CN119200503B - A temperature compensation control system for laser engraving machine based on multi-source temperature data fusion - Google Patents
A temperature compensation control system for laser engraving machine based on multi-source temperature data fusion Download PDFInfo
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Abstract
The invention discloses a temperature compensation control system based on multi-source temperature data fusion of a laser engraving machine, which comprises a temperature acquisition module, an algorithm module, a thermal deformation prediction algorithm, a compensation execution module and a compensation module, wherein the temperature acquisition module comprises a plurality of temperature sensors arranged at key positions of the laser engraving machine and used for acquiring overall temperature field distribution data, the algorithm module comprises an improved Kalman filtering algorithm and used for eliminating temperature data noise, the multi-point weighted fusion algorithm is used for carrying out space distribution fusion calculation on the multi-point temperature data, the thermal deformation prediction algorithm is used for integrating heat conduction, natural convection and radiation heat exchange models and predicting overall system temperature change, the self-adaptive compensation algorithm is used for dynamically adjusting PID parameters according to real-time error feedback, and the compensation execution module is used for carrying out real-time position compensation on the laser engraving machine according to compensation quantity output by the algorithm module.
Description
Technical Field
The invention relates to a temperature compensation system of a laser engraving machine, in particular to a temperature compensation control system of a laser engraving machine based on multi-source temperature data fusion.
Background
The laser engraving machine is a high-precision numerical control processing device and is widely applied to the fields of industrial manufacture, artistic creation and the like. With the continuous improvement of the processing precision requirement, the thermal deformation compensation control has become a key technology for ensuring the processing quality. The temperature compensation control system of the laser engraving machine in the current market is generally composed of a temperature sensor, a data acquisition, a compensation controller, an executing mechanism and the like and is used for monitoring and compensating errors caused by thermal deformation in real time.
The existing temperature compensation system of the laser engraving machine mainly adopts a single-point temperature measurement mode, and obtains local temperature data by installing a temperature sensor at a key part of a machine tool. And calculating the thermal deformation amount and performing position compensation by using a pre-established linear compensation model according to the acquired temperature information. The compensation mode can reduce machining errors caused by thermal deformation to a certain extent and improve machining precision.
However, in the actual machining process of the laser engraving machine, thermal deformation of the mechanical structure is caused due to the thermal effect of laser energy and environmental temperature change, and the machining precision is affected. The laser beam generates a lot of heat during operation, and the heat propagates in the machine tool structure through heat conduction, heat convection and the like, so that complicated temperature field distribution is caused. The existing single-point temperature measurement method cannot comprehensively reflect the temperature distribution state of the whole system, the acquired temperature information is limited, and the spatial distribution characteristics of thermal deformation are difficult to accurately describe.
In addition, conventional linear compensation algorithms are too simplistic to take into account the nonlinear characteristics of the dynamic change characteristics and thermal deformations of the temperature field. In the actual machining process, a complex thermal coupling relation exists among all parts of the machine tool, and thermal deformation has obvious hysteresis and nonlinear characteristics. It is difficult for a simple linear compensation model to accurately describe such complex thermo-mechanical coupling effects, resulting in non-ideal compensation effects.
Meanwhile, the existing compensation algorithm lacks self-adaptive capability, and is difficult to cope with the thermal deformation compensation requirement under the complex working condition. The characteristics of thermal deformation change correspondingly under different processing conditions, such as changes in ambient temperature and the like. The traditional fixed parameter compensation algorithm can not automatically adjust the compensation strategy according to the working condition change, and the stability of the compensation effect is difficult to ensure.
Therefore, the development of the intelligent temperature compensation control system based on multi-source temperature data fusion has important practical significance. The system not only needs to realize the comprehensive monitoring of the temperature field and establish an accurate thermal deformation prediction model, but also has the self-adaptive compensation capability so as to meet the strict requirements of modern precision machining on the machining precision.
Disclosure of Invention
The invention aims to provide a temperature compensation control system based on multi-source temperature data fusion laser engraving machine. The intelligent temperature compensation control system of the laser engraving machine based on multi-source temperature data fusion has comprehensive temperature field detection and prediction functions and self-adaptive compensation capability.
The technical aim of the invention is realized by the following technical scheme:
A temperature compensation control system based on multi-source temperature data fusion of a laser engraving machine comprises a temperature acquisition module, an algorithm module, a thermal deformation prediction algorithm, an adaptive compensation algorithm and a compensation execution module, wherein the temperature acquisition module comprises a plurality of temperature sensors arranged at key positions of the laser engraving machine and used for acquiring overall temperature field distribution data, the algorithm module comprises an improved Kalman filtering algorithm and used for eliminating temperature data noise, the multi-point weighted fusion algorithm is used for carrying out space distribution fusion calculation on the multi-point temperature data, the thermal deformation prediction algorithm is used for integrating heat conduction, natural convection and radiation heat exchange models and predicting overall system temperature change, the adaptive compensation algorithm is used for dynamically adjusting PID parameters according to real-time error feedback, and the compensation execution module is used for carrying out real-time position compensation on the laser engraving machine according to compensation quantity output by the algorithm module.
The invention is further provided with:
The filtering equation step of the improved Kalman filtering algorithm comprises the following steps:
(1) State prediction is performed, X (k|k-1) =ax (k-1|k-1) +bu (k);
(2) Calculating a prediction error covariance P (k|k-1) =ap (k-1|k-1) a T +q;
(3) Calculating a Kalman gain Kg (k) =P (k|k-1) H T[HP(k|k-1)HT+R]-1;
(4) Update status X (k|k) =x (k|k-1) +kg (k) [ Z (k) -HX (k|k-1) ];
(5) Updating error covariance P (k|k) = [ I-Kg (k) H ] P (k|k-1);
Wherein X is a system state vector, k is a current moment, k-1 is a previous moment, X (k|k-1) is a priori estimate (predicted value) of the k moment, X (k|k) is a posterior estimate (corrected value) of the k moment, X (k-1|k-1) is an optimal estimate of the k-1 moment, A is a state transition matrix describing how the system state transitions from the k-1 moment to the k moment, B is a control input matrix, H is an observation matrix mapping the state space to the observation space, I is an identity matrix, U (k) is a control input vector, Z (k) is an actual measured value, P (k|k-1) is a priori estimated error covariance, P (k|k) is a posterior estimated error covariance, P (k-1|k-1) is an estimated error covariance of the previous moment, Q is a process noise covariance matrix, R is a measured noise covariance matrix, kg (k Kalman gain) is T is a matrix transpose, -1 is a matrix inverse of the matrix.
The temperature data weighted fusion method is further characterized in that the temperature data weighted fusion method comprises the steps of (1) obtaining temperature values Ti of all measuring points, (2) determining weight coefficients wi of all measuring points through a least square method, (3) calculating temperature values of any point in space, wherein, q is heat flux density, k is heat conductivity coefficient, and Deltax is space distance, and T (x, y, z) =Σ (wi. Ti)/Σwi- (q/k) Deltax.
The invention further provides that the thermal deformation prediction algorithm comprises the following steps:
(1) Solving the heat conduction equation:
;
(2) Boundary heat exchange is calculated, wherein q=h (Ts-T infinity) +epsilon sigma (Ts 4-T∞4), h is a convection heat transfer coefficient, ts is surface temperature, T infinity is ambient temperature, epsilon is emissivity, and sigma is Stefan-Boltzmann constant.
The invention is further provided with:
The adaptive compensation algorithm comprises the following steps:
(1) Calculating compensation quantity C (t) =Kp.e (t) +Ki ≡e (t) dt+Kd.de (t)/dt;
(2) Dynamically adjusting PID parameters:
dKp/dt=γp·e(t)·|e(t)|
dKi/dt=γi·e(t)·∫e(t)dt
dKd/dt=γd·e(t)·de(t)/dt;
Wherein γp, γi, γd are learning rate parameters.
The invention is further provided that the calculation formula of the thermal deformation amount is as follows:
Wherein DeltaL is the length variation, L 0 is the initial length of the component, alpha is the linear thermal expansion coefficient of the material, T is the current temperature, T 0 is the reference temperature, Is a temperature gradient.
In summary, the intelligent temperature compensation control system of the laser engraving machine based on the multi-source temperature data fusion has the advantages that the technical limitation that the traditional laser engraving machine only depends on single-point temperature measurement is overcome, and a compensation control scheme based on the multi-source temperature data fusion is provided. Through the multipoint temperature sensor network arranged at the key part of the machine tool, the accurate description of the whole machine tool temperature field distribution is realized by combining the improved Kalman filtering and the multipoint weighted fusion algorithm. In the aspect of thermal deformation prediction, the thermodynamic theory is combined with the boundary compensation algorithm, so that not only is a heat conduction equation considered, but also natural convection and radiation heat exchange caused by the ambient temperature are incorporated into a calculation model, and the problem that a traditional linear compensation model cannot accurately describe a complex thermal-mechanical coupling effect is solved. The intelligent compensation method based on multi-source data fusion remarkably improves the prediction and compensation capability of the system for thermal deformation under complex working conditions.
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Fig. 1 is a flow chart of an embodiment.
Detailed Description
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "page", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature.
In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected via an intervening medium, or in communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The present invention will be described in further detail with reference to the accompanying drawings.
A temperature compensation control system based on multi-source temperature data fusion laser engraving machine comprises a temperature acquisition module for data acquisition of temperatures of multiple points. The system comprises an algorithm module for processing the multi-point temperature data and calculating the compensation quantity, and a compensation execution module for correspondingly adjusting the compensation quantity. Fig. 1 is a flowchart of the overall process.
The temperature acquisition module comprises a plurality of high-precision PT100 temperature sensors arranged on the laser engraving machine, the sampling frequency is 10Hz, and the measurement precision is +/-0.1 ℃. According to different weights of temperature field measurement, 4 measuring points are arranged on a spindle box, 4 measuring points are arranged on a workbench, 2 measuring points are arranged on a machine tool upright post, and 2 measuring points are arranged at the ambient temperature. And measuring the temperature of each measuring point in real time through a temperature sensor, and collecting temperature data into an algorithm module.
2. Algorithm module
The algorithm module comprises a filtering algorithm for filtering and eliminating noise on the acquired multi-point temperature data, a multi-point fusion algorithm for weighting the multi-point temperature, a thermal deformation prediction algorithm for predicting the temperature change of the whole system, a boundary compensation algorithm for calculating the compensation quantity of the environmental temperature boundary condition and an adaptive algorithm for calculating the compensation quantity.
The filtering algorithm adopts an improved Kalman filtering algorithm to eliminate temperature noise, and the filtering equation is as follows:
①X(k|k-1)=AX(k-1|k-1)+BU(k)
②P(k|k-1)=AP(k-1|k-1)AT+Q
③Kg(k)=P(k|k-1)HT[HP(k|k-1)HT+R]-1
④X(k|k)=X(k|k-1)+Kg(k)[Z(k)-HX(k|k-1)]
⑤P(k|k)=[I-Kg(k)H]P(k|k-1)
x is a system state vector, k is a current moment, k-1 is a previous moment, X (k|k-1) is a priori estimate (predicted value) of the k moment, X (k|k) is a posterior estimate (corrected value) of the k moment, X (k-1|k-1) is an optimal estimate of the k-1 moment, A is a state transition matrix describing how the system state transitions from the k-1 moment to the k moment, B is a control input matrix, H is an observation matrix mapping the state space to the observation space, I is an identity matrix, U (k) is a control input vector, Z (k) is an actual measured value, P (k|k-1) is an a priori estimated error covariance, P (k|k) is a posterior estimated error covariance, P (k-1|k-1) is an estimated error covariance of the previous moment, Q is a process noise covariance matrix, R is a measured noise covariance matrix, kg (k) is a Kalman gain, T is a transpose, -1 is a matrix inversion.
Above, the equation ① is a state prediction equation, which predicts the state of the temperature change in the next time. Equation ② is a prediction error covariance equation used to calculate the uncertainty created during the prediction process. Equation ③ is a Kalman gain calculation, which determines the weight ratio of the predicted value to the measured value. Equation ④ is a state update equation for integrating the predicted value and the measured value, and equation ⑤ is an update error covariance.
After the calculation of the filtering algorithm, the measuring noise of the temperature sensor can be filtered, smoother and more accurate temperature data can be provided, the measuring precision of the system is improved, and the stability of the subsequent compensation quantity calculation is improved.
The multi-point fusion algorithm is mainly used for calculating multi-point temperature field distribution and mainly comprises a temperature data weighted fusion formula:
T(x,y,z)=Σ(wi·Ti)/Σwi-(q/k)Δx
Ti is the temperature value of the ith measuring point, wi is the corresponding weight coefficient, and the temperature value is determined through least square optimization.
And integrating the temperature data of the multiple points according to the weights through the weighted fusion calculation.
The thermal deformation prediction algorithm is based on thermodynamic theory, and considers a thermal conduction equation of a material as follows:
T is a temperature field function, T is time, D is a thermal diffusivity, ρ is a material density, c is a specific heat capacity, and q is a heat flux density.
X is the transverse movement direction of the workbench;
y, the longitudinal movement direction of the workbench;
z, laser head lifting direction;
representing the second partial derivative of temperature in the x direction, describing the heat conduction in the x direction;
representing the second partial derivative of temperature in the y direction, describing the heat conduction in the y direction;
representing the second partial derivative of temperature in the z direction, describing the heat conduction in the z direction;
the temperature field distribution of the whole system can be obtained by inputting the multi-point data into an algorithm module and solving the partial differential equation.
The boundary condition of the boundary compensation algorithm considering the ambient temperature considers the natural convection and radiation heat exchange process, namely:
q=h(Ts-T∞)+εσ(Ts4-T∞4)
Wherein, the letter meaning of the above formula is as follows:
q total heat flux density (heat flux) in W/m2, representing heat transfer rate per unit area
Convective heat transfer term h (Ts-T infinity):
h, the convective heat transfer coefficient, in W/(mK), describes the heat transfer capacity caused by air flow, and is related to the air flow state, the shape of the object, and the like
Ts, object surface temperature, in K (Kelvin)
T infinity ambient temperature (far field temperature) in K
Radiation heat exchange term epsilon sigma (Ts 4-T∞4):
Epsilon-emissivity (emissivity), dimensionless, ranging from 0 to 1, describing the surface emissivity of an object, complete blackbody epsilon=1, actual object epsilon <1, sigma: stefan-Boltzmann constant, 5.67×10 -8W/(m²·K4)
Ts 4 fourth power of object surface temperature
T-infinity 4 -fourth power of ambient temperature
The boundary compensation algorithm considering the ambient temperature can be used for calculating the heat loss of the whole structure of the machine tool, predicting the distribution of the temperature field, evaluating the heat dissipation effect, optimizing the thermal deformation compensation calculation and improving the compensation precision.
After the temperature field distribution calculation is completed, the thermal deformation amount can be calculated, so that the compensation amount required by the multi-point fusion temperature place can be obtained. The specific formula is as follows:
Wherein DeltaL, the length change (thermal deformation) is usually in millimeters (mm)
L 0 initial length of the component in millimeters (mm)
Alpha, the linear thermal expansion coefficient of the material is 1/DEGC or K -1
T is the current temperature in degrees Celsius (C) or Kelvin (K)
T 0 reference temperature (initial temperature) in degrees Celsius (C) or Kelvin (K)
K: coefficient of thermal conductivity
Temperature gradient
By the above formula, the thermal deformation amount of each component of the laser engraving machine can be predicted, and further the compensation amount can be calculated, and can be used for predicting and evaluating the influence degree of the thermal deformation.
And after the thermal deformation amount of the multipoint fusion temperature field distribution is obtained, correspondingly calculating the compensation amount through a self-adaptive algorithm.
The adaptive algorithm includes an improved PID control equation, which is specifically as follows:
C(t)=Kp·e(t)+Ki·∫e(t)dt+Kd·de(t)/dt
Wherein, C (t) is compensation quantity, e (t) is thermal deformation error, kp, ki, kd are self-adaptive PID parameters, and the adjustment is carried out through real-time error feedback:
dKp/dt=γp·e(t)·|e(t)|
dKi/dt=γi·e(t)·∫e(t)dt
dKd/dt=γd·e(t)·de(t)/dt
wherein the meaning of each letter in the formula is as follows:
Kp scaling factor
Ki integral coefficient
Kd differential coefficient
DKp/dt, dKi/dt, dKd/dt, rate of change of these parameters over time
E (t) the current error, i.e. the difference between the target value and the actual value
Absolute value of error of i e (t)
Integral term of error, denoted cumulative error
Derivative term of error, de (t)/dt, representing the rate of change of error
Gamma p learning rate of proportional term
Gamma i learning rate of integral term
Gamma d learning rate of differentiation term
The adjustment rate of dKp/dt=γ p.e (t) |e (t) |the scaling factor is proportional to the current error and the absolute value of the error
DKi/dt=γ i.e (t) ≡e (t) dt: the rate of adjustment of the integral coefficient is proportional to the current error and the accumulated error
DKd/dt=γ d.e (t). De (t)/dt: the rate of adjustment of the differential coefficient is proportional to the current error and the rate of change of the error
Through the compensation amount formula, the control parameters can be adaptively adjusted according to the thermal deformation amount, and the compensation amount can be adaptively calculated and adjusted according to self-learning of a time algorithm aiming at different situations.
The result of calculating the compensation amount by the above algorithm is as follows:
X'=X+Cx
Y'=Y+Cy
Z'=Z+Cz
wherein Cx, cy and Cz are compensation amounts calculated in the directions of X, Y and Z axes, X, Y and Z are initial position coordinates when the system is started, and X ', Y ', Z ' are position coordinates adjusted by the compensation amounts.
The compensation execution module comprises a high-precision displacement sensor, the resolution of the high-precision displacement sensor can be set to be 0.1 mu m, compensation is realized through a position instruction of a CNC control system of the laser engraving machine, and the corresponding coordinate position is changed.
For a better understanding of the present application, the present application provides corresponding experiments demonstrating the temperature compensation mechanism of the present application. The method mainly comprises the following steps:
1. Setting conditions:
1. setting environmental parameters including temperature of 25+/-1 ℃, relative humidity of 45+/-5% and air pressure of 101.325+/-0.5 kPa.
2. Setting equipment parameters, namely laser power 2000W, workbench size 1500mm multiplied by 1000mm, spindle rotation speed 12000r/min, positioning accuracy +/-2 mu m and repeated positioning accuracy +/-1 mu m.
3. The measuring equipment comprises 12 PT100 temperature sensors (precision: + -0.1 ℃), 3 high-precision displacement sensors (resolution: 0.1 μm), a roundness measuring instrument (precision: 0.1 μm) and a flatness measuring instrument (precision: 0.1 μm).
4. Static thermal characteristic test, wherein the duration is 4 hours, the sampling interval is 10 minutes, and the measurement items are the temperature of each measuring point and the displacement of the key position
5. Dynamic processing test, wherein the duration time is 8 hours, the sampling interval is 30 minutes, and the measurement items include temperature field, thermal deformation amount and processing precision
6. Standard test piece is processed by 45 # steel, 100mm phi by 50mm in size, and the processing characteristics are excircle, end face and step face
2. Data results
1. Complete data of temperature field distribution:
2. thermal deformation data (uncompensated):
3. Compensating control parameter self-adaptive adjustment data:
4. Compensated position error data:
5. standard test piece machining accuracy data:
3. Analysis of results
1. The temperature field characteristics are that the temperature rise of the main shaft box is maximum and reaches 13.5 ℃, the temperature rise of the workbench is inferior and reaches 8.0 ℃, the temperature rise of the upright post is minimum and reaches 5.0 ℃, and the temperature field tends to be stable after 6 hours
2. The thermal deformation compensation effect is that the uncompensated maximum thermal deformation is 123.2 plus or minus 0.9 mu m, the compensated maximum residual error is 6.9 plus or minus 0.2 mu m, the compensation efficiency is 94.4%, and the system response time is 85ms
3. Machining accuracy analysis of roundness retention of <0.9 μm in offset increment, flatness retention of <0.8 μm in offset increment, dimensional accuracy of + -5 μm in maintenance, surface roughness of <0.3 μm in Ra value increment
4. The system stability is that the temperature compensation parameter is self-adaptively adjusted stably, the 8-hour continuous operation is free from abnormality, and the compensation effect fluctuates with time within minus or plus 0.3 mu m
4. Conclusion(s)
1. The temperature compensation system can reduce the error caused by thermal deformation from 120 mu m to 7 mu m, and the compensation efficiency is over 94 percent.
2. The dynamic characteristic is that the response time of the system is less than 100ms, the self-adaptive adjustment of the compensation parameters is stable, and the overshoot phenomenon is avoided.
3. And the machining precision is controlled within +/-5 mu m after compensation, so that the high-precision machining requirement is met.
4. The system reliability 8-hour continuous operation test shows that the system has good stability and reliability and the compensation effect is durable and stable.
For a more convenient understanding of the computational process of the present application, the present application illustrates and provides a complete algorithmic computational process analysis:
1. temperature data acquisition
34.8+/-0.1 ℃ Of main spindle box
29.9+/-0.2 ℃ Of workbench
Column at 27.5+ -0.15 DEG C
Ambient temperature 25.0.+ -. 0.1 °c
2. Kalman filter processing
(1) System parameters a=1 (state transition), h=1 (observation matrix), q=0.001 (process noise), r=0.01 (measurement noise)
(2) Spindle box filtering:
X(k|k-1)=34.7°C
P(k|k-1)=0.041
Kg(k)=0.804
X(k|k)=34.78°C
P(k|k)=0.008
filtering results:
Headstock 34.78 DEG C
Working table 29.85 DEG C
Column at 27.48 DEG C
Environment 24.98 deg.C
3. Temperature field calculation (considering boundary conditions)
(1) Boundary heat flow calculation, convection coefficient h=12w/(m2·k), emissivity ε=0.92, stefan-Boltzmann constant σ=5.67× -8W/(m²·K4), surface temperature ts=31.22 ℃ = 304.37K, and ambient temperature T infinity= 24.98 ℃ = 298.13K
(2) And (3) calculating the heat flux density:
q=h(Ts-T∞)+εσ(Ts4-T∞4)
=12(304.37-298.13)+0.92×5.67×10-8(304.374-298.134)
=74.88W/m2
(3) And (3) calculating a temperature field:
Material parameters are ρ=2700kg/m 3 (density), c=896j/(kg·k) (specific heat capacity), k=237W/(m·k) (thermal conductivity coefficient), α=k/(ρc) =9.8x -5 m2/s (thermal diffusivity).
Temperature gradient:
(4) Multipoint temperature fusion (considering boundary effects):
weight w1=0.4, w2=0.35, w3=0.25
T(x,y,z)=(0.4×34.78+0.35×29.85+0.25×27.48)/1-0.316×Δx=31.22-0.00316=31.217°C
(5) Solving a heat conduction equation:
wherein the set period deltat=1s, Δx=Δy= Δz=0.01 m
=31.217+9.8×10-5×(-0.316)+3.1×10-5=31.217°C
4. Thermal deformation calculation
Characteristic length L 0 = 800mm, linear expansion coefficient α = 23 x 10 -6/°c, temperature difference Δt = 31.217-24.98 = 6.237 ℃;
deformation calculation:
ΔL=L0αΔT=800×23×10-6×6.237=0.1149mm=114.9μm
PID Compensation calculation
Initial parameters:
Kp=0.85,Ki=0.06,Kd=0.03
learning rate γ p=0.001,γi=0.0001,γd =0.0002
Error e (t) =114.9 μm
Parameter updating:
dKp/dt=0.001×114.9×114.9=13.202
dKi/dt=0.0001×114.9×∫114.9dt=1.377
dKd/dt=0.0002×114.9×(de/dt)=0.459
Updated data:
Updated PID parameters (Δt=1s):
Kp_new=Kp+dKp/dt×Δt=0.85+13.202×1=14.052
Ki_new=Ki+dKi/dt×Δt=0.06+1.377×1=1.437
Kd_new=Kd+dKd/dt×Δt=0.03+0.459×1=0.489
taking the X direction as an example:
Cx(t)=Kp_new·ex(t)+Ki_new·∫ex(t)dt+Kd_new·dex(t)/dt
Wherein:
ex (t) =114.9×cos (θ x)μm(θx is the heat distortion and X-axis clamping angle)
∫ex(t)dt=114.9×cos(θx)×Δtμm·s
dex(t)/dt=0.2×cos(θx)μm/s
Assume an angle with the coordinate axis:
θ x =12° (X axis)
Θ y =8° (Y axis)
Θ z =5° (Z axis)
Substituting into X direction to calculate:
ex(t)=114.9×cos(12°)=112.3μm
∫ex(t)dt=112.3×1=112.3μm·s
dex(t)/dt=0.2×cos(12°)=0.196μm/s
Cx=14.052×112.3+1.437×112.3+0.489×0.196=112.1 μm (taking 1/15 of the final compensation amount, avoiding overcompensation)
Similarly, Y direction was calculated as ey (t) =114.9×cos (8 °) =113.8 μm, cy=113.6 μm;
z direction ez (t) =114.9×cos (5 °) =114.4 μm, cz=114.3 μm
Calculating the compensation quantity:
Cx=112.1μm
Cy=113.6μm
Cz=114.3μm
6. Coordinate compensation execution
Initial position (100.000,200.000,300.000) mm
Compensated coordinates:
X'=100.000+0.1121=100.1121mm
Y'=200.000+0.1136=200.1136mm
Z'=300.000+0.1143=300.1143mm
In summary, the intelligent temperature compensation control system of the laser engraving machine based on the multi-source temperature data fusion has the advantages that the technical limitation that the traditional laser engraving machine only depends on single-point temperature measurement is overcome, and a compensation control scheme based on the multi-source temperature data fusion is provided. Through the multipoint temperature sensor network arranged at the key part of the machine tool, the accurate description of the whole machine tool temperature field distribution is realized by combining the improved Kalman filtering and the multipoint weighted fusion algorithm. In the aspect of thermal deformation prediction, the thermodynamic theory is combined with the boundary compensation algorithm, so that not only is a heat conduction equation considered, but also natural convection and radiation heat exchange caused by the ambient temperature are incorporated into a calculation model, and the problem that a traditional linear compensation model cannot accurately describe a complex thermal-mechanical coupling effect is solved. The intelligent compensation method based on multi-source data fusion remarkably improves the prediction and compensation capability of the system for thermal deformation under complex working conditions.
The present embodiment is only for explanation of the present invention and is not to be construed as limiting the present invention, and modifications to the present embodiment, which may not creatively contribute to the present invention as required by those skilled in the art after reading the present specification, are all protected by patent laws within the scope of claims of the present invention.
Claims (5)
1. Temperature compensation control system based on multisource temperature data fuses laser engraving machine, characterized by comprising:
The temperature acquisition module comprises a plurality of temperature sensors arranged at key positions of the laser engraving machine and is used for acquiring overall temperature field distribution data;
an algorithm module comprising:
the improved Kalman filtering algorithm is used for eliminating temperature data noise;
the multi-point weighted fusion algorithm is used for carrying out space distribution fusion calculation on the multi-point temperature data;
the thermal deformation prediction algorithm integrates a heat conduction, natural convection and radiation heat exchange model and is used for predicting the temperature change of the whole system;
The self-adaptive compensation algorithm dynamically adjusts PID parameters according to real-time error feedback;
the compensation execution module is used for carrying out real-time position compensation on the laser engraving machine according to the compensation quantity output by the algorithm module;
The temperature data weighted fusion method comprises the steps of (1) obtaining temperature values Ti of all measuring points, (2) determining weight coefficients wi of all measuring points through a least square method, (3) calculating temperature values of any point in space, wherein, q is heat flux density, k is heat conductivity coefficient, and Deltax is space distance.
2. The temperature compensation control system of the multi-source temperature data fusion laser engraving machine according to claim 1, the method is characterized in that the filtering equation step of the improved Kalman filtering algorithm comprises the following steps:
(1) State prediction is performed, X (k|k-1) =ax (k-1|k-1) +bu (k);
(2) Calculating a prediction error covariance P (k|k-1) =ap (k-1|k-1) a T +q;
(3) Calculating a Kalman gain Kg (k) =P (k|k-1) H T[HP(k|k-1)HT+R]-1;
(4) Update status X (k|k) =x (k|k-1) +kg (k) [ Z (k) -HX (k|k-1) ];
(5) Updating error covariance P (k|k) = [ I-Kg (k) H ] P (k|k-1);
Wherein X is a system state vector, k is a current moment, k-1 is a previous moment, X (k|k-1) is a priori estimate (predicted value) of the k moment, X (k|k) is a posterior estimate (corrected value) of the k moment, X (k-1|k-1) is an optimal estimate of the k-1 moment, A is a state transition matrix describing how the system state transitions from the k-1 moment to the k moment, B is a control input matrix, H is an observation matrix mapping the state space to the observation space, I is an identity matrix, U (k) is a control input vector, Z (k) is an actual measured value, P (k|k-1) is a priori estimated error covariance, P (k|k) is a posterior estimated error covariance, P (k-1|k-1) is an estimated error covariance of the previous moment, Q is a process noise covariance matrix, R is a measured noise covariance matrix, kg (k Kalman gain) is T is a matrix transpose, -1 is a matrix inverse of the matrix.
3. The multi-source temperature data fusion based laser engraving machine temperature compensation control system of claim 1, wherein the thermal deformation prediction algorithm step comprises:
(1) Solving the heat conduction equation:
Wherein T is a temperature field function, T is time, D is a thermal diffusion coefficient, ρ is a material density, c is a specific heat capacity, q is a heat flow density, x is a transverse movement direction of a workbench, y is a longitudinal movement direction of the workbench, and z is a laser head lifting direction; representing the second partial derivative of temperature in the x direction, describing the heat conduction in the x direction; representing the second partial derivative of temperature in the y direction, describing the heat conduction in the y direction; representing the second partial derivative of temperature in the z direction, describing the heat conduction in the z direction;
(2) Boundary heat exchange is calculated, wherein q=h (Ts-T infinity) +epsilon sigma (Ts 4-T∞4), h is a convection heat transfer coefficient, ts is surface temperature, T infinity is ambient temperature, epsilon is emissivity, and sigma is Stefan-Boltzmann constant.
4. The temperature compensation control system of the multi-source temperature data fusion laser engraving machine according to claim 1, the self-adaptive compensation algorithm is characterized by comprising the following steps:
(1) Calculating compensation quantity C (t) =Kp.e (t) +Ki ≡e (t) dt+Kd.de (t)/dt;
(2) Dynamically adjusting PID parameters:
dKp/dt=γp·e(t)·|e(t)|
dKi/dt=γi·e(t)·∫e(t)dt
dKd/dt=γd·e(t)·de(t)/dt;
Wherein γp, γi, γd are learning rate parameters.
5. The temperature compensation control system of a laser engraving machine based on multi-source temperature data fusion according to claim 3, wherein the thermal deformation prediction algorithm step further includes thermal deformation calculation, and a specific calculation formula is:
Wherein DeltaL is the length variation, L 0 is the initial length of the component, alpha is the linear thermal expansion coefficient of the material, T is the current temperature, T 0 is the reference temperature, Is a temperature gradient.
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