JP2019111648A - Machine learning device and thermal displacement correction device - Google Patents

Abstract

To provide a machine learning device and a thermal displacement correction device both capable of deriving a highly accurate correction formula, and further implementing a highly accurate collection at a low cost.SOLUTION: A machine learning device comprises: a measured data acquisition part 11 for acquiring measured data groups; a thermal displacement amount acquisition part 12 for acquiring actually measured data of thermal displacement amount of a machine element; and a storage part 13 for storing teacher data comprising the measured data groups acquired by the measured data acquisition part as input data and the actually measured data of thermal displacement amount of the machine element measured by the thermal displacement amount acquisition part 12 as labels in association with each other; and a calculation learning part 14 for performing machine learning on the basis of the measured data groups and the actually measured data of the thermal displacement amount of machine element, to define a thermal displacement amount prediction formula for calculating the thermal displacement amount of the machine element on the basis of the measured data group.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a machine learning device and a thermal displacement correction device applied to a machine tool.

  As one of the main factors of processing error in a machine tool, there is a problem that relative thermal displacement occurs between a tool and a work due to thermal expansion of machine elements of the machine tool. More specifically, as components of a machine tool, for example, a spindle, a spindle device, a bed, a column, a work table, a tool and the like can be mentioned. Heat generation due to rotation of the spindle, heat generation by the spindle drive motor, heat absorption by the coolant supplied from the coolant supply device to the tool, heat absorption by the lubricant oil supplied from the lubricating oil supply device to the spindle bearing, etc. The components may thermally deform and cause relative thermal displacement between the tool and the workpiece.

  On the other hand, machining is performed by correcting the command value of the numerical control device that controls the machine tool, taking into consideration the influence of thermal expansion of the spindle due to various heat sources such as the heat source near the spindle of the machine tool and the outside air temperature. Techniques for improving the accuracy have been used conventionally (e.g., Patent Document 1).

Japanese Patent Application Laid-Open No. 7-75937

  However, in the technique described in Patent Document 1, it is merely mentioned that a plurality of temperature sensors are installed as a method of acquiring the characteristic value of the machine tool, and in the case where high accuracy correction is guaranteed. It was not.

  In addition, since it takes time for the heat measured by the temperature sensor to spread and be thermally expanded due to heat conduction, it is necessary to evaluate the time delay for highly accurate correction, and the correction equation becomes complicated.

  Further, since the structure and members change depending on the machine on which the numerical control device is mounted, the optimum thermal displacement correction equation changes depending on the type of machine.

  In addition, since the external heat source changes depending on the use environment, such as the presence of surrounding heat sources and entrances, the appropriate temperature sensor arrangement changes, and the optimum correction equation also changes accordingly. In addition, increasing the number of measuring instruments in order to position the temperature sensor leads to an increase in production costs and maintenance costs.

  SUMMARY OF THE INVENTION In view of such circumstances, the present invention provides a machine learning device and a thermal displacement correction device capable of performing highly accurate correction formula derivation, that is, highly accurate correction itself at low cost. The purpose is

  (1) A machine learning apparatus according to the present invention is a machine tool (for example, a machine tool 35 described later) having a thermally expanding machine element, temperature data of the machine element and its surroundings, and / or operating state data of the machine element A machine learning apparatus (for example, machine learning apparatus 10, 10A, 10B described later) that optimizes a calculation formula for estimating the thermal displacement amount of the machine element based on a measurement data group including A measurement data acquisition unit (for example, measurement data acquisition unit 11 described later) for acquiring a data group, and a thermal displacement amount acquisition unit for acquiring an actual measurement value of the thermal displacement amount of the mechanical element (for example, a thermal displacement amount acquisition unit described later 12) and the measurement data group acquired by the measurement data acquisition unit is used as input data, and the measured value of the thermal displacement amount of the mechanical element acquired by the thermal displacement amount acquisition unit is a label Machine learning based on a storage unit (for example, a storage unit 13 described later) that stores data in association with each other as teacher data, the measurement data group, and the measured value of the thermal displacement amount of the machine element A calculation formula learning unit (for example, calculation formula learning section 14 described later) which sets a thermal displacement quantity prediction calculation formula for calculating the thermal displacement quantity of the mechanical element based on the measurement data group, and the calculation formula learning The storage unit is an estimated value of the thermal displacement of the mechanical element calculated by substituting the measurement data group within a predetermined period stored in the storage as teaching data in the thermal displacement prediction calculation equation, and the storage. The thermal displacement prediction equation is set based on the difference between the measured value of the thermal displacement of the mechanical element within the predetermined period and stored as a label in a part.

  (2) In the machine learning device according to (1), the measurement data acquisition unit (for example, the measurement data acquisition unit 11 described later) further adds measurement data to the measurement data group and / or from the measurement data group The second measurement data group is acquired by excluding measurement data, and the second measurement data group is stored as input data in the storage unit (for example, storage unit 13 described later), and the calculation formula learning unit (For example, the calculation formula learning unit 14 described later) may further set a second thermal displacement amount prediction calculation formula for calculating the thermal displacement amount of the mechanical element based on the second measurement data group.

  (3) In the machine learning device according to (2), the contribution degree determination unit (for example, the contribution degree determination unit 15 described later) that determines the contribution degree to the prediction of the thermal displacement amount of the measurement data included in the measurement data group The contribution degree determination unit may calculate a first thermal displacement amount predicted value and a thermal displacement amount calculated by a first thermal displacement amount prediction calculation formula set based on a measurement data group including measurement data of a contribution degree calculation target. Second thermal displacement amount calculated by a second thermal displacement amount prediction calculation formula set based on the second measurement data group excluding the first error which is an error from the actual measurement value and the measurement data of the contribution degree calculation target The degree of contribution of the measurement data for which the degree of contribution calculation is to be calculated may be determined based on the difference between the predicted displacement amount and the second error that is the error between the measured thermal displacement amount.

  (4) In the machine learning device according to (3), optimized measurement data consisting of a combination of measurement data that provides the best accuracy using a preset number of measurement data among the measurement data groups currently acquired. An optimization measurement data selection unit (for example, an optimization measurement data selection unit 16 described later) for selecting a group is further provided, and the optimization measurement data selection unit determines the contribution from the currently acquired measurement data group The measurement data group from which the measurement data with the least degree of contribution determined by the part has been removed is selected as the first measurement data group, and the contribution degree determination is made from the i-th (1 ≦ i) measurement data group The measurement data group excluding the measurement data with the least degree of contribution determined by the unit is repeatedly selected as the (i + 1) -th measurement data group, whereby a preset number of measurement data is deleted. It may be selected to optimize measurement data group consisting of data.

  (5) In the machine learning device of (1) to (4), the thermal displacement prediction equation may use a first-order lag element of measurement data included in the measurement data group.

  (6) In the machine learning device according to any one of (1) to (5), the thermal displacement prediction equation may use a time shift element of measurement data included in the measurement data group.

  (7) In the machine learning device of (1) to (6), the thermal displacement prediction calculation formula may be set based on machine learning by a neural network.

  (8) In the machine learning device according to any one of (1) to (6), the calculation formula learning unit (for example, the calculation formula learning unit 14 described later) is configured to perform the learning based on machine learning using L2 regularization multiple regression analysis. A prediction calculation formula of the thermal displacement may be set.

  (9) In the machine learning device according to any one of (1) to (6), the calculation formula learning unit (for example, the calculation formula learning unit 14 described later) calculates the thermal displacement amount by using sparse regularization learning. May be set.

  (10) The machine learning device according to (9), further including: a detection unit (for example, a detection unit 17 described later) that detects measurement data that does not contribute to accuracy improvement of thermal displacement prediction included in the measurement data group; The detection unit may perform detection based on a prediction calculation formula of the thermal displacement amount set by sparse regularization learning.

  (11) The machine learning device of (1) to (10) may be included in a control device (for example, the control device 30 described below) of the machine tool (for example, the machine tool 35 described below).

  (12) The thermal displacement correction device according to the present invention is based on the thermal displacement prediction calculation equation set by the machine learning device (for example, machine learning device 10, 10A, 10B described later) of (1) to (11). And a correction amount calculation unit (for example, a correction amount calculation unit 22 described later) which calculates a correction amount corresponding to the thermal displacement amount of the mechanical element calculated from the measurement data group, and the correction amount calculation unit And a correction unit (for example, a correction unit described later) that corrects the machine position of the machine element based on the correction amount of the machine element.

  (13) The thermal displacement correction device of (12) may be included in a control device (for example, the control device 30 described later) of the machine tool (for example, the machine tool 35 described later).

  According to the present invention, it is possible to carry out the derivation of a highly accurate correction formula, and hence the highly accurate correction itself, at low cost.

It is a block diagram showing a thermal displacement amendment system concerning a 1st embodiment of the present invention. It is a block diagram showing details of a machine learning device and a thermal displacement correction device according to the first embodiment of the present invention. It is a block diagram showing the details of the machine tool and control device concerning a 1st embodiment of the present invention. It is a flowchart which shows the operation | movement at the time of the machine learning in the machine learning apparatus concerning 1st Embodiment of this invention. It is a flowchart which shows the example of the neural network used for the machine learning in 1st Embodiment of this invention. It is a flowchart which shows the example of the neural network used for the machine learning in 1st Embodiment of this invention. It is a flowchart which shows the example of the neural network used for the machine learning in 1st Embodiment of this invention. It is a flowchart which shows the operation | movement at the time of correction | amendment in the thermal displacement correction apparatus which concerns on 1st Embodiment of this invention. It is a block diagram which shows the detail of the machine learning apparatus based on 2nd Embodiment of this invention. It is a flowchart which shows the operation | movement at the time of contribution degree determination in the machine learning apparatus which concerns on 2nd Embodiment of this invention. It is a flow chart which shows operation of optimization measurement data group selection in a machine learning device concerning a 2nd embodiment of the present invention. It is a block diagram which shows the detail of the machine learning apparatus based on 3rd Embodiment of this invention. It is a block diagram showing operation at the time of detection of measurement data which does not contribute to accuracy improvement in a machine learning device concerning a 3rd embodiment of the present invention.

First Embodiment
Hereinafter, a first embodiment of the present invention will be described based on the drawings. FIG. 1 is a block diagram showing a thermal displacement correction system according to the present embodiment. FIG. 2 is a block diagram showing the details of the machine learning device and the thermal displacement correction device according to the present embodiment. FIG. 3 is a block diagram showing details of the machine tool and control device according to the present embodiment.

<Configuration of Thermal Displacement Correction System 100>
First, the configuration of the thermal displacement correction system 100 according to the present embodiment will be described. The thermal displacement correction system 100 includes a machine learning device 10, a thermal displacement correction device 20, a control device 30, a machine tool 35, and a network 40, as shown in FIG. The machine learning device 10, the thermal displacement correction device 20, the control device 30, and the machine tool 35 may be one or more.

  Here, the control device 30 and the machine tool 35 are connected in a communicable manner in a one-to-one set. For example, a plurality of sets of the control device 30 and the machine tool 35 may be installed in the same factory, or may be installed in different factories.

  The machine learning device 10, the thermal displacement correction device 20, the control device 30, and the machine tool 35 are connected to the network 40, and can communicate with each other via the network 40. . The network 40 is, for example, a LAN (Local Area Network) built in a factory, the Internet, a public telephone network, or a combination of these. There are no particular limitations on the specific communication method in the network 40, and whether it is wired connection or wireless connection. Note that the thermal displacement correction device 20 and the control device 30 may not be communicated using the network 40 but may be directly connected via a connection unit, and the machine learning device 10 and the control device 30 are connected via a connection unit. Directly connected.

  Next, the functions of these devices included in the thermal displacement correction system 100 will be described based on FIG. FIG. 2 is a block diagram showing functional blocks included in each device. In addition, since each thermal displacement correction | amendment apparatus 20 has an equivalent function, respectively, only 1 unit is shown in figure in FIG. Similarly, since each control device 30 and each machine tool 35 also have the same function, only one is shown in FIG. The illustration of the network 40 existing between the devices is omitted.

  As shown in FIG. 3, in the machine tool 35, cutting is performed by a main shaft on which a cutter is attached and which is rotated by the main shaft motor 36, and a feed shaft for feeding the main shaft. That is, the cutter is rotated by the spindle motor 37 driving the spindle 36 and is fed out by the feed shaft motor 39 driving the feed shaft 38. In addition, although the Example demonstrates the machine tool 35 as a cutting machine, it is not limited to this.

The control device 30 controls the machine tool 35 to perform predetermined cutting by sending a control signal to the machine tool 35 as shown in FIGS. 2 and 3. The control device 30 stores a plurality of processing programs 31 determined according to the processing content of the workpiece. Then, the control device 30 reads out and interprets the processing program 31 to extract the conditions for cutting (for example, the acceleration / deceleration frequency of the spindle, the number of rotations, the cutting load, the cutting time), and position command data etc. The main spindle motor 37 and the feed axis motor 39 of the machine tool 35 are driven based on the program reading / interpreting unit 32 output to the thermal displacement correction device 20 and the position command data after thermal displacement correction output from the thermal displacement correction device 20 Motor control unit 33 for producing an operation command to be performed, a motor drive amplifier 34A for amplifying the operation command and outputting it to the spindle motor 37 of the machine tool 35, and a motor drive amplifier 34B for outputting to the feed shaft motor 39 ing. Further, the program reading / interpreting unit 32 may extract conditions of cutting (for example, the acceleration / deceleration frequency of the spindle, the number of rotations, the cutting load, the cutting time) and output it to the thermal displacement correction device 20. In addition, the control device 30 may output information obtained in real time from the spindle motor 37 and / or the feed shaft motor 39 to the thermal displacement correction device 20 regarding the number of rotations, the cutting time, and the like.
Further, in the control device 30, there are a plurality of terminals for connecting sensors attached to the machine tool 35 in order to obtain measurement data. By connecting and disconnecting the sensor cable from this terminal, it is possible to add, remove, or change the location of the sensor connected to the control device 30. Moreover, it is also possible to change the arrangement of the sensors installed on the machine tool 35. Note that the sensor layout change can be treated as a mode in which the sensor is removed from the installation place of the machine tool 35 and the sensor is added to the installation change place.

  As shown in FIG. 2, the machine learning apparatus 10 learns a thermal displacement amount prediction calculation formula in the machine tool 35 by supervised machine learning. Therefore, the machine learning apparatus 10 includes a measurement data acquisition unit 11, a thermal displacement amount acquisition unit 12, a storage unit 13, and a calculation formula learning unit 14.

  The measurement data acquisition unit 11 acquires a measurement data group from the control device 30. Here, the measurement data may include temperature data of the machine element of the machine tool 35 and the periphery measured by the temperature sensor. Further, the measurement data may be operation state data of the machine element of the machine tool 35, specifically, for example, the temperature of the rotation speed of the spindle of the machine tool 35, the coolant flow rate to the spindle, the amount of lubricating oil to the spindle bearing, etc. It may also include physical property values in places where the sensor can not be attached.

  The thermal displacement amount acquisition unit 12 acquires an actual measurement value of the thermal displacement amount of the mechanical element of the machine tool 35 detected by, for example, a probe.

  The storage unit 13 uses the measurement data group acquired by the measurement data acquisition unit 11 as input data, associates the measured values of the thermal displacement amount of the mechanical element acquired by the thermal displacement amount acquisition unit 12 with each other as labels, and transmits the teacher data Remember as.

The calculation formula learning unit 14 performs machine learning based on the measured data group and the measured value of the thermal displacement amount of the mechanical element, thereby calculating the thermal displacement amount of the mechanical element based on the measured data group Set the prediction formula.
More specifically, since there are a plurality of independent variables in the measurement data group, the calculation formula learning unit 14 instructs the storage unit 13 to calculate the thermal displacement amount to be calculated by multiple regression of the generalized linear model. The estimated value of the thermal displacement of the mechanical element calculated by substituting the measured data group within the predetermined period stored as data, and the thermal displacement of the mechanical element within the predetermined period stored as the label in the storage unit 13 Based on the difference from the actual measurement value, for example, the thermal displacement prediction equation is set so as to minimize the difference by the least square method or the like.
Specifically, the calculation formula learning unit 14 sets measurement data (input data) to X1, X2,... Xn, and heat is generated for each of the components such as the spindle, bed, and column that constitute the machine tool. When the predicted value of displacement is f (X1, X2, ..., Xn) (n is a natural number) and the actual value of thermal displacement is YL, f (X1, X2, ..., Xn) and YL Set the thermal displacement prediction equation that minimizes the difference between

As shown in FIG. 2, the thermal displacement correction device 20 includes a correction amount calculation unit 22 as a correction amount calculation unit, and a correction unit 24 as a correction execution unit.
The correction amount calculation unit 22 calculates a correction amount corresponding to the thermal displacement amount of the mechanical element calculated from the measurement data group (determination data) based on the thermal displacement amount prediction calculation equation set by the machine learning device 10. .
The correction unit 24 corrects the machine position of the mechanical element based on the correction amount of the mechanical element calculated by the correction amount calculation unit 22. Alternatively, the correction unit 24 transmits the correction amount of the mechanical element to the control device 30. More specifically, as shown in FIG. 3, the correction unit 24 corrects the cutting conditions output from the program reading / interpreting unit 32 of the control device 30 using the correction amount of the mechanical element. And outputs position command data to the motor control unit 33.

<Operation at machine learning>
Next, an operation at the time of machine learning in the thermal displacement correction system 100 according to the present embodiment will be described. FIG. 4 is a flowchart showing the operation of the machine learning device 10 at the time of machine learning.

In step S11, the measurement data acquisition unit 11 of the machine learning device 10 acquires a measurement data group from the control device 30. More specifically, the measurement data acquisition unit 11 acquires temperature data and / or operation state data of the machine element of the machine tool 35 and the periphery thereof. The operating state data may include, for example, the rotational speed of the main shaft, the coolant flow rate, and the lubricating oil flow rate.
For example, as the measurement data, not the data of the temperature itself but data of the amount of temperature change may be acquired. Furthermore, data on the amount of temperature change from the initial temperature may be acquired as data on the amount of temperature change, or data on the amount of temperature change from the previously measured temperature to the temperature measured this time may be acquired.
Further, the heat absorption amount by the coolant and the lubricant heat absorption amount may be included as the operation state data.

  In step S12, the thermal displacement acquisition unit 12 of the machine learning device 10 acquires an actual measurement value of the thermal displacement of the mechanical element of the machine tool 35, which is detected by, for example, a probe. Specifically, for example, components in the X, Y, and Z axial directions of the thermal displacement amount may be measured, and a set of those measured values may be used as an actual measurement value.

  In step S13, the storage unit 13 of the machine learning device 10 uses the measurement data group acquired by the measurement data acquisition unit 11 as input data, and the measured value of the thermal displacement amount of the mechanical element acquired by the thermal displacement amount acquisition unit 12 As a label, as a set associated with each other, and stored as teacher data.

  In step S14, the calculation formula learning unit 14 of the machine learning device 10 executes machine learning based on the teacher data. An example of a specific method of machine learning will be described later.

  In step S <b> 15, the calculation formula learning unit 14 of the machine learning device 10 determines whether to end machine learning or repeat machine learning. Here, the condition for terminating machine learning can be arbitrarily determined. Here, when the machine learning is ended (S15: YES), the process proceeds to step S16. When machine learning is repeated (S15: NO), the process returns to step S11 and repeats the same process.

  In step S16, the machine learning device 10 transmits the thermal displacement amount prediction calculation equation set by machine learning up to that point to each thermal displacement correction device 20 via the network 40.

  Further, the storage unit 13 of the machine learning device 10 stores the thermal displacement amount prediction calculation formula. Thus, when a thermal displacement amount prediction calculation equation is requested from the newly installed thermal displacement correction device 20, the thermal displacement amount prediction calculation equation can be transmitted to the thermal displacement correction device 20. Further, when new teacher data is acquired, further machine learning can be performed.

<Example of machine learning method>
As described above, in step S14 of FIG. 4, the formula learning unit 14 performs machine learning using teacher data, and an example of the method will be described in detail.

As a first method, based on the multiple regression of the generalized linear model, the square of the estimated value of the thermal displacement calculated with the thermal displacement prediction equation Y = a1X1 + a2X2 +... AnXn and the actual value of the thermal displacement It is possible to infer and set a coefficient that minimizes the error by machine learning using the least squares method. Here, Y is a thermal displacement amount estimated value, X 1, X 2,..., X n are measurement data values, and a 1, a 2,..., An are coefficients determined by multiple regression.
Specifically, when the measurement data is Xk and the label is YL,

を、複数の教師データに渡って合計した値が最小となるような係数ａｋの組を求める。なお、ｋは自然数、ｎは任意の整数であり、ｋ≦ｎである。

A set of coefficients ak is calculated such that the value of the sum over plural pieces of teacher data is minimized. Here, k is a natural number, n is an arbitrary integer, and k ≦ n.

In the first method, it is possible to carry out multiple regression analysis in which the L2 regularization term is taken into consideration, instead of the normal multiple regression analysis. That is, the value obtained by adding the L2 regularization term to the squared error between the thermal displacement estimated value calculated by the thermal displacement prediction calculation formula Y = a1 × 1 + a2 × 2 +. Can be set by inference by machine learning. Here, similarly to the above, Y is a thermal displacement estimated value, X 1, X 2... X n are each measured data values, and a 1, a 2 ... an are determined by multiple regression considering L 2 regularization terms It is a coefficient.
Specifically, when the measurement data is Xk and the label is YL,

を、複数の教師データに渡って合計した値が最小となるような係数ａｋの組を求める。なお、ｎは自然数であり、学習に使用する教師データの測定点数を意味する。また、λはハイパーパラメータであり、機械学習を行う前に、予め設定されるパラメータである。

A set of coefficients ak is calculated such that the value of the sum over plural pieces of teacher data is minimized. In addition, n is a natural number and means the number of measurement points of teacher data used for learning. Also, λ is a hyper parameter, which is a parameter set in advance before machine learning.

  Also, in the first method, it is possible to perform sparse regularization. For example, it is possible to perform multiple regression analysis in consideration of the L1 regularization term. Specifically, when the measurement data is Xk and the label is YL,

を、複数の教師データに渡って合計した値が最小となるような係数ａｋの組を求める。なお、ｎは自然数であり、学習に使用する教師データの測定点数を意味する。また、λはハイパーパラメータであり、機械学習を行う前に、予め設定されるパラメータである。λを大きく設定するほど、ａｋにおいて０となる項の数が増加する効果がある。

A set of coefficients ak is calculated such that the value of the sum over plural pieces of teacher data is minimized. In addition, n is a natural number and means the number of measurement points of teacher data used for learning. Also, λ is a hyper parameter, which is a parameter set in advance before machine learning. As the λ is set to a larger value, the number of terms that are zero at ak can be increased.

  Although L2 regularization and L1 regularization were used as an example of regularization, this is an example and it is not limited to this.

  In the first method, it is also possible to use a first-order delay element of measurement data or a time shift element of measurement data as input data when performing the above-described machine learning. Specifically, assuming that the estimated value of the thermal displacement amount at time t is Y (t) and the measured value of the sensor Xk at time t is Xk (t), the thermal displacement amount using the first-order lag element of the measurement data The prediction formula is

となり、機械学習により、係数ａｋ、ｂｋ、Ｔｋを求める。なお、ΔｔｋはセンサＸｋの測定値のサンプリングタイムである。

The coefficients ak, bk, and Tk are obtained by machine learning. Here, Δtk is a sampling time of the measured value of the sensor Xk.

  Further, assuming that the estimated value of the thermal displacement amount at time t is Y (t) and the measured value of the sensor Xk at time t is Xk (t), the thermal displacement amount prediction equation using the time shift element of the measurement data is

となり、機械学習により、係数ａｋτ、Ｔｋを求める。なお、ΔｔｋはセンサＸｋの測定値のサンプリングタイムである。

The coefficients akτ and Tk are determined by machine learning. Here, Δtk is a sampling time of the measured value of the sensor Xk.

  Further, learning may be performed by adding various regularization terms such as L1 regularization terms and L2 regularization terms after using first-order lag elements of measurement data and time shift elements of measurement data. In this case, regularization terms for various parameters such as ak, akτ, bk and Tk are added.

As a second method, it is possible to implement machine learning using a known neural network.
For example, it is possible to use a single layer neural network as shown in FIG. In FIG. 5, the spindle thermal displacement estimated value is determined based on temperature data A, temperature data B, temperature data C, and operation state data A, and the feed axis is based on temperature data B, temperature data D, and operation state data B. Although the thermal displacement estimated value is obtained, this is an example and is not limited thereto.

It is also possible to use a multi-layered neural network as shown in FIGS. 6A and 6B. In particular, a recurrent neural network in which the output of the intermediate layer simultaneously becomes the input of the intermediate layer as described in FIG. 6A, and the history from the past to the present for a predetermined time as described in FIG. 6B. It is effective to use a time delay type feed forward neural network or the like in which data, for example, temperature data At, temperature data At-1, and temperature data At-2 simultaneously become input values.
Further, as the input data of the neural network, a time shift element of the measurement data (a first order delay element of the measurement data and / or a time shift element of the measurement data) may be used.
Further, when performing learning of a neural network, for example, learning may be performed to which various regularization terms such as L2 regularization terms are added.
Although only one intermediate layer is described in FIGS. 6A and 6B, the present invention is not limited to this, and it is possible to set an arbitrary number of intermediate layers. Further, in FIG. 6A, although the temperature data A, the temperature data B, and the operation state data A are input, the spindle thermal displacement estimated value and the feed shaft thermal displacement estimated value are output, but this is an example. It is not limited to this. In FIG. 6B, the temperature data At, the temperature data At-1, and the temperature data At-2 are input, and the spindle thermal displacement estimated value and the feed shaft thermal displacement estimated value are output, but this is an example. There is no limitation to this.

<Operation at correction>
Next, the operation at the time of correction in the thermal displacement correction system 100 according to the present embodiment will be described. FIG. 7 is a flowchart showing the operation of the thermal displacement correction device 20 at the time of this correction.

  In step S21, the correction amount calculation unit 22 calculates the correction amount corresponding to the thermal displacement amount of the mechanical element calculated from the measurement data group based on the thermal displacement amount prediction calculation equation set by the machine learning device 10. .

In step S22, the correction unit 24 offsets the thermal displacement amount by correcting the mechanical position of the mechanical element based on the correction amount of the mechanical element calculated by the correction amount calculation unit 22.
Although different from the description in FIG. 7, in step S22, the correction unit 24 may transmit the correction amount of the mechanical element to the control device 30. More specifically, the correction unit 24 corrects the coordinate position output from the program reading / interpreting unit 32 of the control device 30 using this correction amount of the machine element, and outputs position command data to the motor control unit 33. Alternatively, the machining program 31 may be executed after correcting the machining program 31 in advance using this correction amount.

<Effects of the First Embodiment>
As described above, in the present embodiment, in the machine learning apparatus 10, the measurement data group including the temperature data of the machine element of the machine tool 35 having the thermally expanding machine element and its periphery and / or the operating state data of the machine element. Based on the thermal displacement prediction equation for estimating the thermal displacement of the machine element can be optimized by machine learning.

  In addition, the temperature change at each point for acquiring measurement data is delayed by heat conduction, and the temperature change is reflected on the thermal displacement, so that the machine learning method capable of considering the delay is effective.

  Further, according to the operation environment of the machine tool 35 and the type of the machine tool 35, it is possible to tune the correction formula based on the thermal displacement prediction calculation formula and the thermal displacement prediction calculation formula to improve the accuracy.

Second Embodiment
Hereinafter, a second embodiment of the present invention will be described based on the drawings. FIG. 8 is a block diagram showing the details of the machine learning apparatus according to the present embodiment. FIG. 9 is a flowchart showing an operation at the time of contribution degree determination in the machine learning device according to the present embodiment. FIG. 10 is a flow chart showing the operation of selecting the optimization measurement data group in the machine learning apparatus according to the present embodiment.

<Configuration of Thermal Displacement Correction System 100A>
As compared with the thermal displacement correction system 100 according to the first embodiment, in the thermal displacement correction system 100A according to the second embodiment, the constituent elements of the machine learning apparatus 10A according to the second embodiment are as shown in FIG. In addition to the component of the machine learning apparatus 10 which concerns on 1st Embodiment, it differs by the point by which the contribution determination part 15 and the optimization measurement data selection part 16 are added. The other configuration is basically the same as that of the first embodiment described above, so the same reference numerals are given to the same members, and the description thereof is omitted.

The contribution degree determination unit 15 determines the contribution degree to the prediction of the thermal displacement amount of each measurement data included in the measurement data group.
More specifically, the contribution degree determination unit 15 calculates the first thermal displacement amount prediction calculation formula set by machine learning based on the first measurement data group including the measurement data of the contribution degree calculation target. The first error (absolute value) which is the error between the thermal displacement predicted value and the thermal displacement actual value, and the second set by machine learning based on the second measurement data group excluding the measurement data of the contribution degree calculation target Based on the difference between the second thermal displacement amount predicted value calculated by the thermal displacement amount prediction calculation formula and the second error (absolute value) which is an error between the thermal displacement amount actual measurement value, the measurement data of the contribution degree calculation target Determine the degree of contribution. Specifically, it can be determined that the degree of contribution of the measurement data of the degree of contribution calculation is larger as the difference between the first error and the second error is larger.
When determining the difference between the first error and the second error, it is preferable to make the determination based on a set of first errors and a set of second errors corresponding to a plurality of teacher data sets. In this case, for example, the average value or the maximum value of the difference between the first error and the second error can be used.

The optimization measurement data selection unit 16 selects, for example, an optimization measurement data group consisting of a combination of a preset number of measurement data excluding measurement data having a small degree of contribution, among the currently acquired measurement data groups. Do. Here, “the number of measurement data” means, for example, the number of types of measurement data, which is different for each sensor that measures the measurement data.
More specifically, the optimization measurement data selection unit 16 removes the measurement data group obtained by removing the measurement data with the smallest contribution degree determined by the contribution degree determination unit 15 from the measurement data group currently acquired, It is selected as the first measurement data group. Next, the optimization measurement data selection unit 16 removes the measurement data group obtained by removing the measurement data with the least contribution degree determined by the contribution degree determination unit 15 from the i-th (1 ≦ i) -th measurement data group. By repeatedly selecting as the (i + 1) th measurement data group, an optimization measurement data group consisting of a preset number of measurement data is selected. Here, i is a natural number.

<Operation when determining contribution degree>
Next, an operation of determining the contribution degree of measurement data included in the measurement data group in the machine learning device 10A will be described. FIG. 9 is a flowchart showing the operation of the machine learning device 10A at the time of determination of the degree of contribution.

  In step S31, according to the flow described in FIG. 4, the calculation formula learning unit 14 sets a first thermal displacement amount prediction calculation formula based on the first measurement data group including all the measurement data and the thermal displacement amount actual measurement value. Do.

  In step S <b> 32, the contribution degree determination unit 15 calculates a first thermal displacement predicted value using the first thermal displacement prediction equation.

  In step S33, the contribution degree determination unit 15 calculates a first error (absolute value) which is an error between the first thermal displacement amount predicted value and the thermal displacement amount actual measurement value.

  In step S34, according to the flow described in FIG. 4, the calculation formula learning unit 14 performs second thermal displacement amount prediction calculation based on the second measurement data group excluding the measurement data of the contribution degree calculation target and the thermal displacement amount actual value. Set the formula.

  In step S35, the contribution degree determination unit 15 calculates a second predicted thermal displacement value using the second predicted thermal displacement equation.

  In step S36, the contribution degree determination unit 15 calculates a second error (absolute value) which is an error between the second thermal displacement amount predicted value and the thermal displacement amount actual measurement value.

  Steps S31 to S36 may be performed in parallel as shown in FIG. 9, or may be performed continuously.

  In step S37, the contribution degree determination unit 15 determines the contribution degree of the measurement data of the contribution degree calculation target based on the difference between the first error and the second error. Specifically, it can be determined that the degree of contribution of the measurement data to be subjected to the degree of contribution calculation increases as the difference increases.

<Operation when selecting optimized measurement data group>
Next, the operation of the machine learning device 10A when selecting an optimized measurement data group including a predetermined number of measurement data by removing measurement data with a small degree of contribution will be described. FIG. 10 is a flowchart showing the operation of the machine learning device 10A at the time of determination of the degree of contribution.

  In step S41, the optimization measurement data selection unit 16 sets the number of measurement data to be used finally. In addition, the number of measurement data to be used finally is assumed to be less than the number of measurement data at our site.

  In step S42, the contribution degree determination unit 15 determines the degree of contribution of each measurement data forming the currently acquired measurement data group according to the flow described in FIG.

  In step S43, the optimization measurement data selection unit 16 removes the measurement data with the smallest contribution from the currently acquired measurement data group, and sets this measurement data group as the “first measurement data group”. Do.

  In step S44, the optimization measurement data selection unit 16 sets i = 1 as an initial value.

  In step S45, the optimization measurement data selection unit 16 selects the i-th measurement data group.

  In step S46, the contribution degree determination unit 15 determines the contribution degree of each measurement data forming the i-th measurement data group according to the flow described in FIG.

  In step S47, the optimization measurement data selection unit 16 removes the measurement data with the smallest contribution from the i-th measurement data group, and sets this measurement data group as the “i + 1st measurement data group”.

  In step S48, the optimization measurement data selection unit 16 determines whether the number of measurement data in the "i + 1st measurement data group" is equal to the number of measurement data set in step S41. If the number of measurement data is equal to the initially set number (S48: YES), the process is ended. That is, the “i + 1st measurement data group” at the end of the process of step S <b> 48 is the optimized measurement data group. If the number of measured data is not equal to the initially set number (S48: NO), the process proceeds to step S47.

  In step S47, the optimization measurement data selection unit 16 increments i by one. Thereafter, the process returns to step S45.

<Effects of the Second Embodiment>
As described above, in the second embodiment, in addition to the effects exhibited by the first embodiment, it is possible to slim the measurement data group by removing the measurement data with a small contribution degree from the measurement data group.

  Furthermore, as described above, since the sensor connected to the terminal of the control device 30 is removable, it is possible to reduce the number of sensors by removing the sensor that contributes little to improvement in accuracy or changing the position of the sensor. It is possible to make highly accurate correction. In addition, the reduction of sensors leads to cost cutting and ease of maintenance. In particular, after installing a large number of sensors in advance and acquiring measurement data, calculate contribution degree by automatic analysis by machine learning, and perform correction with high accuracy with few sensors by deleting sensors with small contribution. Is possible.

Third Embodiment
Hereinafter, a third embodiment of the present invention will be described based on the drawings. FIG. 11 is a block diagram showing the details of the machine learning device according to the present embodiment. FIG. 12 is a flowchart showing an operation at the time of detection of measurement data which does not contribute to accuracy improvement in the machine learning device according to the present embodiment.

<Configuration of Thermal Displacement Correction System 100B>
In the thermal displacement correction system 100B according to the third embodiment, as compared to the thermal displacement correction system 100 according to the first embodiment, the constituent elements of the machine learning device 10B are the same as those of the machine learning device 10 as shown in FIG. In addition to the components, a detection unit 17 is added. The other configuration is basically the same as that of the first embodiment described above, so the same reference numerals are given to the same members, and the description thereof is omitted. In the third embodiment, measurement data that does not contribute to accuracy improvement is detected by using sparse regularization learning.

  The detection unit 17 detects measurement data that does not contribute to the accuracy improvement of the thermal displacement amount prediction based on the thermal displacement amount prediction equation set by the sparse regularization learning.

<Operation at detection>
Next, the operation of determining the contribution degree of the measurement data included in the measurement data group in the machine learning device 10B will be described. FIG. 12 is a flow chart showing the operation of the machine learning device 10B at the time of determination of the degree of contribution.

  In step S51, the measurement data acquisition unit 11 of the machine learning device 10B acquires a measurement data group from the control device 30. More specifically, the measurement data acquisition unit 11 acquires temperature data of the machine element of the machine tool 35 and the periphery thereof and / or operation state data of the machine tool 35.

  In step S52, the thermal displacement acquisition unit 12 of the machine learning device 10B acquires the measured value of the thermal displacement of the mechanical element of the machine tool 35, which is detected by, for example, an eddy current sensor. Specifically, for example, components in the X, Y, and Z axial directions of the thermal displacement amount may be measured, and a set of those measured values may be used as an actual measurement value.

  In step S53, the storage unit 13 of the machine learning device 10B uses the measurement data group acquired by the measurement data acquisition unit 11 as input data, and the measured value of the thermal displacement amount of the mechanical element acquired by the thermal displacement amount acquisition unit 12 As a label, as a set associated with each other, and stored as teacher data.

  In step S54, the calculation formula learning unit 14 of the machine learning device 10 performs machine learning by sparse regularization using this teacher data.

  In step S55, measurement data Xk for which the coefficient ak = 0 is detected. By doing this, the detection unit 17 detects measurement data that does not contribute to the improvement in the accuracy of the thermal displacement amount prediction, based on the thermal displacement amount prediction equation set by the sparse regularization learning.

  In the machine learning device 10A according to the second embodiment, the measurement data group is optimized by using the detection unit 17 instead of the contribution degree determination unit 15 and combining it with the optimization measurement data selection unit 16. Is also possible. More specifically, the detection unit 17 detects measurement data that does not contribute to the accuracy improvement of the thermal displacement amount prediction such that the coefficient ak = 0, and the optimization measurement data selection unit 16 is currently acquiring By removing the measurement data that does not contribute to the accuracy improvement of the thermal displacement amount from the measurement data group, it is possible to select the slim measurement data group.

<Advantages of the Third Embodiment>
As described above, in the third embodiment, it is possible to achieve the same effects as the effects of the second embodiment.

Other Embodiments
The embodiment described above is a preferred embodiment of the present invention, but the scope of the present invention is not limited to the above embodiment, and various changes may be made without departing from the scope of the present invention. Implementation is possible.

[Modification 1]
In the above embodiment, the thermal displacement prediction calculation formula is a polynomial based on the multiple regression of the generalized linear model. However, the present invention is not limited to this, and may be based on the multiple regression of a non-linear model.

[Modification 2]
In the above embodiment, although the technology for optimizing the measurement data group by deleting the measurement data has been described, the invention is not limited thereto, and the measurement data group is optimized by adding the measurement data. It is also good. Specifically, measurement data may be added when the accuracy of the thermal displacement prediction equation set as a result of machine learning is less than a threshold. Furthermore, after deleting one measurement data, another measurement data may be added.
In particular, when a sensor maintenance worker or an end user side of the machine tool adds a sensor, the accuracy of the thermal displacement correction is improved by the automatic tuning of the correction equation based on the thermal displacement prediction equation.
Further, for example, in order to improve the accuracy of the thermal displacement correction, for example, machine learning may be performed using measurement data groups obtained by changing the arrangement position of the temperature sensor. Also in this case, the error between the thermal displacement predicted value calculated by the thermal displacement prediction equation obtained after repositioning and the thermal displacement amount actual measurement value, and obtained based on the machine learning based on the measurement data group before the repositioning. By evaluating the difference between the thermal displacement amount predicted value calculated by the thermal displacement correction formula and the error between the thermal displacement amount actual measurement value, it can be determined whether the accuracy is improved.

  Moreover, in said embodiment, although the machine tool 35 was used as the cutting machine, it is not limited to this. The machine tool 35 may be, for example, a wire electric discharge machine or a laser machine.

[Modification 4]
In addition, the control device 30 may be configured to include the thermal displacement correction device 20.
Alternatively, the control device 30 may be configured to include the machine learning device 10, 10A, 10B.

[Modification 5]
The machine learning devices 10, 10A, and 10B in the above embodiments may be computer systems including a CPU. In that case, the CPU reads a program stored in a storage unit such as a ROM, for example, and according to this program, the computer is used as a measurement data acquisition unit 11, a thermal displacement amount acquisition unit 12, a storage unit 13, a formula learning unit 14, The contribution degree determination unit 15, the optimization measurement data selection unit 16, and the detection unit 17 are executed.

10 10A 10B Machine Learning Device 11 Measurement Data Acquisition Unit 12 Thermal Displacement Acquisition Unit 13 Storage Unit 14 Calculation Formula Learning Unit 15 Contribution Degree Determination Unit 16 Optimization Measurement Data Selection Unit 17 Detection Unit 20 Thermal Displacement Correction Device 22 Correction Amount Calculation Unit 24 correction unit 30 control device 35 machine tool 40 network 100 100A 100B thermal displacement correction system

Claims (13)

A calculation equation for estimating the thermal displacement of the machine element based on a measurement data group including temperature data of the machine element and its surroundings and / or operating condition data of the machine element having a thermally expanding machine element A machine learning device that is optimized by machine learning, and
A measurement data acquisition unit that acquires the measurement data group;
A thermal displacement amount acquisition unit that acquires an actual measurement value of a thermal displacement amount of the mechanical element;
A storage in which the measurement data group acquired by the measurement data acquisition unit is used as input data, and the measured values of the thermal displacement amounts of the mechanical elements acquired by the thermal displacement acquisition unit are associated with each other as labels and stored as teacher data Department,
Thermal displacement prediction calculation to calculate the thermal displacement of the mechanical element based on the measurement data group by performing machine learning based on the measurement data group and the measured value of the thermal displacement amount of the mechanical element A formula learning unit for setting the formula;
The calculation formula learning unit is an estimated value of the thermal displacement amount of the mechanical element calculated by substituting the measurement data group within a predetermined period stored as teaching data in the storage unit in the thermal displacement amount prediction calculation equation. A machine learning apparatus for setting the thermal displacement prediction equation based on the difference between the measured value of the thermal displacement of the mechanical element within the predetermined period stored as a label in the storage unit. Furthermore, the measurement data acquisition unit
Acquiring a second measurement data group by adding measurement data to the measurement data group and / or excluding measurement data from the measurement data group,
Storing the second measurement data group as input data in the storage unit;
The calculation formula learning unit further includes
The machine learning device according to claim 1, wherein a second thermal displacement prediction calculation formula is set to calculate a thermal displacement of the mechanical element based on the second measurement data group. It further comprises a contribution degree determination unit that determines the degree of contribution of the measurement data contained in the measurement data group to the prediction of the thermal displacement amount,
The contribution degree determination unit
A first error which is an error between the first thermal displacement predicted value calculated by the first thermal displacement prediction equation set based on the measurement data group including the measurement data of the contribution degree calculation target and the thermal displacement actual value Second thermal displacement predicted value calculated based on the second thermal displacement prediction equation set based on the second measurement data group excluding the error and the measurement data for the contribution degree calculation target and the thermal displacement actual measurement The machine learning apparatus according to claim 2, wherein the degree of contribution of the measurement data to be subjected to the calculation of the degree of contribution is determined based on a difference between a second error that is an error from the value and the second error. The optimization measurement data selection unit is further configured to select an optimization measurement data group consisting of a combination of measurement data with the best accuracy using a preset number of measurement data among the measurement data groups currently acquired. Equipped
The optimization measurement data selection unit
The measurement data group obtained by removing the measurement data with the smallest contribution degree determined by the contribution degree determination unit from the currently acquired measurement data group is selected as a first measurement data group,
The measurement data group obtained by removing the measurement data with the least contribution degree determined by the contribution degree determination unit from the i-th (1 ≦ i) -th measurement data group is selected as the (i + 1) th measurement data group 4. The machine learning apparatus according to claim 3, wherein an optimization measurement data group consisting of a preset number of measurement data is selected by repeating the process.   The machine learning apparatus according to any one of claims 1 to 4, wherein the thermal displacement prediction calculation formula uses a first-order lag element of measurement data included in the measurement data group.   The machine learning apparatus according to any one of claims 1 to 5, wherein the thermal displacement prediction calculation formula uses a time shift element of measurement data included in the measurement data group.   The machine learning apparatus according to any one of claims 1 to 6, wherein the thermal displacement prediction calculation formula is set based on machine learning by a neural network.   The said calculation formula learning part sets the prediction calculation formula of the said thermal displacement amount to any one of the Claims 1-6 based on the machine learning using the multiple regression analysis which considered L2 regularization terms. Machine learning device as described.   The machine learning device according to any one of claims 1 to 6, wherein the calculation formula learning unit sets a prediction calculation formula of the thermal displacement amount using sparse regularization learning. The measurement data group further includes a detection unit that detects measurement data that does not contribute to the accuracy improvement of the thermal displacement amount prediction,
The detection unit is
10. The machine learning apparatus according to claim 9, wherein the detection is performed based on a prediction calculation formula of the thermal displacement amount set by sparse regularization learning.   The machine learning device according to any one of claims 1 to 10, wherein the machine learning device is included in a control device of the machine tool. It corresponds to the thermal displacement of the said mechanical element calculated from the said measurement data group based on the thermal displacement prediction equation set by the machine learning apparatus in any one of Claims 1-11. A correction amount calculation unit that calculates the correction amount;
A correction unit that corrects the machine position of the machine element based on the correction amount of the machine element calculated by the correction amount calculation unit;
Thermal displacement correction device for machine tools equipped with   The thermal displacement correction device according to claim 12, wherein the thermal displacement correction device is included in a control device of the machine tool.

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