WO2023017560A1

WO2023017560A1 - Evaluation system and evaluation program for evaluating machined surface quality

Info

Publication number: WO2023017560A1
Application number: PCT/JP2021/029508
Authority: WO
Inventors: 威趙; 宏之河村; 誠彰相澤
Original assignee: ファナック株式会社
Priority date: 2021-08-10
Filing date: 2021-08-10
Publication date: 2023-02-16

Abstract

Provided is an evaluation system with which it is possible to easily evaluate machined surface quality pertaining to a machined surface of a machined workpiece.　This evaluation system comprises: a machined surface simulator unit for simulating a machined surface that is the result of machining by using position information and tool information; a machined surface analysis unit for outputting a first analysis result, in which the state of a machined surface is quantified, on the basis of the result of the machined-surface simulation carried out by the machined surface simulator unit; a machined surface measurement analysis unit for outputting a second analysis result, in which the state of the machined surface is quantified, on the basis of a measurement result obtained by measuring a machined surface of a machined workpiece that is actually machined by a work machine using the position information; and a difference calculation unit for calculating the difference between the first analysis result and the second analysis result.

Description

Evaluation system and evaluation program for evaluating machined surface quality

The present invention relates to an evaluation system and evaluation program for evaluating machined surface quality, and more particularly to an evaluation system and evaluation program for evaluating the machined surface quality of a workpiece machined by a machine tool.

A machined surface quality evaluation device capable of quantifying an evaluation index of a non-defective work is described in Patent Document 1, for example.
Patent Literature 1 describes that a machined surface quality evaluation device determines an evaluation result of the machined surface quality of a work by an observer based on the inspection result of the machined surface quality of the work by an inspection device. Then, in Patent Document 1, the machined surface quality evaluation device includes a machine learning device that learns the evaluation result of the machined surface quality of the workpiece by the observer corresponding to the inspection result by the inspection device, and the machine learning device is the inspection device. A state observation unit that observes the result of inspection of the quality of the machined surface of the workpiece as a state variable, a label data acquisition unit that acquires label data indicating the evaluation result of the quality of the machined surface of the workpiece by the observer, a state variable, and the label data and a learning unit that learns by associating with.

Japanese Patent Laid-Open Publication No. 2002-300003 describes a grinding machine assisting device and assisting method for assisting in optimizing the grinding conditions of a workpiece.
In Patent Document 2, a support device includes a state information acquisition unit that acquires grinding conditions including setting states of a plurality of operation command data as state information, and an evaluation result of an evaluation object obtained under the grinding conditions, such as a grinding An evaluation result acquisition unit that acquires the grinding quality of the workpiece after processing, a reward calculation unit that calculates a reward for the state information based on the evaluation result, and a reinforcement learning based on the state information and the reward, generated in the state information. A policy storage unit for storing a policy regarding adjustment of the operation command data according to the state information and the policy, and operation command data to be adjusted and an adjustment amount of the operation command data from among a plurality of candidates for the operation command data that can be adjusted based on the state information and the policy. and an action information output unit capable of outputting the content of the decision made by the action decision unit to the control device as action information.

JP 2018-181218 A JP 2020-157425 A

It is not easy to evaluate how far the machined surface quality of the machined surface of the workpiece differs from the ideal machined surface quality when machining with a machine tool.
Therefore, an evaluation system and an evaluation program that can easily evaluate the machined surface quality of the machined surface of the machined workpiece by comparing the machined surface quality of the machined surface of the machined workpiece with the machined surface quality of the ideal machined surface. was wanted.

(1) A first aspect of the present disclosure utilizes position information for machining a workpiece using a cutting tool and tool information having a feature amount including at least the shape of the cutting tool to obtain a machining result A machined surface simulator that simulates the machined surface of
a machined surface analysis unit that outputs a first analysis result in which the state of the machined surface is quantified based on the result of the machined surface simulation by the machined surface simulator unit;
a machined surface measurement analysis unit that outputs a second analysis result in which the state of the machined surface is quantified based on the measurement result of measuring the machined surface of the workpiece actually machined by the machine tool using the position information;
a difference calculation unit that calculates the difference between the first analysis result and the second analysis result;
is a rating system with

(2) A second aspect of the present disclosure provides a computer with:
A process of simulating a machined surface of a machining result using position information for machining a workpiece using a cutting tool and tool information having a feature amount including at least the shape of the cutting tool;
A process of outputting a first analysis result in which the state of the machined surface is digitized based on the result of the simulation of the machined surface;
A process of outputting a second analysis result in which the state of the machined surface is digitized based on the measurement result of measuring the machined surface of the workpiece actually machined by the machine tool using the position information;
a process of calculating a difference between the first analysis result and the second analysis result;
is an evaluation program that executes

According to each aspect of the present disclosure, it is possible to easily evaluate the machined surface quality of the machined surface of the machined workpiece by comparing the machined surface quality of the machined surface of the machined workpiece and the machined surface quality of the ideal machined surface. can be done.

1 is a block diagram showing a configuration example of an evaluation system according to an embodiment of the present disclosure; FIG. FIG. 10 is a diagram showing a first display example of an image displayed on the display screen of the result display section of the evaluation system; FIG. 11 is a diagram showing a second display example of an image displayed on the display screen of the result display section of the evaluation system; 4 is a flow chart showing the operation of the evaluation system according to one embodiment of the present disclosure;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
FIG. 1 is a block diagram showing one configuration example of an evaluation system according to one embodiment of the present disclosure.

As shown in FIG. 1, the evaluation system 10 for evaluating the machined surface quality of the present embodiment includes a machined surface simulator 11, a machined surface analysis unit 12, a machined surface measurement analysis unit 13, a difference calculation unit 14, and a result display unit 15. , and a display setting unit 16 . In addition to the evaluation system 10, FIG. 1 also shows a workpiece 30, a machining surface measuring device 20 for measuring the machining surface of the machining workpiece 30, and a machine tool 40 for producing the machining workpiece 30. FIG.

The machined surface simulator 11 calculates a portion of the machined surface to be machined based on tool information having a feature amount including at least the shape of the cutting tool and position information for machining the workpiece using the cutting tool, and performs a machined surface simulation. I do. The tool information is, for example, the type of cutting tool such as a ball end mill, and the shape of the tool such as the ball radius. The position information is, for example, trajectory data of a machining program, trajectory data after processing such as interpolation and acceleration/deceleration by a computer numerical controller (CNC), trajectory data of servo feedback of a servo control device that controls a motor, or Tool movement trajectory data such as scale feedback trajectory data of a linear scale attached to a machine.

Based on the machining simulation results output from the machined surface simulator 11, the machined surface analysis unit 12 obtains a first surface texture parameter, which is a first analysis result of analyzing the state of the machined surface, and calculates a difference calculation unit 14. output to The first surface quality parameter is the analysis result of the ideal machined surface.
Surface texture parameters are parameters for evaluating surface roughness, for example, parameters defined in ISO 25178, arithmetic mean height Sa, maximum height Sz, surface texture aspect ratio (autocorrelation) Str, minimum autocorrelation length Sal, root mean square slope Sdq, and the like can be used.

The machined surface measurement analysis unit 13 obtains a second surface texture parameter, which is a second analysis result, from the measured surface data (measurement result) input from the machined surface measurement device 20, and outputs the second surface texture parameter to the difference calculation unit 14. Output. The second surface quality parameter is an analysis result of the measured processed surface based on the measured surface data.
Although the machined surface analysis unit 12 and the machined surface measurement analysis unit 13 are provided separately, they may be shared by one analysis unit. and a machined surface measurement analysis unit 13.

It should be noted that, for example, when obtaining the arithmetic mean height Sa or the maximum height Sz as the second surface texture parameter, height data is required. A laser microscope, a white interference microscope, or the like, for example, is used as the machined surface measuring device 20 to obtain height data. The height data may be graphed height data. The machined surface measuring device 20 measures the machined workpiece 30 machined by the machine tool 40 based on the position information, and inputs height data, which is actually measured surface data, to the machined surface measurement analysis unit 13 .

The difference calculator 14 calculates the difference between the first surface texture parameter output from the machined surface analysis unit 12 and the second surface texture parameter output from the machined surface measurement analysis unit 13 .
For example, the first surface texture parameters output from the machined surface analysis unit 12 include an arithmetic mean height Sa1, a maximum height Sz1, a surface texture aspect ratio (autocorrelation) Str1, a minimum autocorrelation length Sal1, and a root mean square Let be the square root slope Sdq1. Then, the second surface texture parameters output from the machined surface measurement analysis unit 13 are the arithmetic mean height Sa2, the maximum height Sz2, the surface texture aspect ratio (autocorrelation) Str2, the minimum autocorrelation length Sal2, and the square Suppose that the root-mean-square slope is Sdq2. In that case, the difference calculation unit 14 calculates the difference ΔSa (ΔSa=|Sa1−Sa2|) of the arithmetic mean height Sa, the difference ΔSz (ΔSz=|Sz1−Sz2|) of the maximum height Sz, and the aspect ratio of the surface texture ( Autocorrelation) Str difference ΔStr (ΔStr=|Str1-Str2|), minimum autocorrelation length Sal difference ΔSal (ΔSal=|Sal1-Sal2|) and root mean square slope Sdq difference ΔSdq (ΔSdq=|Sdq1- Sdq2|). Then, the difference calculation unit 14 outputs the obtained difference ΔSa, difference ΔSz, difference ΔStr, difference ΔSal, and difference ΔSdq to the result display unit 15 .
The difference calculation unit 14 outputs the obtained differences together with the first and second surface texture parameters corresponding to each difference to the result display unit 15 .

Note that the difference in the surface texture parameters output from the difference calculation unit 14 may not be all of the difference ΔSa, the difference ΔSz, the difference ΔStr, the difference ΔSal, and the difference ΔSdq, and may be one or more of these differences. Alternatively, it may be a value obtained by weighting and adding a plurality of these differences.

The information output from the difference calculation unit 14 only needs to include the difference in the surface texture parameters, and the information other than the difference is appropriately determined according to the information displayed on the result display unit 15. When the result display unit 15 displays only the difference between the first surface texture parameter and the second surface texture parameter, the difference calculation unit 14 displays the difference between the first surface texture parameter and the second surface texture parameter. may be output to the result display unit 15. The result display unit 15 may select and display information to be displayed from the information output from the difference calculation unit 14 based on display items set by the display setting unit 16, which will be described later.

Table 1 is a table showing five differences and the first and second surface texture parameters corresponding to each difference, which are output from the difference calculation section 14 . In Table 1, the ideal surface texture parameter represents the first surface texture parameter, the measured surface texture parameter represents the second surface texture parameter, and the difference surface texture parameter represents the first surface texture parameter. The difference from the second surface texture parameter is shown.

The result display unit 15 uses the display setting unit 16 to display the difference between the first and second surface texture parameters output from the difference calculation unit 14 and the first and second surface texture parameters corresponding to each difference. Display with the set display method.
The display setting unit 16 sets display items and a display format to be displayed on the result display unit 15 .

(First display example)
The display setting unit 16 sets, as display items, a first surface texture parameter that is an ideal surface texture parameter, a second surface texture parameter that is an actually measured surface texture parameter, and a first surface texture parameter and a second surface texture parameter. Set the difference from the property parameter, and set the radar chart as the display format. Here, the first and second surface texture parameters are respectively arithmetic mean height Sa, maximum height Sz, surface texture aspect ratio (autocorrelation) Str, minimum autocorrelation length Sal, and root mean square slope Sdq is.

The result display unit 15 generates an image to be displayed on the display screen based on the display items and the display format set by the display setting unit 16, and displays the image on the display screen.
FIG. 2 is a diagram showing a first display example of an image displayed on the display screen of the result display section.
In FIG. 2, the thick solid line indicates the first surface texture parameter, which is the ideal surface texture parameter, and the thick dashed line indicates the second surface texture parameter, which is the measured surface texture parameter.
In FIG. 2, as the ideal surface texture parameters and the measured surface texture parameters, the arithmetic mean height Sa, the maximum height Sz, the surface texture aspect ratio (autocorrelation) Str, the minimum autocorrelation length Sal, and the root mean square Slope Sdq is shown. In FIG. 2, the differences between the ideal surface texture parameters and the measured surface texture parameters are the difference ΔSa in the arithmetic mean height Sa, the difference ΔSz in the maximum height Sz, and the difference in aspect ratio (autocorrelation) Str of the surface texture. ΔStr, the difference ΔSal of the minimum autocorrelation length Sal, and the difference ΔSdq of the root-mean-square slope Sdq are shown.
FIG. 2 shows the measured surface texture parameter values when the ideal surface texture parameter value is “1” and the difference between the ideal surface texture parameter values and the measured surface texture parameter values. ing.

From FIG. 2, it can be seen that the arithmetic mean height Sa and the maximum height Sz are surfaces in which the actually measured surface texture parameters are smaller than the ideal surface texture parameters and the peaks or valleys are low. From FIG. 2, it can be seen that the root-mean-square slope Sdq of the actually measured surface texture parameter is larger than the ideal surface texture parameter, and that the actually measured surface is steeper with greater undulations than the ideal surface.

(Second display example)
The display setting unit 16 sets, as display items, a first surface texture parameter that is an ideal surface texture parameter, a second surface texture parameter that is an actually measured surface texture parameter, and a first surface texture parameter and a second surface texture parameter. A difference from the property parameter is set, and a table and a vertical bar graph are set as the display format.
As in the first display example, the first and second surface texture parameters are the arithmetic mean height Sa, the maximum height Sz, the surface texture aspect ratio (autocorrelation) Str, the minimum autocorrelation length Sal, and the squared is the root-mean-square slope Sdq.

The result display unit 15 generates an image to be displayed on the display screen based on the display items and the display format set by the result display unit 15, and displays the image on the display screen.
FIG. 3 is a diagram showing a second display example of an image displayed on the display screen of the result display section.
The table shown in FIG. 3 is the same as Table 1 described above. In the vertical bar graph shown in FIG. 3, ideal , a second surface texture parameter that is a measured surface texture parameter, and a difference between the first surface texture parameter and the second surface texture parameter. In FIG. 3, similarly to FIG. 2, when the value of the ideal surface texture parameter is set to "1", the value of the actually measured surface texture parameter, and the value of the ideal surface texture parameter and the value of the measured surface texture parameter. are shown.

From the table in FIG. 3, the numerical value of the first surface texture parameter, which is the ideal surface texture parameter, the actually measured surface texture parameter, and the difference between the first surface texture parameter and the second surface texture parameter. From the vertical bar graph in FIG. 3, similarly to the first display example, the arithmetic mean height Sa and the maximum height Sz indicate that the measured surface texture parameters are smaller than the ideal surface texture parameters, and that the surface has low peaks or valleys. I understand. Further, from the vertical bar graph in FIG. 3, as in the first display example, the root-mean-square slope Sdq indicates that the actually measured surface texture parameter is larger than the ideal surface texture parameter, and that the actually measured surface has greater undulations and is steeper than the ideal surface. It can be seen that the surface is smooth.

The functional blocks of the evaluation system 10 have been described above. In order to implement these functional blocks, the evaluation system 10 includes an arithmetic processing unit such as a CPU (Central Processing Unit). The evaluation system 10 also includes an auxiliary storage device such as a HDD (Hard Disk Drive) that stores various programs such as applications or an OS (Operating System), and an arithmetic processing unit that is temporarily necessary for executing the program. It has a main storage device such as RAM (Random Access Memory) that stores data to be processed.

Then, in the evaluation system 10, the arithmetic processing unit reads the application or OS from the auxiliary storage device, and performs arithmetic processing based on the application or OS while deploying the read application or OS in the main storage device. Further, the arithmetic processing unit controls various hardware provided in each device based on the result of this arithmetic operation. This implements the functional blocks of the evaluation system 10 in this embodiment. In other words, this embodiment can be realized by cooperation of hardware and software.

Next, the operation of the evaluation system 10 according to this embodiment will be described with reference to the flowchart of FIG. In the following description, the operation for displaying only the difference between the first surface texture parameter and the second surface texture parameter will be described.
In step S11, the evaluation system 10 determines whether either the tool information and the position information or the measurement information, which is the measurement result, has been input, or whether neither of the information has been input. The evaluation system 10 proceeds to step S12 when tool information and position information are input, proceeds to step S14 when measurement information is input, and repeats the determination of step S11 when neither information is input. conduct.
The specific contents of the tool information, position information, and measurement information are as described above.

In step S12, the machined surface simulator 11 calculates the portion of the machined surface to be removed based on the tool information and position information, and performs a machined surface simulation.

In step S13, the machined surface analysis unit 12 obtains a first surface texture parameter, which is a first analysis result of analyzing the state of the machined surface, based on the machining simulation results output from the machined surface simulator 11, Output to the difference calculation unit 14 .

In step S14, the machined surface measurement analysis unit 13 obtains a second surface texture parameter as a second analysis result from the measured surface data (measurement result) input from the machined surface measurement device 20, and calculates the difference Output to the calculation unit 14 .
The specific contents of the first and second surface texture parameters in steps S13 and S14 are as described above.

In step S15, the difference calculation unit 14 determines whether or not the first and second analysis results (two analysis results) have been input. If two analysis results have been input, the difference calculation unit 14 proceeds to step S16. If two analysis results have not been input, the difference calculation unit 14 makes the determination of step S15 again.

In step S<b>16 , the difference calculation unit 14 calculates the difference between the first analysis result and the second analysis result, and outputs the difference to the result display unit 15 .

In step S<b>17 , the result display unit 15 displays the difference between the first and second surface texture parameters output from the difference calculation unit 14 in the display method set by the display setting unit 16 .

According to the present embodiment, it is possible to determine how far the machined surface of the workpiece to be machined is from the ideal machined surface based on the magnitude of the difference, making it possible to more easily compare machined surface qualities. .

<Another example of machined surface analysis>
In the above description, the machined surface analysis unit 12 obtains the first surface texture parameter as the first analysis result from the machined surface simulation result of the machined surface simulator 11, outputs the first surface texture parameter to the difference calculation unit 20, and An example in which the measurement analysis unit 13 obtains a second surface texture parameter, which is a second analysis result, from the measured surface data (measurement result) input from the machined surface measurement device 20, and outputs the second surface texture parameter to the difference calculation unit 14. However, the first and second analysis results are not particularly limited as long as they can represent the state of the machined surface.

For example, the machined surface analysis unit 12 uses height data, analytical values of height data (including surface texture parameters), graphed height data, or graphed An analytical value (including a surface texture parameter) of the height data can be obtained and output to the difference calculation unit 20 . In addition, the processed surface analysis unit 12 obtains, from the results of the processed surface simulation by the processed surface simulator 11, image data of photographs or moving images, analysis values of the image data, bidirectional reflectance distribution function (BRDF) data of the processed surface ( BRDF data hereinafter), an analysis value of the BRDF data, graphed BRDF data, or an analysis value of the graphed BRDF data can be obtained and output to the difference calculation unit 14 . Here, the bidirectional reflectance distribution function (BRDF) is a function that indicates the angular distribution characteristics of reflected light when light is incident from a specific angle.

When the machined surface analysis unit 12 uses the result of the machined surface simulation as image data, obtains the analysis value of the pixel value, and outputs it to the difference calculation unit 20, the image data can be created by any of the following methods. .
(1) The height difference of the machined surface is represented by the size of the pixel value.
(2) Simulate the reflection when light is applied from a position other than directly above the processing surface, and express the magnitude of the reflected light by the magnitude of the pixel value.
A histogram feature quantity, which is an analysis value, is calculated from the pixel values of the created image data. The histogram feature amount is, for example, the average value of pixel values, the variance value, the contrast, the skewness, the kurtosis, the energy, the entropy, and the like.

The machined surface measurement analysis unit 13 obtains a second analysis result of the same kind as the first analysis result from the measured surface data using the machined surface measurement device 20 and outputs the second analysis result to the difference calculation unit 14 .
For example, the machined surface measurement analysis unit 13 obtains a second surface texture parameter from surface data actually measured using the machined surface measurement device 20 and outputs the second surface texture parameter to the difference calculation unit 14 .

Further, the machined surface measurement analysis unit 13 may use surface data actually measured using the machined surface measuring device 20 as image data, obtain an analysis value of the pixel value, and output it to the difference calculation unit 14 . A microscope, a digital camera, or the like, for example, is used as the machined surface measuring device 20 to obtain image data. The image data may be photograph data or video data.

Furthermore, the machined surface measurement analysis unit 13 may obtain a bidirectional reflectance distribution function (BRDF) of the machined surface using surface data actually measured using the machined surface measurement device 20, and output the bidirectional reflectance distribution function (BRDF) to the difference calculator 14. . For example, a goniophotometer or the like is used as the processed surface measuring device 20 for measuring the spectral angle or the diffraction angle in order to obtain the bidirectional reflectance distribution function. The BRDF data of the machined surface may be graphed data.

Although the above-described embodiments are preferred embodiments of the present invention, the scope of the present invention is not limited to only the above-described embodiments, and various modifications have been made without departing from the gist of the present invention. Any form of implementation is possible. For example, it is possible to implement in a modified form as described below.

<Modified example of configuration of evaluation system>
In the above-described embodiment, the evaluation system 10 includes the machined surface simulator 11, the machined surface analysis unit 12, the machined surface measurement analysis unit 13, the difference calculation unit 14, the result display unit 15, and the display setting unit 16. showing.
However, the result display unit 15 and the display setting unit 16 may be separately provided outside the evaluation system 10. In this case, the evaluation system 10 includes the machined surface simulator 11, the machined surface analysis unit 12, the machined surface measurement analysis unit 13 and a difference calculation unit 14 .
Some or all of the components of evaluation system 10 may be provided within the machine tool. For example, the machined surface simulator 11 and the machined surface analysis unit 12 can be provided in the machine tool, and the analysis results can be input to the difference calculation unit 14 outside the machine tool. Also, the machined surface measurement analysis unit 13 can be provided in the machined surface measuring device 20 .

The evaluation system 10 may be shared by multiple machine tools. By operating a plurality of machine tools with the same machining program to fabricate workpieces and comparing the machined surface quality using the evaluation system 10, the user can evaluate the performance of the plurality of machine tools.

Also, the embodiments described above can be realized by hardware, software, or a combination thereof. Here, "implemented by software" means implemented by a computer reading and executing a program. When configured by hardware, part or all of each embodiment, for example, integrated circuits such as LSI (Large Scale Integrated circuit), ASIC (Application Specific Integrated Circuit), gate array, FPGA (Field Programmable Gate Array) ( IC).

In addition, when part or all of the above-described embodiments are configured by a combination of software and hardware, a hard disk, ROM, or other storage that stores a program describing all or part of the operation of the evaluation system shown in the flow chart In a computer composed of a unit, a DRAM that stores data necessary for calculation, a CPU, and a bus that connects each unit, information necessary for calculation is stored in the DRAM, and the program is executed by the CPU. can be done.

Programs can be stored and supplied to computers using various types of computer readable media. Computer readable media includes various types of tangible storage media. Computer-readable media include, for example, magnetic recording media (e.g., hard disk drives), magneto-optical recording media (e.g., magneto-optical discs), CD-ROMs (Read Only Memory), CD-Rs, CD-R/Ws, semiconductor memories (eg, mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash ROM, or RAM (random access memory)).

The evaluation system and evaluation program for evaluating machined surface quality according to the present disclosure can take various embodiments having the following configurations, including the embodiments described above.

(1) A machined surface simulator that simulates a machined surface as a result of machining, using position information for machining a workpiece using a cutting tool and tool information having feature quantities including at least the shape of the cutting tool. a part (for example, a machined surface simulator 11);
A machined surface analysis unit (for example, a machined surface analysis unit 12) that outputs a first analysis result in which the state of the machined surface is quantified based on the result of the machined surface simulation by the machined surface simulator unit;
A machined surface measurement analysis unit (for example, a machined surface measurement analysis unit 13);
a difference calculation unit (for example, difference calculation unit 14) that calculates the difference between the first analysis result and the second analysis result;
A rating system with
According to this evaluation system, it is possible to easily evaluate the machined surface quality of the machined surface of the machined workpiece by comparing the machined surface quality of the machined surface of the machined workpiece with the machined surface quality of the ideal machined surface. .

(2) The above (1) comprising a result display unit (for example, the result display unit 15) that displays the difference, and a display setting unit (for example, the display setting unit 16) that sets the display method of the difference. The rating system described in .

(3) The evaluation system according to (1) or (2) above, wherein the first and second analysis results are surface texture parameters.

(4) The evaluation system according to (1) or (2) above, wherein the first and second analysis results are histogram feature quantities.

(5) The evaluation system according to (1) or (2) above, wherein the first and second analysis results are bidirectional reflectance distribution function data of the processed surface.

(6) to the computer,
A process of simulating a machined surface of a machining result using position information for machining a workpiece using a cutting tool and tool information having a feature amount including at least the shape of the cutting tool;
A process of outputting a first analysis result in which the state of the machined surface is digitized based on the result of the simulation of the machined surface;
A process of outputting a second analysis result in which the state of the machined surface is digitized based on the measurement result of measuring the machined surface of the workpiece actually machined by the machine tool using the position information;
a process of calculating a difference between the first analysis result and the second analysis result;
An evaluation program that runs
According to this evaluation program, it is possible to easily evaluate the machined surface quality of the machined surface of the machined workpiece by comparing the machined surface quality of the machined surface of the machined workpiece with the machined surface quality of the ideal machined surface. can.

10 evaluation system 11 machined surface simulator 12 machined surface analysis unit 13 machined surface measurement analysis unit 14 difference calculation unit 15 result display unit 16 display setting unit 20 machined surface measurement device 30 machined workpiece 40 machine tool

Claims

a machined surface simulator section for simulating a machined surface as a result of machining using position information for machining a workpiece using a cutting tool and tool information having feature amounts including at least the shape of the cutting tool;
a machined surface analysis unit that outputs a first analysis result in which the state of the machined surface is quantified based on the result of the machined surface simulation by the machined surface simulator unit;
a machined surface measurement analysis unit that outputs a second analysis result in which the state of the machined surface is quantified based on the measurement result of measuring the machined surface of the workpiece actually machined by the machine tool using the position information;
a difference calculation unit that calculates the difference between the first analysis result and the second analysis result;
A rating system with
The evaluation system according to claim 1, comprising a result display section for displaying the difference, and a display setting section for setting a display method of the difference.
The evaluation system according to claim 1 or claim 2, wherein the first and second analysis results are surface texture parameters.
The evaluation system according to claim 1 or 2, wherein the first and second analysis results are histogram feature quantities.
　The evaluation system according to claim 1 or 2, wherein the first and second analysis results are bidirectional reflectance distribution function data of the processed surface.
to the computer,
A process of simulating a machined surface of a machining result using position information for machining a workpiece using a cutting tool and tool information having a feature amount including at least the shape of the cutting tool;
A process of outputting a first analysis result in which the state of the machined surface is digitized based on the result of the simulation of the machined surface;
A process of outputting a second analysis result in which the state of the machined surface is digitized based on the measurement result of measuring the machined surface of the workpiece actually machined by the machine tool using the position information;
a process of calculating a difference between the first analysis result and the second analysis result;
An evaluation program that runs