Numerical control device, machining system, numerical control method, and machining method

Abstract

The numerical control device (3) is characterized by having: a control characteristic setting section (32) that generates a group of control characteristic parameters, that is, a parameter set, indicating a control characteristic of a controller that controls the drive system (20); a coupled simulation section (34) that calculates process information indicating a result obtained by simulating a process of a workpiece (W) by a tool (23) using a controller that controls the work machine (2) using the control characteristic indicated by the parameter set based on an influence of an action of the drive system (20) and dynamics of a member that vibrates in an action of the work machine (2) on the process (M) of the workpiece (W) by the tool (23); and a process evaluation section (35) that evaluates a size of a processing error when the work machine (2) is controlled using the parameter set based on the process information, and selects the parameter set used for the control of the work machine (2) based on a result of the evaluation.

Description

Technical Field

[0001] This invention relates to CNC devices, machining systems, CNC methods, and machining techniques for controlling machine tools. Background Technology

[0002] A machine tool is a machining device capable of performing removal operations, which involves applying force or energy to the workpiece using a cutting tool, thereby removing unwanted portions from the workpiece. The machine tool has a spindle drive system that rotates the cutting tool or the workpiece, and a feed drive system that changes the relative position of the cutting tool and the workpiece. A CNC device drives the spindle drive system and the feed drive system according to operating instructions generated based on a CNC program, thereby machining the workpiece. However, even when the machine tool is controlled according to the instructions described in the CNC program, machining errors sometimes occur due to various factors that prevent it from performing the machining operations as instructed.

[0003] Patent Document 1 discloses a technique that calculates the displacement of the tool caused by the cutting resistance applied to the tool during machining, thereby reproducing the characteristics of the machined surface. In the method described in Patent Document 1, parameters representing the dynamic characteristics of the tool are pre-stored, and the displacement of the tool center when the cutting resistance is generated, corresponding to the cutting thickness calculated by simulation, is regarded as the machining error.

[0004] Patent Document 1: Japanese Patent Application Publication No. 2013-132733 Summary of the Invention

[0005] However, according to the aforementioned prior art, there is a problem that machining errors cannot be reduced with high precision. In the technology described in Patent Document 1, the deflection of the cutting tool is predicted, and the displacement of the tool center is considered as the machining error. However, in reality, during the operation of the machine tool, the machining process and the mechanical dynamics of the components that vibrate during the operation of the drive system and the machine tool interact with each other. Here, the machining process refers to the series of processes in which the cutting edge of the tool penetrates the workpiece to generate chips and forms a machined surface, while the mechanical dynamics refer to the dynamic characteristics of the components that vibrate when vibrations are transmitted from vibration sources inside and outside the machine tool. Therefore, the method described in Patent Document 1 cannot accurately evaluate machining errors and cannot reduce them with high precision.

[0006] The present invention is proposed in view of the above circumstances, and its purpose is to obtain a CNC device that can reduce the machining error of working machinery with high precision.

[0007] To address the aforementioned issues and achieve the objective, the CNC device of the present invention controls a machine tool having a drive system by assigning operating commands to the machine tool. This drive system includes a spindle drive system that drives a spindle used for machining a workpiece or for rotating the workpiece, and a feed drive system that drives a feed axis that changes the relative position of the workpiece and the tool. The CNC device is characterized by having: a control characteristic setting unit that generates a set of control characteristic parameters, i.e., a parameter set, representing the control characteristics of a controller that controls the drive system; a coupling simulation unit that calculates process information representing the result of simulating machining when the machine tool is controlled using a controller with the control characteristics shown in the parameter set, based on the influence of the drive system's operation and the dynamics of components vibrating during the machine tool's operation on the machining process of the workpiece via the tool; and a process evaluation unit that evaluates the magnitude of machining errors when using the parameter set based on the process information, and selects the parameter set used for controlling the machine tool based on the evaluation results.

[0008] The effects of the invention

[0009] According to the present invention, the following effect is achieved: the machining error of the working machine can be reduced with high precision. Attached Figure Description

[0010] Figure 1 This is a diagram showing the functional structure of the processing system involved in Implementation Method 1.

[0011] Figure 2 It means Figure 1 A diagram showing an example of the physical structure of a machine tool.

[0012] Figure 3 It means Figure 1 The diagram shows the relationship between the spindle drive system, mechanical dynamics, and machining process.

[0013] Figure 4 It is Figure 3 The diagram shows the physical quantities along with the physical structure of the working machine.

[0014] Figure 5 It means Figure 1 The diagram shows the relationship between the feed drive system, mechanical dynamics, and machining process.

[0015] Figure 6 It is Figure 5 The diagram shows the physical quantities along with the physical structure of the working machine.

[0016] Figure 7 It is used for Figure 1The diagram illustrates an example of a spindle drive control model.

[0017] Figure 8 It is used for Figure 1 The diagram illustrates an example of a feed drive control model.

[0018] Figure 9 This indicates that it will be used to... Figure 1 The graph shown is a graph of the gain curves for each frequency, illustrating the response of the drive system when an operating command is input.

[0019] Figure 10 This indicates that it will be used to... Figure 1 The diagram shows the phase curves of the drive system's response to an operating command at each frequency.

[0020] Figure 11 It is used for the purpose of Figure 1 The diagram illustrates an example of a parameter set generated by the control characteristic setting unit.

[0021] Figure 12 It means Figure 1 The diagram shows the first example of the relationship between the state of the cutting tool and the state of the machined surface of the workpiece during the operation of the machine.

[0022] Figure 13 It means Figure 1 The second example of the relationship between the state of the cutting tool and the state of the machined surface of the workpiece during the operation of the machine is shown in the figure.

[0023] Figure 14 It means Figure 1 The figure shows the third example of the relationship between the state of the cutting tool and the state of the machined surface of the workpiece during the operation of the machine.

[0024] Figure 15 It is used for Figure 1 The flowchart illustrates the operation of the numerical control device shown.

[0025] Figure 16 This is a diagram showing the functional structure of the processing system involved in Implementation Method 2.

[0026] Figure 17 It is used for Figure 16 The flowchart illustrates the operation of the numerical control device shown.

[0027] Figure 18 It means and Figure 16 A diagram showing an example of the structure of a learning device related to a numerical control device.

[0028] Figure 19 It is used for Figure 18 The flowchart illustrates the learning process of the learning device shown.

[0029] Figure 20 It means and Figure 16 A diagram showing an example of the structure of the inference device related to the numerical control device.

[0030] Figure 21 It is used for Figure 20 The flowchart illustrates the operation of the inference device shown.

[0031] Figure 22 This is a diagram showing the structure of the processing system involved in Embodiment 3.

[0032] Figure 23 This is a diagram showing dedicated hardware for implementing the functions of the numerical control device, learning device, and inference device involved in embodiments 1 to 3.

[0033] Figure 24 This is a diagram showing the structure of the control circuit used to implement the functions of the numerical control device, learning device, and inference device involved in embodiments 1 to 3. Detailed Implementation

[0034] The numerical control device, machining system, numerical control method, and machining method according to embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Furthermore, in the following description, multiple structural elements having the same function are sometimes distinguished by adding a hyphen and a number after a common number. When it is not necessary to distinguish multiple structural elements having the same function individually, only the common number is marked.

[0035] Implementation method 1.

[0036] Figure 1 This diagram illustrates the functional structure of the machining system 1 according to Embodiment 1. The machining system 1 includes a machine tool 2 and a numerical control device 3. The numerical control device 3 assigns operating instructions generated based on instructions described in the numerical control program 4 to the machine tool 2, thereby controlling the machine tool 2. Furthermore, the machine tool 2 and the numerical control device 3 can be located in close proximity, or, if the numerical control device 3 is connected in a manner capable of controlling the machine tool 2, the numerical control device 3 can be located in a location far from the machine tool 2.

[0037] The machine tool 2 has a spindle drive system 21, one or more feed drive systems 22, a cutting tool 23 for machining the workpiece W, and a worktable 24 for holding the workpiece W.

[0038] The spindle drive system 21 includes a spindle motor 211 and a spindle drive mechanism 212 driven by the spindle motor 211. A tool 23 is connected to the spindle drive system 21, and the spindle drive system 21 enables the tool 23 to rotate. An encoder (not shown) representing angle information of the spindle drive system 21 is included in either the spindle motor 211 or the spindle drive mechanism 212.

[0039] The feed drive system 22 includes a servo motor 221 and a feed drive mechanism 222 driven by the servo motor 221. The feed drive system 22 is capable of changing the relative position of the tool 23 and the workpiece W. The servo motor 221 and the feed drive mechanism 222 include encoders (not shown) that represent position information of the feed drive system 22. A worktable 24 or a tool 23 for loading the workpiece W is connected to the feed drive system 22. By moving the worktable 24 or the tool 23, the feed drive system 22 can change the relative position of the tool 23 and the workpiece W. Furthermore, in... Figure 1 In the example shown, the machine tool 2 is configured with a feed drive system 22-1 that moves the cutting tool 23 and a feed drive system 22-2 that moves the worktable 24. Both the cutting tool 23 and the worktable 24 can be moved, but only the cutting tool 23 or only the worktable 24 can be moved. The goal is to change the relative position of the workpiece W held by the cutting tool 23 and the worktable 24. The feed drive system 22 changes the relative position of the cutting tool 23 and the workpiece W, thereby allowing the cutting tool 23 to cut the workpiece W along the machining path.

[0040] The spindle drive system 21 and feed drive system 22 are connected to the CNC device 3, and control the spindle motor 211 and servo motor 221 through operation commands given by the CNC device 3. Hereinafter, when referring specifically to the spindle drive system 21 and feed drive system 22, they will be referred to as drive system 20. Furthermore, the series of processes in which the cutting edge of the tool 23 penetrates into the workpiece W to generate chips and forms a machined surface is called machining process M.

[0041] Figure 2 It means Figure 1 The diagram shows an example of the physical structure of the machine tool 2. The worktable 24 has a horizontal surface on which the workpiece W is placed. The spindle drive mechanism 212 is configured to position the cutting tool 23 above the workpiece W held by the worktable 24. The spindle motor 211 is arranged adjacent to the spindle drive mechanism 212. The spindle of the spindle drive system 21, which includes the spindle motor 211 and the spindle drive mechanism 212, is perpendicular to the horizontal surface of the worktable 24, and the spindle drive system 21 rotates the cutting tool 23 around the spindle.

[0042] The feed drive mechanism 222-1 of the feed drive system 22-1, which moves the tool 23, is connected to the tool 23 via a component including a spindle drive mechanism 212 on which the tool 23 is mounted. The servo motor 221-1 of the feed drive system 22-1 is disposed adjacent to the feed drive mechanism 222-1. The feed axis of the feed drive system 22-1 is parallel to the spindle, and the feed drive system 22-1 moves the tool 23 up and down along the feed axis.

[0043] The feed drive mechanism 222-2 of the feed drive system 22-2 that moves the worktable 24 is connected to the worktable 24. The servo motor 221-2 of the feed drive system 22-2 is arranged adjacent to the feed drive mechanism 222-2. The feed axis of the feed drive system 22-2 is in the horizontal plane of the worktable 24, and the feed drive system 22-2 moves the worktable 24 in the horizontal direction. Furthermore, only one feed drive system 22 that moves the worktable 24 has been described here, but the machine tool 2 may also have a feed drive system 22 that is perpendicular to the feed axis of the feed drive system 22-2 and has a feed axis in the horizontal plane of the worktable 24.

[0044] Furthermore, the physical structure shown here is an example used to simplify the explanation; the physical structure of machine 2 is not limited to... Figure 2 The example shown is as follows. For example, the feed drive system 22 of the machine tool 2 can be one or more than or equal to three. The direction of the spindle and feed axis is also an example. In addition, the worktable 24 is an example of a mechanism for holding the workpiece W. It can hold the workpiece W, as long as it is a structure that can control the relative position of the workpiece W with respect to the tool 23.

[0045] Return to Figure 1 The numerical control device 3 has an instruction generation unit 31, a control characteristic setting unit 32, a storage unit 33, a coupling simulation unit 34, a process evaluation unit 35, and a drive control unit 36.

[0046] The CNC program 4 contains multiple instructions that direct the movement of the spindle and feed axes of the machine tool 2. For example, the instructions in the CNC program 4 specify the path of the tool 23 by its relative position to the workpiece W. The instructions specifying the path of the tool 23 include multiple position instructions specifying positions along the path. Furthermore, the CNC program 4 also includes spindle speed instructions indicating the spindle's rotational speed at the positions indicated by each position instruction, and feed speed instructions indicating the movement speed of the feed axes. The CNC program 4 can be supplied to the CNC device 3 from outside the CNC device 3, or it can be stored internally by the CNC device 3.

[0047] The instruction generation unit 31 parses the instructions recorded in the CNC program 4 and generates continuous operation instructions for controlling the machine tool 2. The instruction generation unit 31 generates operation instructions that cause the machine tool 2 to execute the instructions recorded in the CNC program 4. The instruction generation unit 31 outputs the generated operation instructions to the coupling simulation unit 34 and the drive control unit 36 ​​respectively.

[0048] The control characteristic setting unit 32 generates a set of parameters, i.e., control characteristic parameters, representing the characteristics of the controller in the drive control unit 36 ​​described later. This set of control characteristic parameters is called a parameter set. Details regarding the control characteristic parameters will be described later. The control characteristic setting unit 32 generates one or more parameter sets and outputs the generated parameter sets to the coupling simulation unit 34 and the drive control unit 36, respectively.

[0049] The storage unit 33 stores the machining process model 331, the dynamics model 332, the spindle drive control model 333, the feed drive control model 334, and the machining condition information 335. The storage unit 33 can output the stored information to the coupled simulation unit 34. The machining condition information 335 includes, for example, tool shape information and the feed rate when using the tool 23. The tool shape information includes the number of cutting edges, tool diameter, and torsion angle of the tool 23. Details regarding the machining process model 331, the dynamics model 332, the spindle drive control model 333, and the feed drive control model 334 will be described later.

[0050] Regarding the cutting process performed by the machine tool 2, since it is a physical phenomenon in which the machining process M and mechanical dynamics influence each other, it is preferable to perform an analysis that combines the machining process and mechanical dynamics in order to manage or control the machining state. Here, the machining process refers to the series of processes in which the tip of the cutting tool 23 penetrates into the workpiece W to generate chips and forms a machined surface. Mechanical dynamics refers to the dynamic movement of components that vibrate due to internal and external vibration sources during machining on the machine tool 2. Here, the term "component" refers to the components constituting the machine tool 2, and may also include the cutting tool 23 and the workpiece W.

[0051] The drive system 20 is controlled by the CNC device 3, thereby moving the tool 23 along a predetermined path relative to the workpiece W while it rotates. During the cutting of the workpiece W by the tool 23, a cutting force F is generated between the tool 23 and the workpiece W. c The interference force F passes through the component. d Transmitted to the feed drive system 22 as disturbance torque T d This is transmitted to the spindle drive system 21. Due to the interference force F applied to the feed drive system 22... dTherefore, when the position of the feed drive system 22 is based on the position of the tool 23 when it is not cutting the workpiece W, the position is related to the interference force F. d The amplitude and frequency of the vibration change accordingly. Similarly, if a disturbance torque T is applied to the spindle drive system 21... d The rotation angle of the spindle drive system 21 will vary relative to the rotation angle of the tool 23 when it is not cutting the workpiece W.

[0052] The above relationships are illustrated using the accompanying diagram. Figure 3 It means Figure 1 The diagram shows the relationship between the spindle drive system 21, mechanical dynamics, and machining process M. Figure 4 It is Figure 3 The diagram shows the physical quantities and the physical structure of the working machine 2. If the CNC device 3 assigns an operation command to the spindle drive system 21, the spindle motor 211 drives the spindle drive mechanism 212, causing the components of the working machine 2, including the tool 23, to rotate and process the workpiece W. Here, if the spindle drive system 21 is controlled as the spindle drive system angle θ1 based on the operation command, the actual angle of the tool 23 is affected by the tool-side mechanical dynamics MD1 and becomes the tool angle θ2. The tool 23 penetrates into the workpiece W, performing a series of machining processes M that simultaneously generate chips and form a machined surface. The cutting torque T generated at this time... c The component is affected by the mechanical dynamics MD1 of the tool side, which acts as a disturbance torque T. d Feedback is sent to the spindle drive system 21. The working machine 2 outputs the feedback signal to the CNC device 3. When subjected to disturbance torque T d When the state of the spindle drive system 21 is different from the operation command, the CNC device 3 changes the operation command based on the feedback signal transmitted from the spindle drive system 21.

[0053] Figure 5 It means Figure 1 The diagram shows the relationship between the feed drive system 22-2, mechanical dynamics, and machining process M. Figure 6 It is Figure 5The diagram shows the physical quantities along with the physical structure of the working machine 2. If the CNC device 3 assigns an operation command to the feed drive system 22-2, the workpiece W is machined via the relative motion between the tool 23 and the workpiece W. At this time, the servo motor 221-2 of the feed drive system 22-2 drives the feed drive mechanism 222-2 based on the operation command, resulting in a drive system displacement r1 on the worktable 24. The actual displacement generated in the workpiece W is influenced by the workpiece-side mechanical dynamics MD2 at the time the drive system displacement r1 is generated, becoming a component displacement r2. The cutting force F generated at this time... c The interference force F passes through the component. d Feedback is given to the feed drive system 22-2. When subjected to disturbance force F... d When the state of the feed drive system 22-2 is different from the operation command, the CNC device 3 changes the operation command based on the feedback signal transmitted from the feed drive system 22-2.

[0054] In addition, the above explanation is used Figures 3 to 6 The spindle drive system 21 and the feed drive system 22 are described separately, but the transmission of displacement and force during machining occurs simultaneously in the spindle drive system 21 and the feed drive system 22.

[0055] As described above, in machining, the system that constitutes the machining process M, mechanical dynamics, and drive system 20 is coupled together. The CNC device 3 participates in the machining process M via the drive system 20 and mechanical dynamics. Furthermore, during the cutting process between the tool 23 and the workpiece W, a cutting force F is generated. c The machining point disappears as chips are generated, making it impossible to install sensors to directly detect the cutting force F. c Therefore, in order to accurately evaluate the cutting process involving the movement of the tool 23 and the workpiece W, it is necessary to simulate the operation of the spindle drive system 21 and the feed drive system 22, in addition to the machining process M and mechanical dynamics.

[0056] Next, specific examples of the machining process model 331, dynamics model 332, spindle drive control model 333, and feed drive control model 334 stored in the storage unit 33 will be described. These models are used in the simulation performed by the coupled simulation unit 34, which will be described later.

[0057] Machining process model 331 represents the machining characteristics between the cutting tool 23 and the workpiece W. More specifically, machining process model 331 is the cutting force F generated in response to the positional relationship between the cutting tool 23 and the workpiece W. cA mathematical model is used to represent this. The following formula (1) is the cutting force F during the contact between the cutting tip of the tool 23 and the workpiece W. c An example of a formula for expression. Formula (1) uses the relative cutting resistance K. c Edge force coefficient K e The minute thickness Δa of the cross-section of tool 23, the removal thickness h of the workpiece W, and the rotation angle of tool 23. The minute cutting force ΔF of each section of tool 23 is expressed at time t. c The total cutting force F generated by the feed of tool 23. c It is possible to use the small cutting force ΔF shown in equation (1) c The calculation is performed by adding the forces along the axial direction of the tool 23. The removal thickness h of the workpiece W is the distance between the previous machining surface and the current machining surface in the radial direction of the tool 23. Equation (1) shows that the cutting force F can be reduced by the sum of a force proportional to the removal thickness h and a certain amount of force called the edge force. c Perform the calculation.

[0058] Formula 1

[0059]

[0060] The cutting thickness h can be expressed by the following formula (2). The cutting thickness h is expressed by a component representing the nominal cutting thickness determined by the feed rate c of the tool 23 for each cutting edge, a component representing the vibration of the relative relationship between the tool 23 and the workpiece W, and a component representing the increase or decrease in cutting thickness caused by the difference in the rotation radius of each cutting edge when the tool 23 has multiple cutting edges. The component representing the vibration of the relative relationship between the tool 23 and the workpiece W is expressed by the radial component u of the tool 23, which represents the relative displacement between the tool 23 and the workpiece W at the instant of cutting the workpiece surface. r The tool radius component w is the relative displacement between the tool 23 transferred to the pre-processing surface and the workpiece W. r The difference is represented by the component of the increase or decrease in cut thickness caused by the difference in the rotation radius of each cutting edge, which is represented by the rotation radius correction amount Δe of the cutting edge of tool 23.

[0061] Formula 2

[0062]

[0063] Equation (1) is an example of machining process model 331, but machining process model 331 is not limited to the example above. For example, it could also be a voxel representing the shape of the tool 23 and the shape of the workpiece W, applied to the cutting force F. c The model used for calculation.

[0064] Dynamic model 332 represents the dynamic characteristics of a component that vibrates during the operation of the machine tool 2. Specifically, dynamic model 332 is a mathematical model of the dynamic displacement of a component when a dynamic force is applied to it. For example, a cutting force F is applied to the workpiece W connected to the drive system 20. c The action of the processing object W can be represented by the following formula (3).

[0065]

Formula 3

[0066]

[0067] Equation (3) is an example of an equation representing the vibration of the workpiece W. The cutting force F generated between the tool 23 and the workpiece W... c It is expressed using the relative displacement u between the cutting tool 23 and the workpiece W, the relative displacement v of the drive system 20, the equivalent mass m of the workpiece W, the equivalent viscosity coefficient C of the workpiece W, and the equivalent spring constant K of the workpiece W. Equation (3) represents the cutting force F. c The object being processed, W, acts as the interfering force, F. d And the mechanical dynamics transmitted to the drive system 20.

[0068] Furthermore, the dynamic model 332 is not limited to formula (3). For example, it could be a model that represents the shape of the workpiece W using voxels and uses FEM (Finite Element Method) to calculate the displacement of the component during vibration. Furthermore, while the dynamic model 332 described here only represents the vibration of the workpiece W, it could also represent the vibration of the tool 23 or other components instead of the workpiece W. Alternatively, the dynamic model 332 could represent the vibration of both the tool 23 and the workpiece W.

[0069] The spindle drive control model 333 is a mathematical model representing the spindle drive system 21 of the machine tool 2 and the controller, i.e., the spindle drive controller, which exists in the drive control unit 36 ​​of the CNC device 3 and controls the spindle drive system 21. Figure 7 It is used for Figure 1 The diagram illustrates an example of a spindle drive control model 333. The spindle drive control model 333, when given a spindle rotation angle command, operates under the influence of the cutting torque T. c The resulting disturbance torque T dThis is a mathematical model of the spindle drive system 21 under conditions where the position and speed of the spindle drive system 21 are controlled by the position controller and speed controller of the spindle drive controller. As this mathematical model, if a spindle rotation angle command θ is input to the controller... r Then, the actual rotation angle θ of the spindle is output. Here, K pp1 K vp1 K vi1 These are the control gains, specifically the proportional gain K used for position control. pp1 The proportional gain K used for speed control vp1 Integral gain K used for speed control vi1 P1(s) is the torque-to-position transfer function of the entire spindle drive system 21, where s is a complex number. P1(s) can be determined based on the actual response of the spindle drive system 21 using known system determination methods. Furthermore, while the spindle drive system 21 is modeled as a single-inertial system here, it can also be modeled as a multi-inertial system. Additionally, a feedforward controller can be added to the spindle drive controller. Furthermore, the controller can be constructed with correction amounts added to compensate for error factors such as friction corresponding to the rotation angle.

[0070] The feed drive control model 334 is a mathematical model representing the feed drive system 22 of the machine tool 2 and the controller, i.e. the feed drive controller, that controls the feed drive system 22 within the drive control unit 36 ​​of the CNC device 3. Figure 8 It is used for Figure 1 The diagram illustrates an example of the feed drive control model 334. The feed drive control model 334 is based on the position command given to the feed drive system, under the influence of the cutting force F. c The resulting disturbance force F d This is a mathematical model of the position and speed of the feed drive system 22 controlled by the position controller and speed controller of the feed drive controller, when the data is transmitted to the feed drive system 22. This mathematical model is established when a position command x is input to the feed drive system. r Then, the actual position x of the feed drive system is output. Here, K pp2 K vp2 K vi2 These are the control gains, specifically the proportional gain K used for position control. pp2 The proportional gain K used for speed control vp2 Integral gain K used for speed control vi2P2(s) is the force-to-position transfer function of the entire feed drive system 22, where s is a complex number. P2(s) can be determined based on the actual response of the feed drive system 22 using known system determination methods. Furthermore, while the feed drive system 22 is modeled as a single-inertial system here, it can also be modeled as a multi-inertial system. Additionally, a feedforward controller can be added to the feed drive controller. The controller can be constructed with a correction amount added to compensate for error factors such as friction corresponding to position or velocity. This correction amount is, for example, a value obtained by multiplying the position or velocity by a coefficient parameter. Furthermore, the controller can be constructed with a signal processing filter for passing through or removing vibrations in a specific frequency band using a frequency parameter.

[0071] Here, the control characteristic parameters are explained. These parameters represent the control characteristics of the controller that controls the drive system 20. Figure 7 and Figure 8 In this context, the control characteristic parameter is the control gain of the controller. Furthermore, the control characteristic parameter is not limited to the control gain; when a correction amount is added to the controller to correct for error factors, the coefficient parameter determining the correction amount is equivalent to the control characteristic parameter. Additionally, when the controller has a signal processing filter for passing through or removing vibrations in a specific frequency band, the frequency parameter specifying the frequency band of the signal processing filter is equivalent to the control characteristic parameter. Moreover, the control characteristic parameter is not limited to a constant value; it can be a value that varies over time.

[0072] If the values ​​of the control characteristic parameters are different, the drive system 20 exhibits different frequency characteristics. Specifically, in the drive system 20, the values ​​of the control characteristic parameters vary in terms of the follow-through of operating commands and the suppression of disturbances. The correspondence between the control characteristic parameters and the frequency characteristics of the drive system 20 will be explained next. Figure 9 This indicates that it will be used to... Figure 1 The diagram shows the gain curves for each frequency illustrating the response of the drive system 20 when an operating command is input. Figure 9 The diagram shows the gain response for three parameter sets, referred to as Group 1, Group 2, and Group 3. The gain response differs for each parameter set, and the peak frequencies of the gain response for each of Group 1, Group 2, and Group 3 are different from each other.

[0073] Figure 10 This indicates that it will be used to... Figure 1 The diagram shows the phase curves of the drive system 20's response to an operating command at each frequency. Figure 10 In, with Figure 9Similarly, phase responses are shown for three parameter sets, referred to as Group 1, Group 2, and Group 3. The phase response differs for each parameter set; the frequency of phase changes differs in each of the three groups. For example... Figure 9 and Figure 10 As shown, if the values ​​of the control characteristic parameters are different, the drive system 20 exhibits different frequency characteristics.

[0074] Here, the parameter set generated by the control characteristic setting unit 32 will be described in detail. As described above, the control characteristic setting unit 32 generates one or more parameter sets. Controllers exist in the form of spindle drive systems 21 and feed drive systems 22. A parameter set is a collection of control characteristic parameters that determine the characteristics of the controllers corresponding to the spindle drive systems 21 and feed drive systems 22 of the machine tool 2. Each parameter set may contain one type of control characteristic parameter for one controller, or it may contain multiple types of control characteristic parameters for one controller. For example, if the machine tool 2 has one spindle drive system 21 and two feed drive systems 22, the CNC device 3 has three controllers. In this case, one parameter set may correspond to each of the three controllers and contain the values ​​of the three control characteristic parameters, or it may contain the values ​​of multiple types of control characteristic parameters for each controller. Figure 7 and Figure 8 In the example shown, when the machine tool 2 has one spindle drive system 21 and two feed drive systems 22, the parameter set with respect to the spindle drive controller can each include one proportional gain K for position control. pp1 The proportional gain K used for speed control vp1 and the integral gain K used for speed control vi1 Regarding the feed drive controller, each value can include two proportional gains K for position control. pp2 The proportional gain K used for speed control vp2 and the integral gain K used for speed control vi2 The value of . Furthermore, in Figure 7 and Figure 8 The diagram shows a controller that includes both proportional and integral gain, but a controller that includes derivative gain can also be used.

[0075] The control characteristic setting unit 32 can set the same control characteristic parameter value for each of multiple controllers. Furthermore, here, "same value for control characteristic parameter" means that the values ​​of the same type of control characteristic parameter are the same across multiple controllers. When the control characteristic setting unit 32 can set the same type of control characteristic parameter for each of multiple controllers, it can set the values ​​of the same type of control characteristic parameter set for each of the multiple controllers to be the same. For example, the control characteristic setting unit 32 can set the proportional gain K between the feed drive controller used to control the feed drive system 22-1 and the feed drive controller used to control the feed drive system 22-2. pp2 , proportional gain K vp2 and integral gain K vi2 Each controller is set to the same value. As described above, when multiple controllers operate with control characteristic parameters of the same value, the multiple controllers exhibit frequency characteristics that are equal to each other. Therefore, the system including the drive system 20 and components has the same frequency characteristics regardless of the feed direction relative to the workpiece W of the tool 23.

[0076] Alternatively, the control characteristic setting unit 32 can set different values ​​for control characteristic parameters for each of the multiple controllers. Furthermore, here, different values ​​for control characteristic parameters mean that the values ​​of the same type of control characteristic parameter differ among the multiple controllers. When multiple types of control characteristic parameters can be set for one controller, the control characteristic setting unit 32 can set different values ​​for at least a portion of the multiple types of control characteristic parameters. As described above, when the multiple controllers operate with different values ​​for control characteristic parameters, each controller exhibits different frequency characteristics. Therefore, the system including the drive system 20 and the components exhibits different frequency characteristics depending on the feed direction relative to the workpiece W of the tool 23.

[0077] Furthermore, the control characteristic setting unit 32 can set different values ​​for control characteristic parameters for each direction of motion of the feed drive system 22 corresponding to the controller. Here, for example... Figure 2As shown, a machine tool 2 having one spindle drive system 21 and multiple feed drive systems 22 corresponding to the spindle drive system 21 has been described. However, when the machine tool 2 has multiple spindle drive systems 21, it may also have multiple feed drive systems 22 with the same direction of motion. Regarding the machine tool 2 with multiple feed drive systems 22 with the same direction of motion, the control characteristic setting unit 32 can set the same control characteristic parameter value for the multiple feed drive systems 22 with the same direction of motion, and set different control characteristic parameters for the multiple feed drive systems 22 with different directions of motion. In this case, the gain response or phase response of the system synthesized from the components that vibrate during the operation of the machine tool 2 and the feed drive systems 22 will be different for each direction of motion of the feed drive systems 22.

[0078] As another example, the relative trajectories of the control characteristic setting unit 32 and the tool 23 with respect to the workpiece W can be adjusted so that the control characteristic parameters of each of the multiple controllers can change over time. Figure 11 It is used for the purpose of Figure 1 The diagram illustrates an example of the parameter set generated by the control characteristic setting unit 32. Figure 11 The diagram shows the relative trajectory L drawn by the tool 23 relative to the workpiece W during the operation of the machine tool 2. The feed direction R1 and the normal direction R2 of the feed direction R1 at each position P on the relative trajectory L change at their respective positions P. For example, if the relative trajectory L is curved, the feed direction R1-1 at position P-1 is different from the feed direction R1-2 at position P-2, and the normal direction R2-1 at position P-1 is different from the normal direction R2-2 at position P-2. Here, the normal direction R2 represents the normal direction of the relative trajectory L at a certain position P of the tool 23, and is perpendicular to the feed direction R1 at that position P. The control characteristic setting unit 32 changes the control characteristic parameters at each moment in such a way that the frequency characteristics of the tool 23 in the feed direction R1 and the normal direction R2 are different from each other. In this case, the gain response or phase response of the combined system of the component that vibrates during the operation of the machine tool 2 and the feed drive system 22 is different from each other in the feed direction R1 and normal direction R2 relative to the workpiece W of the tool 23.

[0079] Here, the mechanism of the machining process M coupled with mechanical dynamics is explained. Figure 12 It means Figure 1 The diagram shows a first example of the relationship between the state of the cutting tool 23 and the state of the machined surface of the workpiece W during the operation of the machine tool 2. Figure 12The diagram shows the first machined surface a1 of the workpiece W and the movement trajectory b1 of the tool 23 during cutting of the first machined surface a1. The movement trajectory b1 represents the vibration of the tool 23 during cutting. The thickness v1 of the chips during cutting of the first machined surface a1 is represented by the difference between the first machined surface a1 and the movement trajectory b1 of the tool 23.

[0080] Next, the second machining surface a2 becomes the surface of the workpiece W after the surface of the first machining surface a1 has been removed by the cutting tool 23, forming a shape corresponding to the movement trajectory b1 of the cutting tool 23 when cutting the first machining surface a1. In the first example, the cutting tool 23 vibrates during the operation of the machine tool 2, thus transferring the vibration of the cutting tool 23 during the cutting of the first machining surface a1 onto the second machining surface a2. Furthermore, the thickness v2 of the chips when cutting the second machining surface a2 is represented by the difference between the second machining surface a2 and the movement trajectory b2 of the cutting tool 23. Therefore, the thickness v2 of the chips when cutting the second machining surface a2 varies depending on the shape of the second machining surface a2 and the vibration of the cutting tool 23 during the cutting of the second machining surface a2. Therefore, the thickness of the chips of the workpiece W varies due to the vibration of the cutting tool 23 when cutting each machining surface.

[0081] exist Figure 12 In the first example shown, the chip thickness v1 when machining the first machining surface a1 and the chip thickness v2 when machining the second machining surface a2 are the same. As described above, the machining process M is stable, and the possibility of vibration divergence is low.

[0082] Figure 13 It means Figure 1 The diagram shows a second example of the relationship between the state of the cutting tool 23 and the state of the machined surface of the workpiece W during the operation of the machine tool 2. Figure 13 The diagram shows the third machining surface a3 of the workpiece W and the movement trajectory b3 of the tool 23 when cutting the third machining surface a3. The thickness v3 of the chips when cutting the third machining surface a3 is represented by the difference between the third machining surface a3 and the movement trajectory b3 of the tool 23.

[0083] Next, the fourth machining surface a4 becomes the surface of the workpiece W after the surface of the third machining surface a3 has been removed by the cutting tool 23, and its shape corresponds to the movement trajectory b3 of the cutting tool 23 when cutting the third machining surface a3. In the second example, the cutting tool 23 vibrates with a larger amplitude than in the first example during the operation of the machine tool 2, and the vibration of the cutting tool 23 when cutting the third machining surface a3 is transferred to the fourth machining surface a4. In addition, the thickness v4 of the chips when cutting the fourth machining surface a4 is represented by the difference between the fourth machining surface a4 and the movement trajectory b4 of the cutting tool 23.

[0084] Regarding the amplitude of the vibration of tool 23, and Figure 12 Compared to the first case shown, Figure 13 The second example shown is larger. Therefore, the difference between the chip thickness v3 when machining the third machining surface a3 and the chip thickness v4 when machining the fourth machining surface a4 is larger than the difference between thickness v1 and thickness v2 in the first example. As described above, the machining process M is unstable and has a high probability of causing chatter.

[0085] Figure 14 It means Figure 1 The diagram shows a third example of the relationship between the state of the cutting tool 23 during the operation of the machine tool 2 and the state of the machined surface of the workpiece W. Figure 14 The diagram shows the fifth machining surface a5 of the workpiece W and the movement trajectory b5 of the tool 23 when cutting the fifth machining surface a5. The thickness v5 of the chips when cutting the fifth machining surface a5 is represented by the difference between the fifth machining surface a5 and the movement trajectory b5 of the tool 23.

[0086] Next, the sixth machining surface a6 becomes the surface of the workpiece W after the surface of the fifth machining surface a5 has been removed by the tool 23, and its shape corresponds to the movement trajectory b5 of the tool 23 when cutting the fifth machining surface a5. In the third example, the tool 23 vibrates during the operation of the machine tool 2, and the vibration of the tool 23 when cutting the fifth machining surface a5 is transferred to the sixth machining surface a6. In addition, the thickness v6 of the chips when cutting the sixth machining surface a6 is represented by the difference between the sixth machining surface a6 and the movement trajectory b6 of the tool 23. Here, the difference between the phase of the vibration of the tool 23 when cutting the fifth machining surface a5 and the phase of the vibration of the tool 23 when cutting the sixth machining surface a6 is larger than the difference between the phase of the vibration of the tool 23 when cutting the first machining surface a1 and the phase of the vibration of the tool 23 when cutting the second machining surface a2 in the first example, and the phases are approximately reversed. Therefore, the difference between the chip thickness v5 when machining the fifth machining surface a5 and the chip thickness v6 when machining the sixth machining surface a6 is larger than the difference between thickness v1 and thickness v2 in the first example. As described above, the machining process M is also unstable, similar to the second example, and has a high probability of causing chatter.

[0087] If used Figures 12 to 14 As explained, the chip thickness changes depending on the vibration state of the tool 23. In particular, the amplitude and phase of the vibration of the tool 23 have a significant impact on the stability of the machining process M. Therefore, by appropriately controlling the amplitude and phase of the vibration of the tool 23, the machining process M can be stabilized.

[0088] The coupling simulation unit 34 assigns the operation command output by the command generation unit 31 to the machine tool 2 and calculates the process information, which represents the simulation result of machining when the machine tool 2 is controlled by a controller with control characteristics shown in the parameter set generated by the control characteristic setting unit 32. The process information includes parameters that can be used to compare machining errors, such as the removal thickness of the workpiece W and the cutting force F. c Interference force F d Here, the thickness of the workpiece W removed is the thickness of the chips removed by the tool 23. The coupling simulation unit 34 can simulate the machining process M by including the effects of the operation of the drive system 20, including the spindle drive system 21 and the feed drive system 22, and the dynamics of the components that vibrate during the operation of the machine tool 2. The coupling simulation unit 34 performs the simulation with the number of parameter sets generated by the control characteristic setting unit 32, and generates process information representing the simulation results with the number of parameter sets. The coupling simulation unit 34 outputs the generated process information to the process evaluation unit 35.

[0089] The coupling simulation unit 34 can use the machining process model 331, the dynamics model 332, the spindle drive control model 333, and the feed drive control model 334 to simulate the machining performed by the machine tool 2 for each parameter set. Specifically, the coupling simulation unit 34 sets the control characteristic parameters of the spindle drive controller in the spindle drive control model 333 and the control characteristic parameters of the feed drive controller in the feed drive control model 334 to the values ​​shown in the parameter set generated by the control characteristic setting unit 32. For the machining process model 331, the dynamics model 332, the spindle drive control model 333, and the feed drive control model 334, it assigns the operation command output by the command generation unit 31 using the specified machining conditions, thereby simulating the machining performed by the machine tool 2 and calculating the process information representing the simulation results. At this time, the coupling simulation unit 34 can use the machining process model 331, the dynamics model 332, the spindle drive control model 333, the feed drive control model 334, and the machining condition information 335 stored in the storage unit 33. When using the processing condition information 335 stored in the storage unit 33, the specified processing conditions become the processing conditions shown in the processing condition information 335.

[0090] The coupling simulation unit 34 performs a simulation that couples the machining process M between the tool 23 and the workpiece W, the mechanical dynamics of the components of the machine tool 2, the operation of the spindle drive system 21, and the operation of the feed drive system 22. In the coupling simulation unit 34, based on... Figures 3 to 6 The relationships shown refer to the coupled model formed by combining the machining process model 331, the dynamics model 332, the spindle drive control model 333, and the feed drive control model 334. Using the values ​​of the control characteristic parameters shown in the parameter set, under the machining conditions described in the machining condition information 335, the values ​​are applied to the drive signal when the operation command is given, the spindle drive system angle θ1, the drive system displacement r1, the tool angle θ2, the component displacement r2 of the feed system, the removal thickness h of the workpiece W, and the cutting torque T. c Cutting force F c Interference torque T d Interference force F d The simulation unit 34 performs a coupled simulation by simulating the feedback signal and calculating the timing and frequency components. The coupled simulation unit 34 performs the simulation using the number of parameter sets generated by the control characteristic setting unit 32, and outputs the simulation results as process information.

[0091] The process evaluation unit 35 evaluates the magnitude of the machining error when using the parameter set generated by the control characteristic setting unit 32 for each parameter set based on multiple process information output by the coupled simulation unit 34, and selects the parameter set used for controlling the machine tool 2 based on the evaluation results. The process evaluation unit 35 outputs a selection signal indicating the selected parameter set to the drive control unit 36.

[0092] If used Figures 12 to 14 As explained, the machining process M coupled with mechanical dynamics can sometimes become unstable depending on the relationship between the vibration of the machining surface and the tool 23. The characteristics of the spindle drive system 21 and the feed drive system 22 change based on the values ​​of the control characteristic parameters shown in the parameter set generated by the control characteristic setting unit 32. Since the tool 23 and the worktable 24 that holds the workpiece W are connected to at least one of the spindle drive system 21 and the feed drive system 22, the mechanical dynamics of the tool 23 and the workpiece W can be indirectly changed by the parameter set generated by the control characteristic setting unit 32. Therefore, the coupling simulation unit 34 performs coupling simulation for each parameter set generated by the control characteristic setting unit 32, thereby enabling the process evaluation unit 35 to evaluate the stability of the machining process M for each parameter set. The process evaluation unit 35 selects the parameter set that makes the machining process M most stable among all generated parameter sets. Thus, the machine tool 2 can be controlled using the parameter set that minimizes machining errors.

[0093] The following describes an example of the evaluation method in the process evaluation unit 35. The process evaluation unit 35 can evaluate the magnitude of the processing error based on the time variation of the cut-off thickness h of the workpiece W. The process information generated by the coupled simulation unit 34 includes the cut-off thickness h of the workpiece W calculated based on the operation command. The smaller the increase in the cut-off thickness h of the workpiece W, the smaller the processing error is evaluated by the process evaluation unit 35. The process evaluation unit 35 can select the parameter set that minimizes the increase in cut-off thickness h as the parameter set used for the control of the machine tool 2. Here, the cut-off thickness h is equivalent to using... Figures 12 to 14 The thickness of the cut chip, as described, represents the thickness between the machined surface and the movement trajectory of the tool 23. If a vibration known as chatter occurs between the tool 23 and the workpiece W, its amplitude increases over time, leading to a deterioration of machining errors. Therefore, by evaluating the time-dependent change in the cut thickness h, the process evaluation unit 35 can select the parameter set that minimizes the vibration between the tool 23 and the workpiece W. This parameter set, which minimizes the vibration between the tool 23 and the workpiece W, stabilizes the machining process M and minimizes the machining errors caused by the vibration between the tool 23 and the workpiece W.

[0094] In addition, the process evaluation unit 35 can evaluate the disturbance force F when executing operation instructions for each parameter set. d Or the disturbance torque T d The maximum amplitude is used to evaluate the magnitude of machining errors. Disturbance force F d Or the disturbance torque T d The smaller the maximum amplitude, the smaller the processing error evaluated by the process evaluation unit 35. The process evaluation unit 35 can select the parameter set that minimizes the maximum amplitude as the parameter set used for the control of the machine tool 2. Disturbance force F d Or the disturbance torque T d The smaller the maximum amplitude, the less the disturbance force F d Or the disturbance torque T d The vibration caused by this becomes smaller. Therefore, by adjusting the disturbance force F... d Or the disturbance torque T d The maximum amplitude is selected as the minimum set of parameters, thereby minimizing the machining error caused by the vibration of the drive system 20.

[0095] Furthermore, the process evaluation unit 35 can compare the time waveform of the process information calculated by the coupled simulation unit 34 with a preset target contour, and evaluate the magnitude of the machining error based on the deviation from the target contour. The target contour is a contour where the machining error is less than or equal to the allowable value, and is, for example, preset within the process evaluation unit 35. The smaller the deviation from the target contour, the smaller the machining error is evaluated by the process evaluation unit 35. The process evaluation unit 35 can also evaluate the deviation from the target contour based on loss functions such as squared error, or based on machine learning methods such as pattern matching. The process evaluation unit 35 selects the parameter set that minimizes the deviation from the target contour, thereby minimizing the machining error.

[0096] The process evaluation unit 35 can use any one of the above-mentioned evaluation methods to evaluate the magnitude of the processing error, or it can combine the above-mentioned evaluation methods.

[0097] The drive control unit 36 ​​controls the drive system 20 of the machine tool 2 according to the operation commands generated by the command generation unit 31. Internally, the drive control unit 36 ​​includes a spindle drive controller for controlling the spindle drive system 21 and a feed drive controller for controlling the feed drive system 22. The spindle drive controller monitors the signals from the encoder of the spindle drive system 21 and outputs commands to the spindle motor 211 such that the position and speed of the spindle drive system 21 are equal to the values ​​specified by the operation commands. The feed drive controller monitors the signals from the encoder of the feed drive system 22 and outputs commands to the servo motor 221 such that the position and speed of the feed drive system 22 are equal to the values ​​specified by the operation commands. Based on the selection signal output by the process evaluation unit 35, the drive control unit 36 ​​selects the parameter set used for controlling the machine tool 2 from the parameter set output by the control characteristic setting unit 32, and sets the values ​​of the control characteristic parameters of the spindle drive controller and the feed drive controller to the values ​​of the control characteristic parameters shown in the selected parameter set.

[0098] Figure 15 It is used for Figure 1 The flowchart illustrates the operation of the CNC device 3. When the machining system 1 starts operating, the instruction generation unit 31 of the CNC device 3 reads the CNC program 4, parses the read CNC program 4, and generates operation instructions based on the CNC program 4. Additionally, the control characteristic setting unit 32 generates a set of parameters (step S101) containing one or more control characteristic parameters. The instruction generation unit 31 outputs the generated operation instructions to the coupling simulation unit 34 and the drive control unit 36, and the control characteristic setting unit 32 outputs the generated parameter set to the coupling simulation unit 34 and the drive control unit 36.

[0099] The coupled simulation unit 34 uses the operation commands output by the command generation unit 31 and the parameter sets output by the control characteristic setting unit 32 to perform coupled simulation for each parameter set and calculate process information (step S102). The coupled simulation unit 34 outputs the calculated process information to the process evaluation unit 35.

[0100] The process evaluation unit 35 evaluates the process information, assesses the magnitude of the machining error for each parameter set, and selects the parameter set used for controlling the machine tool 2 (step S103). The process evaluation unit 35 outputs a selection signal indicating the selected parameter set to the drive control unit 36.

[0101] The drive control unit 36 ​​controls the operation of the machine tool 2 using the selected parameter set based on the selection signal output by the process evaluation unit 35 (step S104). The instruction generation unit 31 determines whether the reading of all instructions recorded in the CNC program 4 has been completed (step S105). If the reading is not completed (step S105: No), the instruction generation unit 31 repeats the process from step S101. If the reading is completed (step S105: Yes), the machining system 1 stops operating.

[0102] As described above, in the machining system 1 of Embodiment 1, the CNC device 3 includes the influence of the operation of the drive system 20 and the dynamics of the components that vibrate during the operation of the machine tool 2 on the machining process M of the workpiece W being machined by the tool 23. For each parameter set, it calculates process information representing the result of simulating the machining process when operation commands generated based on the CNC program 4 are assigned to the machine tool 2. Based on the evaluation results of the process information, it selects the parameter set used for controlling the machine tool 2. The drive control unit 36 ​​of the CNC device 3 uses the control characteristic parameters shown in the selected parameter set to control the machine tool 2 based on the operation commands. Therefore, even when machining errors arise due to the interaction between the machining process, the operation of the drive system 20, and the mechanical dynamics of the components that vibrate during the operation of the machine tool 2, the CNC device 3 can reduce machining errors.

[0103] The control characteristic setting unit 32 generates a set of control characteristic parameters, i.e., a parameter set, that determines the control characteristics of the drive system 20, so the machining efficiency of the machining system 1 remains unchanged. In particular, when a set of control characteristic parameters with different control characteristics in the feed direction R1 and the normal direction R2 of the tool 23 relative to the workpiece W is generated, the operation of the drive system 20 can achieve a constant ratio between the dynamic characteristics of the mechanical dynamics in the feed direction R1 and the dynamic characteristics of the mechanical dynamics in the normal direction R2 of the tool 23 at all positions of the tool 23.

[0104] The coupled simulation unit 34 calculates process information when a running command is given under specified machining conditions, using a parameter set generated by the control characteristic setting unit 32. This includes a machining process model 331 representing the machining characteristics between the tool 23 and the workpiece W; a dynamic model 332 representing the dynamic characteristics of components vibrating during the operation of the machine tool 2; a spindle drive control model 333 representing the spindle drive system 21 and the spindle drive controller that controls the spindle drive system 21; and a feed drive control model 334 representing the feed drive system 22 and the feed drive controller that controls the feed drive system 22. By using coupled simulation with mathematical models, the influence of the running command on the machining process M via the drive system 20 and mechanical dynamics can be accurately evaluated.

[0105] In Embodiment 1, a storage unit 33 is provided in the CNC device 3 to store the machining process model 331, the dynamic model 332, the spindle drive control model 333, the feed drive control model 334, and the machining condition information 335 representing the machining conditions. However, the storage unit 33 may also be provided outside the CNC device 3.

[0106] Furthermore, the storage unit 33 can also store different models and machining conditions corresponding to the machining operations described in the CNC program 4. The coupling simulation unit 34 can perform simulations using different models and machining conditions corresponding to the machining operations. Additionally, in Embodiment 1, the machine tool 2 is assumed to have one spindle drive system 21 and one or more feed drive systems 22, but the machine tool 2 can also have multiple spindle drive systems 21. Even when the machine tool 2 has multiple spindle drive systems 21, the same operation can be performed. Figure 15 The actions shown are sufficient.

[0107] In addition, in Embodiment 1, a working machine 2, such as a machining center, is described, in which a cutting tool 23 is connected to the spindle drive system 21 and the cutting tool 23 rotates. However, the working machine 2 may also be configured as an NC (Numerically Controlled) lathe, in which a workpiece W is connected to the spindle drive system 21 and the workpiece W rotates.

[0108] Implementation method 2.

[0109] Figure 16 This is a diagram illustrating the functional structure of the machining system 1a according to Embodiment 2. Functional structures having the same functions as in Embodiment 1 are labeled with the same reference numerals as in Embodiment 1, and repeated descriptions are omitted. The following mainly describes the differences from Embodiment 1. The difference between machining system 1a and machining system 1 is that operating instructions are generated based on simulation results.

[0110] The machining system 1a includes a machine tool 2 and a numerical control device 3a. Like the numerical control device 3, the numerical control device 3a controls the machine tool 2 based on instructions described in the numerical control program 4. The numerical control device 3a includes an instruction generation unit 31, a control characteristic setting unit 32a, a storage unit 33a, a coupling simulation unit 34a, a process evaluation unit 35, and a drive control unit 36.

[0111] Similar to storage unit 33, storage unit 33a stores machining process model 331, dynamics model 332, spindle drive control model 333, feed drive control model 334, and machining condition information 335, and outputs the stored information to coupled simulation unit 34a. Storage unit 33a can also output the stored information to control characteristic setting unit 32a.

[0112] Similar to the coupling simulation unit 34, the coupling simulation unit 34a calculates process information representing the results of simulating machining when the machine tool 2 is controlled using a parameter set generated by the control characteristic setting unit 32. The coupling simulation unit 34a outputs the calculated process information to the process evaluation unit 35 and also to the control characteristic setting unit 32a.

[0113] The control characteristic setting unit 32a generates a parameter set and outputs it to the coupled simulation unit 34a. Furthermore, the control characteristic setting unit 32a acquires the process information generated by the coupled simulation unit 34a using the generated parameter set, and modifies the parameter set based on the acquired process information.

[0114] Here, the above description is used. Figures 12 to 14 The correction of the parameter set is explained. The simulation results, shown in the process information obtained from the coupled simulation unit 34a, are as follows: Figure 13 or Figure 14 In cases where the chip thickness varies significantly, the control characteristic setting unit 32a modifies the control characteristic parameters by changing at least one of the gain or phase in the frequency response of the drive system 20. For example, as Figure 13 As shown in the second example, when the vibration of the tool 23 is in phase but has a large amplitude, the control characteristic setting unit 32a increases or decreases the control gain, or changes the stopband so that the stopband of the band-stop filter includes that frequency. By making the changes as described above, the control characteristic setting unit 32a can reduce the amplitude of the vibration in the frequency band that includes the vibration of the tool 23.

[0115] In addition, as other examples, such as Figure 14As shown in the third example, when the amplitude of the vibration of the tool 23 is small, but the phase of the vibration transferred to the machined surface of the workpiece and the phase of the vibration of the tool 23 during cutting of that surface are out of phase, the control characteristic setting unit 32a increases or decreases the control gain, or changes the phase compensation amount of the phase compensation filter. By making the changes as described above, the phase difference between the phase of the vibration transferred to the machined surface of the workpiece and the phase of the vibration of the tool 23 during cutting of that surface can be reduced. The control characteristic setting unit 32a changes the parameter set according to the above example based on the process information, thereby achieving the desired effect. Figure 12 As shown, this can reduce the variation in chip thickness, stabilize the machining process M, and reduce machining errors.

[0116] Figure 17 It is used for Figure 16 The flowchart illustrates the operation of the CNC device 3a. When the machining system 1a starts operating, the instruction generation unit 31 of the CNC device 3a reads the CNC program 4, parses the read CNC program 4, generates operation instructions for the machine tool 2 to execute the instructions described in the CNC program 4, and generates a first parameter set (step S201). The instruction generation unit 31 outputs the generated operation instructions to the coupling simulation unit 34a and the drive control unit 36, and the control characteristic setting unit 32a outputs the generated first parameter set to the coupling simulation unit 34a and the drive control unit 36.

[0117] The coupled simulation unit 34a uses the first parameter set generated by the control characteristic setting unit 32a to perform coupled simulation when executing the operation command output by the machine tool 2 execution command generation unit 31, and generates process information (step S202). The coupled simulation unit 34a outputs the generated process information to the process evaluation unit 35 and the control characteristic setting unit 32a respectively.

[0118] The control characteristic setting unit 32a modifies the parameter set based on the process information output after executing step S202, and generates a second parameter set (step S203). The control characteristic setting unit 32a outputs the generated second parameter set to the coupled simulation unit 34a and the drive control unit 36.

[0119] The coupled simulation unit 34a uses the second parameter set output by the control characteristic setting unit 32a to perform coupled simulation when controlling the machine tool 2 and generates process information (step S204). The coupled simulation unit 34a outputs the generated process information to the process evaluation unit 35 and the control characteristic setting unit 32a respectively.

[0120] The process evaluation unit 35 compares and evaluates multiple process information, evaluates the magnitude of machining error when using each parameter set, and selects the parameter set used for controlling the machine tool 2 (step S205). The process evaluation unit 35 outputs a selection signal indicating the selected parameter set to the drive control unit 36.

[0121] The drive control unit 36 ​​controls the operation of the machine tool 2 using the selected parameter set based on the selection signal output by the process evaluation unit 35 (step S206). The instruction generation unit 31 determines whether the reading of all instructions recorded in the CNC program 4 has been completed (step S207). If the reading is not completed (step S207: No), the instruction generation unit 31 repeats the process from step S201. If the reading is completed (step S207: Yes), the machining system 1a stops operating.

[0122] Furthermore, in the above example, the control characteristic setting unit 32a generates a second parameter set after correcting the first parameter set based on process information representing the simulation results of controlling the machine tool 2 using the first parameter set. The process evaluation unit 35 selects the parameter set used for controlling the machine tool 2 from the first and second parameter sets. However, the control characteristic setting unit 32a may also generate more than or equal to three parameter sets. For example, the control characteristic setting unit 32a may generate multiple parameter sets based on one piece of process information. Alternatively, the control characteristic setting unit 32a may repeatedly correct the parameter sets. For example, the control characteristic setting unit 32a may generate a third parameter set based on process information representing the simulation results of controlling the machine tool 2 using the second parameter set. In this case, a method can be used that employs machine learning to explore parameter sets that can reduce the vibration of the cut-off thickness of the workpiece W, using the amplitude or phase of the vibration component of the cut-off thickness of the workpiece W as an evaluation value.

[0123] Figure 18 It means and Figure 16 The diagram shows an example of the structure of the learning device 50 associated with the CNC device 3a. The learning device 50 can, for example, be set in... Figure 16 The numerical control device 3a shown may also be an information processing device different from the numerical control device 3a. The learning device 50 has a learning data acquisition unit 51 and a model generation unit 52.

[0124] The learning data acquisition unit 51 acquires the parameter set generated by the control characteristic setting unit 32a and the corresponding process information, that is, the process information representing the simulation results when the machine tool 2 is controlled using the values ​​of the control characteristic parameters shown in the parameter set, and uses this as learning data. The learning data acquisition unit 51 can output the acquired learning data to the model generation unit 52. Furthermore, the learning data acquisition unit 51 can acquire all or part of the process information. For example, the learning data acquisition unit 51 can acquire parameters representing the magnitude of machining errors from the process information and use them as learning data. For example, the learning data acquisition unit 51 can acquire the cut-off thickness of the workpiece W, or the amplitude or phase of the vibration component of the cut-off thickness of the workpiece W and use it as learning data.

[0125] The model generation unit 52 learns a new parameter set based on learning data, including a parameter set and process information representing simulation results when the machine 2 is controlled using the parameter set. That is, the model generation unit 52 generates a trained model for inferring the new parameter set based on the process information of the CNC device 3a. The model generation unit 52 outputs the generated trained model to the trained model storage unit 53.

[0126] The learning algorithm used by the model generation unit 52 can employ known algorithms such as teacher-led learning, teacherless learning, and reinforcement learning. As an example, we will explain the application of reinforcement learning. In reinforcement learning, the agent, or intelligent agent, observes the parameters of the environment representing the current state and decides on the action to be taken. The environment changes dynamically through the agent's actions, and the agent is rewarded accordingly. The agent repeats this action, learning the action strategy that yields the highest reward after a series of actions. As representative methods of reinforcement learning, Q-learning and TD-learning are known. For example, in the case of Q-learning, the general update formula for the action value function Q(s, a) is expressed by the following formula (4).

[0127]

Formula 4

[0128]

[0129] In equation (4), s t a represents the state of the environment at time t. t Indicates the action at time t. Through action a t The state changes to s t+1 r t+1The value represents the reward resulting from changes in its state, γ represents the discount rate, and α represents the learning coefficient. Furthermore, γ takes values ​​within the range of 0 < γ ≤ 1, and α takes values ​​within the range of 0 < α ≤ 1. The modified operation instruction becomes action a. t Process information becomes state s t The best action a for 50 pairs of states at time t in the learning device. t To learn.

[0130] The update formula, expressed by equation (4), states that if the action value Q of action a with the highest Q value at time t+1 is greater than the action value Q of action a executed at time t, then the action value Q is increased; conversely, if the action value Q is less than or equal to the action value Q of action a executed at time t, then the action value Q is decreased. In other words, the action value function Q(S, a) is updated in a way that makes the action value Q of action a at time t close to the best action value at time t+1. Thus, the best action value Q in a certain environment is continuously propagated to the action values ​​Q in previous environments.

[0131] As shown above, when a trained model is generated through reinforcement learning, the model generation unit 52 has a reward calculation unit 54 and a function update unit 55.

[0132] The reward calculation unit 54 calculates the reward based on the parameter set and process information. The reward calculation unit 54 calculates the reward r based on a reward reference D that includes a reward increase reference D1 and a reward decrease reference D2. For example, the reward reference D is determined based on the magnitude of the machining error shown in the process information. As a parameter representing the magnitude of the machining error, for example, the amplitude of the vibration component of the cut-off thickness of the workpiece W is used. For example, the reward increase reference D1 can be set to a value where the amplitude of the vibration component of the cut-off thickness of the workpiece W is less than a threshold, and the reward decrease reference D2 can be set to a value where the amplitude of the vibration component of the cut-off thickness of the workpiece W is greater than or equal to the threshold. When the reward increase reference D1 is satisfied, the reward calculation unit 54 increases the reward r by, for example, by assigning a reward of "+1", and decreases the reward r by, for example, by assigning a reward of "-1" when the reward decrease reference D2 is satisfied. The reward calculation unit 54 outputs the calculated reward r to the function update unit 55. Furthermore, as another example, in addition to the amplitude of the vibration component of the chip thickness, the phase of the vibration component of the cut-off thickness of the workpiece W can also be used as a parameter to represent the magnitude of the machining error. Here, the phase of the vibration component of the cut-off thickness is the phase of the vibration superimposed on the chip shape at the instant the cutting tip of the tool 23 begins to cut off the workpiece W. In this case, the return increase reference D1 can be set to a value within a predetermined range for the phase of the vibration component of the cut-off thickness of the workpiece W, and the return decrease reference D2 can be set to a value outside the aforementioned range for the phase of the vibration component of the cut-off thickness of the workpiece W.

[0133] The function update unit 55 updates the function used to determine the parameter set according to the reward r calculated by the reward calculation unit 54, and outputs it to the trained model storage unit 53. For example, in the case of Q-learning, the action value function Q(s) expressed by equation (4) is updated. t a t This is used as a function to calculate the modified operation instructions.

[0134] Repeat the above learning process. The trained model storage unit 53 stores the action value function Q(s) updated by the function update unit 55. t a t That is, storing the trained model.

[0135] Next, use Figure 19 The learning process performed by the learning device 50 will be explained. Figure 19 It is used for Figure 18 The flowchart illustrates the learning process of the learning device 50 shown.

[0136] The learning data acquisition unit 51 acquires the parameter set generated by the control characteristic setting unit 32a and the process information corresponding to the parameter set as learning data (step S301).

[0137] The model generation unit 52 calculates the reward r based on the parameter set and process information contained in the learning data obtained by the learning data acquisition unit 51 (step S302). Specifically, the reward calculation unit 54 acquires the parameter set and process information, and determines whether to increase or decrease the reward r based on a predetermined reward benchmark D (step S303).

[0138] If the reward calculation unit 54 determines that the reward r should be increased (step S303: increase), it increases the reward r (step S304). If the reward calculation unit 54 determines that the reward r should be decreased (step S303: decrease), it decreases the reward r (step S305).

[0139] The function update unit 55 updates the action value function Q(s) stored in the trained model storage unit 53 based on the reward r calculated by the reward calculation unit 54. t a t Update (step S306).

[0140] The learning device 50 repeats the above steps S301 to S306, generating the action value function Q(s). t a t Store it as a trained model.

[0141] In addition, Figure 18The trained model storage unit 53 is located outside the learning device 50, but the learning device 50 may also have the trained model storage unit 53 internally. Furthermore, when the learning device 50 is located within the CNC device 3a, the trained model storage unit 53 may be located in the same storage device as the storage unit 33a, or it may be located in a different storage device.

[0142] Figure 20 It means and Figure 16 This diagram shows an example of the structure of the inference device 60 associated with the numerical control device 3a. The inference device 60 has a data acquisition unit 61 and an inference unit 62. The inference device 60 can be provided in the numerical control device 3a, or it can be an information processing device different from the numerical control device 3a. For example, the inference device 60 can be provided in the instruction generation unit 31 of the numerical control device 3a.

[0143] The data acquisition unit 61 acquires the process information output by the coupled simulation unit 34a. The data acquisition unit 61 outputs the acquired data to the inference unit 62.

[0144] The inference unit 62 uses the trained model stored in the trained model storage unit 53 to infer a new parameter set based on the process information obtained by the data acquisition unit 61. That is, the inference unit 62 inputs the process information output by the data acquisition unit 61 into the trained model, thereby enabling it to infer a parameter set suitable for the process information.

[0145] Furthermore, in the above, the inference device 60 outputs the parameter set using a trained model obtained by machine learning from data obtained from the CNC device 3a, but it can also obtain a trained model from another CNC device 3a and output a new parameter set based on the trained model.

[0146] Figure 21 It is used for Figure 20 The flowchart illustrates the operation of the inference device 60. The data acquisition unit 61 of the inference device 60 acquires process information as data for inference (step S401) and outputs the acquired data to the inference unit 62.

[0147] The inference unit 62 inputs the inference data, i.e., process information, obtained in step S401, into the trained model stored in the trained model storage unit 53 (step S402). The inference unit 62 outputs the result, i.e., the parameter set, obtained by inputting the process information into the trained model (step S403). In addition, the instruction generation unit 31 of the numerical control device 3a obtains the parameter set output by the inference unit 62 and outputs the obtained parameter set to the coupled simulation unit 34a.

[0148] Furthermore, while the inference unit 62 is described above as using reinforcement learning as a learning algorithm, the learning algorithm used by the inference unit 62 is not limited to reinforcement learning. In addition to reinforcement learning, the inference unit 62 can also use teacher-assisted learning, teacherless learning, or semi-teacher-assisted learning as learning algorithms.

[0149] In addition, the learning algorithm used by the model generation unit 52 can also be deep learning, which learns by extracting the feature quantity itself, and can perform machine learning according to other well-known methods such as neural networks, genetic programming, functional reasoning programming, support vector machines, etc.

[0150] Furthermore, the learning device 50 and the inference device 60 can each be connected to the CNC device 3a via a network, and can be separate devices from the CNC device 3a. Alternatively, the learning device 50 and the inference device 60 can each be built into the CNC device 3a. Furthermore, the learning device 50 and the inference device 60 can each reside on a cloud server.

[0151] Furthermore, the model generation unit 52 can learn the parameter set using learning data obtained from multiple CNC devices 3a. Additionally, the model generation unit 52 can obtain learning data from multiple CNC devices 3a used in the same area, or it can learn the parameter set using learning data collected from multiple CNC devices 3a operating independently in different areas. Moreover, the CNC device 3a collecting the learning data can be added to or removed from the object midway through the process. Furthermore, the learning device 50, after learning the parameter set with respect to a certain CNC device 3a, can be applied to other CNC devices 3a, and the parameter set can be updated by relearning with respect to those other CNC devices 3a.

[0152] As described above, the CNC device 3a according to Embodiment 2 generates a first parameter set and a second parameter set after correcting the first parameter set based on usage process information, and selects the parameter set used for controlling the machine tool 2 based on the evaluation results of each parameter set. The control characteristic setting unit 32a generates a new parameter set based on the simulation results executed by the coupling simulation unit 34a, and therefore can generate a parameter set based on the characteristics of the drive system 20, mechanical dynamics, and machining process M. Therefore, the CNC device 3a can efficiently reduce machining errors.

[0153] Furthermore, the CNC device 3a can generate other parameter sets based on the simulation results of the corrected parameter set. In this case, each time the correction is repeated, a parameter set that can further reduce machining errors can be generated. When the parameter set is repeatedly corrected, the learning device 50 can be used to learn the corrected parameter set through machine learning. The CNC device 3a can use the parameter set output by the inference device 60, which uses the learning results of the learning device 50, i.e., the trained model, to infer the parameter set. By using machine learning, the CNC device 3a can exploratoryly generate parameter sets, thus the machining system 1a can generate parameter sets that reduce machining errors without pre-preparing rules for correcting the parameter set.

[0154] Implementation method 3.

[0155] Figure 22 This is a diagram showing the structure of the processing system 1b according to Embodiment 3. Furthermore, structural elements having the same functions as in Embodiment 1 are labeled with the same reference numerals as in Embodiment 1, and repeated descriptions are omitted. Hereinafter, the parts that differ from Embodiments 1 and 2 will be mainly described.

[0156] The machining system 1b includes a machine tool 2b and a numerical control device 3b. The machine tool 2b includes a spindle drive system 21, a feed drive system 22, a cutting tool 23, a worktable 24, and a sensor 25.

[0157] Sensor 25 detects the vibration of components that vibrate during the operation of the machine tool 2b. Sensor 25 may be, for example, an acceleration sensor or a force sensor. Alternatively, sensor 25 may be an encoder pre-installed within the drive system 20 for feedback control of the drive system 20. Sensor 25 is connected to the CNC device 3b, and the signal acquired by sensor 25, i.e., the sensor signal, is output to the CNC device 3b.

[0158] The numerical control device 3b includes an instruction generation unit 31, a control characteristic setting unit 32b, a storage unit 33, a coupling simulation unit 34, a process evaluation unit 35, and a drive control unit 36. The difference between the numerical control device 3b and embodiments 1 and 2 is that it generates a parameter set based on the sensor signal output by the sensor 25.

[0159] The control characteristic setting unit 32b can generate the first parameter set using the same method as the control characteristic setting unit 32 according to Embodiment 1. Furthermore, the control characteristic setting unit 32b can modify the first parameter set based on the sensor signal output by the sensor 25 to generate a new parameter set, namely the second parameter set. Specifically, the control characteristic setting unit 32b can pre-store a correspondence table between the amplitude of the sensor signal and the values ​​of the control characteristic parameters internally, and generate the parameter set based on the sensor signal using this correspondence table. Alternatively, the control characteristic setting unit 32b can also determine the values ​​of the control characteristic parameters using machine learning methods such as pattern matching when the sensor signal is input.

[0160] The operation of the CNC device 3b, besides using sensor signals when correcting the parameter set, is similar to... Figure 17 The CNC device 3a shown operates the same way, so detailed explanations are omitted here.

[0161] Furthermore, in the above description, the control characteristic setting unit 32b is configured to generate a second parameter set by correcting the first parameter set using sensor signals. However, the parameter set correction can also be performed sequentially. That is, the control characteristic setting unit 32b can further correct the parameter set by detecting sensor signals when controlling the machine tool 2b using the parameter set generated using sensor signals. In this case, the amplitude or phase of the vibration component of the sensor signal can be used as an evaluation value, and machine learning methods such as reinforcement learning can be used to explore a parameter set that reduces the vibration of the sensor signal.

[0162] In the case of using machine learning, for example, it is possible to use Figure 18 The training device 50 shown is used to obtain a trained model. Figure 20 The inference device 60 shown acquires a parameter set from the trained model. In this case, the "process information" acquired by the learning data acquisition unit 51 and the data acquisition unit 61 in the description of Embodiment 2 above will be referred to as "sensor signals," thereby omitting the description of the method for generating the parameter set used by the CNC device 3b according to Embodiment 3. In this case, the parameter set acquired by the learning data acquisition unit 51 is a parameter set corresponding to the sensor signals; specifically, it is the parameter set used for controlling the machine tool 2b when acquiring the sensor signals.

[0163] As explained above, regarding the CNC device 3b according to Embodiment 3, the machine tool 2b has a sensor 25, and the control characteristic setting unit 32b of the CNC device 3b can correct the parameter set based on the sensor signal. Therefore, the control characteristic setting unit 32b can correct the parameter set in accordance with the state of vibration actually occurring in the machine tool 2b, and can efficiently generate a parameter set that reduces machining errors.

[0164] Furthermore, by using machine learning to exploratoryly generate parameter sets, the processing system 1b can generate parameter sets that reduce processing errors without pre-preparing correction rules for the parameter sets.

[0165] Next, the hardware structure of the numerical control devices 3, 3a, 3b, the learning device 50, and the inference device 60 involved in embodiments 1 to 3 will be described. The instruction generation unit 31, control characteristic setting units 32, 32a, 32b, coupling simulation unit 34, 34a, process evaluation unit 35, and drive control unit 36 ​​of the numerical control devices 3, 3a, and 3b; the learning data acquisition unit 51 and model generation unit 52 of the learning device 50; and the data acquisition unit 61 and inference unit 62 of the inference device 60 are implemented by processing circuits. These processing circuits can be implemented using dedicated hardware or control circuits using a CPU (Central Processing Unit).

[0166] When the aforementioned processing circuits are implemented using dedicated hardware, they are implemented through... Figure 23 The processing circuit 90 shown is used for this purpose. Figure 23 This is a diagram illustrating the dedicated hardware used to implement the functions of the numerical control devices 3, 3a, 3b, learning device 50, and inference device 60 involved in embodiments 1 to 3. The processing circuit 90 is a single circuit, a composite circuit, a programmable processor, a parallel-programmable processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof.

[0167] When the above processing circuit is implemented using the CPU's control circuit, the control circuit is, for example, Figure 24 The control circuit 91 of the structure shown. Figure 24 This is a diagram showing the structure of the control circuit 91 used to implement the functions of the numerical control devices 3, 3a, 3b, learning device 50, and inference device 60 involved in embodiments 1 to 3. Figure 24As shown, the control circuit 91 has a processor 92 and a memory 93. The processor 92 is a CPU, also known as an arithmetic unit, microprocessor, microcomputer, DSP (Digital Signal Processor), etc. The memory 93 is, for example, a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable ROM), EEPROM (Electrically EPROM), disk, floppy disk, optical disk, compact disk, mini disk, DVD (Digital Versatile Disk), etc.

[0168] When the aforementioned processing circuit is implemented by the control circuit 91, it is achieved by the processor 92 reading and executing the program stored in the memory 93 corresponding to the processing of each structural element. Furthermore, the memory 93 is also used as temporary storage for each process executed by the processor 92.

[0169] Furthermore, the program executed by the processor 92 can be provided by storing it in a storage medium or by providing it via a communication path. Additionally, the functions of the numerical control devices 3, 3a, 3b, the learning device 50, and the inference device 60 described in embodiments 1-3 can be used... Figure 23 The processing circuit 90 shown or Figure 24 It can be implemented by any of the control circuits 91 shown, or the processing circuit 90 and the control circuit 91 can be used in combination.

[0170] The structure shown in the above embodiments is an example, and it can also be combined with other known technologies, and the embodiments can be combined with each other. Without departing from the spirit of the subject, some parts of the structure can be omitted or changed.

[0171] Explanation of the label

[0172] 1. Machining systems (1a, 1b), 2. Machine tools (2b), 3. CNC devices (3a, 3b), 4. CNC program, 20. Drive system, 21. Spindle drive system, 22. Feed drive systems (22-1, 22-2), 23. Tool, 24. Worktable, 25. Sensor, 31. Instruction generation unit, 32. Control characteristic setting unit (32, 32a, 32b), 33. Storage unit (33a), 34. Coupled simulation unit (34a), 35. Process evaluation unit, 36. Drive control unit, 50. Learning device, 51. Learning data acquisition unit, 52. Model generation unit, 53. Training model storage unit, 54. Feedback calculation unit, 55. Function update unit, 60. Inference device, 6 1. Data Acquisition Unit, 62. Inference Unit, 90. Processing Circuit, 91. Control Circuit, 92. Processor, 93. Memory, 211. Spindle Motor, 212. Spindle Drive Mechanism, 221, 221-1, 221-2. Servo Motors, 222, 222-1, 222-2. Feed Drive Mechanisms, 331. Machining Process Model, 332. Dynamic Model, 333. Spindle Drive Control Model, 334. Feed Drive Control Model, 335. Machining Condition Information, a1. First Machining Surface, a2. Second Machining Surface, a3. Third Machining Surface, a4. Fourth Machining Surface, a5. Fifth Machining Surface, a6. Sixth Machining Surface, b1-b6 Movement Trajectory, c. Feed Amount, F c Cutting force, F d Interference force, L relative trajectory, M machining process, MD1 tool-side mechanical dynamics, MD2 workpiece-side mechanical dynamics, P, P-1, P-2 positions, R1 feed direction, r1 drive system displacement, R2 normal direction, r2 component displacement, T c Cutting torque, T d Interference torque, thickness v1~v6, workpiece W, spindle drive system angle θ1, tool angle θ2.

Claims

1. A numerical control device that controls a machine tool by assigning operating commands to the machine tool having a drive system, the drive system comprising a spindle drive system for driving a tool used to machine an object or a spindle rotating the object, and a feed drive system for driving a feed axis that changes the relative position of the tool and the object. The CNC device is characterized by having: The control characteristic setting unit generates a set of control characteristic parameters, i.e., a parameter set, representing the control characteristics of the controller that controls the drive system. The coupled simulation unit calculates process information representing the results of simulating machining using a controller with control characteristics shown in the parameter set, based on the influence of the drive system's actions and the dynamics of components vibrating during the machine's actions on the machining process of the workpiece via the cutting tool; and... The process evaluation unit evaluates the process information when multiple parameter sets are used respectively, and selects the parameter set used for controlling the machine tool from among the multiple parameter sets based on the evaluation results. The process information refers to one or more of the following: cut thickness, cutting force, and interference force. As the parameter set, the gain response or phase response of the system after combining the feed drive system and the component is different for each motion direction of the feed drive system.

2. The CNC device according to claim 1, characterized in that, The coupled simulation unit sets the control characteristic parameters in the mathematical model representing the spindle drive system and the controller controlling the spindle drive system (i.e., the spindle drive controller) and the mathematical model representing the feed drive system and the controller controlling the feed drive system (i.e., the feed drive controller) to the values ​​shown in the parameter set. For the mathematical model representing the machining characteristics between the tool and the workpiece (i.e., the machining process model), the mathematical model representing the dynamic characteristics of the component (i.e., the dynamic model), the spindle drive control model, and the feed drive control model, the unit calculates the process information when the operation command is given using the specified machining conditions for each parameter set.

3. The CNC device according to claim 2, characterized in that, It also includes a storage unit that stores the machining process model, the dynamics model, the spindle drive control model, the feed drive control model, and machining condition information representing the machining conditions. The coupled simulation unit uses the machining process model, the dynamics model, the spindle drive control model, the feed drive control model, and the machining condition information stored in the storage unit to generate the process information.

4. The CNC device according to any one of claims 1 to 3, characterized in that, The control characteristic setting unit generates parameter sets with different values ​​for the control characteristic parameters of each of the multiple controllers.

5. The CNC device according to any one of claims 1 to 3, characterized in that, The control characteristic setting unit causes the control characteristic parameters to change over time.

6. The CNC device according to claim 5, characterized in that, The control characteristic setting unit generates a set of parameters that make the gain response or phase response of the system after combining the feed drive system and the component different from each other in the feed direction and the normal direction of the feed direction relative to the workpiece.

7. The numerical control device according to any one of claims 1 to 3, characterized in that, The control characteristic setting unit generates the parameter set based on the process information.

8. The numerical control device according to claim 7, characterized in that, It also has: The learning data acquisition unit acquires learning data including the process information and the parameter set corresponding to the process information; as well as The model generation unit uses the learning data to generate a trained model for inferring new parameter sets based on the process information.

9. The numerical control device according to claim 7, characterized in that, It also has: The data acquisition unit acquires the process information; and The inference unit uses a trained model to infer a new parameter set based on the process information, and outputs the new parameter set based on the process information obtained by the data acquisition unit.

10. The numerical control device according to any one of claims 1 to 3, characterized in that, The machine tool also includes a sensor that detects vibrations of the component during processing and outputs a sensor signal. The control characteristic setting unit generates the parameter set based on the sensor signal.

11. The numerical control device according to claim 10, characterized in that, It also has: The learning data acquisition unit acquires learning data including the sensor signals and a parameter set corresponding to the sensor signals; and The model generation unit uses the learning data to generate a trained model for inferring new parameter sets based on the sensor signals.

12. The numerical control device according to claim 10, characterized in that, It also has: The data acquisition unit acquires the sensor signal; and The inference unit uses a trained model to infer a new set of parameters based on the sensor signals, and outputs the new set of parameters based on the sensor signals acquired by the data acquisition unit.

13. A processing system, characterized in that, have: A machine tool having a drive system comprising a spindle drive system for driving a spindle used to rotate a tool for machining an object or said object, and a feed drive system for driving a feed axis that changes the relative position of said tool and said object. as well as A numerical control device assigns operating commands to the machine tool, thereby controlling the machine tool. The numerical control device has: The control characteristic setting unit generates a set of control characteristic parameters, i.e., a parameter set, representing the control characteristics of the controller that controls the drive system. The coupled simulation unit calculates process information representing the result of simulating machining using a controller with control characteristics shown in the parameter sets, based on the influence of the actions of the drive system and the dynamics of the components vibrating in the actions of the working machine on the machining process of the workpiece through the tool. as well as The process evaluation unit evaluates the process information when multiple parameter sets are used respectively, and selects the parameter set used for controlling the machine tool from among the multiple parameter sets based on the evaluation results. The process information refers to one or more of the following: cut thickness, cutting force, and interference force. As the parameter set, the gain response or phase response of the system after combining the feed drive system and the component is different for each motion direction of the feed drive system.

14. The processing system according to claim 13, characterized in that, It also includes a learning device, which has: The learning data acquisition unit acquires learning data including the parameter set generated by the CNC device and process information representing the simulation results when the machine is controlled using the parameter set; as well as The model generation unit uses the learning data to generate a trained model for inferring new parameter sets based on the process information.

15. The processing system according to claim 13, characterized in that, It also has an inference device, which has: The data acquisition unit acquires the process information generated by the numerical control device; and The inference unit uses a trained model to infer a new parameter set based on the process information, and outputs the new parameter set based on the process information obtained by the data acquisition unit.

16. The processing system according to claim 13, characterized in that, The working machine also has a sensor that detects the vibration of the moving component and outputs a sensor signal. The processing system also has a learning device, which has the following features: The learning data acquisition unit acquires learning data including the parameter set generated by the CNC device and the sensor signals when the working machine is controlled using the parameter set; as well as The model generation unit uses the learning data to generate a trained model for inferring new parameter sets based on the sensor signals.

17. The processing system according to claim 13, characterized in that, The working machine also has a sensor that detects the vibration of the moving component and outputs a sensor signal. The processing system also has an inference device, which has the following features: The data acquisition unit acquires the sensor signals during the operation of the working machinery; and The inference unit uses a trained model to infer a new set of parameters based on the sensor signals, and outputs the new set of parameters based on the sensor signals acquired by the data acquisition unit.

18. A numerical control method for controlling a machine tool having a drive system by assigning operating commands to the machine tool, the drive system comprising a spindle drive system for driving a tool used to machine an object or a spindle rotating the object, and a feed drive system for driving a feed axis that changes the relative position of the tool and the object. The CNC method is characterized by including the following steps: Generate a set of control characteristic parameters, i.e., a group of control characteristic parameters representing the control characteristics of the controller that controls the drive system; The process information, which includes the influence of the action of the drive system and the dynamics of the components that vibrate during the action of the working machine on the machining process of the workpiece by the cutting tool, is calculated for each of the multiple parameter sets, representing the result of a simulation of machining when the working machine is controlled using a controller with control characteristics shown in the parameter sets. as well as The process information is evaluated when multiple parameter sets are used respectively, and based on the evaluation results, the parameter set used for controlling the machine tool is selected from among the multiple parameter sets. The process information refers to one or more of the following: cut thickness, cutting force, and interference force. As the parameter set, the gain response or phase response of the system after combining the feed drive system and the component is different for each motion direction of the feed drive system.

19. A machining method comprising machining an object by controlling the drive system of a machine tool having a drive system, the drive system comprising a spindle drive system for driving a tool used to machine the object or a spindle rotating the object, and a feed drive system for driving a feed axis that changes the relative position of the tool and the object. The processing method is characterized by including the following steps: Generate a set of control characteristic parameters, i.e., a group of control characteristic parameters representing the control characteristics of the controller that controls the drive system; The process information, which includes the influence of the action of the drive system and the dynamics of the components that vibrate during the action of the working machine on the machining process of the workpiece by the cutting tool, is calculated for each of the multiple parameter sets, representing the result of a simulation of machining when the working machine is controlled using a controller with control characteristics shown in the parameter sets. The process information is evaluated when multiple parameter sets are used respectively, and the parameter set used for controlling the machine is selected from the multiple parameter sets based on the evaluation results; The working machine is controlled using the selected set of parameters; as well as Controlled using the aforementioned parameter set, the drive system operates to machine the workpiece using the cutting tool. The process information refers to one or more of the following: cut thickness, cutting force, and interference force. As the parameter set, the gain response or phase response of the system after combining the feed drive system and the component is different for each motion direction of the feed drive system.

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