Method for adapting machining resistance estimation, program for adapting machining resistance estimation, and machine tool

Abstract

To highly accurately estimate a processing resistance during processing.SOLUTION: An adaptation method for processing resistance estimation for estimating, in a machine tool including a blade rest to which a tool is attached and a workpiece holding mechanism by which a workpiece is held, a processing resistance when the workpiece is processed using the tool by moving at least one of the blade rest and the workpiece holding mechanism as a moving body by rotational driving of a ball screw, includes a first estimation step of obtaining a mass of the moving body, a viscous friction coefficient in a driving system of the moving body, and a Coulomb friction in the driving system of the moving body by using a motion equation relating to a movement of the moving body and using torsion torque of the ball screw; and a second estimation step of estimating a processing resistance, based on the mass, viscous friction coefficient, and Coulomb friction of the moving body estimated in the first estimation step.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for adapting machining resistance estimation, a program for adapting machining resistance estimation, and a machine tool. [Background technology]

[0002] In the above technical field, Patent Document 1 discloses a technique for estimating cutting force by using a cutting force observer. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-085058 A [Non-patent literature]

[0004] [Non-Patent Document 1] Shota Yamada, Hiroshi Fujimoto, "Minimum-Variance Load-Side External Torque Estimation Robust Against Modeling and Measurement Errors", IEEJ Journal of Industry Applications, 2020, Volume 9, Issue 2, Pages 117-124 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technique described in the above document does not support parameters that vary in real time, and therefore it is not possible to accurately estimate the machining resistance.

[0006] An object of the present invention is to provide a technique for solving the above-mentioned problems. [Means for solving the problem]

[0007] In order to achieve the above object, the method according to the present invention comprises: In a machine tool having a tool rest on which a tool is attached and a work holding mechanism on which a work is held, at least one of the tool rest and the work holding mechanism is moved as a moving body by the rotational drive of a ball screw, so that the work is held by the tool. cutting When cutting force Estimate cutting force 1. A method for adapting an estimation, comprising: a first estimation step of calculating a mass of the moving body, a viscous friction coefficient in a drive system of the moving body, and a Coulomb friction in the drive system of the moving body using an equation of motion related to the movement of the moving body and a torsional torque of the ball screw; based on the mass of the moving body estimated in the first estimation step, the viscous friction coefficient, and the Coulomb friction, cutting force a second estimation step of estimating Includes cutting force This is an adaptive estimation method.

[0008] In order to achieve the above object, the machine tool according to the present invention comprises the above cutting force Estimated using the adaptive estimation method cutting force The machine tool is equipped with an output unit that outputs the above signal during machining.

[0009] In order to achieve the above object, the program according to the present invention comprises: In a machine tool having a tool rest on which a tool is attached and a work holding mechanism on which a work is held, at least one of the tool rest and the work holding mechanism is moved as a moving body by the rotational drive of a ball screw, thereby enabling the tool to be machined when the work is machined. cutting force Estimate cutting force An estimation adaptation program, comprising: a first estimation step of calculating a mass of the moving body, a viscous friction coefficient in a drive system of the moving body, and a Coulomb friction in the drive system of the moving body using an equation of motion related to the movement of the moving body and a torsional torque of the ball screw; Based on the mass of the moving body estimated in the first estimation step, the viscous friction coefficient, and the Coulomb friction, cutting force a second estimation step of estimating Have your computer run cutting force This is an estimation adaptation program. Effect of the Invention

[0010] According to the present invention, the machining resistance can be estimated with high accuracy during machining. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing a configuration of a machine tool according to a first embodiment. [Diagram 2] FIG. 11 is a block diagram showing the configuration of a machine tool according to a second embodiment. [Diagram 3] 13 is a diagram for explaining the contents of processing by a cutting force observer according to the second embodiment. FIG. [Figure 4] 13 is a diagram for explaining the contents of processing by a cutting force observer according to the second embodiment. FIG. [Diagram 5] 13A and 13B are diagrams for explaining the effect of the cutting force observer according to the second embodiment. [Figure 6] FIG. 11 is a diagram illustrating control of a machine tool according to a third embodiment. [Figure 7] FIG. 11 is a diagram illustrating control of a machine tool according to a third embodiment. [Figure 8] FIG. 11 is a diagram illustrating control of a machine tool according to a third embodiment. [Figure 9] FIG. 11 is a diagram illustrating control of a machine tool according to a third embodiment. [Figure 10] FIG. 11 is a diagram illustrating control of a machine tool according to a third embodiment. [Figure 11] FIG. 11 is a diagram illustrating control of a machine tool according to a third embodiment. [Figure 12] FIG. 11 is a diagram illustrating control of a machine tool according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. However, the components described in the following embodiment are merely examples, and are not intended to limit the technical scope of the present invention.

[0013] [First embodiment] A machine tool 100 according to a first embodiment of the present invention will be described with reference to Fig. 1. The machine tool 100 estimates the machining resistance when a tool 121 is used to machine a workpiece 123.

[0014] As shown in FIG. 1, machine tool 100 includes a tool rest 122 on which a tool 121 is attached, a workpiece holding mechanism 124 on which a workpiece 123 is held, and further includes estimating units 101 and 102.

[0015] In the machine tool 100, at least one of the tool rest 122 and the workpiece holding mechanism 124 is moved as a moving body by the rotational drive of the ball screw 125.

[0016] The estimation unit 101 uses an equation of motion 111 relating to the movement of the moving body and a torsional torque 112 of a ball screw to obtain a mass 113 of the moving body, a viscous friction coefficient 114 in the driving system of the moving body, and a Coulomb friction 115 in the driving system of the moving body.

[0017] The estimation unit 102 estimates a machining resistance 116 when a workpiece 123 is machined using a tool 121 based on the mass 113 of the moving body, the viscous friction coefficient 114 , and the Coulomb friction 115 estimated by the estimation unit 101 .

[0018] According to the above configuration, it is possible to realize adaptation of machining resistance estimation, and to estimate the machining resistance with high accuracy during machining.

[0019] [Second embodiment] Next, a machine tool according to a second embodiment of the present invention will be described with reference to Fig. 2. Fig. 2 is a diagram for explaining the configuration of a machine tool 200 according to this embodiment.

[0020] In FIG. 2, a machine tool 200 includes a controller 201 , a servo amplifier 202 , a stage driving unit 203 , a tool spindle 205 , a cutting tool 206 , and an output unit 207 .

[0021] The controller 201 executes position control over a stage driving unit 203 via a servo amplifier 202 .

[0022] The servo amplifier 202 controls the torque of a motor 231 for driving a stage driving unit 203 by supplying a current Ia corresponding to a command value from the controller 201 to the motor 231 .

[0023] The stage driving unit 203 includes a motor 231, a ball screw 232, and a stage 233, and moves the workpiece 204 fixed to the stage 233 to a specified position by driving and rotating the ball screw 232.

[0024] The motor 231 generates a motor torque according to the current from the servo amplifier 202, thereby rotating the screw shaft of the ball screw 232 via a coupling. The rotary encoder 234 detects the rotation angle θ of the motor 231. M is detected and fed back to the controller 201 via the servo amplifier 202.

[0025] The ball screw 232 converts the rotational motion transmitted from the motor 231 into linear motion, and moves the stage 233 along the screw axis.

[0026] The linear encoder 235 detects the position X of the stage 233. t is detected and fed back to the controller 201.

[0027] The controller 201 includes, as functional components, a position control unit 211, a disturbance observer 212, and a cutting force observer 213. These functional components are realized by a CPU (Central Processing Unit) as hardware that executes various programs read from a memory.

[0028] The position control unit 211 moves the stage 233 on which the workpiece 204 is attached, and at the same time adjusts the position of the tool spindle 205, so that the workpiece 204 is cut by the cutting tool 206. The position control unit 211 adjusts the rotation angle θ M is the state information, and the position X of the stage 233 t Based on the state information and the output information, the current I a The command value I aref The disturbance observer 212 outputs the current I a , the rotation angle θ of the motor 231 M , and position X of stage 233 t The disturbance is estimated based on the above, and a compensation current is derived to compensate for the disturbance and improve the robustness of the system.

[0029] The command value I output from the position control unit 211 aref A compensation current generated by the disturbance observer 212 is added to this and the resulting value is provided to the servo amplifier 202.

[0030] When the workpiece 204 loaded on the stage 233 moves and comes into contact with the blade of the cutting tool 206, the cutting tool 206 applies a cutting force F cut The workpiece 204 is cut by the cutting force F cut Estimate.

[0031] The cutting force observer 213 determines the current I a (Input information), Position X of stage 233 t (output information), and the rotation angle θ of the motor 231 M (Status information) is used to calculate the cutting force F cutEstimate.

[0032] The output unit 207 outputs the estimated machining resistance during machining. The output unit 207 may derive and output at least one of a tool load, a tool wear, and a tool breakage based on a time-series value of the machining resistance during machining of the workpiece 204.

[0033] FIG. 3 is a diagram showing a model of the stage driving unit 203 for explaining an outline of the cutting force estimation method executed by the cutting force observer 213. In FIG.

[0034] In FIG. 3, the motor 231 of the rotation system and the stage 233 of the linear motion system (with the workpiece 204 loaded thereon) serving as the load are expressed as a two-inertia system model. The motor torque T M Friction torque T for rotational movement at fric =(Ph / 2π)θ M There exists a friction force F against the movement of the stage 233. fric exists.

[0035] Under these conditions, the rotational motion of the motor 231 is expressed as follows: L The load travels x L At that time, the cutting tool 206 cuts into the workpiece 204, and a cutting force F cut This results in:

[0036] FIG. 4 is a functional block diagram showing a detailed functional configuration of the cutting force observer 213. In FIG. cut is the cutting force, F CL is the Coulomb friction, M L is the stage mass, B L represents the viscosity coefficient of the stage. Ph represents the lead of the ball screw (the linear movement generated by one rotation). T s is the torsional torque of the ball screw, T M is the motor torque and the current I a and a conversion factor (not shown). M is the motor inertia, B Mdenotes the viscosity coefficient of the motor. α M is a predetermined coefficient between 0 and 1, and indicates the mixing ratio (eg, the one that minimizes the error) of the two methods of estimating the torsional torque (estimation in the motor section and estimation in the ball screw section).

[0037] The cutting force observer 213 in FIG. 4 is based on the robust torsional torque estimation described in Non-Patent Document 1. Block 401 functions as an estimation unit and is a block that realizes a parameter determination algorithm based on the recursive least squares method (RLS). Block 401 calculates the equation of motion related to the movement of a moving body and the torsional torque T s Using the above, the mass of the moving object M L , the viscous friction coefficient B in the drive system of the moving object L , and the Coulomb friction in the drive system of the moving object F CL Request.

[0038] Here, the equation of motion in this embodiment is as follows:

number

[0039] That is, block 401 uses RLS to calculate the stage displacement x L The estimated torsional torque T s From the stage parameter, mass M L , viscous friction coefficient B L , Coulomb friction F CL and the DC component of the cutting force F cut DC Estimate.

[0040] The RLS-based parameter determination algorithm is expressed by the following equation:

number

[0041] Block 401 is a model parameter, L , viscous friction coefficient B L , Coulomb friction F CLBy using the above, the cutting force observer 213 adapts (sequentially updates) the stage-dependent components 402, 403. This allows the cutting force F to be calculated in real time with high accuracy under environmental conditions that change depending on the cutting process and temperature changes during machining. cut In other words, the block 404 as a whole functions as an estimation unit that estimates the machining resistance when machining a workpiece with a tool, based on the successively estimated mass of the moving body, the viscosity coefficient, and the Coulomb friction.

[0042] In block 401, the least squares method is used for the estimation, but the present invention is not limited to this, and the estimation may be performed using other recursive least squares estimation algorithms or a Kalman filter. This is because there are other methods such as the forgetting factor method other than the recursive least squares algorithm shown in FIG.

[0043] In block 401, by estimating the DC component of the cutting force in addition to the mass of the moving body, the viscous friction coefficient, and the Coulomb friction, the mass of the moving body, the viscous friction coefficient, and the Coulomb friction can be estimated with high accuracy.

[0044] The cutting force observer 213 can estimate the cutting force in a wide frequency band to begin with, but the recursive least squares method used in block 401 requires that the mass of the moving body, the viscous friction coefficient, Coulomb friction, and the cutting force vary only slowly over time. However, in reality, the cutting force also varies at high frequencies. Therefore, a low-pass filter is inserted to cut the high-frequency components of the above equation of motion shown in Equation 1, and this is used in the regression model (Equation 2) used in the recursive least squares method. As a result, by estimating the parameters of the moving body including the DC component of the cutting force, it becomes possible to accurately determine the three model parameters (the mass of the moving body, the viscous friction coefficient, and the Coulomb friction).

[0045] As described above, according to this embodiment, it is possible to estimate the cutting resistance with high accuracy in real time during machining.

[0046] Accurate estimation of cutting force makes it possible to determine tool load, unexpected vibration, chatter vibration, etc., and ultimately to derive conditions that prevent cutting force from becoming excessively large. It also makes it possible to predict tool breakage and chatter vibration.

[0047] 5 is a graph showing the verification result of the adaptation method of machining resistance estimation according to the present embodiment. As shown in FIG. 5, by using the above-mentioned algorithm, the cutting force can be estimated with high accuracy.

[0048] In Fig. 5, the dashed line indicates the cutting force inputted for verification, the solid line indicates the estimation result by the cutting force estimation mechanism based on adaptation according to the present embodiment, and the dotted line indicates the estimation result by the cutting force estimation mechanism not based on adaptation. In the verification, estimation was performed when the design value of the driven part mass in the cutting force estimation mechanism was different from the actual value. The estimation results using the non-adaptation based estimation mechanism, shown by the dotted line, have a large error compared to the actual cutting force, shown by the dashed line. On the other hand, the adaptation-based estimation mechanism in this embodiment can estimate the cutting force shown by the dashed line well.

[0049] [Third embodiment] Next, a machine tool according to a third embodiment of the present invention will be described with reference to Fig. 6. This embodiment differs from the second embodiment in that elements (2) and (3) in Fig. 6 are added for cutting force estimation. Other configurations and operations are similar to those of the second embodiment, so the same configurations and operations are denoted by the same reference numerals and detailed descriptions thereof will be omitted.

[0050] In this embodiment, advanced adaptive identification is realized by three elements (1) to (3) shown in Fig. 6. Each element will be described in detail.

[0051] (1) Separation of parameter estimation considering frequency components of cutting force As in the second embodiment, the offset (DC value) is estimated as one of the model parameters, taking into account the frequency components (frequency characteristics) of the cutting force. In addition, a parameter estimation mechanism is used in addition to the cutting force observer (decoupling of cutting force estimation and parameter estimation). The separation of cutting forces and parameter estimation is RLS Tuned by (s).

[0052] As shown in FIG. 7, the equation of motion 701 of the part for which parameters are to be estimated is formulated, and model parameters 702 to 704 are estimated so that the errors on both sides of the equation are minimized. This allows the observer to be redesigned. However, since the cutting force Fcut 705 in the equation of motion 701 is unknown, the equation of motion 701 cannot be solved. Focusing on the frequency components of the actual cutting force shown in FIG. 8, it can be seen that it is composed of an offset component 801 and harmonic components 802 and 803. Therefore, the parameters are estimated in a band where only the offset component 801 is visible, and the equation of motion 701 is solved by treating the cutting force Fcut 705 in the equation of motion 701 as known. This enabled the cutting force estimation and parameter estimation to be decoupled in separate frequency bands.

[0053] (2) Estimation based on sample differences When applying the above method (1), in the second embodiment, the cutting force offset component is assumed to be a constant value. However, strictly speaking, the cutting force offset component is not a constant value, and may fluctuate at the start of cutting or during cutting. In this case, in the second embodiment, the conditions under which the model parameters can be estimated (assuming a constant value) are limited, and may not match the actual situation. Therefore, as shown in FIG. 9, the difference 901 in the cutting force between samples is estimated as the offset component of the cutting force. As a result, even in the equation of motion for such a difference between samples, components other than the cutting force (Coulomb friction, viscous friction coefficient, inertia) can be derived by statistical analysis using a Kalman filter or a recursive least squares method, as in the second embodiment, so that abrupt changes in the cutting force can be removed as noise, and only gentle changes in the cutting force are estimated, and the parameter estimation is made more realistic and robust by considering the fluctuations in the cutting force offset component.

[0054] (3) Dynamic switching of estimation formulas 10, model parameters 1001 to be estimated include inertia 1011, viscous friction coefficient 1012, fluctuation rate of cutting force offset 1013, and Coulomb friction force 1014, but to estimate the Coulomb friction force 1014, a fluctuation 1015 of the sign function of the speed is required as a state quantity, but unless the sign of the speed between samples is inverted, this fluctuation 1015 will always be 0. In that case, except when the speed is inverted, the Coulomb friction force 1014 and the fluctuation rate of the cutting force 1013 become separate and cannot be estimated (the condition for convergence of the parameters to the true value is not satisfied in the statistical calculation).

[0055] Therefore, the Coulomb friction force 1014 is estimated only when the speed is reversed, and when the speed is not reversed (non-reversed), the Coulomb friction is excluded from the estimation target. By satisfying the parameter convergence condition in a wide range of situations, the fluctuation rate of the Coulomb friction and the cutting force are separated, and the estimation accuracy of each is improved.

[0056] Specifically, when there is a velocity reversal, the Coulomb friction force F CLThe fluctuation rate of the cutting force is also estimated (Equation 1002). When there is no speed judgment, the Coulomb friction force F is estimated by using the past Coulomb friction component 1031 (the Coulomb friction component when there is a speed reversal) as the input. CL In other words, the two equations 1002 and 1003 are dynamically switched depending on whether or not the speed is reversing.

[0057] As shown in FIG. 11, when calculating the recursive least squares method or the Kalman filter, the formula contains an error covariance matrix 1101 as an intermediate variable. In other words, the covariance matrix 1101 is stored in the parameter vector 1102, which is the estimation result. In this case, when switching between the two equations 1002 and 1003, the estimated parameter vector 1102 and the error covariance matrix 1101 should be inherited.

[0058] Therefore, when the situation changes from speed inversion to no speed inversion, the parameters are taken over as shown in 1103, and when the situation changes from no speed inversion to speed inversion, the parameters are taken over as shown in 1104.

[0059] In other words, as shown in Figure 12, when an estimator with a smaller order is running, the covariance matrix and other parameters are updated in the background, and a reasonable value 1201 is stored internally and used next time. When switching to a lower order, a more reasonable value 1202 is substituted. By the above processing, it is possible to achieve a trade-off between the convergence condition and the estimation opportunity, and it is possible to expand the estimation opportunity while satisfying the convergence condition.

[0060] According to this embodiment, the cutting force estimation mechanism is updated taking into account gradually changing inertia and viscous friction coefficient, so that the cutting force can be estimated at any time with high accuracy.

[0061] In addition, estimation based on the sample difference improves stability at the start of cutting (when the assumption that the DC component of the cutting force is constant does not hold) and can handle cases where the cutting force fluctuates gently. Furthermore, by switching the estimation equation (regressor), the accuracy of cutting force estimation in low frequency components can be improved.

[0062] [Other embodiments] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the technical scope of the present invention. In addition, systems or devices that combine the separate features included in each embodiment in any way are also included in the technical scope of the present invention.

[0063] The present invention may be applied to a system consisting of multiple devices, or to a single device. Furthermore, the present invention is also applicable to a case where an adaptation program for realizing the functions of the embodiments is supplied to a system or device and executed by a built-in processor. In order to realize the functions of the present invention by a computer, a program installed on a computer, a medium storing the program, a server for downloading the program, and a processor for executing the program are also included in the technical scope of the present invention. In particular, at least a non-transitory computer readable medium storing a program for causing a computer to execute the processing steps included in the above-mentioned embodiments is included in the technical scope of the present invention.

Claims

1. A method for adapting cutting force estimation in a machine tool having a tool post on which a tool is attached and a work holding mechanism for holding a workpiece, in which at least one of the tool post and the work holding mechanism is moved as a moving body by rotational driving of a ball screw, and a cutting force is estimated when the workpiece is cut using the tool, the method comprising: a first estimation step of calculating a mass of the moving body, a viscous friction coefficient in a drive system of the moving body, and a Coulomb friction in the drive system of the moving body using an equation of motion related to the movement of the moving body and a torsional torque of the ball screw; a second estimation step of estimating the cutting force based on the mass of the moving body estimated in the first estimation step, the viscous friction coefficient, and the Coulomb friction; The method for adapting cutting force estimation includes:

2. 2. The method for adapting cutting force estimation according to claim 1, wherein in the first estimation step, the mass of the moving body, the viscous friction coefficient, and the Coulomb friction are estimated using a least squares method.

3. the tool is a cutting tool, 3. The method for adapting cutting force estimation according to claim 1, wherein the first estimation step further comprises estimating a DC component of the cutting force applied by the tool.

4. 2. The method for adapting cutting force estimation according to claim 1, wherein in the first estimation step, a mass of the moving body, the viscous friction coefficient, and the Coulomb friction are estimated using a Kalman filter.

5. 5. The method for adapting cutting force estimation according to claim 1, wherein the equation of motion relating to the movement of the moving body is an equation of motion having an inter-sample difference as a term.

6. The method for adapting cutting force estimation according to any one of claims 1 to 5, wherein in the first estimation step, an estimation equation is dynamically switched between when the speed of the moving body is reversing and when it is not reversing, and when there is no reversal of the speed of the moving body, no estimation of the Coulomb friction is performed.

7. 7. A machine tool comprising: an output unit that outputs a cutting force estimated by the method for adapting cutting force estimation according to claim 1 during machining.

8. The machine tool according to claim 7 , wherein the output unit derives and outputs at least one of a tool load, a tool wear, and a tool breakage based on a time-series value of the cutting force during cutting of the workpiece.

9. A cutting force estimation adaptation program for estimating a cutting force when machining a workpiece using a tool in a machine tool having a tool post on which a tool is attached and a workpiece holding mechanism for holding a workpiece, the cutting force being estimated by moving at least one of the tool post and the workpiece holding mechanism as a moving body by rotational driving of a ball screw, the program comprising: a first estimation step of calculating a mass of the moving body, a viscous friction coefficient in a drive system of the moving body, and a Coulomb friction in the drive system of the moving body using an equation of motion related to the movement of the moving body and a torsional torque of the ball screw; a second estimation step of estimating a cutting force based on the mass of the moving body estimated in the first estimation step, the viscous friction coefficient, and the Coulomb friction; A cutting force estimation adaptation program that causes a computer to execute the above.

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