WO2011137891A1 - Procédé pour reconnaître un broutage, dispositif de surveillance de machine-outil et machine-outil associée - Google Patents

Procédé pour reconnaître un broutage, dispositif de surveillance de machine-outil et machine-outil associée Download PDF

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Publication number
WO2011137891A1
WO2011137891A1 PCT/DE2011/000454 DE2011000454W WO2011137891A1 WO 2011137891 A1 WO2011137891 A1 WO 2011137891A1 DE 2011000454 W DE2011000454 W DE 2011000454W WO 2011137891 A1 WO2011137891 A1 WO 2011137891A1
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WO
WIPO (PCT)
Prior art keywords
signal
speed
machine tool
tool
frequency
Prior art date
Application number
PCT/DE2011/000454
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German (de)
English (en)
Inventor
Berend Denkena
Hans-Christian Möhring
Kai Martin Litwinski
Felix HACKELÖER
Original Assignee
Gottfried Wilhelm Leibniz Universität Hannover
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Publication of WO2011137891A1 publication Critical patent/WO2011137891A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
    • B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
    • B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
    • B23Q17/0971—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining by measuring mechanical vibrations of parts of the machine
    • B23Q17/0976—Detection or control of chatter
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/404—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control arrangements for compensation, e.g. for backlash, overshoot, tool offset, tool wear, temperature, machine construction errors, load, inertia
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
    • G05B19/4065—Monitoring tool breakage, life or condition
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/37—Measurements
    • G05B2219/37435—Vibration of machine
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/49—Nc machine tool, till multiple
    • G05B2219/49176—Compensation of vibration of machine base due to slide movement

Definitions

  • the invention relates to a method for detecting chatter in a cutting process by means of a machine tool, a machine tool monitoring device and a machine tool.
  • the background of the invention is rattle, which may also be referred to as regenerative rattle.
  • Regenerative rattle involves feedback between nonuniform machining of the workpiece and vibration of the machine tool. Due to the vibration of the machine tool, the workpiece is processed non-uniformly and due to the non-uniform processing, the process forces change, which in turn lead to an excitation of the machine tool. Regenerative chattering leads to poor quality components and can cause tool breakage and machine damage
  • a machine tool monitoring device in which by means of a broadband vibration sensor of a high-pass filter for damping machine noise low frequency, a signal level detector for rectifying and low-pass filtering of the signal, means for determining a current average signal level and means for Comparing the current signal level with the current average signal level, a detection of chattering is to be achieved.
  • a broadband vibration sensor of a high-pass filter for damping machine noise low frequency a signal level detector for rectifying and low-pass filtering of the signal
  • means for determining a current average signal level means for Comparing the current signal level with the current average signal level
  • the invention has for its object to reduce disadvantages in the prior art.
  • the invention solves the problem by a method for detecting chatter in a machining process by means of a machine tool, comprising the steps of: (i) determining a rotational speed of the machine tool, (ii) detecting an electrical variable which correlates with a process force of the machining process, (iii) digitizing the electrical quantity with a sampling frequency so that a time-dependent digital total (Iv) stochastically estimating a speed correlated, for example, meshing, correlated portion in the digital signal, (v) subtracting the speed correlated portion from the total signal to obtain a process signal, (vi) determining an instability parameter, which encodes a signal strength of the process signal from the process signal and (vii) outputting a warning signal when the instability parameter exceeds a preset value.
  • the invention solves the problem by a process monitoring device having a data input for reading in data of a machine control of a machine tool and a microprocessor, which is set up to carry out such a method.
  • the invention solves the problem by a machine tool having a spindle, a tool holder which can be driven by means of the spindle, and an electric controller for actuating the spindle, the controller being designed to automatically carry out such a method.
  • the machine tool is understood in particular to mean a cutting machine tool.
  • it is a machine tool for geometrically determined machining.
  • it can also be a machine tool for geometrically indeterminate machining.
  • the invention can be used particularly advantageously in grinding machines and milling machines.
  • a readout of a machine control of the machine tool is understood as determining a rotational speed of the machine tool.
  • the setpoint value for the speed is also stored in a digital memory in machine tools. The reading of such a memory is a determination of the speed.
  • electrical variables which correlates with a process force of the cutting process, in particular the motor current of the spindle and / or one of the feed axes of the spindle is understood.
  • electrical variables can also be detected by sensors, such as acceleration sensors, strain sensors, which are attached to the machine tool or the tool, and microphones which record an acoustic emission of the machining process.
  • the process force is understood in particular to mean a cutting force, a passive force and / or a feed force.
  • the stochastic estimation of the speed-correlated component in the digital signal means estimation in the mathematical sense.
  • the instability parameter is understood to mean any quantity that is large when chattering occurs, and which is then very small or zero when the chipping process is completely chatter-free.
  • the instability parameter may be, for example, the sum or the integral over the square of the process signal. It is also possible that the sum or the integral is calculated over a fixed interval. It is favorable, however, if this interval is a sliding interval. It is also possible to represent the instability parameter as a root over the sum of squares, as a moving average over the magnitude, from a statistical height curve, as a sliding filtered value, or as a parameter generated by a neural network.
  • a warning signal is meant, in particular, that a human perceptible or imperceptible signal is output that encodes the condition that at least one of given the likelihood of rattling.
  • machining processes are in principle periodic processes.
  • the spectrum of process forces shows for a stable and thus targeted process essentially shares in the speed and the higher-frequency multiple of the speed, the harmonics, an example of a milling process, this is in particular the cutting engagement frequency, ie the number of cutting times the speed.
  • regenerative chatter usually occurs at a natural frequency of the machine tool or the machine tool system and thus is usually not a multiple of the speed.
  • the rotational speed or a multiple of the rotational speed corresponds to the natural frequency
  • the invention is based on the finding that such processes are generally stable and therefore usually unproblematic in practice. It is therefore sufficient for practical purposes to monitor the cases where the rotational frequency or harmonic thereof is not equal to the rotational speed.
  • the speed-correlated fraction can be estimated stochastically. As a result, the amplitude and the phase of the speed-correlated component in the electrical variable are determined particularly accurately. It is also possible that the speed itself is estimated.
  • One aspect of the invention is therefore that the rotational speed of the machine tool is determined and that the amplitude and the phase are determined by stochastic estimation of the speed-correlated components.
  • the sampling frequency is less than twice the speed, in particular the sampling frequency is at most the speed. According to the Shannon-Therorie, frequency be recognized in a signal only if the sampling frequency is greater than twice the frequency to be searched.
  • the stochastic estimation comprises stochastically estimating an amplitude of a signal component with the rotational speed and an amplitude of a signal component with a harmonic of the rotational speed.
  • the harmonics of the rotation frequency are called.
  • the process forces depend on the position of the tool in a non-linear manner.
  • the cutting force has a time course in which a plurality of frequency components are included, which belong to the harmonics of the excitation frequency, namely when milling the meshing frequency. If, in addition to the amplitude of the signal part with the number of revolutions, the amplitudes of a number of revolutions with a harmonic of the revolutions are detected, the proportion of the total signal, which is due to the proper cutting process, can be determined particularly accurately. If that is the case
  • the machining process is preferably a stochastic estimation of an amplitude having the meshing frequency and an amplitude of a signal component of at least one harmonic of the meshing frequency.
  • the stochastic estimation is performed on an unfiltered signal.
  • Methods are known in the prior art in which the signal is filtered before and / or after digitizing. This is to ensure that only frequencies suitable for rattling are considered.
  • the chatter frequency can be determined with sufficient accuracy only in the context of a complex teaching process in order to achieve reliable results with such methods.
  • pre-filtering is not necessary, which advantageously increases the accuracy of the rattle detection.
  • At least one process parameter of the cutting process is also changed due to the warning signal. If the cutting process is milling, turning or grinding, for example, the feed is reduced until the instability parameter is again below the threshold value. Alternatively and additionally, it is also possible that a speed of the spindle of the machine tool is changed.
  • the chip removal volume is one of the most important parameters with regard to the economic efficiency of a machining process. It is therefore desirable to always work with the highest possible feed. Since currently the rattle can not be detected reliably, is used with feeds that are below the maximum possible feeds to avoid tool breakage due to chattering. Because rattling can be reliably detected with the method according to the invention, it is always possible to use the maximum possible feed rate. drive. Only when rattling occurs, the feed is reduced, and thus averted a threatening tool breakage or a sinking component quality. The inventive method thus allows an increase in the Zeitspanvolumens.
  • the method according to the invention is particularly advantageously a method for detecting chatter in a milling process.
  • the milling tool has at least two cutting edges, wherein a tooth engagement frequency is determined as an alternative to the rotational speed or in addition to the rotational speed and wherein as a speed-correlated component, a signal component with the tooth-engaging frequency and optionally of harmonics thereof is determined.
  • the sampling frequency is at most 700 hertz. This leads to a particularly low effort in the scanning of the electrical variable.
  • the electrical variable comprises a drive current of the spindle and / or a feed axis of the machine tool.
  • the electrical quantity is a scalar or a vector. It is thus possible to use the motor current in addition to a further electrical variable, such as, for example, an acceleration measured on the tool, on the tool holder or on another part of the machine tool.
  • a method for detecting wear is preferred with the above-mentioned steps, in which a temporal development of an amplitude of an ner harmonic of the rotational frequency or the meshing frequency is determined, wherein when a threshold value is exceeded, a warning signal is output.
  • this threshold value is an average value determined at the beginning of the procedure. The more a tool wears, the larger the harmonic components become. At the beginning of the cutting process, for example in the first minute, the cutting edge is still sharp.
  • the strength (for example in the form of the amplitude or its square) of a selected harmonic as the first harmonic can be determined by averaging.
  • the amplitude of the selected harmonic increases significantly in the course of the method, this indicates wear on the tool and a warning signal can be output so that the tool is changed. This makes it possible to always use a sufficiently unworn tool and at the same time not having to change the tool too early.
  • a machine tool monitoring device with the features of claim 9 is also possible.
  • This machine tool monitoring device can be an external component which can be connected to a machine control of the machine tool via a bidirectional interface.
  • An advantage of such a machine tool monitoring device is that existing machine tools can be retrofitted.
  • FIG. 1 shows a schematic view of a machine tool according to the invention, six diagrams illustrating the effect of the inventions to the invention method in a milling process with ge ringem noise and rattling at the end, a stable process with low noise,
  • FIG. 4 shows a stable process with a high noise component
  • FIG. 5 shows the diagrams according to FIG. 2 in a high-noise milling process and instability at the end
  • Figure 6 is a schematic view of a machine tool according to the invention.
  • FIG. 1 shows a machine tool 10 according to the invention, in the present case a milling machine.
  • the machine tool 10 has a spindle 12 with a tool holder 14 and an electric control 16 for driving the spindle and not shown feed drives, by means of which the spindle 12 at least in an xy plane, but especially in a z-direction, can be positioned ,
  • the controller 16 comprises a digital memory 18 in which a program according to the invention described above is stored. In addition, a value for a rotational speed n of the spindle 12 is stored in the digital memory 18. The controller 16 automatically controls the spindle 12 to rotate at the speed n. It is advantageous if the machine tool 10, as in the present embodiment, has a motor current detection device 20, with which a motor current i motor can be detected, from which the power P (t) dependent on a time t can be calculated. Alternatively or in addition to the motor current detection device 20, a machine tool 10 according to the invention, as in the present case, may have an acceleration sensor 22, which may be fastened to the tool holder 14.
  • the machine tool 10 it is possible, but not necessary, for the machine tool 10, as in the present case, to have a microphone 24 for picking up sound emissions arising during the machining of a workpiece 26.
  • the signal generated by the microphone 24 represents an electrical quantity G. The same applies to an acceleration a of the tool holder.
  • a tool 28 is accommodated in the present case in the form of a milling cutter, which is driven by the spindle 12.
  • the tool holder 14 is accelerated, which is recorded by the acceleration sensor 22.
  • the determined acceleration a is transmitted, for example, via a radio interface 30 to a receiver 32.
  • the motor current i otor (t) is detected by the motor current detection device 20 and sent to the controller 16.
  • a force gauge which operates for example on the basis of a strain gauge, may be present.
  • the force gauge 34 is arranged on the tool holder 14 or the spindle 12.
  • component F x of the process force F p which acts on the tool 28 at the point of engagement with the workpiece 26.
  • the dynamometer 34 is calibrated by applying a known force to the tool 28 at the cutting edge and simultaneously measuring the signal from the dynamometer 34. Thus, a calibration curve is obtained, from which it is possible to deduce in the process from the measurement signal of the force meter 34 to the acting process force F p .
  • the upper left partial image (a) shows the signal obtained by the force meter 34 in arbitrary units, for example the emitted voltage, as a function of the time t.
  • partial image (c) the signal according to partial image (a) is shown for a longer period of time.
  • the partial image (d) shows the overall signal 36, with the individual data points present at the discrete times t K being connected to one another in a continuous curve. It can be seen from the partial image (c) that the milling process becomes unstable at the time ti due to chattering. This is also evident in the overall signal 36.
  • the overall signal 36 is processed in the controller 16 (FIG. 1) by an estimator in the form of a microprocessor by means of a stochastic estimator, in the present case by means of a Kalman filter, which supplies a speed-correlated component 38.
  • This speed Relative fraction 38 is shown in the partial image (e).
  • the speed-correlated component 38 indicates the estimated value for the component which corresponds in the overall signal 36 to a harmonic oscillator which corresponds approximately to one frequency f tooth, namely the tooth meshing frequency.
  • the meshing frequency is the product of the speed n (in hertz and the number N of blades of the tool 28).
  • the speed-correlated component 38 is subtracted from the overall signal 36 (FIG. 2) and an instability parameter Q is calculated therefrom. This happens, for example, as follows:
  • the electrical size is called
  • S (t k ) denotes formula 3.
  • the mathematical method for calculating the estimated speed-correlated proportion S (t k ) will be described later.
  • the instability parameter Q can then be called
  • k a ktue ii denotes the currently active counting parameter k, which counts through the times t k .
  • the partial image (f) shows the time profile of the instability parameter Q. It can be seen that at the time t-1, when the chattering begins, the instability parameter increases. In addition, the root of the floating sum of squares of the electrical quantity G, as shown in the partial image (a), is drawn in a dashed line. It can be seen that both the instability parameter Q and the raw signal make the occurrence of rattling recognizable. However, the signal-to-background ratio is greater in the method of the invention.
  • FIG. 3 shows a stable milling process with a low noise component.
  • sub-image (f) it can be seen that the instability indicator Q always remains close to zero, whereas a parameter determined according to a method known from the prior art, which is shown as a dashed-dotted line, shows an increase. In order for such an increase not to be interpreted as rattling, training would have to take place in the present process according to the prior art. This is dispensable with the method according to the invention.
  • FIG. 4 shows a stable process with a high noise component. It can also be seen in sub-picture (f) that the instability indicator Q always remains at zero, whereas a parameter calculated according to the prior art can differ greatly from zero, from which it could be erroneously concluded that chattering occurs.
  • FIG. 5 shows an unstable process with a high noise component, in which rattle begins at the time t.sub.i.
  • the onset of rattling is with the inventive method on the basis of the instability parameter Q Good to recognize, since the instability parameter Q changes significantly from the time ti.
  • a parameter calculated according to the prior art also shows an increase, this is only slightly pronounced in relation to the previous values, so that it is difficult to detect chattering.
  • FIG. 6 shows a machine tool monitoring device 40, which has a bidirectional interface 42 to the machine tool 10.
  • the machine tool monitoring device 40 is configured to communicate with the controller 16, receive the rotational speed n and the magnitude G, for example the acceleration a, the force F p , the force F and / or a loudspeaker signal from the microphone 24.
  • the microphone 24 can also be part of the machine tool monitoring device 40.
  • the machine tool monitoring device 40 may transmit the warning signal via the interface 42, whereupon the controller 16 reduces a feed of the tool holder 14.
  • the method of chatter detection consists of several steps.
  • the process-relevant part is removed from the subsampled signal with the aid of a Kalman filter.
  • this proportion is deducted from the original measurement signal so that only a residual signal remains, which is not related to the process.
  • the amplitude of this residual signal is finally extracted by means of an RMS filter.
  • the reconstruction of the process signal from the subsampled signal is based, for example, on the Kalman filter described in Kaiman, R .: A new approach to linear filtering and prediction problems. Transaction of the ASME, Journal of Basic Engineering 82, pp. 35-45, 1960.
  • the dynamic system machine tool is modeled in state space.
  • the Kalman filter is an optimal predictor-corrector estimator - optimal in the sense that the error covariance matrix P of the state vector is minimized.
  • the system behavior is modeled and discretized for the signal processing.
  • the prediction step takes place in which an a-priori state estimation vector i i is calculated according to Equation 7 from the past state ⁇ ⁇ _.
  • the states are corrected by returning the estimation error of the outputs - the difference between an output signal generated by the a-priori estimation and the sensor signal - via the Kalman gain K to the state vector.
  • the Kalman Gain is determined such that the a-posteriori error covariance of the state vector V k is minimized.
  • the measurement noise is represented here by the covariance matrix R k .
  • p 4 (i -K t c d ) p Formula 1 1
  • the signal relevant for the chatter detection is a harmonic signal with the frequency of the cutting interventions, which is derived from the original signal. must be extracted.
  • the signal thus extracted is subtracted from the original measurement signal. There remains a residual signal, which essentially contains only the dominant chatter vibrations.
  • RMS root mean square

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Manufacturing & Machinery (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mechanical Engineering (AREA)
  • Automatic Control Of Machine Tools (AREA)
  • Machine Tool Sensing Apparatuses (AREA)

Abstract

L'invention concerne un procédé pour reconnaître un broutage lors d'un processus d'usinage par enlèvement de matière s'effectuant au moyen d'une machine-outil (10). Ce procédé comprend les étapes suivantes: (i) déterminer une vitesse de rotation (n) de la machine-outil (10), (ii) enregistrer une grandeur électrique (G) qui est corrélée à une force (Fp) du processus d'usinage par enlèvement de matière, (iii) numériser la grandeur électrique (G) à l'aide d'une fréquence de balayage (fAbtast) de manière à obtenir un signal total numérique (36) fonction du temps, (iv) évaluer de manière stochastique une partie (38) du signal total numérique, qui est corrélée à la vitesse de rotation, (v) soustraire la partie (38), qui est corrélée à la vitesse de rotation, du signal total (36) pour obtenir un signal d'usinage, (vi) déterminer un paramètre d'instabilité (Q), qui code l'intensité du signal d'usinage, à partir du signal d'usinage, et (vii) produire un signal d'alerte lorsque le paramètre d'instabilité (Q) dépasse une valeur de seuil préréglée.
PCT/DE2011/000454 2010-05-04 2011-04-21 Procédé pour reconnaître un broutage, dispositif de surveillance de machine-outil et machine-outil associée WO2011137891A1 (fr)

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DE102010019419.0A DE102010019419B4 (de) 2010-05-04 2010-05-04 Verfahren zum Erkennen von Rattern, Werkzeugmaschinen-Überwachungsvorrichtung und Werkzeugmaschine

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US10503142B2 (en) * 2016-08-02 2019-12-10 Dr. Johannes Heidenhain Gmbh Method and device for controlling a milling machine

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EP3118593A1 (fr) * 2015-07-17 2017-01-18 Siemens Aktiengesellschaft Procede et systeme de detection d'oscillations auto-excitees
JP6495797B2 (ja) * 2015-10-19 2019-04-03 オークマ株式会社 工作機械の主軸異常検出装置及び主軸異常検出方法
TWI607830B (zh) 2016-11-15 2017-12-11 財團法人工業技術研究院 工具機進給裝置設計系統及其方法
CN108958164A (zh) * 2017-05-23 2018-12-07 大族激光科技产业集团股份有限公司 一种数控机床的停机控制方法及装置
DE102018206865B4 (de) * 2018-05-04 2021-08-05 Audi Ag Verfahren zur Bearbeitung eines Rohbauteils durch eine Bearbeitungsmaschine und Bearbeitungsmaschine zur Bearbeitung eines Rohbauteils

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US10503142B2 (en) * 2016-08-02 2019-12-10 Dr. Johannes Heidenhain Gmbh Method and device for controlling a milling machine
JP2018043317A (ja) * 2016-09-14 2018-03-22 オークマ株式会社 工作機械

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