which does not consider the signal attenuation effect of deep hole processing. Especially when the processing depth is deep, the signal-to-noise ratio of the existing monitoring system drops significantly, resulting in an increase in the missed detection rate. The above defects make it difficult for existing technologies to meet the needs of complex working conditions such as composite material stack drilling and large aspect ratio micro-hole processing.

[0004] Therefore, there is an urgent need for a drilling processing control method that can solve the above problems. Summary of the Invention

[0005] In order to solve the deficiencies in the prior art, the present invention aims to provide a method and system for dynamically controlling a drilling tool of a machine tool.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] In a first aspect, a method for dynamically controlling a drilling tool of a machine tool is provided, comprising:

[0008] A method for dynamically controlling a drilling tool of a machine tool, characterized by comprising:

[0009] Real-time acquisition of drilling processing data, including spindle motor current, motor voltage, processing depth, spindle speed, motor operating frequency, time information, and motor internal resistance;

[0010] Constructing a cutting impedance dynamic model, inputting the spindle motor current, the motor voltage, the spindle speed, the motor internal resistance, the motor operating frequency, and the time information into the cutting impedance dynamic model to obtain cutting impedance, and determining an impedance mutation amount based on the cutting impedance;

[0011] Determining a current phase difference offset and a reference phase difference, and calculating a tool phase distortion index based on the current phase difference offset and the reference phase difference;

[0012] Fusion of the tool phase distortion index and the impedance mutation amount to generate a comprehensive health index;

[0013] A hierarchical dynamic control strategy is executed according to the comprehensive health index value and the processing depth.

[0014] Furthermore, the calculation formula of the cutting impedance dynamic model is:

[0015]

[0016] Among them, V m is the motor voltage, in volts (V); I is the spindle motor current, in amperes (A); R0 is the motor internal resistance, in ohms (Ω); ΔZ is the impedance mutation, in ohms (Ω); Z cut is the cutting resistance, in ohms; Z avg is the standard impedance value of the current material; ω is the angular frequency, unit rad / s; is the power factor angle, in rad; ΔT is the time difference between adjacent zero crossing points; f is the motor operating frequency, in Hertz (Hz); n is the spindle speed; and t is the time information.

[0017] Furthermore, the calculation formula of the impedance mutation amount is:

[0018]

[0019] Where ΔZ is the impedance mutation, in ohms; Z cut(t) is the cutting resistance at time t, in ohms; Z avg is the standard impedance value of the current material; t2-t1 is the integration interval and dynamic time window.

[0020] Furthermore, determining the current phase difference offset and the reference phase difference, and calculating the tool phase distortion index according to the current phase difference offset and the reference phase difference, includes:

[0021] According to the spindle speed and the machining depth, a corresponding reference phase difference is obtained by matching from a reference phase matrix, wherein the reference phase matrix is ​​constructed based on the spindle speed and the machining depth;

[0022] Establishing a dynamic model of the main shaft three-phase current phase difference, and calculating the current phase difference offset by combining the DC bus voltage, the motor operating frequency, the initial phase difference, the quadrature-axis current, the motor direct-axis inductance, and the motor direct-axis quadrature-axis inductance;

[0023] The tool phase distortion index is calculated based on the difference between the reference phase difference and the current phase difference offset.

[0024] Furthermore, the calculation formula of the current phase difference offset is:

[0025]

[0026] Where Δθ is the current phase difference offset, in radians; f is the motor operating frequency, in Hertz; L d is the motor direct axis inductance, in Henry; L q is the motor quadrature axis inductance, in Henry; i q is the quadrature axis current; V dc is the DC bus voltage, in volts V; θ0 is the initial phase difference, in radians rad.

[0027] Furthermore, the calculation formula of the tool phase distortion index is:

[0028]

[0029] Where PDI is the tool phase distortion index, in radians; N is the number of sampling points; Δθ0 is the reference phase difference; k is the depth attenuation coefficient, in the unit of 1 / m, with a value range of 0.02-0.1, which compensates for the signal attenuation caused by the increase in machining depth; e -kH is the depth attenuation factor, k = 0.02-0.1 / mm, which compensates for the signal attenuation caused by the increase in processing depth; H is the processing depth, unit is mm; Δθ is the current phase difference offset.

[0030] Furthermore, the generation expression of the comprehensive health index is:

[0031]

[0032] Where HI is the comprehensive health index, dimensionless; α is the weight coefficient of progressive wear; β is the weight coefficient of sudden failure; ΔZ is the impedance mutation; ΔZ max The maximum impedance change allowed.

[0033] Furthermore, the step of executing a hierarchical dynamic control strategy based on the comprehensive health index value and the processing depth parameter includes:

[0034] Primary adjustment: when the comprehensive health index is less than or equal to 1, a dynamic compensation feed speed strategy is executed to dynamically adjust the feed speed;

[0035] Secondary adjustment: when the comprehensive health index is greater than 1 and less than or equal to 1.2, a spindle speed compensation strategy adaptive to machining depth is executed to dynamically adjust the spindle speed;

[0036] Emergency chip removal program: When the comprehensive health index is greater than 1.2, the emergency chip removal program is triggered.

[0037] Furthermore, the dynamic compensation feed speed is a real-time calculation of the compensation amount based on the comprehensive health index, and the calculation formula is:

[0038]

[0039] Where Δv f is the feed speed adjustment; K v is the speed compensation coefficient, unit is mm / s; HI is the comprehensive health index; H is the processing depth, unit is mm; H max is the maximum processing depth, in mm;

[0040] And / or, the adjustment calculation formula of the spindle speed compensation strategy for adaptive machining depth is:

[0041]

[0042] Among them, Δn is the spindle speed adjustment amount; γ is the time constant in seconds, unit is seconds; K n is the speed compensation base, unit is rpm; K v is the speed compensation coefficient, unit is mm / s; H is the processing depth, unit is mm; H max is the maximum processing depth, in mm; t is the time information;

[0043] And / or, the triggering of the emergency chip removal program includes: stopping feeding and controlling the spindle to reverse; increasing the coolant flow; applying high-frequency vibration to assist chip removal; and re-detecting the comprehensive health index to decide whether to resume processing or shut down.

[0044] In a second aspect, a system for dynamically controlling a machine tool drilling tool is provided. The system is used to implement a method for dynamically controlling a machine tool drilling tool as described in the first aspect, comprising:

[0045] A data acquisition module is used to collect drilling processing data in real time, wherein the drilling processing data includes spindle motor current, motor voltage, processing depth, spindle speed, motor operating frequency, time information, and motor internal resistance;

[0046] an impedance model module, configured to construct a cutting impedance dynamic model, input the spindle motor current, the motor voltage, the spindle speed, the motor internal resistance, the motor operating frequency, and the time information into the cutting impedance dynamic model to obtain the cutting impedance, and determine the impedance mutation amount based on the cutting impedance;

[0047] A phase index module is used to determine a current phase difference offset and a reference phase difference, and calculate a tool phase distortion index based on the current phase difference offset and the reference phase difference;

[0048] A health index module, configured to fuse the tool phase distortion index and the impedance mutation amount to generate a comprehensive health index;

[0049] A hierarchical control module is used to execute a hierarchical dynamic control strategy according to the comprehensive health index value and the processing depth.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention provides a dynamic control method for machine tool drilling tools. By multiplexing the machine tool's electrical signals (quadrature-axis current, DC bus voltage, etc.) and integrating the cutting impedance mutation and phase distortion index to generate a comprehensive health index (HI), this method achieves coordinated detection of tool wear (progressive failure) and tool breakage / chipping (sudden failure). The comprehensive health index provides a precise quantitative assessment of the tool's health status, enabling tool health monitoring without additional sensors. Furthermore, through hierarchical control, compared to traditional methods, the method boasts higher detection accuracy, lower false alarm rate, and faster response speed, while also reducing hardware costs and extending tool life.

[0052] 2. The present invention constructs a dynamic model of cutting impedance based on the electrical signal of the motor body, which can achieve real-time inversion of cutting load without external sensors, thus reducing hardware costs;

[0053] 3. The present invention uses the ReLU function to quickly respond to sudden faults and the sigmoid function to smooth slowly varying signals to avoid false triggering;

[0054] 4. The present invention adopts a depth compensation factor to compensate for signal attenuation during deep hole processing, thereby improving sensitivity during deep hole processing and avoiding missed detection due to signal attenuation;

[0055] 5. The present invention achieves a progressive response from speed compensation to emergency chip removal through the coupling decision of HI comprehensive health index and processing depth, thereby extending tool life and reducing chip breaking failure rate;

[0056] 6. The present invention supports adaptive calibration of spindle speed and material type by adopting a phase matrix model, which shortens the system reconfiguration time, improves the efficiency compared with traditional manual parameter setting, and has universal applicability in multiple working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0058] Figure 1 This is a flow chart of Example 1 of the present invention;

[0059] Figure 2 This is a system block diagram in Example 2 of the present invention. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0061] Example 1: A method for dynamically controlling a drilling tool of a machine tool, such as Figure 1 As shown, the following steps are included:

[0062] S1: Real-time acquisition of drilling processing data, including spindle motor current, motor voltage, processing depth, spindle speed, motor operating frequency, time information, and motor internal resistance;

[0063] S2: Construct a cutting impedance dynamic model, input the spindle motor current, motor voltage, spindle speed, motor internal resistance, motor operating frequency and time information into the cutting impedance dynamic model to obtain the cutting impedance, and determine the impedance mutation amount based on the cutting impedance;

[0064] S3: determining the current phase difference offset and the reference phase difference, and calculating the tool phase distortion index according to the current phase difference offset and the reference phase difference;

[0065] S4: Fusion of tool phase distortion index and impedance mutation to generate a comprehensive health index;

[0066] S5: Execute hierarchical dynamic control strategy according to the comprehensive health index value and processing depth.

[0067] In step S1, the spindle motor current and motor voltage are directly obtained through the built-in current / voltage Hall element of the servo driver. The motor internal resistance is obtained from the motor factory parameters or the ratio of voltage to current when no-load; the time information comes from the system timer; the spindle speed comes from the spindle speed signal, which is fed back through the encoder; the processing depth comes from the feed axis (Z axis) servo driver of the modern CNC machine tool. The built-in position encoder directly reads the encoder pulse number and converts it into displacement without the need for an additional sensor; the spindle speed comes from the spindle servo driver integrated photoelectric encoder or rotary transformer, which feeds back the speed in real time, and is fed back through f m The encoder pulse frequency and the number of pulses per revolution m are calculated using the formula: The motor operating frequency comes from For synchronous motors, the operating frequency is strictly proportional to the speed and is directly calculated from the speed n and the number of pole pairs p: No additional measurement is required; collect the instantaneous value of three-phase current (i A 、i B 、i C ) is transformed into the quadrature and direct axis current (i d 、i q ), take the quadrature axis current i q Used for subsequent calculations.

[0068] In step S2, the cutting impedance dynamic model is based on the coupling of motor control theory and cutting mechanics. By real-time acquisition of the voltage, current and other intrinsic signals of the machine tool spindle motor and combining the dynamic phase angle change, an impedance model that can reflect the tool-workpiece interaction state is constructed to monitor the cutting state in real time.

[0069] The calculation formula of the cutting impedance dynamic model is:

[0070]

[0071] Among them, V m is the motor voltage, in volts (V); I is the spindle motor current, in amperes (A); R0 is the motor internal resistance, in ohms (Ω); ΔZ is the impedance mutation, in ohms (Ω); Z cut is the cutting resistance, in ohms; Z avg is the standard impedance value of the current material; ω is the angular frequency, unit rad / s; is the power factor angle, in rad; ΔT is the time difference between adjacent zero crossing points; f is the motor operating frequency, in Hertz (Hz); and n is the spindle speed.

[0072] The impedance mutation amount is determined according to the cutting impedance. The calculation formula of the impedance mutation amount is:

[0073]

[0074] Where ΔZ is the impedance mutation, in ohms; Z cut(t) is the cutting resistance at time t, in ohms; Z avg is the standard impedance value of the current material; t2-t1 is the integration interval, the dynamic time window, usually 50ms to 200ms, balancing real-time performance and noise suppression.

[0075] The data required by the cutting impedance dynamic model is completely dependent on the electrical signal of the machine tool body, which reduces the hardware cost; the delay from signal acquisition to ΔZ calculation is low, meeting the real-time control requirements; through Z avg Dynamic calibration and phase angle compensation enable the model to adapt to different materials and processing parameters (speed, feed), achieving real-time, sensorless, high-precision monitoring, optimizing the processing process and reducing failures.

[0076] In step S3, the current phase difference offset and the reference phase difference are determined, and the tool phase distortion index is calculated based on the current phase difference offset and the reference phase difference, including: matching the corresponding reference phase difference from the reference phase matrix according to the spindle speed and the machining depth, wherein the reference phase matrix is ​​constructed based on the spindle speed and the machining depth;

[0077] In some embodiments, the expression of the reference phase matrix is:

[0078] Δθ0(n,H)=a0+a1n+a2H+a3nH+a4n 2 +a5H 2 ;

[0079] Among them, Δθ0(n,H) is the reference phase difference under the action of spindle speed n and drilling depth H; a0 is the zero-state phase offset, which represents the initial phase difference when the speed is 0 and the depth is 0, and the unit is rad; a1 is the speed linear coefficient, which represents the weight of the individual influence of the speed on the phase difference, and the unit is rad / rpm; a2 is the depth linear coefficient, which represents the weight of the individual influence of the machining depth on the phase difference, and the unit is rad / mm; a3 is the speed-depth interaction coefficient, which represents the weight of the coupling effect of the speed and depth, and the unit is rad / (rpm·mm); a4 is the speed quadratic coefficient, which represents the nonlinear effect of the speed on the phase difference, such as the electromagnetic saturation effect, and the unit is rad / rpm 2 ; a5 is the depth quadratic coefficient, which represents the nonlinear effect of depth on phase difference, such as thermal deformation in deep hole processing, unit rad / mm 2 ; a0 reflects the machine tool assembly error, such as the inherent phase difference caused by the motor-tool axis deviation; a1n+a4n 2 Describes the changes in electromagnetic characteristics caused by speed changes, such as the inductor saturation effect; a2H+a5H 2It characterizes the mechanical load accumulation caused by the increase in depth, such as the phase lag caused by the increase in cutting force; a3nH reflects the dynamic coupling effect between speed and depth.

[0080] In some embodiments, a0-a5 are determined by an offline calibration experiment, and the process is as follows:

[0081] First, under no-load condition, the spindle is controlled to rotate at different speeds (n1, n2, ..., n k ) idling, measuring the reference phase difference Δθ0(n,0); at a fixed speed, gradually increase the machining depth (H1, H2, ..., H m ), measure Δθ0(n,H). Combine multiple sets of (n,H) data and fit the polynomial coefficients (a0-a5) using the least squares method.

[0082] Considering that the original three-phase current phase difference offset Δθ contains electromagnetic noise, such as PWM modulation noise and mechanical vibration interference, it cannot be directly used for accurate judgment. Furthermore, Δθ varies significantly for the same tool at different speeds n and depths H, necessitating decoupling through a benchmark model. Furthermore, a single measured Δθ cannot represent progressive wear. For example, crater wear requires cumulative wear exceeding 0.2 mm to be detected. However, accumulated differentials (PDI) can quantify small offsets into a trackable indicator. Therefore, a dynamic model of the spindle's three-phase current phase difference was established to calculate Δθ for subsequent analysis.

[0083] In step S3, it also includes establishing a dynamic model of the main shaft three-phase current phase difference, and calculating the current phase difference offset by combining the DC bus voltage, motor operating frequency, initial phase difference, quadrature-axis current, motor direct-axis inductance and motor direct-axis quadrature-axis inductance.

[0084] The calculation formula of phase difference offset is:

[0085]

[0086] Where Δθ is the current phase difference offset, in radians; f is the motor operating frequency, in Hertz; L d is the motor direct axis inductance, in Henry H; L q is the motor quadrature axis inductance, in Henry H; i q is the quadrature axis current; V dc is the DC bus voltage, in volts V; θ0 is the initial phase difference, in radians rad.

[0087] In some embodiments, the calibration is performed by a no-load calibration experiment. The specific steps are as follows: the experimental setting conditions are that the spindle is idling and the machining depth H = 0, at which time the tool does not contact the workpiece, and the three-phase current phase difference offset Δθ is measured at different speeds (n = 100, 200, ..., 2000 rpm) kong,i, according to the formula Calculate θ0.

[0088] In step S3, the tool phase distortion index is calculated by combining the difference between the reference phase difference and the current phase difference offset. The calculation formula of the tool phase distortion index is:

[0089]

[0090] Where PDI is the tool phase distortion index, in radians; N is the number of sampling points; Δθ0 is the reference phase difference; k is the depth attenuation coefficient, in the unit of 1 / m, with a value range of 0.02-0.1, which compensates for the signal attenuation caused by the increase in machining depth; e -kH is the depth attenuation factor; H is the current processing depth, in mm; Δθ is the current phase difference offset.

[0091] The PDI tool phase distortion index solves the problem that traditional phase monitoring methods do not consider the impact of depth on the signal, which leads to misjudgment in deep hole processing. By introducing an exponential decay compensation mechanism, the phase sensitivity is adaptively adjusted with depth. Even at a depth of Z = 80mm, the minimum phase change that the system can detect is 0.1°, and the actual measurement error does not exceed 0.1.

[0092] In step S4, a comprehensive health index is generated based on the real-time motor phase difference offset, the baseline phase difference and the physical parameters of the impedance model (electromagnetic characteristics and cutting mechanics coupling) to distinguish between PDI-dominated progressive wear and ΔZ-dominated sudden failure, and achieve hierarchical response.

[0093] The comprehensive health index generated by integrating the tool phase distortion index and impedance mutation includes:

[0094]

[0095] Wherein, HI is the comprehensive health index, which is dimensionless; α is the progressive wear weight coefficient, which represents the contribution weight of the phase distortion index (PDI) to the comprehensive health index (HI), reflecting the long-term impact of progressive tool wear (such as edge blunting and coating peeling) on ​​the health state, and the value range is: α∈[0.4, 0.8], which is calibrated by experiments; β is the sudden failure weight coefficient, which represents the contribution weight of the impedance mutation (ΔZ) to HI, reflecting the instantaneous impact of sudden tool failure (such as edge breakage and chip blockage) on the health state, and the value range is: β∈[0.2, 0.6], and satisfies α+β=1, usually set α=0.6, β=0.4; ΔZ is the impedance mutation; ΔZ max The maximum impedance change allowed.

[0096] The weight coefficients α and β are adjusted dynamically, and the weights can be adaptively adjusted according to the processing stage. For example, rough processing: α = 0.7 (focusing on wear monitoring); fine processing: β = 0.6 (focusing on sudden fault interception).

[0097] The HI comprehensive health index (HI) achieves precise quantitative assessment of tool health through multimodal signal fusion and nonlinear function mapping. PDI reflects progressive tool wear, such as electromagnetic phase shift caused by edge blunting and coating peeling, while ΔZ characterizes sudden faults, such as transient impedance anomalies caused by chipping and chip blockage. By synergizing the multimodal signals of PDI and ΔZ, a comprehensive fault model is constructed to cover all fault modes. The sigmoid function amplifies late-stage wear risks, while the ReLU function focuses on sudden anomalies to enhance nonlinear sensitivity. Adaptive material calibration and α / β weighting enable HI to adapt to complex working conditions.

[0098] In step S5, a hierarchical dynamic control strategy is executed according to the comprehensive health index value and the processing depth parameter, including: primary adjustment: when the comprehensive health index is less than or equal to 1, the dynamic compensation feed speed strategy is executed to dynamically adjust the feed speed; secondary adjustment: when the comprehensive health index is greater than 1 and less than or equal to 1.2, the spindle speed compensation strategy for adaptive processing depth is executed to dynamically adjust the spindle speed; emergency chip removal program: when the comprehensive health index is greater than 1.2, the emergency chip removal program is triggered.

[0099] In step S5, the dynamic compensation feed speed is calculated in real time based on the comprehensive health index. The calculation formula is:

[0100]

[0101] Speed ​​compensation coefficient K v The value range of is as follows:

[0102]

[0103] Where Δv f is the feed speed adjustment; K v is the speed compensation coefficient, unit is mm / s; HI is the comprehensive health index; H is the processing depth, unit is mm; H max It is the maximum processing depth, in mm.

[0104] The adjustment calculation formula of the spindle speed compensation strategy for adaptive machining depth is:

[0105]

[0106] Among them, Δn is the spindle speed adjustment amount; γ is the time constant in seconds, unit is seconds; K n is the speed compensation base, unit is rpm; K vis the speed compensation coefficient, unit is mm / s; H is the processing depth, unit is mm; H max It is the maximum processing depth, in mm.

[0107] Triggering the emergency chip removal procedure includes: stopping feed and controlling spindle reverse rotation; increasing coolant flow; applying high-frequency vibration to assist chip removal; and re-testing the comprehensive health index to decide whether to resume processing or shut down.

[0108] Example 2, a machine tool drilling tool dynamic control system, the system is used to implement a machine tool drilling tool dynamic control method as described in Example 1, such as Figure 2 As shown, it includes a data acquisition module, an impedance model module, a phase index module, a health index module and a hierarchical control module.

[0109] Among them, the data acquisition module is used to collect drilling processing data in real time. The drilling processing data includes spindle motor current, motor voltage, processing depth, spindle speed, motor operating frequency, time information, and motor internal resistance; the impedance model module is used to construct a cutting impedance dynamic model, and the spindle motor current, motor voltage, spindle speed, motor internal resistance, motor operating frequency and time information are input into the cutting impedance dynamic model to obtain the cutting impedance, and determine the impedance mutation amount based on the cutting impedance; the phase index module is used to determine the current phase difference offset and the reference phase difference, and calculate the tool phase distortion index based on the current phase difference offset and the reference phase difference; the health index module is used to fuse the tool phase distortion index and the impedance mutation amount to generate a comprehensive health index; the hierarchical control module is used to execute a hierarchical dynamic control strategy based on the comprehensive health index value and processing depth.

[0110] Working principle: The present invention multiplexes the electrical signals of the machine tool body (cross-axis current, DC bus voltage, etc.) and integrates the cutting impedance mutation and phase distortion index to generate a comprehensive health index (HI), thereby realizing the coordinated detection of tool wear (progressive fault) and tool breakage / chipping (sudden fault); through the comprehensive health index, an accurate quantitative assessment of the tool health status is achieved, and tool health monitoring without additional sensors is realized; in addition, through hierarchical control, compared with traditional methods, the detection accuracy is high, the false alarm rate is low, the response speed is high, the hardware cost is reduced, and the tool life is extended.

[0111] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0115] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dynamically controlling a drilling tool of a machine tool, characterized in that: include: Real-time acquisition of drilling processing data, including spindle motor current, motor voltage, processing depth, spindle speed, motor operating frequency, time information, and motor internal resistance; Constructing a cutting impedance dynamic model, inputting the spindle motor current, the motor voltage, the spindle speed, the motor internal resistance, the motor operating frequency, and the time information into the cutting impedance dynamic model to obtain cutting impedance, and determining an impedance mutation amount based on the cutting impedance; Determining a current phase difference offset and a reference phase difference, and calculating a tool phase distortion index based on the current phase difference offset and the reference phase difference; Fusion of the tool phase distortion index and the impedance mutation amount to generate a comprehensive health index; A hierarchical dynamic control strategy is executed according to the comprehensive health index value and the processing depth.

2. The method for dynamic control of a drilling tool of a machine tool according to claim 1, characterized in that: The calculation formula of the cutting impedance dynamic model is: Among them, V m is the motor voltage, in volts (V); I is the spindle motor current, in amperes (A); R0 is the motor internal resistance, in ohms (Ω); ΔZ is the impedance mutation, in ohms (Ω); Z cut is the cutting resistance, in ohms; Z avg is the standard impedance value of the current material; ω is the angular frequency, unit rad / s; is the power factor angle, in rad; ΔT is the time difference between adjacent zero crossing points; f is the motor operating frequency, in Hertz (Hz); n is the spindle speed; and t is the time information.

3. The method for dynamic control of a drilling tool of a machine tool according to claim 1, characterized in that: The calculation formula of the impedance mutation is: Where ΔZ is the impedance mutation, in ohms; Z cut(t) is the cutting resistance at time t, in ohms; Z avg is the standard impedance value of the current material; t2-t1 is the integration interval and dynamic time window.

4. The method for dynamically controlling a drilling tool of a machine tool according to claim 1, wherein: The determining of the current phase difference offset and the reference phase difference, and calculating the tool phase distortion index according to the current phase difference offset and the reference phase difference, includes: According to the spindle speed and the machining depth, a corresponding reference phase difference is obtained by matching from a reference phase matrix, wherein the reference phase matrix is ​​constructed based on the spindle speed and the machining depth; Establishing a dynamic model of the main shaft three-phase current phase difference, and calculating the current phase difference offset by combining the DC bus voltage, the motor operating frequency, the initial phase difference, the quadrature-axis current, the motor direct-axis inductance, and the motor direct-axis quadrature-axis inductance; The tool phase distortion index is calculated based on the difference between the reference phase difference and the current phase difference offset.

5. The method for dynamically controlling a drilling tool of a machine tool according to claim 4, wherein: The calculation formula of the current phase difference offset is: Where Δθ is the current phase difference offset, in radians; f is the motor operating frequency, in Hertz; L d is the motor direct axis inductance, in Henry; L q is the motor quadrature axis inductance, in Henry; i q is the quadrature axis current; V dc is the DC bus voltage, in volts V; θ0 is the initial phase difference, in radians rad.

6. A method for dynamically controlling a drilling tool of a machine tool according to claim 4, characterized in that: The calculation formula of the tool phase distortion index is: Where PDI is the tool phase distortion index, in radians; N is the number of sampling points; Δθ0 is the reference phase difference; k is the depth attenuation coefficient, in units of 1 / m; e -kH is the depth attenuation factor; H is the processing depth, unit is mm; Δθ is the current phase difference offset.

7. The method for dynamically controlling a drilling tool of a machine tool according to claim 1, wherein: The generation expression of the comprehensive health index is: Where HI is the comprehensive health index, dimensionless; α is the weight coefficient of progressive wear; β is the weight coefficient of sudden failure; ΔZ is the impedance mutation; ΔZ max The maximum impedance change allowed.

8. The method for dynamically controlling a drilling tool of a machine tool according to claim 1, wherein: The step of executing a hierarchical dynamic control strategy according to the comprehensive health index value and the processing depth parameter includes: Primary adjustment: when the comprehensive health index is less than or equal to 1, a dynamic compensation feed speed strategy is executed to dynamically adjust the feed speed; Secondary adjustment: when the comprehensive health index is greater than 1 and less than or equal to 1.2, a spindle speed compensation strategy adaptive to machining depth is executed to dynamically adjust the spindle speed; Emergency chip removal program: When the comprehensive health index is greater than 1.2, the emergency chip removal program is triggered.

9. The method for dynamically controlling a drilling tool of a machine tool according to claim 8, wherein: The dynamic compensation feed speed is a real-time calculation of the compensation amount based on the comprehensive health index, and the calculation formula is: Where Δv f is the feed speed adjustment; K v is the speed compensation coefficient, unit is mm / s; HI is the comprehensive health index; H is the processing depth, unit is mm; H max is the maximum processing depth, in mm; And / or, the adjustment calculation formula of the spindle speed compensation strategy for adaptive machining depth is: Among them, Δn is the spindle speed adjustment amount; γ is the time constant in seconds, unit is seconds; K n is the speed compensation base, unit is rpm; K v is the speed compensation coefficient, unit is mm / s; H is the processing depth, unit is mm; H max is the maximum processing depth, in mm; t is the time information; And / or, the triggering of the emergency chip removal program includes: stopping feeding and controlling the spindle to reverse; increasing the coolant flow; applying high-frequency vibration to assist chip removal; and re-detecting the comprehensive health index to decide whether to resume processing or shut down.

10. The dynamic control system for drilling tools of a machine tool according to claim 1, characterized in that: The system is used to implement a method for dynamically controlling a machine tool drilling tool as described in any one of claims 1 to 9, comprising: A data acquisition module is used to collect drilling processing data in real time, wherein the drilling processing data includes spindle motor current, motor voltage, processing depth, spindle speed, motor operating frequency, time information, and motor internal resistance; an impedance model module, configured to construct a cutting impedance dynamic model, input the spindle motor current, the motor voltage, the spindle speed, the motor internal resistance, the motor operating frequency, and the time information into the cutting impedance dynamic model to obtain the cutting impedance, and determine the impedance mutation amount based on the cutting impedance; A phase index module is used to determine a current phase difference offset and a reference phase difference, and calculate a tool phase distortion index based on the current phase difference offset and the reference phase difference; A health index module, configured to fuse the tool phase distortion index and the impedance mutation amount to generate a comprehensive health index; A hierarchical control module is used to execute a hierarchical dynamic control strategy according to the comprehensive health index value and the processing depth.

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