Patent Description:
<CIT> discloses a machine tool that includes a nozzle for supplying a coolant to a portion of a workpiece that is to be machined using a tool, and the machine tool cools the workpiece and the tool, and removes chips generated by machining.

Also, <CIT> discloses a chip processing device by which chips are collected, together with a coolant, in a coolant tank installed below a portion to be machined, and the collected chips are transported to the outside of a machine tool by a chip conveyor disposed below the coolant tank.

<CIT> discloses a chip processing device with the features of the preamble of claim <NUM>.

A machine tool as disclosed in <CIT> is provided with a coolant ejection mechanism including a nozzle that is pivotable relative to a tool holder attachment portion of a tool post, and the nozzle is appropriately pivoted toward an accumulation location of chips in the machine tool so as to eject the coolant, whereby chips scattered on a pallet or a table can be cleaned.

However, this is a time-consuming, inefficient operation because an operator who has visually confirmed the accumulation state of the chips scattered on the pallet or the table needs to adjust the orientation of the nozzle by operating a control board.

In view of the above-described problem, an object of the present invention is to provide a chip processing device and a chip processing method for a machine tool that enable the orientation of a nozzle to be automatically adjusted such that a cleaning fluid is ejected toward an area where chips are accumulated.

The present invention solves this problem with a system with the features of claim <NUM> and a method with the features of claim <NUM>. A chip processing device for a machine tool according to the present invention is a chip processing device for a machine tool, including a cleaning nozzle control unit that controls a cleaning nozzle for spraying a cleaning fluid onto chips scattered during machining so as to guide the chips to a chip collection portion, wherein the cleaning nozzle control unit includes: a position estimation unit that estimates an accumulation position of generated chips by analyzing machining conditions of a workpiece; and a nozzle orientation adjustment unit that adjusts an orientation of the cleaning nozzle toward the accumulation position estimated by the position estimation unit, wherein the position estimation unit includes a machine learning device from which an accumulation position of the chips is output when a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool are input to the machine learning device, and wherein the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by a (FEM) analysis performed by a FEM analysis device based on the machining parameter and the in-machine shape.

A chip processing method for a machine tool according to the present invention is a chip processing method for a machine tool, including a cleaning nozzle control step of controlling a cleaning nozzle for spraying a cleaning fluid onto chips scattered during machining so as to guide the chips to a chip collection portion, wherein the cleaning nozzle control step includes: a position estimation step of estimating an accumulation position of generated chips by analyzing machining conditions of a workpiece, and a nozzle orientation adjustment step of adjusting an orientation of the cleaning nozzle toward the accumulation position estimated in the position estimation step, wherein the position estimation step is a step of inputting, to a machine learning device, a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool, and causing the machine learning device to output an accumulation position of the chips, and wherein the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by a FEM analysis performed by a FEM analysis device based on the machining parameter and the in-machine shape.

The present invention makes it possible to provide a chip processing device and a chip processing method for a machine tool that enable the orientation of a nozzle to be automatically adjusted such that a cleaning fluid is ejected toward an area where chips are accumulated.

While the novel features of the invention are set forth in the appended claims, the invention, both as to organization and content, will be better understood and appreciated, along with other objects and features thereof, from the following detailed description taken in conjunction with the drawings.

A chip processing device for a machine tool according to the present invention is a chip processing device for a machine tool, including a cleaning nozzle control unit that controls a cleaning nozzle for spraying a cleaning fluid onto chips scattered during machining so as to guide the chips to a chip collection portion, wherein the cleaning nozzle control unit includes: a position estimation unit that estimates an accumulation position of generated chips by analyzing machining conditions of a workpiece; and a nozzle orientation adjustment unit that adjusts an orientation of the cleaning nozzle toward the accumulation position estimated by the position estimation unit.

That is, the position estimation unit analyzes the machining conditions of the workpiece, and estimates the accumulation position of chips generated by machining, and the nozzle orientation adjustment unit adjusts the orientation of the cleaning nozzle such that the cleaning fluid is sprayed to the accumulation position of the chips.

As an aspect, it is preferable that the position estimation unit includes: an FEM analysis unit that performs an FEM analysis based on a machining parameter included in an NC program used for machining, to calculate a scattering trajectory of generated chips; and a position calculation unit that calculates an accumulation position of the chips based on the scattering trajectory of the chips calculated by the FEM analysis unit and an in-machine shape that is an internal shape of the machine tool.

That is, the position estimation unit is provided with the FEM analysis unit in order to analyze the machining conditions of the workpiece, and the FEM analysis unit performs an FEM analysis based on the machining parameter included in the NC program, and calculates the scattering trajectory of chips generated by machining. Then, the position calculation unit provided in the position estimation unit calculates the accumulation position of the chips based on the scattering trajectory of the chips and a preset in-machine shape.

As another aspect, it is preferable that the position estimation unit includes a machine learning device from which an accumulation position of the chips is output when a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool are input to the machine learning device.

That is, when the machining parameter and the in-machine shape are input to the machine learning device, the accumulation position of chips that corresponds to the machining parameter and the in-machine shape is output.

Specifically, it is preferable that the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by the FEM analysis based on the machining parameter and the in-machine shape. This configuration is excellent in that it is possible to promptly perform primary learning without requiring training data based on prior verification of the accumulation position using an actual tool machine.

It is more preferable that the machine learning device is a secondary learned device obtained by subjecting the primary learned device to secondary learning, using, as training data, an accumulation position of the chips obtained from captured images of an interior of the machine tool before and after machining. A machine learning device with a higher precision can be realized through accumulation of learning using the training data based on verification of the accumulation position using an actual machine tool.

It is preferable that the chip processing device further includes a machining control unit that moves a tool and a workpiece relative to each other, wherein the chip processing device is configured such that, in parallel with execution of the NC program by the machining control unit, estimation processing for the accumulation position by the position estimation unit is executed, and the nozzle orientation adjustment unit adjusts the orientation of the cleaning nozzle toward the accumulation position.

In this manner, when the machining processing and the cleaning process using the cleaning nozzle are executed in parallel in the order of execution of the NC program, which is a collection of unit machining processes, it is possible not only to efficiently use the storage unit, but also to reduce the wait time, caused by prior estimation processing for the accumulation positions, before start of machining, as compared with a case where a large amount of operation data required for estimating processing for the accumulation positions of chips corresponding to all of the unit machining processes included in the NC program, and for adjustment of the orientation of the cleaning nozzle based on the estimated results is stored in the storage unit before execution of the NC program.

A chip processing method for a machine tool according to the present invention is a chip processing method for a machine tool, including a cleaning nozzle control step of controlling a cleaning nozzle for spraying a cleaning fluid onto chips scattered during machining so as to guide the chips to a chip collection portion, wherein the cleaning nozzle control step includes: a position estimation step of estimating an accumulation position of generated chips by analyzing machining conditions of a workpiece, and a nozzle orientation adjustment step of adjusting an orientation of the cleaning nozzle toward the accumulation position estimated in the position estimation step.

The position estimation step includes: an FEM analysis step of performing an FEM analysis based on a machining parameter included in an NC program used for machining, to calculate a scattering trajectory of generated chips; and a position calculation step of calculating an accumulation position of the chips based on the scattering trajectory of the chips calculated in the FEM analysis step and an in-machine shape that is an internal shape of the machine tool.

The position estimation step is a step of inputting, to a machine learning device, a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool, and causing the machine learning device to output an accumulation position of the chips.

It is preferable that the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as an input data, the scattering trajectory of the chips obtained by the FEM analysis based on the machining parameter included in the NC program and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory and the in-machine shape.

Furthermore, it is preferable that the machine learning device is a secondary learned device obtained by subjecting the primary learned device to secondary learning, using, as training data, an accumulation position of the chips obtained from captured images of an interior of the machine tool before and after machining.

<FIG> show a machining system <NUM> including a machine tool <NUM> in which a chip processing device according to the present invention is incorporated, and a control system <NUM> that controls the machine tool <NUM> based on a preset NC program.

The machine tool <NUM> is a vertical machining center including a bed <NUM>, a saddle <NUM> that moves along a guide surface on the bed <NUM> in a Y-axis direction, a table <NUM> that moves along a guide surface on the saddle <NUM> in an X-axis direction, a column <NUM> provided vertically on the bed <NUM>, and a spindle head <NUM> that moves along a guide surface on the column <NUM> in a Z-axis direction. Although not shown, a cover including a door member capable of opening and closing covers around the machine tool <NUM>, and a control board constituting the control system <NUM> is provided on the outside of the cover.

The saddle <NUM> moves on the bed <NUM> along a linear drive shaft extending in the Y-axis direction when a servomotor MY is driven, the table <NUM> moves on the saddle <NUM> along a linear drive shaft extending in the X-axis direction when a servomotor MX is driven, and the spindle head <NUM> moves on the column <NUM> along a linear drive shaft extending in the Z-axis direction when a servomotor MZ is driven.

A tool <NUM> is held by a tool holder <NUM> provided on the spindle head <NUM>, and the tool <NUM> rotates about a vertical axis when a servomotor MS1 is driven. The table <NUM> is formed in a "C" shape in front view, with a pair of vertical walls 3W disposed so as to be opposed to each other. A work holder <NUM> that holds a work <NUM>, which is a workpiece, is provided on each of the vertical walls 3W, and the work <NUM> held by the work holder <NUM> rotates about a horizontal axis extending along the X axis when a servomotor MS2 is driven. That is, the table <NUM> serves as a work holding portion. For example, when side machining, groove machining, or the like of the work <NUM> is intended, an end mill having cutting edges on an outer circumferential surface and an end face thereof is used as the tool <NUM>.

As a result of the above-described servomotors being driven via a servo control unit based on the preset NC program, the work <NUM> and the tool <NUM> move relative to each other, and the work <NUM> is machined into a desired shape.

A coolant tank <NUM> in which a coolant, which is a fluid supplied for cooling or cleaning is collected is installed below the saddle <NUM>, and the coolant tank <NUM> is configured such that chips generated due to machining are collected, together with the coolant, in the coolant tank <NUM>. A chip conveyor <NUM> is disposed at a bottom portion of the coolant tank <NUM>, and the chips collected in the coolant tank <NUM> are transported to the outside of the machine tool by the chip conveyor <NUM>, and are collected in a collection container.

The machine tool <NUM> is provided with a chip processing device <NUM> that sprays the coolant serving as a cleaning fluid toward an accumulation position of chips <NUM>, and guides the chips <NUM> to the coolant tank <NUM> together with the coolant such that the chips <NUM> generated when the work <NUM> is machined with the tool <NUM> and scattering in the machine tool will not be accumulated on the work <NUM>, the table <NUM>, the saddle <NUM>, and so forth. This is because chips heated to a high temperature by the heat generated during cutting may cause thermal displacement in the work <NUM>, the table <NUM>, the saddle <NUM>, and so forth, so that the machining precision may be reduced, and also because it is very difficult to clean and remove a large amount of accumulated chips at a later time.

As shown in <FIG>, the chip processing device <NUM> includes a cleaning nozzle mechanism <NUM>, and a cleaning nozzle control unit <NUM> that controls the cleaning nozzle mechanism <NUM>.

The cleaning nozzle mechanism <NUM> includes an annular member <NUM> rotatably attached to the outer circumference of one of the work holders <NUM> via a bearing, and a cleaning nozzle <NUM> attached to the annular member <NUM>. The cleaning nozzle <NUM> is attached to a side surface of the annular member <NUM> so as to protrude toward the other vertical wall 3W, and is configured such that a tip of the cleaning nozzle <NUM> can be swung about an axis P2 extending orthogonal to a rotation axis P1 of the work <NUM> by a motor M2 provided in the cleaning nozzle <NUM>.

The annular member <NUM> is configured to be rotatable coaxially with the rotation axis P1 of the work <NUM> via a motor M1 attached to the vertical wall 3W and a gear mechanism for drive transmission. The rotation angle of the annular member <NUM> is adjusted by the motor M1, and the inclination angle of the cleaning nozzle <NUM> is adjusted by the motor M2, whereby the coolant can be sprayed in any given direction from the cleaning nozzle <NUM>. Note that a fluid flow path that guides the coolant to the cleaning nozzle <NUM> is formed in the annular member <NUM>, and, for example, the coolant collected in the coolant tank <NUM> is circulated through and supplied to the fluid flow path via a dust filter and a fluid transport pipe.

The cleaning nozzle control unit <NUM> includes a position estimation unit <NUM> that estimates an accumulation position of chips generated by machining, and a nozzle orientation adjustment unit <NUM> that controls the rotation of the motors M1 and M2 so as to adjust the orientation of the cleaning nozzle <NUM> toward the accumulation position estimated by the position estimation unit <NUM>. The motors M1 and M2 are provided with encoders that detect the rotation positions of the respective drive shafts, and the nozzle orientation adjustment unit <NUM> performs control based on outputs of the encoders such that the rotation position of the annular member <NUM> and the inclination angle of the cleaning nozzle <NUM> become the target rotation position and the target inclination angle.

As shown in <FIG>, the control system <NUM> includes a system control unit <NUM>, a servo control unit <NUM>, which is an example of a machining control unit that controls the above-described servomotors, and a cleaning nozzle control unit <NUM> that controls the cleaning nozzle mechanism <NUM> described above. The control units <NUM>, <NUM>, and <NUM> each include hardware such as a motherboard including a CPU, a ROM, and a RAM, and an I/O board that exchanges various types of control data between the motherboard and the machine tool <NUM> or an operator, and a communication board that exchanges necessary information between the motherboards.

Various programs and various types of data such as a system control program, an NC program, a tool parameter, and in-machine shape data are stored in the ROM of the system control unit <NUM>, a servo control program that controls the various servomotors is stored in the ROM of the servo control unit <NUM>, and a cleaning nozzle control program is stored in the ROM of the cleaning nozzle control unit <NUM>.

In the servo control unit <NUM>, the functional blocks of the positioning control unit <NUM> and the speed control unit <NUM> are implemented by the CPU executing the servo control program. In the cleaning nozzle control unit <NUM>, the functional blocks of the position estimation unit <NUM> and the nozzle orientation adjustment unit <NUM> are implemented by the CPU executing the cleaning nozzle control program.

Furthermore, in the position estimation unit <NUM>, the functional blocks of an FEM analysis unit <NUM> and a position calculation unit <NUM> are implemented, wherein the FEM analysis unit <NUM> performs an FEM analysis based on a machining parameter included in an NC program used for machining, and calculates the scattering trajectory of generated chips, and the position calculation unit <NUM> calculates the accumulation position of chips in the machine tool based on the scattering trajectory of the chips calculated by the FEM analysis unit <NUM> and the in-machine shape.

When a starting switch provided on an operation panel is operated by the operator, the system control unit <NUM> reads out the NC program, and sends a necessary control command to the servo control unit <NUM>. Upon receiving the control command from the system control unit <NUM>, the servo control unit <NUM> controls the servomotors via the positioning control unit <NUM> and the speed control unit <NUM> such that the tool and the work move to predetermined relative positions at a predetermined speed. The configuration of the control command is not particularly limited, and the control command may be in the form of, for example, a pulse train signal capable of specifying the feed amount, the feed speed, and the like necessary for servo control. The control command includes the above-described positions of the tool and the work, and the number of revolutions of the spindle, the feed speed, and so forth.

In parallel with execution of the NC program by the servo control unit <NUM>, the system control unit <NUM> sends the machining conditions, the tool parameter, and the in-machine shape based on the NC program to the position estimation unit <NUM> provided in the data cleaning nozzle control unit <NUM>. The FEM analysis unit <NUM> provided in the position estimation unit <NUM> calculates the scattering trajectory of chips generated by machining by performing an FEM analysis on the machining state of the work <NUM> based on the machining conditions of the work <NUM> and the tool parameter that are included in the NC program, and the position calculation unit <NUM> calculates the accumulation position of the chips based on the scattering trajectory of the chips calculated by the FEM analysis unit <NUM> and the in-machine shape.

Then, the nozzle orientation adjustment unit <NUM> adjusts the orientation of the cleaning nozzle <NUM> by controlling the motors M1 and M2 such that the coolant is sprayed to the accumulation position of the chips calculated by the position calculation unit <NUM>.

That is, the chip processing device for a machine tool includes the servo control unit <NUM> configured to machine the work into a desired shape by relatively moving the spindle head <NUM> on which the tool <NUM> is held via the tool holder <NUM>, and the work holding portion (table <NUM>) on which the work <NUM> is held via the work holder <NUM> based on the NC program. The chip processing device is configured such that, in parallel with execution of the NC program by the servo control unit <NUM>, estimation processing for the accumulation position by the position estimation unit <NUM> is executed, and the nozzle orientation adjustment unit <NUM> adjusts the orientation of the cleaning nozzle <NUM> toward the accumulation position.

<FIG> shows a procedure of machining processing executed by the system control unit <NUM> and the servo control unit <NUM>.

When the starting switch is operated (SA1), the system control unit <NUM> starts the chip conveyor <NUM> (SA2), reads the NC program stored in a storage unit (SA3), analyzes a machining command incorporated in the NC program, and sends a control command to the servo control unit <NUM> (SA4).

The servo control unit <NUM> that has received the control command controls the feed speed and the feed amount of the associated servomotors so as to perform control such that the tool <NUM> and the work <NUM> are located at predetermined relative positions, drives the spindle with the tool <NUM> attached thereto at a predetermined rotational speed via the Z-axis rotation motor MS1, and drives the work <NUM> at a predetermined rotational speed via the X-axis rotation motor MS2 (SA5).

As a result of the processing from step SA3 to step SA5 being repeatedly executed until execution of all of the commands included in the NC program ends, the work <NUM> is machined into a desired shape (SA6, N). When execution of all of the commands ends (SA6, Y), the chip conveyor <NUM> is stopped, and the machining ends.

<FIG> shows a procedure of cleaning nozzle control processing executed by the system control unit <NUM> and the cleaning nozzle control unit <NUM>.

Initially, the in-machine shape data is sent from the system control unit <NUM> to the position calculation unit <NUM>, and then stored in a storage unit provided in the cleaning nozzle control unit <NUM> (SB1).

Subsequently, the system control unit <NUM> reads the NC program stored in the storage unit (SB2), analyzes the machining command incorporated in the NC program, and sends an analysis command and a tool parameter to the FEM analysis unit <NUM> (SB3). Upon receiving the analysis command and the tool parameter (SB4), the FEM analysis unit <NUM> executes a predetermined FEM analysis program, and calculates the scattering trajectory of chips generated due to machining (SB5).

The position calculation unit <NUM> calculates the accumulation position of the chips based on the scattering trajectory of the chips calculated by the FEM analysis unit <NUM> and the in-machine shape data (SB6). The nozzle orientation adjustment unit <NUM> adjusts the orientation of the cleaning nozzle <NUM> by controlling the motors M1 and M2 (SB7) such that the coolant is sprayed to the accumulation position of the chips calculated by the position calculation unit <NUM>, and opens a valve provided in the coolant supply path so as to spray the coolant from the cleaning nozzle <NUM> until execution of the machining command ends (SB8). When the machining command ends (SB9, Y), the nozzle orientation adjustment unit <NUM> closes the valve provided in the coolant supply path so as to stop the supply of the coolant (SB10).

As a result of the processing from step SB2 to step SB <NUM> being repeatedly executed until execution of all of the commands included in the NC program ends (SB11, N), the chips generated during a period in which the work <NUM> is machined into a desired shape can be collected in the coolant tank <NUM> without being accumulated on the table <NUM> or the saddle <NUM>. When execution of all of the commands ends (SB11, Y), the cleaning nozzle control processing ends.

That is, a position estimation step (SB5 and BB6) of estimating the accumulation position of the chips by analyzing the machining conditions of the work, which is a workpiece, and a nozzle orientation adjustment step (SB7) of adjusting the orientation of the cleaning nozzle toward the accumulation position estimated by the position estimation step constitute a cleaning nozzle control step.

An FEM analysis step (SB5) of performing an FEM analysis based on the machining parameter included in the NC program used for machining, to calculate the scattering trajectory of the chips, and a position calculation step (SB6) of calculating the accumulation position of the chips based on the scattering trajectory of the chips calculated by the FEM analysis step and the in-machine shape constitute a position estimation step.

The FEM analysis unit <NUM> is an arithmetic processing unit that calculates generation and scattering directions of chips resulting from cutting or the like, using, for example, AvantEdge (registered trademark), which is software dedicated to chip generation simulation. The FEM analysis unit <NUM> is configured to receive, as inputs, an analysis command and a tool parameter, including, for example, the shapes and the material properties of the workpiece and the tool, and cutting conditions, analyze elastoplastic deformation and heat conduction by numerical arithmetic processing using a finite element method, and output a cutting resistance, a chip shape, a temperature, a stress distribution, a scattering direction, a scattering speed, and so forth. Note that the software for chip generation simulation is not limited to AvantEdge (registered trademark), and it is possible to use another software.

In the finite element method, a structure is divided into triangular elements each composed of three nodes, and analysis related to elastic deformation and plastic deformation is performed based on the fact that a relationship between an external force {f} acting on each node and a displacement {V} of the node is determined by a stiffness matrix [K] from the formula {f} = [K] {V}. The external force {f} and the stiffness matrix [K] are set based on a machining parameter such as a tool parameter and an analysis command.

The tool parameter includes a tool material such as tool steel and cemented carbide, a tool type such as a drill, a milling cutter, and an end mill, and a tool characteristic such as a point angle in the case of a drill, the number of teeth and an entering angle in the case of a milling cutter, and the number of teeth, a bottom tooth shape and a helix angle in the case of an end mill, and includes a material characteristic of the workpiece (work), such as stainless steel and aluminum. The tool parameters are stored in advance in the storage unit provided in the system control unit <NUM>, and a tool parameter necessary for analysis is extracted based on tool specific information or the like defined in the NC program, and the extracted tool parameter is provided to the FEM analysis unit <NUM>.

The analysis command includes, for example, the machining route for the work, the number of revolutions of the tool, the feed speed per tooth, and the cutting depth, and these values are known from the NC program. Furthermore, if necessary, the drive power and the like of the motor that drives the spindle is supplied as a tool load from the servo control unit <NUM> to the FEM analysis unit <NUM> via the system control unit <NUM>. That is, the machining parameter necessary for an FEM analysis is composed of any of the tool load, the tool parameter, and the analysis command, or a combination thereof.

If the arithmetic processing capability of the FEM analysis unit <NUM> is sufficiently secured at the time of executing estimation processing for the accumulation position by the position estimation unit <NUM> in parallel with execution of the NC program by the servo control unit <NUM>, execution of the NC program by the servo control unit <NUM> and estimation processing for the accumulation position by the position estimation unit <NUM> can be synchronized.

However, if there is insufficiency in the arithmetic processing capability of the FEM analysis unit <NUM>, it is possible to adopt a configuration in which a time difference is provided between execution of estimation processing for the accumulation position by the position estimation unit <NUM> and execution of the NC program by the servo control unit <NUM> such that estimation processing for the accumulation position by the position estimation unit <NUM> is performed ahead and the NC program by the servo control unit <NUM> is executed in parallel therewith.

That is, since the NC program is formed by a collection of a plurality of machining processes, by executing estimation processing for the accumulation position by the position estimation unit <NUM> ahead of execution timing of the NC program by the servo control unit <NUM> for each of the machining processes, the coolant can be sprayed in real time to the accumulation position of the chips at the execution timing of the NC program by the servo control unit <NUM>.

Note that instead of performing execution of the NC program by the servo control unit <NUM> and estimation processing for the accumulation position by the position estimation unit <NUM> in parallel, it is possible to adopt a configuration in which the NC program is executed by the servo control unit <NUM> after all of the estimation processing for the accumulation position by the position estimation unit <NUM> has been completed.

The position calculation unit <NUM> may be configured to calculate the drop position and the drop amount of chips that change over time due to machining based on the scattering trajectory of chips calculated from the scattering direction and the scattering speed of chips obtained by the above-described numerical arithmetic processing and in-machine shape data indicating the three-dimensional shapes of the work <NUM>, the saddle <NUM>, the table <NUM>, and so forth.

In the above-described example, an aspect is described in which the position estimation unit <NUM> is composed of the FEM analysis unit <NUM> and the position calculation unit <NUM>. However, it is possible to adopt a configuration in which the position estimation unit includes a machine learning device from which chip accumulation information including at least the accumulation position of chips is output when the above-described machining parameter and the in-machine shape are input to the machine learning device, without including the FEM analysis unit <NUM> and the position calculation unit <NUM>. It is preferable that the chip accumulation information further includes an accumulation amount and an accumulation time.

<FIG> shows a neural network suitable as such a machine learning device.

The neural network is composed of three layers, namely, an input layer, an intermediate layer, and an output layer. Each of the nodes constituting the input layer and each of the nodes constituting the intermediate layer are coupled to each other with a predetermined coupling coefficient Wi, n (n is a product of the number of nodes of the input layer and the number of nodes of the intermediate layer), and each of the nodes constituting the intermediate layer and each of the nodes constituting the output layer are coupled to each other with a predetermined coupling coefficient Wo, m (m is a product of the number of nodes of the intermediate layer and the number of nodes of the output layer).

One segment, which serves as a unit of arithmetic processing, of the storage unit is associated with each of the nodes. For example, if the arithmetic processing is executed in units of <NUM> bits, the value of each of the nodes is represented by <NUM>-bit data.

A value that is input to each of the nodes of the input layer is subjected to weighted addition based on the coupling coefficient Wi, n and an activation function, and the resulting value is input to each of the nodes of the intermediate layer. Furthermore, a value that is input to each of the nodes of the intermediate layer is subjected to weighted addition based on the coupling coefficient Wi, m, and an activation function, and the resulting value is input to each of the nodes constituting the output layer. That is, when the machining parameter and the in-machine shape are input to each of the nodes constituting the input layer, the accumulation position, the accumulation amount, and the accumulation time of chips in the machine tool are output from the output layer. A step function, a sigmoid function, or the like is used as the activation function.

The above-described machining parameter such as a tool material, a tool type, a tool characteristic, the material of the workpiece, the machining route, the number of revolutions of the tool, and the feed speed per tooth, and the in-machine shape are input.

For example, as for the tool material, the tool type, the tool characteristic, and the material of the workpiece, an option for each of these parameters is assigned to each of the nodes of the input layer, and <NUM> is input to the selected node, and zero is input to the non-selected nodes.

As for the quantitative characteristics such as the number of revolutions of the tool, the feed speed per tooth, and the cutting depth, a plurality of value ranges are assigned in advance to each of the nodes of the input layer, and <NUM> is input to the corresponding node, and zero is input to the non-corresponding nodes.

Furthermore, as for the in-machine shape, the interior of the machine tool is divided into a plurality of square regions in plan view. Each of the divided regions is assigned to a node, and the height of the square region resulting from normalization in the range of zero to <NUM> is input to the corresponding node.

The output layer includes a node indicating the accumulation amount of chips and a node indicating the accumulation time for each of the above-described square regions. An accumulation amount is output in the range of zero to <NUM> to the node indicating the accumulation amount of chips, and an elapsed time normalized by the time required from start to end of machining and indicated in the range of zero to <NUM> is output to the node indicating the accumulation time. If zero is output to the node indicating the accumulation amount, it can be determined that no chip will be accumulated in the square region specified by that node. If a value other than zero is output to that node, it can be determined that the closer the value is to <NUM>, the larger the amount of accumulation of chips is.

Such a neural network has been subjected to learning in advance based on training data such that the coupling coefficients Wi, n and Wo, m take optimal values.

This will be specifically described. The accumulation amount and the accumulation time of chips for each square region that have been obtained in advance by performing an FEM analysis on the machining parameter and the in-machine shape for each standard unit machining process using an FEM analysis device are prepared as training data.

Thereafter, an operation of adjusting the coupling coefficients Wi, n and Wo, m is repeatedly performed such that a difference value between the data that is output from the output layer when the corresponding machining parameter and in-machine shape are input to the input layer for each standard unit machining process, and the above-described training data is minimum. As such a learning algorithm, an error propagation method is preferably used.

That is, as shown in <FIG>, the machine learning device is constituted by a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by the FEM analysis based on the machining parameter and the in-machine shape.

In the description of the neural network shown in <FIG>, a primary learned device that has been subjected to learning using, as the training data, an analysis result obtained using the FEM analysis device is used as the machine learning device. However, as shown in <FIG>, it is more preferable that the machine learning device is constituted by a secondary learned device obtained by subjecting the primary learned device to secondary learning using, as training data, an accumulation position of the chips obtained from captured images of an interior of the machine tool before and after machining.

A captured image in plan view of the interior state of an actual machine tool, showing an accumulation state of chips generated when causing the machine tool to execute an NC program defining a unit machining process specified by a predetermined machining parameter, and a captured image of the interior of the machine tool before chips scatters are compared through image processing, and training data indicating how much chips are accumulated in which of a plurality of divided square regions in plan view of the interior of the machine tool is generated. By subjecting the primary learned device to secondary learning using the training data, the coupling coefficients Wi, n and Wo, m can be adjusted to more appropriate values.

Note that the above-described neural networks are illustrative examples, and the number of nodes constituting each of the input layer, the intermediate layer, and the output layer, the type and the format of the input data that is input to the input layer, and the type and the format of the output data that is output from the output layer are not limited to the examples, and can be set as appropriate.

Also, as the learning algorithm, it is possible to use an algorithm other than an error propagation method, and it is also possible to use a neural network that is deepened with an increased number of intermediate layers so as to be subjected to deep learning.

Although not particularly described in the above embodiment, another cleaning nozzle for supplying a coolant to a position where the tool and the work come into contact may be provided for the purpose of lubrication, cooling, chip removal, and the like, in addition to the cleaning nozzle for cleaning and removing chips scattered in the machine tool.

The fluid sprayed from the cleaning nozzle in order to remove the chips scattered in the machine tool is not limited to a coolant, and another fluid such as compressed gas can be used as the cleaning fluid.

In the above-described embodiment, an example is described in which the machine tool <NUM> is constituted by a vertical machining center. However, the machine tool <NUM> to which the present invention is applied is not limited to a vertical machining center, and the present invention can be applied to various machining centers, and also can be applied to a machine tool, such as a lathe, that does not include a spindle head that holds a tool.

Specific structure and attachment position of the cleaning nozzle mechanism <NUM> are also not limited to the above-described embodiment. The cleaning nozzle mechanism <NUM> may be attached to another member such as the spindle head, and the number of cleaning nozzle mechanisms <NUM> may be plural.

The embodiments and aspects of the present invention have been described above. However, the contents of the disclosure may change in the details of the configuration, and the combinations of the elements and the changes of the sequence in the embodiments and aspects can be achieved without departing from the scope and idea of the present invention.

Claim 1:
A system comprising a finite element method, FEM, analysis device and a chip processing device for a machine tool (<NUM>), the chip processing device comprising
a cleaning nozzle control unit (<NUM>) configured to control a cleaning nozzle (<NUM>) for spraying a cleaning fluid onto chips scattered during machining so as to guide the chips to a chip collection portion,
wherein the cleaning nozzle control unit (<NUM>) includes:
a position estimation unit (<NUM>) configured to estimate an accumulation position of generated chips by analyzing machining conditions of a workpiece; and
a nozzle orientation adjustment unit (<NUM>) configured to adjust an orientation of the cleaning nozzle (<NUM>) toward the accumulation position estimated by the position estimation unit (<NUM>),
characterized in that the position estimation unit (<NUM>) includes a machine learning device from which an accumulation position of the chips is output when a machining parameter included in an NC program used for machining and an in-machine shape that is an internal shape of the machine tool (<NUM>) are input to the machine learning device, and
in that the machine learning device is a primary learned device that has been subjected to primary learning in advance, using, as input data, the machining parameter and the in-machine shape, and using, as training data, the accumulation position of the chips calculated based on the scattering trajectory of the chips obtained by a FEM analysis performed by the FEM analysis device based on the machining parameter and the in-machine shape.