CNC machining adaptive path planning method

Abstract

The invention discloses a CNC (computer numerical control) machining adaptive path planning method in the field of numerical control machining control, which comprises the following steps: S10, collecting machining state data in real time through a multi-sensor system, including S11, synchronously measuring three-way cutting force components, namely a main cutting force Fx, a radial force Fy and an axial force Fz, by a piezoelectric force sensor of a main shaft bearing seat; s12, the MEMS vibration sensor integrated with the knife handle collects three-axis vibration signals ax (t), ay (t) and az (t); and S13, an infrared temperature measurement module at the cutting edge of the tool monitors temperature field distribution T (x, y, t). According to the method, a force-vibration-temperature full coupling model is established, multi-dimensional control is achieved through matrix operation and space path compensation, three-way force sensors (Fx, Fy and Fz) are definitely adopted for synchronous monitoring, and the cutting mechanical state is comprehensively captured; the one-sidedness of force field sensing can be effectively solved by adopting multidirectional force cooperative control; coupling modeling and dynamic compensation can solve multi-physics field decoupling; digital twinning real-time verification can solve the problem of compensation hysteresis.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of numerical control machining control, and particularly relates to a CNC machining adaptive path planning method. BACKGROUND

[0002] There are three technical bottlenecks in current CNC machining adaptive control:

[0003] 1. One-sidedness of force field perception:

[0004] Commercial systems (such as Siemens Adaptive Control) only monitor the main cutting force Fx, resulting in:

[0005] Radial chatter cannot be suppressed (Fy is not involved in control)

[0006] Axial tool relief when deep hole machining (Fz is not compensated)

[0007] Test data: In the machining of a certain aircraft structure, the side wall vibration marks (Ra deteriorated from 0.8 μm to 3.2 μm) were caused by ignoring Fy

[0008] 2. Decoupling problem of multiple physical fields:

[0009] Traditional methods process force, vibration and temperature signals independently, ignoring their coupling relationship. For example:

[0010] Cutting heat affects material stiffness (temperature -> force)

[0011] Vibration causes temperature measurement error (vibration -> temperature)

[0012] Academic reference: A study in the International Journal of Machine Tools and Manufacture in 2022 pointed out that multi-field coupling error accounts for 34-61% of the total error

[0013] 3. Compensation hysteresis:

[0014] The mainstream solution (such as patent CN114563008A) adopts the "collection-offline analysis-compensation" mode, with a delay of up to 200-500 ms

[0015] Comparison test: In the machining of a titanium alloy blade, the traditional method caused an overcut of 0.05 mm due to the delay.

[0016] Therefore, the person skilled in the art provides a CNC machining adaptive path planning method to solve the problems raised in the background art. SUMMARY

[0017] The purpose of the present application is to provide a CNC machining adaptive path planning method to solve the problems raised in the background art.

[0018] To achieve the above object, the present application provides the following technical solutions:

[0019] A CNC machining adaptive path planning method, comprising the following steps:

[0020] Step S10, real-time acquisition of machining state data by a multi-sensor system, including:

[0021] S11, piezoelectric force sensor of the spindle bearing seat, synchronous measurement of three-direction cutting force components main cutting force Fx, radial force Fy and axial force Fz;

[0022] S12, MEMS vibration sensor integrated with the tool holder, acquisition of three-axis vibration signals a x (t), a y (t), a z (t);

[0023] S13, infrared temperature measurement module at the cutting edge of the tool, monitoring of temperature field distribution T(x,y,t);

[0024] A vibration-force decoupling model is established, and the interference of vibration on force signals is eliminated by Gram-Schmidt orthogonalization: the algorithm for vibration-force decoupling is:

[0025] \tilde{F}_y=F_y-\frac{\langle F_y,a_y\rangle}{\langle a_y,a_y\rangle}a_y

[0026] Step S20, fusion processing of multi-source data: including

[0027] S21, processing of vibration signals by wavelet packet decomposition algorithm, extraction of vibration energy features in each direction:

[0028]

[0029] S22, establishment of a three-direction force coupling model:

[0030]

[0031] Wherein K is the stiffness matrix, T represents vector transpose C is the damping matrix, D is the tool displacement vector, is the velocity vector; the three-direction force coupling control algorithm is:

[0032] \begin{cases}

[0033] AV = Kp (Fx - Fref) + KpyFy + KpzFz

[0034] Ay = g (Fy, Ey, band), Az = h (Fz, Ez, band)

[0035]

[0036] Step S30, generating adjustment instructions based on multi-physical field coupling analysis: including

[0037] S31, main cutting parameter adjustment:

[0038]

[0039] S32, tool posture compensation:

[0040] Ay = g (Fy, Ey, band), Az = h (Fz, Ez, band)

[0041] S33, thermal deformation compensation:

[0042] ST = ∫∫α (T (x, y, t) - T0) dxdy

[0043] S40, execute multi-dimensional path correction: including generating compensation path based on original path P (u, v):

[0044] P new = P + AV · t + Ay y · n + Az z · b + ST T · k

[0045] Where t, n, b are tangent, normal and binormal vectors respectively;

[0046] S50, execute machining after verifying the feasibility of the corrected path through the digital twin system.

[0047] As a further scheme of the application: in step S10, the force sensor sampling frequency is ≥10kHz, and the vibration sensor is ≥50kHz; the temperature field monitoring resolution is ≤0.1mm / pixel. 2

[0048] As a further scheme of the application: in step S20, the wavelet packet decomposition layer number j = 6, and the frequency band division matches the machine tool natural frequency; the stiffness matrix K is calibrated online through hammering test.

[0049] ​​As a further scheme of the present application: in the step S30, the function f is implemented by a fuzzy PID controller, and the rule base comprises: "IF Fx is High AND dFx / dt is Rising THEN ΔV is Negative Big"; the functions g and h are implemented by a neural network model, and the input comprises historical machining data.

[0050] As a further scheme of the present application: in the step S40, when Fy / Fx>0.3, a vibration prevention path is triggered for re-planning; when Fz continuously exceeds a threshold value, an axial tool withdrawal instruction is generated.

[0051] As a further scheme of the present application: further comprising: based on Fy-Fz plane force ellipse analysis, a tool wear state is analyzed; when an ellipse eccentricity>0.7, a tool change early warning is triggered.

[0052] As a further scheme of the present application: for thin-walled part machining, a feedback control loop of Fy and workpiece deformation is established: δ w =β·∫F y dt; δw is compensated to a machining coordinate system in real time.

[0053] As a further scheme of the present application: in five-axis machining, the Fz component is converted into a tool inclination angle adjustment amount:

[0054] Δγ=arctan(F z / ‖F xy ‖).

[0055] As a further scheme of the present application: a temperature-force coupling model is established:

[0056] F x,comp =F x ·[1+λ(T-T0)]; a force parameter compensation for high-temperature alloy machining.

[0057] As a further scheme of the present application: in the step S50, the digital twin system comprises:

[0058] a) a cutting process physical simulation module;

[0059] b) a machine tool dynamic characteristic database;

[0060] c) an AR-based machining state visualization interface.

[0061] Compared with the prior art, the present application has the beneficial effects that:

[0062] 1. The present application establishes a "force-vibration-temperature" full coupling model, realizes multi-dimensional control through matrix operation and space path compensation, clearly adopts a three-way force sensor (Fx, Fy, Fz) for synchronous monitoring, and fully captures the cutting mechanical state.

[0063] 2、The application can effectively solve the one-sidedness of force field perception by adopting multi-direction force cooperative control; can solve multi-physical field decoupling by coupling modeling and dynamic compensation; and can solve compensation hysteresis by digital twin real-time verification. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0065] In the embodiments of the application, a CNC machining adaptive path planning method comprises the following steps:

[0066] Step S10, real-time acquisition of machining state data by a multi-sensor system, comprising:

[0067] S11, a piezoelectric force sensor of a spindle bearing seat, synchronously measuring three-direction cutting force components, i.e., main cutting force Fx, radial force Fy and axial force Fz;

[0068] S12, a MEMS vibration sensor integrated in a tool holder, collecting three-axis vibration signals a x (t), a y (t), a z (t);

[0069] S13, an infrared temperature measurement module at a cutting edge of a tool, monitoring temperature field distribution T(x, y, t);

[0070] A vibration-force decoupling model is established, and the interference of vibration on force signals is eliminated by Gram-Schmidt orthogonalization: the algorithm for vibration-force decoupling is:

[0071] \tilde{F}_y=F_y-\frac{\langle F_y,a_y\rangle}{\langle a_y,a_y\rangle}a_y

[0072] Step S20, fusion processing of multi-source data: comprising

[0073] S21, processing of vibration signals by a wavelet packet decomposition algorithm, extraction of vibration energy features in each direction:

[0074]

[0075] S22, establishment of a three-direction force coupling model:

[0076]

[0077] Where K is the stiffness matrix, T represents the vector transpose, C is the damping matrix, and D is the tool displacement vector. is the velocity vector;

[0078] The three-way force coupling control algorithm is:

[0079] \begin{cases}

[0080] \Delta V=K_p(F_x-F_{ref})+K_{py}F_y+K_{pz}F_z\\

[0081] \Delta\theta_y=\int_0^t\frac{F_y(t)}{\tau_m}dt\quad (\tau_m is the mechanical constant of the machine tool)

[0082] \end{cases}.

[0083] Step S30: Generate adjustment instructions based on multi-physics field coupling analysis: including

[0084] S31, main cutting parameter adjustment:

[0085]

[0086] S32, tool posture compensation:

[0087] Δθy=g(F, Ey, band), Δθz=h(Fz, Ez, band)

[0088] S33, thermal deformation compensation:

[0089] δT=∫∫α(T(x,y,t)-T0)dxdy

[0090] S40, performing multi-dimensional path correction: including generating a compensation path based on the original path P(u,v):

[0091] P new =P+ΔV·t+Δθ y ·n+Δθ z b+δ T ·k

[0092] Where t, n, and b are the tangential, normal, and binormal vectors, respectively;

[0093] S50, executing processing after verifying the feasibility of the corrected path through the digital twin system.

[0094] By adopting the technical scheme, a "force-vibration-temperature" full coupling model is established, multi-dimensional control is realized through matrix operation and space path compensation, three-way force sensors (Fx, Fy, Fz) are synchronously monitored, and the cutting mechanical state is comprehensively captured.

[0095] In the step S10, the force sensor sampling frequency is greater than or equal to 10 kHz, and the vibration sensor is greater than or equal to 50 kHz; and the temperature field monitoring resolution is less than or equal to 0.1 mm. 2 / pixel.

[0096] In the step S20, the wavelet packet decomposition layer number j is 6, and the frequency band division is matched with the natural frequency of the machine tool; and the stiffness matrix K is calibrated online through a hammering test.

[0097] In the step S30, the function f is realized by using a fuzzy PID controller, and the rule base includes: "IF FxisHigh AND dFx / dt is Rising THEN ΔV is Negative Big"; the functions g and h are realized by using a neural network model, and the input includes historical machining data.

[0098] In the step S40, when Fy / Fx is greater than 0.3, the anti-vibration path is re-planned; when Fz continuously exceeds a threshold value, an axial tool retracting instruction is generated; and when Fy / Fx is greater than 0.3, the anti-vibration path is re-planned to suppress radial chatter.

[0099] In addition, the tool wear state is analyzed based on the Fy-Fz plane force ellipse; and when the ellipse eccentricity is greater than 0.7, a tool change warning is triggered.

[0100] For thin-walled part machining, a feedback control loop of Fy and workpiece deformation is established: δ w = β·∫F y dt; δw is compensated to the machining coordinate system in real time, and the feedback control loop of Fy and workpiece deformation is: δ w = β·∫F y dt, and the thin-walled part machining deformation problem is solved.

[0101] In five-axis machining, the Fz component is converted into a tool inclination angle adjustment amount:

[0102] Δγ = arctan(F z / ‖F xy ‖), which can effectively avoid axial tool retracting.

[0103] A temperature-force coupling model is established:

[0104] F x,comp = F x ·[1+λ(T-T0)]; and the force parameter compensation is used for high-temperature alloy machining.

[0105] wherein, in step S50, the digital twin system comprises:

[0106] a) a cutting process physical simulation module;

[0107] b) a machine tool dynamic characteristic database;

[0108] c) an AR-based machining state visualization interface.

[0109] Example 1: Aero-engine turbine disc machining

[0110] Working condition characteristics:

[0111] Material: nickel-based superalloy GH4169

[0112] Machining feature: deep groove milling (depth-diameter ratio 5:1)

[0113] Challenge: tool let go caused by Fz

[0114] Implementation steps:

[0115] Step 1, force sensor configuration:

[0116] Install Kistler 9129AA three-way force sensor (range ±10kN) at the spindle end

[0117] Sampling frequency: Fx-20kHz, Fy / Fz-15kHz

[0118] Step 2, Fz compensation strategy:

[0119] \Delta Z=\frac{F_z}{k_{tool}}\quad(k_{tool}=2800N / mm)

[0120] Effect comparison:

[0121] Index Conventional method The present invention Groove bottom flatness 0.08mm 0.02mm Tool chipping frequency 3 times / piece 0 times

[0122] As can be seen from the table, compared with the prior art method, the planeness of the machining is higher, the tool chipping frequency is less, and the machining is more stable.

[0123] Example 2: Mobile phone metal frame machining

[0124] Working condition characteristics:

[0125] Material: 6 series aluminum alloy

[0126] Machining feature: thin wall (0.5mm thickness)

[0127] Challenge: workpiece deformation caused by Fy

[0128] Implementation steps:

[0129] Step 1, thin-walled deformation compensation model:

[0130] \delta_w=\frac{L^3}{3EI}F_y\quad(L=15mm,E=69GPa)

[0131] Step 2, real-time compensation:

[0132] Generate reverse offset path in CAM software;

[0133] Compensation period: update every 50ms.

[0134] Effect comparison:

[0135] Index Conventional method The present invention Wall thickness uniformity ±0.12mm ±0.03mm Surface scratch 8 places / piece 0 places

[0136] As can be seen from the table, the uniformity of the wall thickness of the mobile phone metal frame processed by the application is better, and the penguin surface scratch control is better.

[0137] Example 3: processing of medical titanium alloy bone plate

[0138] Working condition characteristics:

[0139] Material: Ti-6Al-4V ELI

[0140] Processing features: micro-hole array (φ0.3mm)

[0141] Challenge: temperature-induced size drift

[0142] Implementation steps:

[0143] Step 1, temperature-force coupling compensation:

[0144] F_{x,corr}=F_x[1+0.003(T-25)]

[0145] Step 2, dynamic adjustment:

[0146] Activate cooling spray when T>120℃

[0147] Effect comparison:

[0148] Index Conventional method The present invention Hole diameter tolerance ±15μm ±5μm Burr height 20μm <5 μm

[0149] As can be seen from the table, the medical titanium alloy bone plate processed by the application has higher hole diameter accuracy and smaller burrs.

[0150] Digital twin system implementation

[0151] 1. Hardware architecture:

[0152] Real-time simulator: NIPXIe-8880 (Xeon 8-core + FPGA)

[0153] Data transmission: EtherCAT (cycle 1 ms)

[0154] 2. Software modules:

[0155] Physical simulation:

[0156] Cutting force prediction based on AdvantEdge

[0157] ANSYS structural deformation solver

[0158] Virtual-real synchronization:

[0159] While machining:

[0160] real_data = get_sensor_data() # Real-time acquisition

[0161] twin_pred = simulate(real_data) # Digital twin prediction

[0162] if error: = compare(real_data, twin_pred) > threshold:

[0163] replan_path()

[0164] Multi-material machining verification data:

[0165] Material Optimal Fy / Fx ratio Allowable Fz threshold value (N) Temperature rise sensitivity coefficient (μm / ℃) Aluminum alloy 6061 0.25 150 0.8 Titanium alloy TC4 0.18 80 1.2 Die steel H13 0.30 200 0.5

[0166] Five-axis machining verification:

[0167] In impeller blade machining:

[0168] Without compensation: tip contour error 0.07mm;

[0169] After applying Δγ compensation: error reduced to 0.02mm.

[0170] Multi-directional force collaborative control can effectively solve the one-sidedness of force field perception; coupled modeling and dynamic compensation can solve the decoupling of multi-physical fields; digital twin real-time verification can solve the lag of compensation. In the fields of aerospace, consumer electronics, medical devices, etc. It has been proven, and completely covers the defects pointed out by the background technology.

[0171] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A CNC machining adaptive path planning method, characterized by: The method includes the following steps: Step S10, collecting processing status data in real time through a multi-sensor system, including: S11, the piezoelectric force sensor of the spindle bearing seat, synchronously measures the three-dimensional cutting force components: main cutting force Fx, radial force Fy, and axial force Fz; S12, MEMS vibration sensor integrated in the tool handle, collects three-axis vibration signals a x (t), a y (t), a z (t); S13, infrared temperature measurement module at the cutting edge of the tool to monitor the temperature field distribution T(x, y, t); Step S20: Fusion processing of multi-source data: including S21. Use wavelet packet decomposition algorithm to process vibration signals and extract all-directional vibration energy characteristics: S22. Establish a three-axis force coupling model: Where K is the stiffness matrix, T represents the vector transpose, C is the damping matrix, and D is the tool displacement vector. is the velocity vector; Step S30: Generate adjustment instructions based on multi-physics field coupling analysis: including S31. Adjustment of main cutting parameters: S32, tool posture compensation: Δθy=g(Fy, Ey, band), Δθz=h(Fz, Ez, band) S33, thermal deformation compensation: δT=∫∫α(T(x,y,t)-T0)dxdy S40, performing multi-dimensional path correction: including generating a compensation path based on the original path P(u,v): P new =P+ΔV·t+Δθ y ·n+Δθ z ·b+d T ·k Where t, n, and b are the tangential, normal, and binormal vectors, respectively; S50, executing processing after verifying the feasibility of the corrected path through the digital twin system.

2. A CNC machining adaptive path planning method according to claim 1, characterized in that: In step S10: the sampling frequency of the force sensor is ≥10kHz, the sampling frequency of the vibration sensor is ≥50kHz; the temperature field monitoring resolution is ≤0.1mm 2 / pixel.

3. The CNC machining adaptive path planning method according to claim 1, characterized in that: In the step S20, the wavelet packet decomposition layer number j=6, and the frequency band division matches the natural frequency of the machine tool; the stiffness matrix K is calibrated online through a hammer test.

4. The CNC machining adaptive path planning method according to claim 1, wherein: In step S30 , function f is implemented using a fuzzy PID controller, whose rule base includes: “IF Fx is High AND dFx / dt is Rising THEN ΔVis Negative Big”; functions g and h use a neural network model, whose input includes historical processing data.

5. The CNC machining adaptive path planning method according to claim 1, characterized in that: In the step S40 , when Fy / Fx>0.3, anti-vibration path re-planning is triggered; when Fz continuously exceeds the threshold, an axial tool retraction instruction is generated.

6. The CNC machining adaptive path planning method according to claim 1, characterized in that: Also includes: The tool wear status is analyzed based on the Fy-Fz plane force ellipse; when the ellipse eccentricity is greater than 0.7, a tool change warning is triggered.

7. The CNC machining adaptive path planning method according to claim 1, characterized in that: For thin-walled parts processing, a feedback control loop between Fy and workpiece deformation is established: w =β·∫F y dt; compensate δw to the machining coordinate system in real time.

8. The CNC machining adaptive path planning method according to claim 1, characterized in that: In five-axis machining, the Fz component is converted into tool inclination adjustment: Δγ=arctan(F z / ‖F xy ‖).

9. The CNC machining adaptive path planning method according to claim 1, characterized in that: Establish a temperature-force coupling model: F x,comp =F x [1+λ(T-T0)]: Force parameter compensation for high-temperature alloy processing.

10. The CNC machining adaptive path planning method according to claim 1, characterized in that: In step S50, the digital twin system includes: a) Cutting process physical simulation module; b) Machine tool dynamic characteristics database; c) AR-based processing status visualization interface.

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