Machined surface texture prediction device

Abstract

To grasp a waviness shape occurring on the surface of a workpiece by oscillation.SOLUTION: A processed face property predictor comprises: a first data acquisition unit, which acquires first oscillation data representing measurement results from a first oscillation sensor measuring oscillation during the processing of a rotary tool; and a waviness prediction unit, which utilizes data extracted every passing cycle of a cutting edge for forming a processed face from the acquired first oscillation data to estimate a waviness shape occurring on the surface of the workpiece by oscillation, and to output information on the estimated waviness shape.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a machining surface property prediction device.

Background Art

[0002] In machining in a machine tool, in order to estimate the machining surface quality of a workpiece, it is preferable to grasp the vibrations of the tool and the workpiece during machining. In the machining state monitoring method described in Patent Document 1, vibrations and sounds during machining are measured, and Fourier analysis is performed on such data to identify the chatter frequency and detect the occurrence of chatter vibrations.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the machining state monitoring method described in Patent Document 1 has a problem that the shape of the undulation generated on the machining surface due to the occurrence of chatter vibrations cannot be grasped.

Means for Solving the Problems

[0005] The present disclosure can be realized in the following forms. According to one embodiment of the present disclosure, a machined surface texture prediction device is provided. This machined surface texture prediction device includes: a first data acquisition unit that acquires first vibration data indicating the measurement result from a first vibration sensor that measures vibrations of a rotary tool during machining; and a undulation shape prediction unit that estimates the undulation shape generated on the surface of a workpiece due to the vibrations, using data extracted from the acquired first vibration data for each cutting edge passage period for forming the machined surface, and outputs information regarding the estimated undulation shape, wherein the undulation shape includes a undulation interval which is the length of one cycle of undulations periodically formed on the surface of the workpiece. In other embodiments of this disclosure, a machined surface texture prediction device is provided. This machined surface texture prediction device includes: a first data acquisition unit that acquires first vibration data indicating measurement results from a first vibration sensor that measures vibrations of a rotary tool during machining; a undulation shape prediction unit that estimates the undulation shape generated on the surface of a workpiece due to the vibrations using data extracted from the acquired first vibration data for each cutting edge passage period for forming the machined surface, and outputs information regarding the estimated undulation shape; a vibration frequency estimation unit that estimates the vibration frequency of the vibrations by performing frequency analysis on the first vibration data; an undulation interval estimation unit that estimates the undulation interval using the vibration frequency; and in the undulation shape prediction unit, The system further includes a wave spacing determination unit that determines whether the magnitude of the difference between a first wave spacing estimated as a wave shape and a second wave spacing estimated by the wave spacing estimation unit is less than or equal to a preset threshold, wherein the wave spacing determination unit determines that the vibration frequency is the frequency that caused the wave shape when the magnitude of the difference between the first wave spacing and the second wave spacing is less than or equal to the threshold, outputs the estimated information regarding the wave shape and the vibration frequency, and determines that there is a high possibility that a vibration different from the vibration of the vibration frequency is occurring when the magnitude of the difference between the first wave spacing and the second wave spacing is greater than the threshold,

[0006] (1) According to one embodiment of the present disclosure, a machined surface texture prediction device is provided. This machined surface texture prediction device includes: a first data acquisition unit that acquires first vibration data indicating the measurement result from a first vibration sensor that measures vibrations of a rotary tool during machining; and a undulation shape prediction unit that estimates the undulation shape generated on the surface of a workpiece due to the vibrations, using data extracted from the acquired first vibration data for each cutting edge passage period for forming the machined surface, and outputs information regarding the estimated undulation shape. This type of machined surface texture prediction device utilizes first vibration data, which represents the vibration of the rotary tool during machining, acquired from a first vibration sensor, to estimate and output the undulation shape generated on the surface of the workpiece due to the vibration, thus enabling the understanding of the undulation shape generated on the machined surface. In addition, since the estimation of the undulation shape does not require complex processing, the decrease in processing speed of the undulation shape estimation process can be suppressed. (2) In the processed surface texture prediction device of the above form, the device further comprises a vibration frequency estimation unit that estimates the vibration frequency of the vibration by performing frequency analysis on the first vibration data, and a undulation interval estimation unit that estimates the undulation interval using the vibration frequency, wherein if the magnitude of the difference between the first undulation interval, which is the undulation interval estimated by the undulation shape prediction unit, and the second undulation interval, which is the undulation interval estimated by the undulation interval estimation unit, is less than or equal to a preset threshold, the device determines that the vibration frequency is the frequency that caused the undulation shape, and outputs the estimated information regarding the undulation shape and the vibration frequency. This type of machined surface texture prediction device determines the magnitude of the difference between the first undulation interval estimated in the undulation shape prediction unit and the second undulation interval estimated in the undulation interval estimation unit, thereby suppressing a decrease in the reliability of the estimated undulation shape. (3) In the machined surface condition prediction device of the above form, the waviness shape prediction unit further comprises a second data acquisition unit that acquires second vibration data indicating the measurement result from a second vibration sensor that measures the vibration of the workpiece, and a relative vibration data calculation unit that calculates relative vibration data between the rotary tool and the workpiece from the first vibration data and the acquired second vibration data, and the waviness shape prediction unit estimates the waviness shape using the calculated relative vibration data and outputs information regarding the estimated waviness shape. According to this type of machined surface texture prediction device, relative vibration data is calculated using first vibration data acquired from a first vibration sensor and second vibration data indicating the vibration of the workpiece acquired from a second vibration sensor, and the undulation shape is predicted using this relative vibration data, thereby further suppressing the decrease in the accuracy of the undulation shape prediction. [Brief explanation of the drawing]

[0007] [Figure 1] This is an explanatory diagram showing the schematic configuration of a machine tool equipped with a machined surface texture prediction device according to the first embodiment. [Figure 2] This is a block diagram showing the schematic configuration of the machined surface texture prediction device according to the first embodiment. [Figure 3] This flowchart shows the procedure for predicting the surface properties of the processed surface according to the first embodiment. [Figure 4] This is a flowchart showing the procedure for the first wave interval estimation process. [Figure 5] This is an explanatory diagram showing an example of the data extracted in step S31 of the first wave interval estimation process. [Figure 6] This is a flowchart showing the procedure for the second wave interval estimation process. [Figure 7] This is an explanatory diagram showing the schematic configuration of a machine tool equipped with a machined surface texture prediction device according to the second embodiment. [Figure 8] This is a block diagram showing the schematic configuration of the machined surface texture prediction device according to the second embodiment. [Figure 9] This flowchart shows the procedure for predicting the surface properties of the processed surface according to the second embodiment. [Modes for carrying out the invention]

[0008] A. First Embodiment: A-1.Device configuration: Figure 1 is an explanatory diagram showing the schematic configuration of a machine tool 100 equipped with a machined surface texture prediction device 70 according to the first embodiment. The machine tool 100 comprises a bed 10, a workpiece table 20, a workpiece spindle housing 30, a column 40, a tool spindle housing 50, a rotary tool T, a first vibration sensor 60, and a machined surface texture prediction device 70. In this embodiment, the machine tool 100 is configured as a horizontal machining center. However, the machine tool 100 is not limited to a horizontal machining center and may be a vertical machining center, grinding machine, milling machine, etc.

[0009] Figure 1 shows arrows representing the three mutually orthogonal coordinate axes: the X, Y, and Z axes. In the following explanation, the direction parallel to the X axis will be referred to as the "X direction," the direction parallel to the Y axis as the "Y direction," and the direction parallel to the Z axis as the "Z direction." The directions indicated by the arrows representing the X, Y, and Z axes are the same in all subsequent figures.

[0010] The bed 10 is fixed to the floor surface. The bed 10 has a pair of X-axis guide rails 11 extending along the X direction and a pair of Z-axis guide rails 12 extending along the Z direction on its upper surface. The pair of Z-axis guide rails 12 are arranged parallel to each other at a predetermined distance in the X direction.

[0011] The workpiece table 20 consists of a movable table 21 and a rotary table 22. The movable table 21 is mounted on the bed 10 via an X-axis guide rail 11 and is configured to move in the X direction along the X-axis guide rail 11 by a linear motor or ball screw mechanism (not shown). The rotary table 22 moves integrally with the movable table 21 and is also held rotatably relative to the movable table 21.

[0012] The workpiece spindle housing 30 is installed on the rotary table 22 and is configured to be integrally movable in accordance with the operations of the moving table 21 and the rotary table 22. The workpiece spindle housing 30 houses at least a part of the workpiece spindle device 31. The workpiece spindle device 31 is rotatably held with respect to the workpiece spindle housing 30. The workpiece spindle device 31 detachably holds the workpiece W and rotates integrally with the workpiece W. Note that the workpiece W corresponds to the "workpiece to be machined" in the present disclosure.

[0013] The column 40 is installed on the bed 10 via the Z-axis guide rail 12 and is configured to be movable in the Z direction along the Z-axis guide rail 12 by a linear motor or a ball screw mechanism (not shown). The column 40 includes a pair of Y-axis guide rails 41 extending along the Y direction. The pair of Y-axis guide rails 41 are arranged in parallel with each other at a predetermined distance in the X direction.

[0014] The tool spindle housing 50 is installed on the column 40 via the Y-axis guide rail 41 and is configured to be movable in the Y direction along the Y-axis guide rail 41 by a linear motor or a ball screw mechanism (not shown).

[0015] The tool spindle housing 50 houses at least a part of the tool spindle device 51. The tool spindle device 51 is rotatably held with respect to the tool spindle housing 50. The tool spindle device 51 detachably holds the rotary tool T.

[0016] The rotary tool T is, for example, a milling cutter, an end mill, a drill, or the like. The rotary tool T is attached to the tool spindle device 51 and rotates integrally with the tool spindle device 51. The rotary tool T machines the workpiece W by contacting the workpiece W while rotating. In the following description, the portion of the rotary tool T that contacts the workpiece W during machining is referred to as the "machining point". Note that the "machining point" can also be regarded as a minute surface having an area.

[0017] The first vibration sensor 60 is installed on the outer surface of the tool spindle housing 50. Preferably, the installation location of the first vibration sensor 60 is closer to the machining point. In this embodiment, the first vibration sensor 60 is configured as an acceleration sensor and measures the acceleration perpendicular to the tool feed direction, caused by vibrations of the tool spindle housing 50, at preset sampling periods. The first vibration sensor 60 transmits the measurement results showing the time change of this acceleration (also referred to as "first vibration data" in the following description) to the machined surface texture prediction device 70. The acquisition and transmission of the first vibration data by the first vibration sensor 60 continues as long as the power to the first vibration sensor 60 is on. Note that the first vibration sensor 60 is not limited to an acceleration sensor; it may also be a velocity sensor or a displacement sensor.

[0018] Figure 2 is a block diagram showing the schematic configuration of the machined surface texture prediction device 70 of the first embodiment. The machined surface texture prediction device 70 uses first vibration data acquired from the first vibration sensor 60 to predict the undulation shape that will occur on the surface of the workpiece W after machining (hereinafter also referred to as the "machined surface") and outputs it to an external device. The machined surface texture prediction device 70 is configured as a computer comprising a CPU 710, a memory 720, and a communication unit 730. In this embodiment, the memory 720 holds the first vibration data transmitted from the first vibration sensor 60.

[0019] The CPU 710 includes a first data acquisition unit 711, a unit conversion unit 712, a undulation shape prediction unit 713, a vibration frequency estimation unit 714, a undulation interval estimation unit 715, and a undulation interval determination unit 716. In this embodiment, the first data acquisition unit 711, the unit conversion unit 712, the undulation shape prediction unit 713, the vibration frequency estimation unit 714, the undulation interval estimation unit 715, and the undulation interval determination unit 716 are functional units realized by the CPU 710 executing a program. The processing in each functional unit will be explained in the machining surface texture prediction processing described later.

[0020] A2. Prediction process for machined surface properties: Figure 3 is a flowchart showing the procedure for predicting the surface texture of the machined surface according to the first embodiment. The machined surface texture prediction device 70 estimates the undulation shape formed on the machined surface by performing the machined surface texture prediction process and outputs the estimated undulation shape to an external device. In this embodiment, the machined surface texture prediction process is pre-programmed to be executed automatically after the completion of machining by the machine tool 100.

[0021] In step S10, the first data acquisition unit 711 reads out the first vibration data that was stored in the memory 720.

[0022] In step S20, the unit conversion unit 712 converts the unit of the read-out first vibration data from "acceleration" to "displacement". In this embodiment, the unit conversion unit 712 converts the unit of the first vibration data to displacement by performing a second integral of the first vibration data with respect to time. Note that if the first vibration sensor 60 is configured as a displacement sensor, the first vibration data with units of displacement is obtained, so this step can be omitted.

[0023] The machined surface texture prediction device 70 executes the first waviness interval estimation process (step S30) and the second waviness interval estimation process (step S40) in parallel. Alternatively, the machined surface texture prediction device 70 may execute steps S30 and S40 in order.

[0024] Figure 4 is a flowchart showing the procedure for the first undulation interval estimation process. In step S31, the undulation shape prediction unit 713 extracts data from the unit-converted data for each cutting edge passage period for forming the machined surface. By extracting data in this way, it is possible to extract data indicating the position perpendicular to the machined surface when the cutting edge contacts the machined surface on a straight line parallel to the Z direction.

[0025] In step S32, the undulation shape prediction unit 713 estimates the undulation shape using the extracted data. In this embodiment, the undulation shape prediction unit 713 estimates the undulation amplitude A, the undulation start position Ps, and the undulation interval as the undulation shape. The undulation amplitude A refers to the amplitude of the undulation perpendicular to the machined surface of the workpiece W. The undulation start position Ps refers to the position where the formation of the undulation begins in the Z direction of the workpiece W, with respect to the measurement start position. The undulation interval refers to the length of one period of the periodically formed undulation.

[0026] Figure 5 is an explanatory diagram showing an example of the data extracted in step S31. The method for estimating the undulation shape in this embodiment will be explained with reference to Figure 5. In Figure 5, the horizontal axis represents the distance relative to the measurement start position, and the vertical axis represents the undulation height. The distance relative to the measurement start position is determined using the elapsed time from the start of measurement and the preset feed rate of the rotary tool T.

[0027] The swell shape prediction unit 713 calculates the swell amplitude A by taking the difference between the swell height at the maximum point Hmax and the swell height at the minimum point Hmin in the extracted data. The swell shape prediction unit 713 also acquires the position where the swell height first exceeds a preset threshold Th as the swell start position Ps. Furthermore, it measures the length d of the swell for 10 periods and calculates the swell interval by dividing the length d by the number of periods, 10, in other words, by taking the average value of the swell intervals for 10 periods. This swell interval will be referred to as the "first swell interval" in the following explanation. After estimating the swell shape, the swell shape prediction unit 713 terminates the first swell interval estimation process. If the undulation amplitude A is less than a preset threshold, it is determined that no undulation has occurred on the machined surface of the workpiece W, and the undulation interval and undulation start position Ps are not acquired. The undulation shape prediction unit 713 then terminates the first undulation interval estimation process.

[0028] Figure 6 is a flowchart showing the procedure for the second undulation interval estimation process of the first embodiment. In step S41, the vibration frequency estimation unit 714 performs a Fourier analysis on the data converted in unit conversion unit 712 to estimate the vibration frequency. More specifically, the vibration frequency estimation unit 714 estimates the vibration frequency to be the peak frequency with the largest peak height among the peak frequencies obtained by Fourier transforming the unit-converted data that do not coincide with an integer multiple of the cutting edge passing frequency for forming the machined surface of the workpiece W. Note that the Fourier analysis in this step corresponds to "frequency analysis" in this disclosure.

[0029] In step S42, the wobble interval estimation unit 715 estimates the wobble interval using the vibration frequency. First, the wobble interval estimation unit 715 calculates the wobble frequency from the remainder obtained by dividing the vibration frequency by the cutting edge passage frequency for forming the machined surface. Specifically, if the remainder is less than half the cutting edge passage frequency for forming the machined surface, the remainder is taken as the wobble frequency. If the remainder is greater than half the frequency, the value obtained by folding half the frequency towards the lower frequency side with respect to the axis of reference is taken as the wobble frequency. Then, the wobble interval estimation unit 715 calculates the wobble interval by dividing the amount of movement of the workpiece W in the Z direction per second relative to the rotating tool T by the wobble frequency. After the completion of this step, the wobble interval estimation unit 715 terminates the second wobble interval estimation process. The wobble interval estimated in this process will be referred to as the "second wobble interval" in the following description.

[0030] After steps S30 and S40 are completed, in step S50 of Figure 3, the swell interval determination unit 716 determines whether the magnitude of the difference between the first swell interval and the second swell interval is less than or equal to a preset threshold. If it is determined that the magnitude of the difference between the first swell interval and the second swell interval is less than or equal to the threshold (step S50: Yes), it can be determined that the vibration frequency estimated by the vibration frequency estimation unit 714 is the frequency of the vibration that caused the swell shape. In this case, the swell interval determination unit 716 outputs the swell shape estimated by the swell shape prediction unit 713 and the vibration frequency estimated by the swell interval estimation unit 715 to an external device via the communication unit 730 (step S60). In this embodiment, "output of swell shape" means the output of information including the swell interval estimated by the swell shape prediction unit 713, the swell amplitude A, and the swell start position Ps. After this step is completed, the machined surface texture prediction device 70 terminates the machined surface texture prediction process.

[0031] On the other hand, if the magnitude of the difference between the first undulation interval and the second undulation interval is determined to be greater than a threshold (step S50: No), it is highly likely that vibrations different from those of the vibration frequency estimated by the vibration frequency estimation unit 714 are occurring. In such cases, it is highly likely that a undulation shape different from the undulation shape estimated by the undulation interval estimation unit 715 is occurring, so the undulation interval determination unit 716 outputs estimation accuracy information in addition to the undulation shape and vibration frequency mentioned above to an external device via the communication unit 730. "Estimated accuracy information" means information indicating that the accuracy of the undulation shape is low. After the completion of this step, the machined surface texture prediction device 70 terminates the machined surface texture prediction process.

[0032] In this embodiment, after the machining surface texture prediction process is completed, the machine tool 100 receives the outputted waviness shape and vibration frequency and, for example, detects abnormalities in the machining state and sets machining conditions that can suppress vibration of the rotary tool T.

[0033] According to the machined surface texture prediction device 70 of the first embodiment described above, the device uses first vibration data indicating the vibration of the rotary tool T during machining, acquired from the first vibration sensor 60, to estimate and output the undulation shape generated on the surface of the workpiece W due to the vibration, thereby enabling the understanding of the undulation shape generated on the machined surface. In addition, since the estimation of the undulation shape does not require complex processing, a decrease in the processing speed of the undulation shape estimation process can be suppressed.

[0034] Furthermore, the processed surface texture prediction device 70 determines the magnitude of the difference between the first undulation interval estimated by the undulation shape prediction unit 713 and the second undulation interval estimated by the undulation interval estimation unit 715, thereby suppressing a decrease in the reliability of the estimated undulation shape.

[0035] B. Second Embodiment: Figure 7 is an explanatory diagram showing the schematic configuration of a machine tool 200 equipped with the machined surface texture prediction device 170 of the second embodiment. Figure 8 is a block diagram showing the schematic configuration of the machined surface texture prediction device 170 of the second embodiment. As shown in Figures 7 and 8, the machined surface texture prediction device 170 of the second embodiment differs from the machined surface texture prediction device 70 of the first embodiment in that it acquires data from a second vibration sensor 160 in addition to the first vibration sensor 60, and that it includes a second data acquisition unit 717 and a relative vibration data calculation unit 718. The device configuration of the machined surface texture prediction device 170 of the second embodiment is the same as that of the machined surface texture prediction device 70 of the first embodiment, so the same components are denoted by the same reference numerals and their detailed description is omitted.

[0036] As shown in Figure 7, the machine tool 200 is equipped with a second vibration sensor 160 in addition to the configuration of the machine tool 100 shown in Figure 1. The second vibration sensor 160 is installed on the outer surface of the workpiece spindle housing 30. Preferably, the installation position of the second vibration sensor 160 is closer to the machining point. The second vibration sensor 160 in this embodiment is configured as an acceleration sensor and measures the acceleration perpendicular to the tool feed direction caused by the vibration of the workpiece spindle housing 30 at preset sampling periods. The second vibration sensor 160 transmits the measurement results showing the time change of this acceleration (also referred to as "second vibration data" in the following description) to the machined surface condition prediction device 170. The acquisition and transmission of the second vibration data by the second vibration sensor 160 is performed continuously as long as the power to the second vibration sensor 160 is on. The second vibration sensor 160 is not limited to an acceleration sensor, but may also be a velocity sensor or a displacement sensor.

[0037] The second data acquisition unit 717 and the relative vibration data calculation unit 718 shown in Figure 8 are functional units realized by the CPU 710 executing a program. The processing in the second data acquisition unit 717 and the relative vibration data calculation unit 718 will be explained in the machining surface texture prediction processing described later.

[0038] Figure 9 is a flowchart showing the procedure for the machining surface texture prediction process in the second embodiment. As shown in Figure 9, the machining surface texture prediction device 170 of the second embodiment differs from the machining surface texture prediction device 70 of the first embodiment in that, in the machining surface texture prediction process, steps S12, S14, and S22 are executed instead of steps S10 and S20 in Figure 3. The other steps in the machining surface texture prediction process are the same as those in the machining surface texture prediction process of the first embodiment, so the same reference numerals are used for the same steps and their detailed explanation is omitted.

[0039] In step S12, the first data acquisition unit 711 reads the first vibration data held in the memory 720, and the second data acquisition unit 717 reads the second vibration data held in the memory 720.

[0040] In step S14, the relative vibration data calculation unit 718 calculates relative vibration data from the read-out first vibration data and second vibration data. Relative vibration data refers to data that shows the relative vibration between the rotating tool T and the workpiece W. The relative vibration data calculation unit 718 calculates relative vibration data by taking the difference between the first vibration data and the second vibration data.

[0041] In step S22, the unit conversion unit 712 converts the unit of the calculated relative vibration data from "acceleration" to "displacement". In this embodiment, similar to the first embodiment, the unit conversion unit 712 converts the unit of the relative vibration data to displacement by performing a second integral of the relative vibration data with respect to time.

[0042] According to the processed surface texture prediction device 170 of the second embodiment described above, relative vibration data is calculated using the first vibration data acquired from the first vibration sensor 60 and the second vibration data acquired from the second vibration sensor 160, and the undulation shape is predicted using this relative vibration data, thereby further suppressing the decrease in the accuracy of the undulation shape prediction.

[0043] C. Other embodiments: (C1) In the above embodiment, the machined surface texture prediction device 70 determines whether the magnitude of the difference between the first waviness interval and the second waviness interval is less than or equal to a preset threshold, and if the magnitude of the difference between the first waviness interval and the second waviness interval is greater than the threshold, it outputs estimation accuracy information in addition to the waviness shape and waviness frequency, but the disclosure is not limited thereto. The machined surface texture prediction device 70 may output the waviness shape and vibration frequency regardless of the magnitude of the difference between the first waviness interval and the second waviness interval. In this form as well, the same effects as in the above embodiment are achieved. In addition, since no determination is made regarding the magnitude of the difference between the first waviness interval and the second waviness interval, the decrease in processing speed of the process for estimating the waviness shape can be further suppressed.

[0044] (C2) In the above embodiment, the machined surface texture prediction device 70 performs machined surface texture prediction processing after the completion of machining by the machine tool 100, but the disclosure is not limited thereto. For example, the machined surface texture prediction device 70 may be programmed to perform machined surface texture prediction processing each time it acquires data for a predetermined amount of time.

[0045] (C3) In the above embodiment, in the processing surface texture prediction process, the waviness interval determination unit 716 outputs the waviness shape estimated by the waviness interval estimation unit 715 as is, but the disclosure is not limited thereto. The waviness interval determination unit 716 may, for example, calculate an estimated value of the waviness amplitude A at the processing point by multiplying the waviness amplitude A by a coefficient that takes into account the vibration transmission characteristics of vibration measurement points with respect to the processing point, which have been identified in advance by experiments or the like, and output such an estimated value. With this embodiment, the decrease in measurement accuracy of the waviness shape can be further suppressed.

[0046] (C4) In the above embodiment, the first vibration data was vibration data perpendicular to the tool feed direction, but the disclosure is not limited thereto. For example, the first vibration sensor 60 may also simultaneously acquire vibration displacement in the tool feed direction, and in the first undulation interval estimation process, the vibration displacement in the tool feed direction may be added to the distance from the measurement start position to output the undulation shape. With such an embodiment, the decrease in the prediction accuracy of the undulation shape can be further suppressed.

[0047] (C5) In the above embodiment, the waviness shape prediction unit 713 calculates and outputs the waviness interval, waviness amplitude A, and waviness start position Ps as the waviness shape, but the disclosure is not limited thereto. For example, the unit may determine whether the waviness amplitude A exceeds a preset threshold, whether it is normal, abnormal, or whether there is a waviness, and output this as linguistic information. Such linguistic information and the waviness shape described above correspond to "information regarding the waviness shape" in the disclosure. With this form, the operator can more easily determine the quality of the processed surface.

[0048] This disclosure is not limited to the embodiments described above, and can be implemented in various configurations without departing from its spirit. For example, the technical features in each embodiment corresponding to the technical features in the embodiments described in the summary of the invention can be replaced or combined as appropriate in order to solve some or all of the above-described problems, or to achieve some or all of the above-described effects. Furthermore, if a technical feature is not described as essential in this specification, it can be deleted as appropriate. [Explanation of symbols]

[0049] 10...Bed, 11...X-axis guide rail, 12...Z-axis guide rail, 20...Workpiece table, 21...Mobile table, 22...Rotating table, 30...Workpiece spindle housing, 31...Workpiece spindle unit, 40...Column, 41...Y-axis guide rail, 50...Tool spindle housing, 51...Tool spindle unit, 60...First vibration sensor, 70, 170...Machining surface texture prediction device, 100, 200...Machine tool, 160...Second vibration sensor 710...CPU, 711...First data acquisition unit, 712...Unit conversion unit, 713...Wave shape prediction unit, 714...Vibration frequency estimation unit, 715...Wave interval estimation unit, 716...Wave interval determination unit, 717...Second data acquisition unit, 718...Relative vibration data calculation unit, 720...Memory, 730...Communication unit, A...Wave amplitude, Hmax...Maximum point, Hmin...Minimum point, Ps...Wave start position, T...Rotating tool, W...Workpiece

Claims

1. A machined surface texture prediction device, A first data acquisition unit acquires first vibration data showing the measurement results from a first vibration sensor that measures vibrations of a rotary tool during machining, A waviness shape prediction unit that uses data extracted from the acquired first vibration data for each cutting edge passage period to form the machined surface to estimate the waviness shape generated on the surface of the workpiece due to the vibration, and outputs information regarding the estimated waviness shape, Equipped with, The aforementioned undulation shape includes an undulation interval which is the length of one cycle of the undulation that is periodically formed on the surface of the workpiece. Machined surface texture prediction device.

2. A machined surface texture prediction device according to claim 1, A vibration frequency estimation unit that estimates the vibration frequency of the vibration by performing frequency analysis on the first vibration data, A swell interval estimation unit that estimates the swell interval using the vibration frequency, Furthermore, If the magnitude of the difference between the first swell interval estimated as the swell shape in the swell shape prediction unit and the second swell interval estimated in the swell interval estimation unit is less than or equal to a preset threshold, the vibration frequency is determined to be the frequency that caused the swell shape, and the estimated information regarding the swell shape and the vibration frequency are output. Machined surface texture prediction device.

3. A machined surface texture prediction device according to claim 1 or claim 2, A second data acquisition unit acquires second vibration data indicating the measurement result from a second vibration sensor that measures the vibration of the workpiece, A relative vibration data calculation unit calculates relative vibration data between the rotating tool and the workpiece from the first vibration data and the acquired second vibration data. Furthermore, The swell shape prediction unit estimates the swell shape using the calculated relative vibration data and outputs information regarding the estimated swell shape. Machined surface texture prediction device.

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