Machining result prediction device

Abstract

To provide a machining result prediction device with superior prediction accuracy for machining results generated by a machine tool.SOLUTION: A machining result prediction device 1, configured to predict a machining result of a workpiece W by a machine tool, includes: a machine model storage unit 2a that stores a machine model 2 modeling a machine tool including a vibration generation unit 4 that generates vibration; a vibration information storage unit 105 that stores vibration information derived from vibration generated by the vibration generation unit 4; a machine behavior prediction unit 110 that predicts machine behavior in the machine model 2, based on the machine model 2, command values derived from machining conditions of the machine tool, and the vibration information; and a machining result prediction unit 111 that predicts a machining result of the workpiece W by the machine tool based on a prediction result from the machine behavior prediction unit 110.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a machining result prediction device. [Background technology]

[0002] Conventionally, in order to evaluate the performance of a machine tool before it is prototyped, a machine model and a machining model of the machine tool are used to predict the machining result of a workpiece. For example, Patent Document 1 discloses a configuration in which a three-dimensional model is used as the machine model of the machine tool. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 4893723 Summary of the Invention [Problem to be solved by the invention]

[0004] Machine tools are composed of many parts and units, and are designed and assembled according to target specifications. The assembled machine tool generates various vibrations due to unbalance of the rotating body, pumps, etc., even before machining a workpiece. Machining is performed in a state where the machine tool is vibrating, but vibrations are also generated by machining, and these vibrations affect the machining accuracy of the workpiece. However, in the development of machine tools up to now, information on the state of vibration generation is often only revealed by actually assembling and operating the machine tool to machine a workpiece. Therefore, in the conventional configuration such as the configuration disclosed in Patent Document 1, the static behavior without taking into account the machine vibration before machining is simulated to predict the machining result, and it is judged whether the machine performance is satisfactory. Therefore, there is room for improvement in making highly accurate predictions.

[0005] The present invention has been made in view of the above-mentioned problems, and aims to provide a machining result prediction device that has excellent prediction accuracy for the machining result by a machine tool. [Means for solving the problem]

[0006] One aspect of the present invention is A machining result prediction device for predicting a machining result of a workpiece by a machine tool, comprising: a storage unit that stores a machine model that models the machine tool including a vibration generating unit that generates vibrations; a vibration information storage unit that stores vibration information based on the vibration generated by the vibration generating unit; a machine behavior prediction unit that predicts a machine behavior in the machine model based on the machine model, a command value based on a machining condition of the machine tool, and the vibration information; a machining result prediction unit that predicts a machining result of the workpiece by the machine tool based on a prediction result of the machine behavior prediction unit; The present invention relates to a processing result prediction device including: Effect of the Invention

[0007] According to the above aspect, a machine model that models a machine tool including a vibration generating unit that generates vibration is used, and the machine behavior in the machine model is predicted based on the machine model, command values, and vibration information based on the vibration generated from the vibration generating unit, and the machine behavior is predicted based on the predicted machine behavior. As a result, the predicted machine behavior is based on the machine behavior including the dynamic behavior that takes into account the vibration from the vibration generating unit in addition to the static behavior that does not take into account the vibration from the vibration generating unit provided in the machine tool, so that a more accurate prediction result can be obtained than in the past. In addition, since there is no need to use an actual machine tool or a prototype, it is possible to shorten the lead time for designing and developing the machine tool and significantly reduce the prototyping cost.

[0008] As described above, according to the above aspect, it is possible to provide a machining result prediction device that has excellent prediction accuracy for the machining result by a machine tool. [Brief description of the drawings]

[0009] [Figure 1] 1 is a conceptual diagram showing a configuration of a processing result prediction device 1 in a first embodiment. [Diagram 2] 3 is a flow diagram illustrating a processing result prediction method of the processing result prediction device 1 in the first embodiment. [Diagram 3] 4 is a view showing a first example of a prediction result of a processing result by the processing result prediction device 1 in the first embodiment. FIG. [Figure 4] 5 is a view showing a second example of a prediction result of the processing result prediction device 1 in the first embodiment. FIG. [Diagram 5] FIG. 11 is a conceptual diagram showing the configuration of a processing result prediction device 1 in a second embodiment. [Figure 6] FIG. 11 is a flow diagram illustrating a processing result predicting method of the processing result predicting device 1 in the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] (Embodiment 1) 1. Overview of the machining result prediction device 1 As shown in Fig. 1, the machining result prediction device 1 in the present embodiment 1 includes a machine model 2 and a machining model 3. The machining result prediction device 1 is configured by a calculation device and a storage device (not shown), and a program that causes the calculation device to realize each configuration described later. Each configuration will be described in detail below.

[0011] 2. Machine Model 2 As shown in Fig. 1, the machine model 2 is a model of a machine tool. The type of machine tool is not particularly limited as long as it processes a workpiece, but in this embodiment 1, the machine model 2 is a model of a table traverse type grinding machine, which is a cylindrical grinding device. The grinding machine may be a wheel head traverse type grinding machine. The machine model 2 is stored in a machine model storage unit 2a, which is a known storage device.

[0012] The machine model 2 is a model of a grinding machine that machines a workpiece W by rotating the workpiece W around its axis while moving a tool T in a direction including a component in the direction of the rotation axis of the workpiece W. In addition, the shape of the workpiece W is not limited and can be any shape, but in this embodiment, a member formed in a shaft shape as shown in Fig. 1 is taken as an example of the workpiece W.

[0013] The machine model 2 includes a bed 10, a table 20, a spindle unit 30, a tailstock unit 40, a grinding wheel head 50, and a pump 60. The spindle unit 30 and the tailstock unit 40 provided on the table 20 function as a workpiece support member that supports the workpiece W. The grinding wheel head 50 functions as a tool support member that supports the grinding wheel T. In other words, the machine model 2 grinds the workpiece W supported by the workpiece support member with the grinding wheel T supported by the tool support member. The machine model 2 further includes a sizing device (not shown) that acquires the outer dimensions of the workpiece W, and a coolant device that supplies coolant to the point where the workpiece W is ground by the grinding wheel T.

[0014] As shown in Fig. 1, bed 10 is placed on a placement surface. Bed 10 is provided with Z-axis guide surface 11 extending in the Z-axis direction on the upper surface on the front side in the X-axis direction, and Z-axis drive mechanism 12 that drives along Z-axis guide surface 11. In this embodiment 1, Z-axis drive mechanism 12 includes ball screw mechanism 12a and Z-axis motor 12b. Ball screw mechanism 12a extends parallel to Z-axis guide surface 11, and Z-axis motor 12b drives ball screw mechanism 12a.

[0015] A Z-axis drive circuit and a Z-axis detector 12c (not shown) are provided to drive the Z-axis drive mechanism 12. The Z-axis drive circuit includes an amplifier circuit and drives the Z-axis motor 12b. The Z-axis detector 12c detects the angle of the rotation shaft of the Z-axis motor 12b. Note that the Z-axis drive mechanism 12 may be configured with a linear motor or the like instead of the ball screw mechanism 12a.

[0016] Bed 10 also has an X-axis guide surface 13 extending in the X-axis direction intersecting with the Z-axis direction on the upper surface on the rear side in the X-axis direction. Bed 10 also has an X-axis drive mechanism 14 that drives along X-axis guide surface 13. In this embodiment, X-axis drive mechanism 14 includes a ball screw mechanism 14a and an X-axis motor 14b. Ball screw mechanism 14a extends parallel to X-axis guide surface 13, and X-axis motor 14b drives ball screw mechanism 14a.

[0017] An X-axis drive circuit and an X-axis detector 14c (not shown) are provided to drive the X-axis drive mechanism 14. The X-axis drive circuit includes an amplifier circuit and drives the X-axis motor 14b. The X-axis detector 14c detects the rotation angle of the rotation shaft of the X-axis motor 14b. Note that the X-axis drive mechanism 14 may be a linear motor or the like instead of the ball screw mechanism 14a.

[0018] Table 20 is formed in an elongated shape, and is supported movably in the Z-axis direction (horizontal left-right direction) on Z-axis guide surface 11 of bed 10. Table 20 is also fixed to a ball screw nut of Z-axis ball screw mechanism 12a, and moves in the Z-axis direction by the rotational drive of Z-axis motor 12b.

[0019] The spindle unit 30 supports the workpiece W and drives and rotates the workpiece W. The spindle unit 30 is disposed on one end side in the Z-axis direction on the table 20. The spindle unit 30 includes a spindle housing 31, a spindle 32, a spindle motor 33, a spindle center 34, a spindle detector 35, and a spindle drive circuit (not shown).

[0020] The spindle housing 31 is fixed onto the table 20. The spindle 32 is rotatably supported by the spindle housing 31 via a bearing. A spindle motor 33 rotates the spindle 32. The spindle center 34 constitutes a workpiece support member that supports an end face of one axial end (the left end in FIG. 1) of the workpiece W. Note that the spindle device 30 may be provided with a chuck that grips the workpiece W as a workpiece support member, instead of the spindle center 34.

[0021] The spindle detector 35 and the spindle drive circuit are provided to drive the spindle motor 33. The spindle detector 35 detects the rotation angle of the rotary shaft of the spindle motor 33. The spindle drive circuit includes an amplifier circuit and drives the spindle motor 33.

[0022] The tailstock device 40 supports the workpiece W together with the spindle device 30. The tailstock device 40 is disposed on the other end side in the Z-axis direction on the table 20. The tailstock device 40 is provided so as to be movable in the Z-axis direction on the table 20. The tailstock device 40 includes a tailstock center 41 and an adjustment mechanism 42. When the machine model 2 grinds the inner peripheral surface of the workpiece W, the tailstock device 40 is not required. The tailstock center 41 constitutes a workpiece support member that supports the end face of the other axial end (the right end in FIG. 1) of the workpiece W. In the present embodiment 1, the tailstock device 40 includes an adjustment mechanism 42. The adjustment mechanism 42 is, for example, constituted by a spring, and is configured so that the tailstock center 41 exerts a pressing force, and when the tailstock center 41 exerts a pressing force on the workpiece W, the spindle center 34 also exerts a pressing force on the workpiece W as a reaction force.

[0023] The wheel head 50 is provided with a grindstone T and rotates the grindstone T. In addition to the grindstone T, the wheel head 50 is provided with a wheel head main body 51, a grindstone spindle 52, a grindstone wheel motor 53, and a grindstone wheel drive circuit (not shown).

[0024] The grindstone T is formed in a disk shape. The grindstone T is used for grinding the outer or inner peripheral surface of the workpiece W. The grindstone head body 51 is formed, for example, in a rectangular shape in a plan view, and is supported on the X-axis guide surface 13 of the bed 10 so as to be movable in the X-axis direction (horizontal front-back direction). The grindstone head body 51 is fixed to a ball screw nut of the X-axis ball screw mechanism 14a, and moves in the X-axis direction by the rotational drive of the X-axis motor 14b. The grindstone head body 51 constitutes a tool support member that supports the grindstone T.

[0025] The grinding wheel spindle 52 is rotatably supported by the wheel head body 51 via a bearing. A grinding wheel T is fixed to the tip of the grinding wheel spindle 52, and the grinding wheel T rotates due to the rotation of the grinding wheel spindle 52. A grinding wheel motor 53 rotates the grinding wheel spindle 52. A hydrostatic bearing, a rolling bearing, or the like is used as the bearing. A grinding wheel drive circuit is provided to drive the grinding wheel motor 53. The grinding wheel drive circuit includes an amplifier circuit, and drives the grinding wheel motor 53.

[0026] The pump 60 supplies coolant and other fluids to the coolant device. The configuration of the pump 60 is not limited, and a known configuration can be adopted.

[0027] 3. Vibration generating unit 4 The grinding machine modeled by the machine model 2 has a vibration generating unit 4. The vibration generating unit 4 generates vibrations by itself when driven, and in this embodiment 1, these include the Z-axis motor 12b, the X-axis motor 14b, the spindle motor 33, the grinding wheel motor 53, and the pump 60. All of these have rotating parts, and generate periodic vibrations by themselves due to imbalance of the rotating parts. In addition to these, the vibration generating unit 4 can also be set to include vibrations generated by movement of the table 20 and the grinding wheel head 50, and vibrations generated by the frequency of AC current when AC current is applied to the machine tool.

[0028] 4. Configuration of machining model 3 The machining model 3 includes a workpiece shape memory unit 101, a rigidity memory unit 102, a command value memory unit 103, a machining condition memory unit 104, a vibration information memory unit 105, a prediction target setting unit 106, a vibration correspondence relationship memory unit 107, a design information similarity evaluation unit 108, a generated vibration identification unit 109, a machine behavior prediction unit 110, a machining result prediction unit 111, and a pass / fail judgment unit 112.

[0029] 4-1. Workpiece shape memory section 101, rigidity memory section 102 The workpiece shape storage unit 101 stores the shape of the workpiece W. The workpiece shape storage unit 101 is configured to update and store the shape of the workpiece W that has changed due to machining. The stiffness storage unit 102 stores the stiffness of each portion of the machine model 2 and the stiffness of the workpiece. The stiffness is calculated by a simulation in the machine model 2.

[0030] 4-2. Command value storage unit 103, machining condition storage unit 104 The command value storage unit 103 stores command values ​​based on the machining conditions of the workpiece W in the machine model 2. The command values ​​include the X-axis value, Y-axis value, and Z-axis value of the machining point by the grindstone T, the rotational speed of the grindstone T, and the rotational speed of the workpiece W. The machining condition storage unit 104 stores the machining conditions of the workpiece W in the machine model 2. The machining conditions include the machining allowance of the machined portion of the workpiece W, the feed speed of the grindstone T or the workpiece W, etc. When the machining conditions stored in the machining condition storage unit 104 are changed, the command values ​​stored in the command value storage unit 103 are changed accordingly.

[0031] 4-3. Vibration information storage unit 105 The vibration information storage unit 105 stores vibration information based on the vibration generated from the vibration generating unit 4 provided in the machine model 2. The vibration information may include the frequency, amplitude, phase, period, and changes over time of the vibration. In the first embodiment, vibration information of the vibration generated from each of the Z-axis motor 12b, the X-axis motor 14b, the spindle motor 33, the grinding wheel motor 53, and the pump 60 is stored.

[0032] 4-4. Prediction target setting unit 106 The prediction target setting unit 106 sets a target to be output as a machining result by a machining result prediction unit described later. The user can set the prediction target by setting an arbitrary configuration or part of the workpiece W. In the present embodiment 1, the roundness or dimensional change of the cylindrical workpiece W is set as the prediction target.

[0033] 4-5. Vibration Correspondence Storage Unit 107 The vibration correspondence storage unit 107 prestores the correspondence between design information of the machine tool for creating the correspondence, machining conditions by the machine tool for creating the correspondence, and vibration generated from the vibration generating unit 4 provided on the machine tool for creating the correspondence. The correspondence can be created in advance based on actual measurement values ​​acquired from the machine tool for creating the correspondence. The design information of the machine tool includes the shape, support rigidity, sharpness of the grindstone, etc. of the machine tool. Since the sharpness of the grindstone in a machine tool deteriorates with use, the design information can be set so that the sharpness of the grindstone deteriorates over time.

[0034] 4-6. Design information similarity evaluation unit 108, generated vibration identification unit 109 The design information similarity evaluation unit 108 evaluates the similarity between the design information constituting the correspondence stored in the vibration correspondence storage unit 107 and the design information of the machine tool in the machine model 2. The generated vibration identification unit 109 identifies the vibration generated from the vibration generating unit 4 in the correspondence based on the design information evaluated to have high similarity based on the evaluation result of the design information similarity evaluation unit 108. Thereby, among the vibration generating units 4 that are the sources of multiple vibrations corresponding to the design information of the machine tool in the correspondence stored in the vibration correspondence storage unit 107, the vibration of the vibration generating unit 4 corresponding to the design information with high similarity is identified.

[0035] 4-7. Machine behavior prediction unit 110 The machine behavior prediction unit 110 predicts the machine behavior of the machine model 2 based on the machine model 2, a command value based on the machining conditions of the machine tool, and the vibration generated from the vibration generating unit 4. The machine behavior is not limited to this, and can be, for example, the grindstone vibration of the grindstone T, the grindstone dynamic characteristics, the workpiece runout of the workpiece W, and the workpiece dynamic characteristics. In the present embodiment 1, the vibration identified by the generated vibration identifying unit 109 is used as the vibration generated from the vibration generating unit 4.

[0036] In this embodiment 1, the machine behavior prediction unit 110 predicts machine behavior including static behavior based on command values ​​based on the machining conditions of the machine tool and design information of the machine tool in the machine model 2, and dynamic behavior corresponding to a prediction target among dynamic behaviors based on vibrations generated from the vibration generating unit 4. Note that, although the static behavior and dynamic behavior of the machine behavior of the machine model 2 can be conceptually distinguished, the machine behavior predicted by the machine behavior prediction unit 110 is output as a mixture of both.

[0037] 4-8. Processing result prediction unit 111 The machining result prediction unit 111 predicts the machining result of the workpiece W by the machine tool which is the model of the machine model 2, based on the prediction result of the machine behavior prediction unit 110. In the present embodiment 1, for the prediction target set by the prediction target setting unit 106, the time-series change in the shape of the workpiece W is predicted based on the prediction result of the machine behavior, and the machining result is predicted.

[0038] 4-9. Quality judgement unit 112 The pass / fail judgment unit 112 judges whether the design information or command values ​​of the machine tool of the machine model 2 are good or bad based on the prediction result of the machining result prediction unit 111. When the pass / fail judgment result of the pass / fail judgment unit 112 is bad, the machining result can be predicted using design information or command values ​​different from the design information or command values ​​that caused the pass / fail judgment result. The criterion for pass / fail judgment can be set appropriately, and for example, the criterion for pass / fail judgment can be whether or not the error from the target value of the dimension or the like specified in the machining conditions is within a predetermined allowable range.

[0039] 5. Method for predicting machining results using machining result prediction device 1 A method for predicting a machining result by the machining result prediction device 1 in this embodiment will be described with reference to the flow shown in Fig. 2. First, in step S1, a machine model 2 of a machine tool created in advance is stored in a machine model storage unit 2a as a preparation step. Also, the shape of a workpiece W is stored in a workpiece shape storage unit 101. Also, a target for predicting a machining result is set in a prediction target setting unit 106. In this embodiment 1, the roundness of a cylindrical workpiece W is set as the prediction target. Also, machining conditions for the machine model 2 are input to a machining condition storage unit 104.

[0040] 2, the stiffness of the machine model 2 is calculated. The calculated stiffness is stored in the stiffness storage unit 102. After that, in step S3, a command value based on the machining conditions stored in the machining condition storage unit 104 is stored in the command value storage unit 103. Note that steps S2 and S3 may be performed in parallel.

[0041] Then, in step S4, the vibration generated by the vibration generating unit 4 is acquired and stored as vibration information in the vibration information storage unit 105. In the present embodiment 1, vibration information of the vibrations generated by each of the Z-axis motor 12b, the X-axis motor 14b, the spindle motor 33, the grinding wheel motor 53, and the pump 60 is stored.

[0042] Thereafter, in step S5, the design information similarity evaluation unit 108 evaluates the similarity between the design information constituting the correspondence stored in the vibration correspondence storage unit 107 and the design information of the machine tool in the machine model 2. In step S6, the generated vibration identification unit 109 identifies the vibration generated from the vibration generating unit 4 in the correspondence based on the design information evaluated to have high similarity based on the evaluation result by the design information similarity evaluation unit 108.

[0043] Next, in step S7, the machine behavior prediction unit 110 predicts the machine behavior including the static behavior based on the command value and design information and the dynamic behavior based on the identified vibration. Then, in step S8, the machining result prediction unit 111 predicts the machining result of the prediction target based on the prediction result of the machine behavior prediction unit 110. Based on the prediction result, the shape of the workpiece W stored in the workpiece shape storage unit 101 is updated for prediction of the next machining result. In the present embodiment 1, as shown in Fig. 3, a time-series change in the shape of the workpiece W is predicted based on the prediction result of the machine behavior, and the roundness of the workpiece W is predicted as the machining result.

[0044] In step S9, the pass / fail judgment unit 112 judges the pass / fail of the design information or the command value of the machine model 2 based on the prediction result of the machining result prediction unit 111. In the present embodiment 1, the pass / fail judgment is performed based on whether or not the error between the roundness, which is the prediction result, and the target value based on the command value, as shown in Fig. 3, or the error between the dimensional change, which is the prediction result, and the target value based on the command value, as shown in Fig. 3, is within a preset allowable range.

[0045] If the quality judgment result of the quality judgment unit 112 is "no", proceed to No in step S9, and in step S10, change the design information or command value to a different one from the design information or command value that caused the quality judgment result, and return to step S2 to perform the subsequent steps S2 to S10 to predict the processing result again. On the other hand, if the quality judgment result of the quality judgment unit 112 is "good" in step S9, proceed to No in step S9 and end the flow.

[0046] 6. Effects The effects of the machining result prediction device 1 of the present embodiment 1 will be described below. According to the machining result prediction device 1 of the present embodiment 1, a machine model 2 that models a machine tool including a vibration generating unit 4 that generates vibration is used, and vibration information based on the machine model 2, command values, and vibration generated from the vibration generating unit 4 and machine behavior in the machine model 2 are predicted, and the machining result of the workpiece by the machine tool is predicted based on the predicted machine behavior. As a result, the predicted machining result is based on the machine behavior including the dynamic behavior that takes into account the vibration from the vibration generating unit 4 provided in the machine tool in addition to the static behavior that does not take into account the vibration from the vibration generating unit 4, so that a more accurate prediction result can be obtained than in the past. In addition, since there is no need to use an actual machine tool or a prototype, it is possible to shorten the lead time for designing and developing the machine tool and significantly reduce the prototype cost.

[0047] Moreover, in this embodiment 1, a prediction target setting unit 106 is provided which sets a prediction target of the machining result of the workpiece W, and the machine behavior prediction unit 110 predicts machine behavior including a static behavior of the machine model 2 based on command values ​​based on the machining conditions of the machine tool and design information of the machine tool in the machine model 2, and a dynamic behavior corresponding to the prediction target among dynamic behaviors of the machine model 2 based on vibration information. Then, the machining result prediction unit 111 predicts the machining result of the prediction target based on the prediction result of the machine behavior prediction unit 110. As a result, the machine behavior prediction unit 110 only needs to predict a dynamic behavior corresponding to the prediction target, and therefore the calculation load can be reduced compared to the case of predicting all dynamic behaviors.

[0048] In addition, in the first embodiment, the vibration correspondence storage unit 107 stores in advance the correspondence between the design information of the machine tool for creating the correspondence, the machining conditions of the machine tool for creating the correspondence, and the vibration generated from the vibration generating unit 4 provided on the machine tool for creating the correspondence, a design information similarity evaluation unit 108 evaluates the similarity between the design information constituting the correspondence stored in the vibration correspondence storage unit 107 and the design information of the machine tool in the machine model 2, and an generated vibration identification unit 109 identifies the vibration generated from the vibration generating unit 4 in the correspondence based on the design information evaluated to have high similarity based on the evaluation result of the design information similarity evaluation unit 108. Then, the machine behavior prediction unit 110 predicts the machine behavior including the dynamic behavior based on the vibration identified by the generated vibration identification unit 109. This can further reduce the calculation load on the machine behavior prediction unit 110.

[0049] In the first embodiment, the machine tool is configured to grind a cylindrical or columnar workpiece W with a grindstone T. The prediction target setting unit 106 sets the roundness of the cylindrical workpiece W as the prediction target. The machine behavior prediction unit 110 calculates the dynamic characteristics of the grindstone T in the machine model 2, the dynamic characteristics of the workpiece W, the vibration of the grindstone T, and the runout of the workpiece W to predict the machine behavior as the dynamic behavior. The machining result prediction unit 111 predicts the time-series change in the shape of the workpiece W based on the prediction result of the machine behavior prediction unit 110, and predicts the roundness or dimensional change of the workpiece W as the machining result. This makes it possible to predict the roundness or dimensional change of the workpiece W with high accuracy.

[0050] Moreover, in this embodiment 1, a pass / fail judgment unit 112 is provided which judges whether the design information or command value of the machine tool of the machine model 2 is pass / fail based on the prediction result of the machining result prediction unit 111, and is configured to predict the machining result using design information or command values ​​different from the design information or command values ​​which caused the pass / fail judgment result when the pass / fail judgment result of the pass / fail judgment unit 112 is negative. This makes it possible to redesign the machine tool or optimize the machining conditions, thereby improving the machining accuracy of the workpiece W.

[0051] As described above, according to the above-described one aspect, it is possible to provide the machining result predicting device 1 having excellent prediction accuracy of the machining result by the machine tool.

[0052] (Embodiment 2) In the above-mentioned embodiment 1, a vibration correspondence storage unit 107 and a design information similarity evaluation unit 108 are provided, and the generated vibration identification unit 109 extracts a correspondence based on design information evaluated to have high similarity from the correspondences stored in the vibration correspondence storage unit 107 based on the evaluation result of the design information similarity evaluation unit 108, and identifies the vibration generated from the vibration generating unit 4. The mechanical behavior prediction unit predicts the mechanical behavior including the dynamic behavior based on the vibration identified by the generated vibration identification unit 109.

[0053] 5, instead, the second embodiment includes a generated vibration estimation unit 113 that calculates and estimates vibration generated from the vibration generating unit 4 based on design information and command values ​​of the machine model, and the machine behavior prediction unit 110 predicts machine behavior including dynamic behavior based on the vibration estimated by the generated vibration estimation unit 113. Note that in the second embodiment, configurations equivalent to those in the first embodiment are denoted by the same reference numerals and descriptions thereof will be omitted.

[0054] A method for predicting a machining result by the machining result prediction device 1 of the second embodiment will be described with reference to the flow shown in FIG. 6. First, steps S1 to S4 are performed in the same manner as in the first embodiment shown in FIG. 2. Then, after step S4 shown in FIG. 6, the process proceeds to step S61, where the generated vibration estimation unit 113 calculates and estimates the vibration generated from the vibration generating unit 4 according to the prediction target of the machining result set by the prediction target setting unit 106. Then, in step S71, the machine behavior prediction unit 110 predicts the machine behavior including the static behavior based on the command value and design information and the dynamic behavior based on the estimated vibration. The following steps S8 to S10 are performed in the same manner as in the first embodiment shown in FIG. 2, and the flow ends.

[0055] According to the machining result prediction device 1 of the second embodiment, the vibration correspondence storage unit 107 and the design information similarity evaluation unit 108 in the first embodiment are unnecessary, and therefore it is not necessary to acquire the above-mentioned correspondence in advance using a machine tool, so that further cost reduction can be achieved. Note that the second embodiment also provides the same effects as the first embodiment, except for the effects of the first embodiment having the vibration correspondence storage unit 107 and the design information similarity evaluation unit 108. [Explanation of symbols]

[0056] 1. Processing result prediction device 2. Machine Model 2a Machine model memory section 3 Machining model 4. Vibration generating section 12b Z-axis motor 14b X-axis motor 20 Tables 33 Spindle motor 50 Grindstone stand 53 Grinding wheel motor 60 Pump 101 Workpiece shape memory section 102 Rigidity memory section 103 Command value memory unit 104 Machining condition memory section 105 Vibration information storage unit 106 Prediction target setting section 107 Vibration Correspondence Memory Unit 108 Design Information Similarity Evaluation Unit 109 Vibration generation identification section 110 Machine Behavior Prediction Department 111 Machining result prediction section 112 Good / bad judgement section 113 Vibration generation estimation unit

Claims

1. A machining result prediction device for predicting a machining result of a workpiece by a machine tool, comprising: a machine model storage unit that stores a machine model that models the machine tool including a vibration generating unit that generates vibrations; a vibration information storage unit that stores vibration information based on the vibration generated by the vibration generating unit; a machine behavior prediction unit that predicts a machine behavior in the machine model based on the machine model, a command value based on a machining condition of the machine tool, and the vibration information; a machining result prediction unit that predicts a machining result of the workpiece by the machine tool based on a prediction result of the machine behavior prediction unit; A processing result prediction device comprising:

2. A prediction target setting unit is provided for setting a prediction target of the machining result of the workpiece, the machine behavior prediction unit predicts the machine behavior including a static behavior of the machine model based on a command value based on a machining condition of the machine tool and design information of the machine tool in the machine model, and a dynamic behavior of the machine model based on the vibration information, the dynamic behavior corresponding to the prediction target; The machining result prediction device according to claim 1 , wherein the machining result prediction unit predicts the machining result of the prediction target based on the prediction result of the machine behavior prediction unit.

3. a vibration correspondence relationship storage unit in which correspondence relationships between design information of a machine tool for creating a correspondence relationship, machining conditions by the machine tool for creating a correspondence relationship, and vibrations generated by a vibration generating unit provided in the machine tool for creating a correspondence relationship are stored in advance; a design information similarity evaluation unit that evaluates a similarity between the design information constituting the correspondence relationship stored in the vibration correspondence storage unit and design information of the machine tool in the machine model; a generated vibration specifying unit that specifies a vibration generated from the vibration generating unit in the correspondence relationship based on design information that is evaluated to have high similarity based on an evaluation result of the design information similarity evaluation unit; Equipped with The machining result prediction device according to claim 2 , wherein the machine behavior prediction unit predicts the machine behavior including the dynamic behavior based on the vibration identified by the generated vibration identification unit.

4. a generated vibration estimation unit that calculates and estimates vibration generated from the vibration generating unit based on the design information of the machine model and the command value, The machining result prediction device according to claim 2 , wherein the machine behavior prediction unit predicts the machine behavior including the dynamic behavior based on the vibration estimated by the generated vibration estimation unit.

5. The machine tool is configured to grind the cylindrical or columnar workpiece with a grinding wheel, The prediction target setting unit sets the roundness of the cylindrical workpiece as the prediction target, the machine behavior prediction unit calculates, as the dynamic behavior, dynamic characteristics of the grinding wheel, dynamic characteristics of the workpiece, vibration of the grinding wheel, and run-out of the workpiece in the machine model to predict the machine behavior; 4. The machining result prediction device according to claim 2 or 3, wherein the machining result prediction unit predicts a time series change in a shape of the workpiece based on a prediction result of the machine behavior prediction unit, and predicts the roundness or dimensional change of the workpiece as the machining result.

6. a quality determination unit that determines quality of design information or the command value of the machine tool of the machine model based on a prediction result of the machining result prediction unit, The machining result prediction device according to any one of claims 1 to 3, configured to predict the machining result using the design information or the command value different from the design information or the command value that caused the pass / fail judgment result of the pass / fail judgment unit when the pass / fail judgment result of the pass / fail judgment unit is not satisfied.

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