System for estimating factors causing decline

Abstract

This invention provides a performance degradation estimation system with improved accuracy in estimating the factors that degrade the performance of machine tools. [Solution] A reduction factor estimation system 1 comprising: a provisional reduction factor estimation unit 221 that estimates a provisional reduction factor HF1 based on a first analysis parameter AP1 and a second comparison analysis parameter CAP2 that can be compared with the first analysis parameter AP1; an estimated workpiece shape data calculation unit 208 that calculates estimated workpiece shape data ESD using a third analysis parameter AP3 that includes the first analysis parameter AP1; a third analysis parameter update unit 210 that causes the estimated workpiece shape data ESD to be calculated based on the updated third analysis parameter AP3; a provisional reduction factor update unit 222 that updates the provisional reduction factor HF based on the updated third analysis parameter AP3 and the second analysis parameter AP2; and a reduction factor setting unit 223 that sets the provisional reduction factor HF1 as reduction factor FC when the numerical data ND and the estimated workpiece shape data ESD are at equivalent levels.

Description

Technical Field

[0001] The present invention relates to a deterioration factor estimation system.

Background Art

[0002] Patent Document 1 (Japanese Patent No. 7441602) discloses a technique for solving problems in machining by using a knowledge model. The machining support system according to the above technique includes a knowledge database that stores various information representing know-how regarding machining methods and machining equipment, an input unit for inputting questions, a control unit that sets search conditions based on the questions and executes a drive solver and a solution solver that search the knowledge database to derive an answer, and a display unit that displays the answer. The answer includes a plurality of countermeasures and the priority order of each countermeasure, and also includes background information representing a search process composed of a plurality of factors for guiding the countermeasures and a search path connecting the plurality of factors.

[0003] According to the above technique, the machining support system sets search conditions based on the questions input to the input unit, searches the knowledge database to derive an answer, and displays the derived answer.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] When the performance of a machine tool deteriorates, the machining quality (accuracy) of a workpiece may deteriorate. In such a case, it is not easy for an operator of the machine tool to identify the factors causing the performance deterioration of the machine tool. Therefore, it may be difficult for the operator to appropriately set the questions for inputting to the machining support system according to the prior art.

[0006] On the other hand, by actually measuring the shape of the workpiece and analyzing the actual machining results based on the measurement results, it is sometimes possible to understand the patterns of deterioration in machining quality that occur in the workpiece, as well as the tendencies for deterioration in machining quality. This can, to some extent, allow for the estimation of the factors causing deterioration in the machining quality of the workpiece.

[0007] However, the factors that cause a decrease in machining quality in a workpiece are diverse. Therefore, even if the decrease in machining quality in a workpiece appears similar, the underlying causes may be different. Furthermore, a decrease in machining quality may occur due to a combination of multiple factors. For this reason, it may be difficult to identify the underlying causes of the decrease in machining quality by focusing only on the decrease in machining quality in a workpiece. For this reason, there is a need to accurately estimate the factors that cause a decrease in machining quality in a workpiece.

[0008] This disclosure was made in view of the aforementioned issues and aims to provide a system for estimating factors that improve the accuracy of estimating factors related to performance degradation of machine tools. [Means for solving the problem]

[0009] One aspect of the present invention is, A numerical data acquisition unit that acquires numerical data including machine tool numerical data obtainable from a machine tool that processes a workpiece, or workpiece numerical data obtainable by actually measuring the shape of the workpiece, A first analysis parameter calculation unit calculates first analysis parameters used to perform a simulation of machining the workpiece using the machine tool, based on the aforementioned numerical data. A second analysis parameter acquisition unit acquires second analysis parameters used to perform a simulation of machining the workpiece by the machine tool in the initial or normal state of the machine tool, A provisional reduction factor estimation unit estimates provisional reduction factors that are the causes of the deterioration in the machining quality of the workpiece, based on the first analysis parameter and a second comparison analysis parameter among the second analysis parameters that can be compared with the first analysis parameter. An estimated workpiece shape data calculation unit calculates estimated workpiece shape data by performing a simulation of machining the workpiece using the machine tool, using a third analysis parameter including the first analysis parameter. An estimation result determination unit determines whether the numerical data and the estimated workpiece shape data are at the same level, based on the numerical data and the estimated workpiece shape data. If the numerical data and the estimated workpiece shape data are not at the same level, the third analysis parameter update unit updates the third analysis parameter until the numerical data and the estimated workpiece shape data are determined to be at the same level, and causes the estimated workpiece shape data calculation unit to calculate the estimated workpiece shape data based on the updated third analysis parameter. A provisional reduction factor update unit updates the provisional reduction factor based on the updated third analysis parameter and the second analysis parameter, A reduction factor setting unit sets the provisional reduction factor as the reduction factor when the numerical data and the estimated workpiece shape data are at the same level, A display unit that displays the aforementioned factors causing the decrease, It is a system for estimating factors causing a decline, which includes the following features. [Effects of the Invention]

[0010] According to one aspect of the present invention, the third analysis parameter can be updated by repeating the simulation. This makes the estimated workpiece shape data closer to numerical data. This improves the accuracy of estimating factors that degrade the machining quality of the workpiece. [Brief explanation of the drawing]

[0011] [Figure 1] This is a block diagram showing the system for estimating factors for degradation according to Embodiment 1. [Figure 2] It is a diagram showing data stored in the memory unit according to Embodiment 1. [Figure 3] It is a block diagram showing the processing unit according to Embodiment 1. [Figure 4] It is a diagram showing a machine tool according to Embodiment 1. [Figure 5] In Embodiment 1, it is a schematic diagram showing the interference state of a workpiece, a grinding wheel, and a rest device in a simulation of machining the workpiece by a machine tool. [Figure 6] In Embodiment 1, it is a schematic diagram showing an FTA. [Figure 7] In Embodiment 1, it is a partially cut-away view showing a decision table. [Figure 8] In Embodiment 1, it is a schematic diagram showing a knowledge model. [Figure 9] It is a block diagram showing an estimated workpiece shape data calculation unit according to Embodiment 1. [Figure 10] In Embodiment 1, it is a schematic diagram showing the interference state between a workpiece and a grinding wheel in a simulation of machining the workpiece by a machine tool. [Figure 11] In Embodiment 1, it is a diagram showing the shape of a workpiece in a simulation of machining the workpiece by a machine tool represented by a group of line segments in the radial direction, and showing the state where the workpiece represented by the line segments in the radial direction interferes with the outer peripheral line of the grinding wheel during machining. [Figure 12] In Embodiment 1, it is a schematic diagram showing workpiece dynamic stiffness, tool dynamic stiffness, and contact dynamic stiffness. [Figure 13] In Embodiment 1, it is a diagram showing roundness measurement data. [Figure 14] In Embodiment 1, it is a diagram showing frequency domain data. [Figure 15] It is the main flow of the reduction factor estimation system according to Embodiment 1. [Figure 16] In Embodiment 1, it is a schematic diagram showing examples of data acquired by a user input data acquisition unit and a numerical data acquisition unit. [Figure 17]It is a diagram showing an image displayed on the display unit according to Embodiment 1. [Figure 18] It is a diagram showing an image displayed on the display unit according to Embodiment 1. [Figure 19] It is a diagram showing an image displayed on the display unit according to Embodiment 1. [Figure 20] It is a diagram showing an image displayed on the display unit according to Embodiment 1. [Figure 21] It is a diagram showing an image displayed on the display unit according to Embodiment 1. [Figure 22] It is a diagram showing an image displayed on the display unit according to Embodiment . [Figure 23] It is a block diagram showing the processing unit according to Embodiment 2. [Figure 24] It is a diagram showing the data stored in the storage unit according to Embodiment 2. [Figure 25] It is a block diagram showing the processing unit according to Embodiment 3. [Figure 26] It is a block diagram showing the comparison data generation unit according to Embodiment 3. [Figure 27] It is a diagram showing the first comparison data according to Embodiment 3. [Figure 28] It is a diagram showing the reference data according to Embodiment 3. [Figure 29] It is a diagram showing the data stored in the storage unit according to Embodiment 3. [Figure 30] It is a block diagram showing the comparison data generation unit according to Embodiment 4. [Figure 31] It is a diagram showing the second comparison data in Embodiment 4. [Figure 32] It is a diagram showing the data stored in the storage unit according to Embodiment 4. [[ID=4|6]] [Figure 33] It is a block diagram showing the processing unit according to Embodiment 5. [Figure 34] It is a diagram showing the data stored in the storage unit according to Embodiment 5. [Figure 35] It is a diagram showing an image displayed on the display unit according to Embodiment 5. [Figure 36]This figure shows the image displayed on the display unit according to Embodiment 5. [Figure 37] This figure shows the image displayed on the display unit according to Embodiment 5. [Figure 38] This figure shows the image displayed on the display unit according to Embodiment 5. [Figure 39] This figure shows the image displayed on the display unit according to Embodiment 5. [Modes for carrying out the invention]

[0012] (Embodiment 1) 1.1 Overall configuration of the reduction factor estimation system 1 Referring to Figure 1, the performance degradation factor estimation system 1 according to Embodiment 1 will be described. The performance degradation factor estimation system 1 comprises a processing unit 2, a network 3, a storage unit 4, a user terminal 5, and a machine tool 6. The performance degradation factor estimation system 1 displays the factors that degrade the performance of the machine tool 6. Multiple machine tools 6 may be connected to the network 3.

[0013] The processing unit 2 includes a communication unit 7 connected to the network 3. The communication unit 7 transmits data regarding the factors causing performance degradation of the machine tool 6 to the user terminal 5 of user U. The network 3 may be the internet or an intranet. However, the processing unit 2 may also be configured to be located on the machine tool 6.

[0014] The storage unit 4 can utilize any storage medium, such as a hard disk drive, RAM (Random Access Memory), ROM (Read Only Memory), or USB (Universal Serial Bus) memory. However, the storage unit 4 may also be configured to be located within the processing unit 2.

[0015] As shown in Figure 2, the memory unit 4 stores user input data UD, numerical data ND, machine tool numerical data MND, workpiece numerical data WND, and roundness measurement data RMD.

[0016] Furthermore, the memory unit 4 stores normal state data NSD, shipment data ASD, user input side factor UF, first numerical factor NF1, second numerical factor NF2, estimated workpiece shape data ESD, workpiece shape analysis data WAD, and frequency domain data FD.

[0017] Furthermore, the storage unit 4 stores the first analysis parameter AP1, the second analysis parameter AP2, the third analysis parameter AP3, the supplementary second analysis parameter SAP2, the comparison second analysis parameter CAP2, and the correspondence relationship data CD. The storage unit 4 may also store data other than those mentioned above.

[0018] User input data UD is data relating to a decrease in the machining quality of the workpiece W, or a decrease in the performance of the machine tool 6 that processes the workpiece W, and includes at least linguistic data or image data, and is data entered by the user U of the machine tool 6.

[0019] Numerical data ND includes machine tool numerical data MND or workpiece numerical data WND. Machine tool numerical data MND is data obtainable from machine tool 6. Workpiece numerical data WND is data obtainable by actually measuring the shape of workpiece W. Roundness measurement data RMD is data obtained by actually measuring the roundness of workpiece W. Roundness measurement data RMD may be measured by precision measuring equipment located on machine tool 6, or by precision measuring equipment separate from machine tool 6. Frequency domain data FD is data obtained by analyzing roundness measurement data RMD.

[0020] User input factor UF is a factor that reduces the machining quality of workpiece W, estimated based on user input data UD. The first numerical factor NF1 is a factor that reduces the machining quality of workpiece W, estimated based on the first analysis parameter AP1 and the second analysis parameter AP2. The second numerical factor NF2 is a factor that reduces the machining quality of workpiece W, estimated based on numerical data ND.

[0021] The estimated workpiece shape data ESD is calculated by performing a simulation of machining the workpiece W using the machine tool 6 with the third analysis parameter AP3, which includes the first analysis parameter AP1. The workpiece shape analysis data WAD is generated by analyzing the shape of the workpiece W based on the numerical data ND.

[0022] Normal state data NSD represents the data for machine tool 6 under normal conditions. Shipment data ASD represents the data for each machine tool 6 at the time of shipment.

[0023] The first analysis parameter AP1 is used to perform a simulation of machining the workpiece W by the machine tool 6 based on numerical data ND. The second analysis parameter AP2 is used to perform a simulation of machining the machine tool 6 by the machine tool 6 in its initial or normal state.

[0024] The third analysis parameter AP3 is an analysis parameter that includes the first analysis parameter AP1, and is used by the estimated workpiece shape data ESD calculation unit to calculate the estimated workpiece shape data ESD.

[0025] The supplementary second analysis parameter SAP2 is an analysis parameter included in the third analysis parameter AP3, and supplements the first analysis parameter AP1 when the estimated workpiece shape data ESD calculation unit cannot perform the simulation with only the first analysis parameter AP1. The comparison second analysis parameter CAP2 is a part of the second analysis parameter AP2 that can be compared with the first analysis parameter AP1.

[0026] The correspondence data CD contains data on the correspondence between the analysis parameters used to perform a simulation of machining the workpiece W using the machine tool 6, and the factors that reduce the machining quality of the workpiece W.

[0027] The processing unit 2 displays the factors causing the machine tool 6 to degrade. The processing unit 2 may be connected to the machine tool 6 via the network 3, or it may be configured as an integral part of the machine tool 6.

[0028] Any machine tool 6 can be appropriately selected as the machine tool 6, such as a grinding machine, a turning machine, or a machining center. In Embodiment 1, a grinding machine is used as an example of the machine tool 6 (hereinafter it may be referred to as grinding machine 6). The machine tool 6 processes the workpiece W, which will be described later.

[0029] User terminal 5 can be any terminal device, such as a mobile phone or tablet.

[0030] 1.2 Machine tools 6 An example of the configuration of the grinding machine 6 and the control device 6a will be described in detail with reference to Figure 4. The grinding machine 6 is an example of a table traverse type cylindrical grinding machine. That is, the grinding machine 6 is configured to move the workpiece W in the axial direction of the workpiece W and to move the grinding wheel T in a direction intersecting the axis of the workpiece W. Furthermore, in Embodiment 1, the grinding machine 6 is described as grinding the cylindrical outer surface of the workpiece W with the grinding wheel T.

[0031] The grinding machine 6 comprises a bed 10, a table 20, a spindle unit 30, a tailstock unit 40, a grinding wheel holder 50, a sizing device 60, a rest device 70, and a control device 6a. The bed 10 is installed on a mounting surface. The bed 10 is formed with a longer width (length in the Z-axis direction) on the front side in the X-axis direction (lower side in Figure 4) and a shorter width on the back side in the X-axis direction (upper side in Figure 4).

[0032] The bed 10 has a Z-axis guide surface 11 extending in the Z-axis direction on the upper surface of the front side in the X-axis direction. Furthermore, the bed 10 is equipped with a Z-axis drive mechanism 12 that drives along the Z-axis guide surface 11. In Embodiment 1, the Z-axis drive mechanism 12 is provided as an example in which a ball screw mechanism 12a and a Z-axis motor 12b are included. The ball screw mechanism 12a extends parallel to the Z-axis guide surface 11, and the Z-axis motor 12b drives the ball screw mechanism 12a.

[0033] To drive the Z-axis drive mechanism 12, a Z-axis drive circuit and a Z-axis detector 12c (not shown) are provided. The Z-axis drive circuit includes an amplifier circuit and drives the Z-axis motor 12b. In Embodiment 1, the Z-axis detector 12c is, for example, an angle detector such as an encoder, which detects the angle of the rotation axis of the Z-axis motor 12b. Note that the Z-axis drive mechanism 12 can also be configured with a linear motor or the like instead of the ball screw mechanism 12a described above.

[0034] Furthermore, the bed 10 is provided with a guide surface 13 on the upper surface of the back side in the X-axis direction, extending in a direction intersecting the Z-axis direction. In Embodiment 1, the guide surface 13 is an X-axis guide surface extending in the X-axis direction perpendicular to the Z-axis. In addition, the bed 10 is provided with an X-axis drive mechanism 14 that drives along the X-axis guide surface 13. In Embodiment 1, the X-axis drive mechanism 14 is provided with a ball screw mechanism 14a and an X-axis motor 14b as an example. The ball screw mechanism 14a extends parallel to the X-axis guide surface 13, and the X-axis motor 14b drives the ball screw mechanism 14a.

[0035] To drive the X-axis drive mechanism 14, an X-axis drive circuit and an X-axis detector 14c (not shown) are provided. The X-axis drive circuit includes an amplifier circuit and drives the X-axis motor 14b. In Embodiment 1, the X-axis detector 14c is, for example, an angle detector such as an encoder, which detects the angle of the rotation axis of the X-axis motor 14b. Note that the X-axis drive mechanism 14 can also be configured with a linear motor or the like instead of the ball screw mechanism 14a described above.

[0036] The table 20 is formed in an elongated shape and is supported on the Z-axis guide surface 11 of the bed 10 so as to be movable in the Z-axis direction (horizontal left-right direction). The table 20 is also fixed to the ball screw nut of the Z-axis ball screw mechanism 12a and moves in the Z-axis direction by the rotational drive of the Z-axis motor 12b.

[0037] The spindle unit 30 constitutes a workpiece support device. The spindle unit 30 supports the workpiece W and rotates the workpiece W. The spindle unit 30 is located on one end of the table 20 in the Z-axis direction. The spindle unit 30 comprises a spindle housing 31, a workpiece spindle 32, a workpiece spindle motor 33, a spindle center 34, a spindle detector 35, and a spindle drive circuit (not shown).

[0038] The spindle housing 31 is fixed on the table 20. The workpiece spindle 32 is rotatably supported in the spindle housing 31 via bearings. The workpiece spindle motor 33 constitutes a drive device that rotationally drives the workpiece spindle 32. The spindle center 34 supports the end face of one axial end of the workpiece W. The spindle center 34 is fixed to the workpiece spindle 32 and is rotatably mounted relative to the spindle housing 31.

[0039] However, if the spindle device 30 includes a rotating member such as a chuck (not shown), the spindle center 34 may be fixed to the spindle housing 31 and configured so as not to rotate relative to the spindle housing 31. Alternatively, the spindle device 30 may be equipped with a chuck for gripping the workpiece W instead of the spindle center 34. The chuck is rotationally driven by being connected to the workpiece spindle 32.

[0040] The spindle detector 35 and the spindle drive circuit are provided to drive the workpiece spindle motor 33. In Embodiment 1, the spindle detector 35 is, for example, an angle detector such as an encoder, which detects the angle of the rotation axis of the workpiece spindle motor 33. The spindle drive circuit includes an amplifier circuit and drives the workpiece spindle motor 33.

[0041] The tailstock 40, together with the spindle 30, constitutes a workpiece support device. The tailstock 40 is located on the other end of the table 20 in the Z-axis direction. The tailstock 40 is fixed on the table 20 and comprises a tailstock center 41 and a ram 42. The tailstock center 41 is supported by the workpiece W by being axially pressed toward the other end face of the workpiece W via a ram 42 that is movable in the Z-axis direction.

[0042] The method of supporting the workpiece W is not particularly limited, and the pressing force of the ram 42 may be configured to be adjustable, and can be controlled by means of adjusting the spring force, means of adjusting the fluid pressure, etc. The tailstock center 41 and the ram 42 may be provided so as to be non-rotatable or so as to be rotatable. Furthermore, the tailstock device 40 may not have a ram 42, and the tailstock center 41 may be positioned in a fixed position relative to the workpiece W. Note that the tailstock device 40 is not necessary when the grinding machine 6 grinds the inner surface of the workpiece W.

[0043] The grinding wheel holder 50 is equipped with a grinding wheel T as a tool and rotates the grinding wheel T. However, in the following description, it may also be referred to as the tool T. In addition to the grinding wheel T, the grinding wheel holder 50 is equipped with a grinding wheel holder body 51, a grinding wheel shaft 52, a motor for the grinding wheel 53, and a drive circuit for the grinding wheel (not shown).

[0044] The grinding wheel T is formed in a disc shape. The grinding wheel T is used to grind the outer or inner surface of the workpiece W. The grinding wheel T is constructed by fixing multiple abrasive grains with a binder. The abrasive grains can be general abrasive grains made of ceramic materials such as alumina or silicon carbide, or superabrasive grains such as diamond or CBN.

[0045] Binders include vitrified (V), resinoid (B), rubber (R), silicate (S), shellac (E), metal (M), electrodeposition (P), and magnesia cement (Mg). Furthermore, grinding wheels T can have either a porous or non-porous structure. Depending on the type of binder and the presence or absence of pores, grinding wheels T can be either elastically deformable or almost elastically indeformable. In elastically deformable grinding wheels T, the elastic modulus differs depending on the type of binder, the presence or absence of pores, and the porosity.

[0046] The grinding wheel base body 51 is formed, for example, in a rectangular shape in 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 grinding wheel base body 51 is also fixed to the 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 grinding wheel base body 51 constitutes a tool support device that supports the grinding wheel T.

[0047] The grinding wheel spindle 52 is rotatably supported on the grinding wheel base body 51 via bearings. A grinding wheel T is fixed to the tip of the grinding wheel spindle 52, and the grinding wheel T rotates as the grinding wheel spindle 52 rotates. In other words, the grinding wheel spindle 52 constitutes the tool spindle. The grinding wheel motor 53 constitutes a drive device that rotationally drives the grinding wheel spindle 52. Hydrostatic bearings and rolling bearings are used as bearings.

[0048] The grinding wheel motor 53 transmits rotational driving force to the grinding wheel shaft 52, for example, via a belt. However, the grinding wheel motor 53 may be arranged coaxially with the grinding wheel shaft 52. Generally, the rotational speed of the grinding wheel T driven by the grinding wheel motor 53 is faster than the rotational speed of the workpiece W driven by the workpiece spindle motor 33. 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.

[0049] As described above, the grinding machine 6, as a machine tool 6, comprises a grinding wheel spindle (tool spindle) 52, a grinding wheel motor (drive device) 53, a workpiece spindle 32, and a workpiece spindle motor 33 as rotating bodies. Although not shown in the figures, the rotating bodies may also include a motor for a coolant supply pump, a motor for a hydraulic pump for lubricating oil supply, and motors for opening and closing valves provided in the coolant and lubricating oil passages.

[0050] The sizing device 60 is provided on the upper surface of the bed 10 and measures the outer diameter of the workpiece W. The sizing device 60 includes, for example, a pair of contactors that can contact the outer circumferential surface of the workpiece W, and measures the outer diameter at the contact points with the workpiece W.

[0051] As shown in Figure 5, the rest device 70 comprises a first arm 71 and a second arm 72. The rest device 70 is configured to support the lower part W1 of the workpiece W with the first arm 71, while the second arm 72 presses the part of the workpiece W opposite to the tool T, W2, toward the tool T, providing sliding support. The rest device 70 is also adjustable in the Z direction via a moving mechanism (not shown) so that the pressing position on the workpiece W can be changed. By pressing the workpiece W toward the tool T, the rest device 70 prevents the workpiece W from deforming and separating from the tool T during machining. Note that the sizing device 60 is not shown in Figure 5.

[0052] The rest device 70 supports the workpiece W, which changes the stiffness of the machining point in the workpiece Wb, thus changing the workpiece spring constant (Kw) in the analysis. When the rest device 70 is present, the workpiece W may be supported by either a cantilever or a double-support. Furthermore, the workpiece spring constant (Kw) may include the support stiffness of the rest device 70.

[0053] The control device 6a is a CNC (Computer Numerical Control) and PLC (Programmable Logic Controller) device that performs machining control. In other words, the control device 6a drives the Z-axis drive mechanism 12 and the X-axis drive mechanism 14, which act as moving devices, based on the grinding program and the measurement results from the sizing device 60, to control the position of the table 20 and the grinding wheel base 50. That is, by controlling the position of the table 20 and the grinding wheel base 50, the control device 6a moves the workpiece W and the grinding wheel T closer together and further apart. Furthermore, the control device 6a controls the spindle device 30 and the grinding wheel base 50. It also controls the rest device 70. In other words, the control device 6a controls the rotation of the workpiece spindle 32 and the grinding wheel T, as well as the amount of indentation of the rest device 70.

[0054] 1.3 Processing Unit 2 Referring to Figure 3, the processing unit 2 will be described. The processing unit 2 includes a user input data acquisition unit 201, a numerical data acquisition unit 202, and an analysis range determination unit 203.

[0055] Furthermore, the processing unit 2 includes a user input side factor estimation unit 204, a first analysis parameter calculation unit 205, a second analysis parameter acquisition unit 206, a first numerical factor estimation unit 207, an estimated workpiece shape data calculation unit 208, an estimation result determination unit 209, a third analysis parameter update unit 210, and a first numerical factor update unit 211.

[0056] Furthermore, the processing unit 2 includes a display unit 212 and a communication unit 7.

[0057] Furthermore, the processing unit 2 includes a workpiece shape analysis unit 213, a second numerical factor estimation unit 214, a correspondence relationship acquisition unit 215, an analysis parameter comparison unit 216, and a third numerical factor estimation unit 217.

[0058] The user input data acquisition unit 201 acquires user input data UD. User input data UD is data relating to a decrease in the machining quality of the workpiece W or a decrease in the performance of the machine tool 6 that processes the workpiece W, and is data entered by the user U of the machine tool 6. User input data UD includes at least language data or image data.

[0059] Language data includes text data entered by user U, audio data recorded by user U, etc. It also includes text data entered by operator OP who receives information from user U via telephone or email, audio data recorded by operator OP, etc. In other words, language data may be data indirectly entered by user U. Language data may include information different from numerical data ND, such as, "A foreign object is in contact with the safety switch of the machine tool," or "The vibration has recently become stronger." Furthermore, if the language data entered by user U includes numerical data ND, such as the dimensions of the workpiece W measured by user U, then the numerical data ND is included in the language data.

[0060] Image data includes image data captured by user U, video data recorded by user U, etc. Furthermore, similar to language data, image data may also be data indirectly input by user U.

[0061] The numerical data acquisition unit 202 acquires numerical data ND, which includes machine tool numerical data MND obtainable from the machine tool 6, or workpiece numerical data WND obtainable by measuring the shape of the workpiece W. The numerical data ND may be automatically stored in the storage unit 4 via the network 3 by an IoT system installed on the machine tool 6. Alternatively, in response to an inquiry from user U, user U or operator OP may manually store the data in the storage unit 4 via the network 3. The numerical data acquisition unit 202 acquires the numerical data ND stored in the storage unit 4 via the network 3. If the storage unit 4 is located in the processing unit 2, the numerical data acquisition unit 202 directly acquires the numerical data ND from the storage unit 4.

[0062] The machine tool numerical data MND obtainable from the machine tool 6 includes servo sampling data of the X-axis motor 14b, servo sampling data of the effective position of the X-axis, servo sampling data of the effective position of the Z-axis, servo sampling data of the grinding wheel spindle 52, servo sampling data of the workpiece spindle motor 33, a fixed-size signal, power data of the grinding wheel motor 53, and the like.

[0063] Furthermore, the machine tool numerical data MND obtainable from the machine tool 6 also includes vibration data from a lubrication pump (not shown) attached to the machine tool 6, vibration data from an accelerometer installed on the bed 10, and the like.

[0064] The workpiece numerical data WND, which can be obtained by actually measuring the shape of the workpiece W, is separate from the machine tool 6 and includes the roundness of the workpiece W, etc., measured by a precision measuring instrument attached to the machine tool 6. Examples of precision measuring instruments include laser-type non-contact precision measuring instruments and contact-type precision measuring instruments.

[0065] The analysis range determination unit 203 determines the range of data types for analyzing the factors causing the deterioration of the machining quality of the workpiece W, based on the user input data UD acquired by the user input data acquisition unit 201 and the numerical data ND acquired by the numerical data acquisition unit 202. The analysis range determination unit 203 determines whether to use only the user input data UD, only the numerical data ND, or both the user input data UD and the numerical data ND.

[0066] If the factors causing the performance degradation of the machine tool 6 can be estimated using only the user input data UD, and if the factors causing the performance degradation of the machine tool 6 cannot be estimated using only the numerical data ND, the analysis range determination unit 203 may either estimate the factors causing the performance degradation of the machine tool 6 using only the user input data UD, or it may estimate the factors causing the performance degradation of the machine tool 6 based on both the user input factors and the numerical data ND.

[0067] Furthermore, if it is not possible to estimate the factors causing the performance degradation of the machine tool 6 using only the user input data UD, but it is possible to estimate the factors causing the performance degradation of the machine tool 6 using only the numerical data ND, the analysis range determination unit 203 may estimate the factors causing the performance degradation of the machine tool 6 using only the numerical data ND, or it may estimate the factors causing the performance degradation of the machine tool 6 based on both the user input factors and the numerical data ND.

[0068] Furthermore, if it is impossible to estimate the factors causing the performance degradation of the machine tool 6 using only the user input data UD, and it is also impossible to estimate the factors causing the performance degradation of the machine tool 6 using only the numerical data ND, the analysis range determination unit 203 estimates the factors causing the performance degradation of the machine tool 6 based on both the user input factors and the numerical data ND.

[0069] The analysis range determination unit 203 can determine, for example, the range of data types used to analyze the factors causing the deterioration of the machining quality of the workpiece W, as described below. However, the method by which the analysis range determination unit 203 determines the range of data types is not limited to the following.

[0070] First, the analysis range determination unit 203 determines the type of deterioration in the machining quality of the workpiece W or the performance of the machine tool 6 from the content of the user input data UD. There are four possible types of deterioration in the machining quality of the workpiece W or the performance of the machine tool 6, for example: failure of the machine tool 6, increase in machining dimensional error of the workpiece W, increase in roundness of the workpiece W, and occurrence of grinding burn of the workpiece W.

[0071] In this case, keywords specific to each aspect are extracted from the user input data UD, and a category classification model using a large-scale language model (LLM) is used to estimate the factors causing the deterioration of the machining quality of the workpiece W from among the four types mentioned above. However, if the system is configured to input the four types of aspects mentioned above at the stage of inputting the user input data UD, it becomes unnecessary to determine the aspect causing the deterioration of the machining quality of the workpiece W.

[0072] Next, the analysis range determination unit 203 determines the type of data to analyze the factors causing the deterioration in processing quality based on the identified type of deterioration in processing quality and the content of the user input data UD. For example, it performs FTA (Fault Tree Analysis) analysis on the identified type of deterioration in processing quality to determine the range of the corresponding factors (see Figure 6).

[0073] Finally, the analysis range determination unit 203 determines whether or not to perform an analysis based on the acquired numerical data ND, and the range of types of analysis parameters based on numerical data ND and analysis parameters not based on numerical data ND, based on the number and type of numerical data ND. This determination, for example, records the presence or absence of machine tool numerical data MND acquired from the machine tool 6 and workpiece numerical data WND acquired by actually measuring the shape of the workpiece W, and determines whether or not to calculate the analysis parameters used by the estimated workpiece shape data calculation unit 208, which will be described later, to perform the simulation, depending on the combination of the presence or absence of machine tool numerical data MND and workpiece numerical data WND.

[0074] The feasibility of calculating analysis parameters is determined, for example, by referring to the determination table shown in Figure 7. This determination table is created by listing the expected calculation methods for each analysis parameter and extracting the usable calculation methods according to the availability of numerical data ND. The determination table shown in Figure 7 summarizes which numerical data ND is required for the calculation method of workpiece stiffness (Kw). If the numerical values ​​in the cells marked with white circles in the determination table can be obtained, the workpiece stiffness (Kw) can be calculated based on the predetermined calculation method. For analysis parameters that are determined to be calculable from numerical data ND by the determination table, the analysis parameters calculated based on the calculation method described in the determination table are used in the simulation by the estimated workpiece shape data calculation unit 208.

[0075] On the other hand, for analysis parameters that were not determined to be calculable from the numerical data ND by the judgment table, the estimated workpiece shape data calculation unit 208 performs a simulation using the second analysis parameter AP2, which will be described later.

[0076] The analysis range determination unit 203 determines, for example, that if it determines that the type of factor can be sufficiently determined by the user input data UD, it will decide to use only the user input data UD. Furthermore, the analysis range determination unit 203 determines, for example, that if it determines that the simulation by the estimated workpiece shape data calculation unit 208 can be performed using the acquired numerical data ND, it will decide to use only the numerical data ND. Finally, the analysis range determination unit 203 determines that both the user input data UD and the numerical data ND are insufficient, it will decide to use both.

[0077] The user input-side factor estimation unit 204 estimates user input-side factors UF, which are factors causing a decrease in the machining quality of the workpiece W, based on the user input data UD. The input-side factor estimation unit may use, for example, FTA90 or a knowledge model. It may also use a natural language model, an image recognition model, an image language multimodal model, etc. Furthermore, the natural language model or the image language multimodal model may include a large-scale language model (LLM). However, the user input-side factor estimation unit 204 may use any other analysis model.

[0078] For example, the FTA90 shown in Figure 6 can be used. The FTA90 is constructed by placing events in each part of a tree structure via logic gates. The FTA90 initially assumes an undesirable event in the product and represents the possible paths leading to that failure, along with the probability of occurrence, in a failure tree diagram. However, the probability of occurrence may be omitted if quantitative analysis is not required.

[0079] Figure 8 shows an example of a knowledge model. Generally, in the field of machining, operators determine machining conditions such as cutting speed and depth of cut per unit time by considering various information such as the material of the workpiece W, the material of the tool, the machining quality of the workpiece W, and the machining cycle time. In this case, a knowledge model is a model of the operator's thought process when they acquire various input information and determine the machining conditions.

[0080] In other words, the knowledge model defines factors such as the material of the workpiece W, the material of the tool T, the machining quality of the workpiece W, the machining cycle time, the cutting speed, and the depth of cut, as well as each of the industrial technology elements that appear in the operator's thought process, and defines the relationships between these factors.

[0081] Conceptually, knowledge models are represented in network form. An example of a knowledge network diagram 80, which represents a knowledge model as a network diagram, will be explained with reference to Figure 8. In this example, a knowledge network diagram 80 relating to a knowledge model in the field of machining will be given. However, knowledge network diagram 80 is not limited to Figure 8.

[0082] As shown in Figure 8, the knowledge network diagram 80 comprises multiple node shapes 81 and link shapes 82 that connect the node shapes 81. The node shapes 81 are represented by arbitrary shapes such as boxes, shapes containing text, icons, etc. The node shapes 81 represent factors in the knowledge model. The link shapes 82 are represented by straight lines, curves, angled lines, etc. In this example, the link shapes 82 are represented by arrow lines to define the direction of the relationship. The link shapes 82 represent the relationships that connect the factors in the knowledge model. In the knowledge network diagram 80 shown in Figure 8, all node shapes 81 are represented by boxes in which text can be written, and the link shapes 82 are represented by arrow lines.

[0083] The user input-side factor estimation unit 204 can estimate the factors causing a decrease in the machining quality of the workpiece W by applying the user input data UD to a knowledge model.

[0084] Furthermore, the user input factor estimation unit 204 can determine which factors within FTA90 are contributing by applying the logic of each branch of FTA90 described above to the user input data UD, such as a natural language model, image recognition model, or image language multimodal model. However, the natural language model and image language multimodal model may include large-scale language models (LLMs).

[0085] The first analysis parameter calculation unit 205 calculates the first analysis parameter AP1, which is used to perform a simulation of machining the workpiece W by the machine tool 6, based on the numerical data ND. As shown in the determination table in Figure 7, the first analysis parameter calculation unit 205 calculates the first analysis parameter AP1 based on a predetermined calculation method using the acquired numerical data ND. In Figure 7, workpiece stiffness Kw is shown as an example of the first analysis parameter AP1. If the acquired numerical data ND is insufficient, the first analysis parameter calculation unit 205 does not calculate the first analysis parameter AP1.

[0086] Examples of the first analysis parameter AP1 include workpiece dynamic stiffness (Mw, Kw, Cw), grinding wheel axis dynamic stiffness (Mt, Kt, Ct), contact dynamic stiffness (Ki, Ci), tangential resistance (Ft), normal resistance (Fn), grinding wheel axis runout, external vibration, deflection due to center pressure, etc.

[0087] The second analysis parameter acquisition unit 206 acquires the second analysis parameter AP2, which is used to perform a simulation of machining the workpiece W by the machine tool 6 in its initial or normal state. The second analysis parameter AP2 is stored in the storage unit 4. The second analysis parameter acquisition unit 206 acquires the second analysis parameter AP2 from the storage unit 4 via the network 3.

[0088] The second analysis parameter AP2 comprises a supplementary second analysis parameter SAP2 and a comparative second analysis parameter CAP2. The supplementary second analysis parameter SAP2 is used to supplement the first analysis parameter AP1 when the machining result analysis unit cannot perform the simulation using only the first analysis parameter AP1. The comparative second analysis parameter CAP2 is a part of the second analysis parameter AP2 that can be compared with the first analysis parameter AP1.

[0089] The estimated workpiece shape data calculation unit 208 calculates the estimated workpiece shape data ESD by performing a simulation of machining the workpiece W by the machine tool 6 using a third analysis parameter AP3 which includes a first analysis parameter AP1. As shown in Figure 2, the third analysis parameter AP3 includes the first analysis parameter AP1 and a supplementary second analysis parameter SAP2.

[0090] The estimated workpiece shape data calculation unit 208 will be described with reference to Figures 9 to 12. The configuration of the estimated workpiece shape data calculation unit 208 will be described with reference to Figure 9. The estimated workpiece shape data calculation unit 208 comprises a command value acquisition unit 101, an estimation unit 102, a dynamic stiffness storage unit 106, a correction amount calculation unit 107, and an output unit 109.

[0091] The command value acquisition unit 101 acquires the third analysis parameter AP3. Based on the third analysis parameter AP3, the command value acquisition unit 101 calculates and generates command values ​​for executing a simulation of machining the workpiece W by the machine tool 6.

[0092] The estimation unit 102 estimates the estimated workpiece shape data ESD based on the third analysis parameter AP3. Specifically, the estimation unit 102 uses the command values ​​acquired by the command value acquisition unit 101 to perform a grinding simulation, thereby estimating at least one of the state of the workpiece W or grinding wheel T during grinding, the shape of the workpiece W, the shape of the grinding wheel T, and the mechanical state of the grinding machine 6.

[0093] The state of the workpiece W includes, for example, the vibration state and temperature state of the workpiece W. The state of the grinding wheel T includes, for example, the vibration state and temperature state of the grinding wheel T, the grinding resistance generated at each part of the outer surface of the grinding wheel T, the cutting performance of the grinding wheel T, and the state of the abrasive grains constituting the grinding wheel T. The state of the abrasive grains includes, for example, the average protrusion amount and the distribution of the abrasive grains. The shape of the workpiece W includes the shape at intermediate stages of grinding and the shape at the end of grinding. The shape of the grinding wheel T includes the shape at intermediate stages of grinding and the shape at the end of grinding. The mechanical state of the grinding machine 6 includes, for example, the vibration state and temperature state of the parts constituting the grinding machine 6.

[0094] In Embodiment 1, the estimation unit 102 performs a process in which the shape of the workpiece W changes sequentially through grinding simulation, and as an example, estimates the shape of the workpiece W, the state of the workpiece W, and the mechanical state of the grinding machine 6. In Embodiment 1, the grinding simulation is performed assuming that the grinding wheel T does not deform. In addition to the above estimation targets, the estimation unit 102 can also estimate the grinding resistance generated at each part of the outer surface of the grinding wheel T.

[0095] The estimation unit 102 includes an interference amount calculation unit 111, a grinding efficiency calculation unit 112, a grinding characteristic determination unit 113, and a grinding resistance calculation unit 114.

[0096] The interference amount calculation unit 111 calculates the interference amount between the workpiece W and the grinding wheel T based on the relative position between the workpiece W and the grinding wheel T, the outer surface shape of the workpiece W, and the outer surface shape of the grinding wheel T, obtained using the command value acquired by the command value acquisition unit 101. The interference amount corresponds to the amount of radial grinding of the workpiece W at each part of the workpiece W in the circumferential direction. In other words, the interference amount is the amount of material removed from the workpiece W by grinding the grinding wheel T, more specifically, the amount of material removed from the workpiece W in the radial direction at each part of the workpiece W in the circumferential direction. As shown in Figure 10, the interference amount is the volume of the part where the workpiece W and the grinding wheel T interfere (shaded area in Figure 10: interference region).

[0097] The interference amount calculation unit 111 geometrically calculates the interference amount through computation. Here, the interference amount calculation unit 111 stores the outer surface shape of the workpiece W and the outer surface shape of the grinding wheel T. As shown in the right-hand portion of Figure 11, the outer surface shape of the workpiece W is represented by a group of radial line segments on a polar coordinate system with the rotation center Ow of the workpiece W as the origin. In other words, the interference amount calculation unit 111 stores a group of line segments connecting the division points (white dots in Figure 11) on the outer surface of the workpiece W, which is divided into equiangled (α) sections, and the rotation center Ow (origin) of the workpiece W, as the outer surface shape of the workpiece W. The division points shown as white dots in Figure 11 are stored as the outer surface shape of the workpiece W before it is removed by the grinding wheel T.

[0098] The interference amount calculation unit 111 determines the intersection points (black dots in Figure 11) of each line segment of the workpiece W and the line representing the outer surface shape of the grinding wheel T, based on the relative position (distance between axes) of the workpiece W and the grinding wheel T and the outer surface shape of the grinding wheel T. The interference amount calculation unit 111 stores the determined intersection points (black dots in Figure 11) as the outer surface shape of the workpiece W after it has been removed by the grinding wheel T. In other words, the interference amount calculation unit 111 modifies the stored outer surface shape of the workpiece W.

[0099] The interference amount calculation unit 111 then subtracts the area of ​​the triangle △Ow-b1-b2, which is formed by points b1 and b2 (the intersection points with the grinding wheel T) after removal and the origin Ow, from the area of ​​the triangle △Ow-a1-a2, which is formed by adjacent points a1 and a2 that define the outer surface shape of the workpiece W before removal and the origin Ow. The area after the subtraction is calculated for all adjacent points that define the outer surface shape of the workpiece W.

[0100] The interference amount calculation unit 111 then calculates the interference amount (removal amount) by summing the areas after each subtraction and multiplying the summed area by the thickness of the workpiece W. In the above case, the area of ​​the removed portion was calculated by calculating the area of ​​two types of triangles and then calculating the difference between those areas. Alternatively, the area of ​​the removed portion may be calculated by directly calculating the quadrilateral a1-a2-b1-b2.

[0101] As shown in Figure 9, the grinding efficiency calculation unit 112 calculates the grinding efficiency (machining efficiency) Z' based on the interference amount calculated by the interference amount calculation unit 111. The grinding efficiency Z' is calculated as the amount of interference per unit time, that is, the volume of the workpiece W that is ground by the grinding wheel T per unit time.

[0102] The grinding characteristics determination unit 113 determines the grinding characteristics kc based on the material of the workpiece W, the type of abrasive grains and binder of the grinding wheel T, and the condition of the outer surface of the grinding wheel T. The condition of the outer surface of the grinding wheel T is expressed, for example, using an index that represents the wear state and cutting performance of the abrasive grains of the grinding wheel T. Here, the grinding characteristics determination unit 113 stores the grinding characteristics for each condition in advance through experiments or analyses.

[0103] The grinding resistance calculation unit 114 calculates the grinding resistance Fn in the direction normal to the outer surface of the workpiece W (X-axis direction) based on the grinding efficiency Z' and the grinding characteristic kc. The grinding resistance Fn is obtained by multiplying the grinding efficiency Z' by the grinding characteristic kc (Fn = kc × Z').

[0104] Furthermore, the grinding characteristic kc has an almost linear relationship such that the grinding resistance Fn in the normal direction (X-axis direction) increases as the grinding efficiency Z' increases. However, this relationship changes when, for example, the grinding wheel T wears down. For example, when the grinding wheel T wears down, the grinding resistance Fn in the normal direction increases with respect to the grinding efficiency Z'.

[0105] The dynamic stiffness memory unit 106 stores the workpiece dynamic stiffness (Mw, Cw, Kw), the tool dynamic stiffness (Mt, Ct, Kt), and the contact dynamic stiffness (Ci, Ki). However, the workpiece dynamic stiffness (Mw, Cw, Kw) may include the workpiece support dynamic stiffness, and the tool dynamic stiffness (Mt, Ct, Kt) may include the tool support dynamic stiffness.

[0106] Referring to Figure 12, the workpiece dynamic stiffness (Mw, Cw, Kw), tool dynamic stiffness (Mt, Ct, Kt), and contact dynamic stiffness (Ci, Ki) will be explained.

[0107] The workpiece dynamic stiffness (Mw, Cw, Kw) is the dynamic stiffness exhibited when the workpiece W is supported by the spindle unit 30 and tailstock unit 40, which constitute the workpiece support members of the grinding machine 6. The workpiece dynamic stiffness (Mw, Cw, Kw) is defined by the damping coefficient Cw and the spring constant Kw. The mass Mw is a value that represents the relationship between the relative acceleration of the workpiece W with respect to the reference position of the grinding machine 6 and the external force acting on the workpiece W. The damping coefficient Cw is a value that represents the relationship between the relative velocity of the workpiece W with respect to the reference position of the grinding machine 6 and the external force acting on the workpiece W. The spring constant Kw is a value that represents the relationship between the relative position of the workpiece W with respect to the reference position of the grinding machine 6 and the external force acting on the workpiece W. Furthermore, the spring constant Kw may include the support stiffness of the rest unit 70.

[0108] Contact dynamic stiffness (Ci, Ki) is the dynamic stiffness between the workpiece W and the grinding wheel T, and is the dynamic stiffness exhibited by the contact between the workpiece W and the grinding wheel T during grinding. Contact dynamic stiffness is defined by the damping coefficient Ci and the spring constant Ki. Note that contact static stiffness, which is distinguished from contact dynamic stiffness, is expressed only by the spring constant K and does not include the damping coefficient C. The damping coefficient Ci in contact dynamic stiffness is a value that represents the relationship between the relative speed between the workpiece W and the grinding wheel T and the external force acting on the workpiece W or the grinding wheel T. The spring constant Ki is a value that represents the relationship between the relative position between the workpiece W and the grinding wheel T and the external force acting on the workpiece W or the grinding wheel T.

[0109] The tool dynamic stiffness (Mt, Ct, Kt) is the dynamic stiffness relating to the grinding wheel base 50, including the grinding wheel T. The tool dynamic stiffness (Mt, Ct, Kt) is defined by the damping coefficient Ct and the spring constant Kt. The mass Mt is a value that represents the relationship between the relative acceleration of the grinding wheel T with respect to the reference position on the grinding wheel base 50 and the external force acting on the grinding wheel T. The damping coefficient Ct is a value that represents the relationship between the relative velocity of the grinding wheel T with respect to the reference position on the grinding wheel base 50 and the external force acting on the grinding wheel T. The spring constant Kt is a value that represents the relationship between the relative position of the grinding wheel T with respect to the reference position on the grinding wheel base 50 and the external force acting on the grinding wheel T. The spring constant Kt may also include the support stiffness of the rest device 70.

[0110] The correction amount calculation unit 107 calculates a correction amount for the relative displacement of the workpiece W and the machine tool 6 in the direction in which the workpiece W and the machine tool 6 approach each other, based on the dynamic stiffness data stored in the dynamic stiffness memory unit. The correction amount for displacement can be calculated from the dynamic stiffness data and the grinding resistance. In other words, the correction amount for displacement can be calculated from the grinding resistance, workpiece dynamic stiffness (Mw, Cw, Kw), tool dynamic stiffness (Mt, Ct, Kt), and contact dynamic stiffness (Ci, Ki).

[0111] The correction amount calculation unit 107 outputs the calculated correction amount to the estimation unit 102. As described above, the estimation unit 102 estimates the target of estimation based on the relative position between the workpiece W and the grinding wheel T, the outer surface shape of the workpiece W, and the outer surface shape of the grinding wheel T, which are acquired by the command value acquisition unit 101. However, due to grinding resistance, the relative position between the workpiece W and the grinding wheel T will be different from the relative position determined by the command value.

[0112] Therefore, when the estimation unit 102 estimates the target to be estimated, it uses the relative position between the workpiece W and the grinding wheel T, which is obtained by the command value acquisition unit 101 plus a correction amount calculated by the correction amount calculation unit 107. In other words, the estimation unit 102 estimates the target to be estimated based on the relative position determined by the command value and the correction amount calculated using each dynamic stiffness data.

[0113] The output unit 109 outputs the estimated workpiece shape data ESD estimated by the estimation unit 102.

[0114] The estimation result determination unit 209 determines whether the numerical data ND and the estimated workpiece shape data ESD are at the same level, based on the numerical data ND and the estimated workpiece shape data ESD. The method for determining whether the numerical data ND and the estimated workpiece shape data ESD are at the same level is not particularly limited.

[0115] The estimation result determination unit 209 can determine, for example, whether the numerical data ND and the estimated workpiece shape data ESD are at the same level, as shown below.

[0116] If, for example, roundness is obtained as numerical data ND for the workpiece W, a harmonic analysis is performed on the measured values ​​of the workpiece W to obtain workpiece shape analysis data WAD. Alternatively, if harmonic analysis data of the workpiece W is obtained as user input data UD, this data may also be used.

[0117] A harmonic analysis is performed on the estimated workpiece shape data (ESD) to obtain the workpiece shape analysis data (WAD).

[0118] The workpiece shape analysis data WAD for workpiece W and the workpiece shape analysis data WAD for estimated workpiece shape data ESD are compared, and the amplitude is compared for the corresponding number of peaks (frequency). If this amplitude is below a predetermined threshold, it is determined that the numerical data ND and the estimated workpiece shape data ESD are at the same level.

[0119] Furthermore, the estimation result determination unit 209 may determine whether the numerical data ND and the estimated workpiece shape data ESD are at the same level by comparing the roundness obtained as numerical data ND of the workpiece W with the roundness obtained by analyzing the estimated workpiece shape data ESD.

[0120] The display unit 212 displays the common factor CF, which is common to the user input factor UF and the first numerical factor NF1, when the estimation result determination unit 209 determines that the numerical data ND and the estimated workpiece shape data ESD are at the same level. Any display device can be appropriately selected as the display unit 212, such as a liquid crystal display, a mobile terminal, or a tablet terminal. The display unit 212 may be configured to be located on the machine tool 6, or it may be configured separately from the machine tool 6.

[0121] Furthermore, the display unit 212 may also display the user input side factor UF, which is not common to the first numerical factor NF1, separately from the common factor CF. The display unit 212 may also display the first numerical factor NF1, which is not common to the user input side factor UF, separately from the common factor CF. The display unit 212 may also display the second numerical factor NF2, which will be described later, separately from the common factor CF. The display unit 212 may also display the third numerical factor, which will be described later, separately from the common factor CF.

[0122] The communication unit 7 transmits the data displayed on the display unit 212 to the user terminal 5 of user U. The communication unit 7 transmits the data to the user terminal 5 via the network 3 using wireless or wired communication. The communication unit 7 may also receive data from user U via the network 3.

[0123] The workpiece shape analysis unit 213 generates workpiece shape analysis data WAD by analyzing the shape of the workpiece W based on the numerical data ND. The method for analyzing the shape of the workpiece W is not particularly limited, and any method can be selected as appropriate.

[0124] The workpiece shape analysis unit 213 can use harmonic analysis, for example, as a method for analyzing the shape of the workpiece W. Harmonics analysis involves decomposing the amplitude and other properties of the wavelength components from the random waveform obtained from the roundness measurement.

[0125] Figure 13 shows an example of roundness measurement data RMD. Roundness measurement data RMD is data on the roundness of the outer shape of a workpiece W, measured by a contact or non-contact sensor. Roundness measurement data RMD is the radius dimension from the center of the workpiece W at a phase from 0° to 360° for the outer shape of the workpiece W. Figure 14 shows an example of harmonic analysis. The horizontal axis is the number of peaks (corners) per revolution of the workpiece W, and the vertical axis is the amplitude of each peak component.

[0126] The second numerical factor estimation unit 214 estimates the second numerical factor NF2, which is a factor in the deterioration of the machining quality of the workpiece W, based on the numerical data ND. The method for estimating the second numerical factor NF2 is not particularly limited, and any method can be adopted.

[0127] The second numerical factor estimation unit 214 can estimate the second numerical factor NF2 by, for example, the following method. The second numerical factor estimation unit 214 acquires analysis data obtained by the workpiece shape analysis unit 213 performing harmonic analysis on the numerical data ND. The second numerical factor estimation unit 214 acquires the value of a component for a specific number of peaks. The second numerical factor estimation unit 214 checks whether the component for the specific number of peaks is at a peak value. If there is a component for a specific number of peaks that is at a peak value, the second numerical factor estimation unit 214 determines whether the peak value is significantly larger than the amplitude of other peaks. Whether the peak value is significantly larger than the amplitude of other peaks can be determined, for example, by comparing its magnitude with a predetermined threshold, or by the ratio of the peak value to the amplitude of other peaks; any method can be adopted.

[0128] Furthermore, the second numerical factor estimation unit 214 quantitatively confirms whether the peak value of a component with a specific number of peaks is the main factor in the increase in roundness. This can be confirmed by comparing the roundness in the analysis result obtained by performing an inverse FFT when the peak value of the component with a specific number of peaks is excluded, with the original roundness numerical data ND that includes the peak value of the component with a specific number of peaks.

[0129] The second numerical factor estimation unit 214 may estimate the "vibration of the rotation source" or "play of the rotation source" of the frequency component corresponding to the specific number of peaks as the second numerical factor NF2 if it can quantitatively confirm that the peak value of a particular number of peaks is significantly larger than the amplitude of other number of peaks, and that the peak value of the component of that particular number of peaks is the main cause of the increase in roundness.

[0130] Furthermore, if no clear peak is found in the harmonic analysis results, the second numerical factor estimation unit 214 may estimate "increase in external vibration" as the second numerical factor NF2. An example of "increase in external vibration" is abnormal rotation of the pump used to circulate the coolant.

[0131] The third analysis parameter update unit 210 updates the third analysis parameter AP3 if the numerical data ND and the estimated workpiece shape data ESD are not at the same level, until the numerical data ND and the estimated workpiece shape data ESD are determined to be at the same level. Based on the updated third analysis parameter AP3, the third analysis parameter AP3 causes the workpiece shape data calculation unit to calculate the estimated workpiece shape data ESD. The third analysis parameter AP3 can be updated based on a numerical optimization algorithm, such as Bayesian optimization.

[0132] The first numerical factor estimation unit 207 estimates the first numerical factor NF1, which is a factor in the deterioration of the machining quality of the workpiece W, based on the first analysis parameter AP1 and the second analysis parameter AP2. The first numerical factor estimation unit 207 compares the first analysis parameter AP1 and the second analysis parameter AP2. If there is a significant discrepancy between the numerical value of the first analysis parameter AP1 and the corresponding numerical value of the second analysis parameter AP2, the first numerical factor estimation unit 207 estimates the elements that affect the first analysis parameter AP1 and the second analysis parameter AP2 as factors that cause a deterioration in the performance of the machine tool 6. The method for determining whether the numerical values ​​are significantly different is not particularly limited. For example, it may be determined by whether the absolute value of the difference between the first analysis parameter AP1 and the second analysis parameter AP2 is greater than or equal to a predetermined threshold, or by whether the ratio of the first analysis parameter AP1 and the second analysis parameter AP2 is greater than or equal to a predetermined threshold.

[0133] The first numerical factor update unit 211 updates the first numerical factor NF1 based on the updated third analysis parameter AP3 and the second analysis parameter AP2 when it is determined that the numerical data ND and the estimated workpiece shape data ESD are at the same level as a result of updating the third analysis parameter AP3.

[0134] The first numerical factor update unit 211 compares the updated third analysis parameter AP3, which has been updated until the numerical data ND and the estimated workpiece shape data ESD are judged to be at the same level, with the second analysis parameter AP2. The first numerical factor update unit 211 compares the updated third analysis parameter AP3 with the second analysis parameter AP2 to identify the analysis parameter where a numerical discrepancy has occurred. The first numerical factor update unit 211 determines whether the numerical discrepancy is significant or not.

[0135] Whether or not the deviation is significant can be determined by comparing its magnitude to a predetermined threshold, or by using a ratio of the values ​​in which deviations have occurred; any method can be adopted. The first numerical factor update unit 211 estimates the analysis parameters in which the deviation of the values ​​is determined to be significant as the decline factors. The first numerical factor update unit 211 updates the analysis parameters estimated to be decline factors as the first numerical factor NF1.

[0136] Furthermore, the first numerical factor update unit 211 compares the third analysis parameter AP3 before the update with the third analysis parameter AP3 after the update. If there are analysis parameters in the third analysis parameter AP3 before the update that deviated significantly from the second analysis parameter AP2, the first numerical factor update unit 211 removes those analysis parameters from the first numerical factor NF1 if the deviation from the second analysis parameter AP2 is no longer significant in the updated third analysis parameter AP3.

[0137] The correspondence acquisition unit 215 acquires correspondence data CD relating to the correspondence between analysis parameters used to perform a simulation of machining the workpiece W by the machine tool 6 and factors that reduce the machining quality of the workpiece W. The correspondence data CD is data relating to the correspondence between factors that reduce the machining quality of a specific workpiece W and analysis parameters associated with these factors.

[0138] The analysis parameter comparison unit 216 determines whether the updated third analysis parameter AP3, updated by the third analysis parameter update unit 210, and the analysis parameters included in the correspondence data CD are at the same level. The method for determining whether the updated third analysis parameter AP3 and the analysis parameters included in the correspondence data CD are at the same level is not particularly limited, and any of the methods described above can be used, for example.

[0139] The third numerical factor estimation unit 217 estimates the factors causing a decrease in the machining quality of the workpiece W as factors causing a decrease in machining quality, based on the analysis parameters that are determined to be at the same level as the updated third analysis parameter AP3, when the updated third analysis parameter AP3 and the analysis parameters included in the corresponding relationship data CD are at the same level.

[0140] 1.4 Operation of Embodiment 1 The operation of Embodiment 1 will be explained with reference to Figures 15 to 22. Figure 15 shows a flowchart of the degradation factor estimation system 1 of Embodiment 1. When the degradation factor estimation system 1 is started, the user input data acquisition unit 201 acquires user input data UD (S1). As shown in Figure 16, user U communicates information regarding the degradation of the machining quality of the workpiece W to operator OP of the machine tool manufacturing and sales company via telephone. Examples of information regarding the degradation of the machining quality of the workpiece W include, for example, "The roundness value is not good." Operator OP inputs the user input data UD transmitted from user U to the analysis range determination unit 203 via the network 3 from the terminal used by operator OP.

[0141] Furthermore, user U may input user input data UD regarding the deterioration of the machining quality of the workpiece W to the analysis range determination unit 203 via network 3 from the terminal used by user U. Examples of user input data UD include the "diameter of the workpiece W" measured by user U.

[0142] The analysis range determination unit 203 acquires machine tool numerical data MND from the machine tool 6 via the network 3 (S2). Examples of workpiece numerical data WND include servo sampling data. The analysis range determination unit 203 also acquires workpiece numerical data WND obtained by actually measuring the workpiece W from a measuring instrument separate from the machine tool 6 via the network 3. Examples of workpiece numerical data WND include roundness measurement data RMD obtained by actually measuring the roundness of the workpiece W.

[0143] The analysis range determination unit 203 determines the range of data types for analyzing the factors causing performance degradation of the machine tool 6 based on the user input data UD and numerical data ND (S3).

[0144] If the analysis range determination unit 203 determines that it is appropriate to estimate the factors causing the decline based on the user input data UD, the process proceeds to the user input factor UF estimation process (S4).

[0145] In the user input factor UF estimation process (S4), the user input factor estimation unit 204 estimates the user input factor UF based on the FTA90 shown in Figure 6 and the knowledge model shown in Figure 8.

[0146] The user input-side factor estimation unit 204 determines, based on the FTA90 shown in Figure 6, which performance aspect of the machine tool 6 has deteriorated to cause a decline in the machining quality of the workpiece W. For example, the user input-side factor estimation unit 204 may determine that "decreased dynamic rigidity" and "decreased grinding wheel sharpness" are the factors causing the deterioration. Based on the FTA90, the user input-side factor estimation unit 204 may limit the factors causing the deterioration to one, or it may list multiple candidates without limiting it to one.

[0147] The user-input-side factor estimation unit 204 determines, based on the knowledge model shown in Figure 8, which of the machine tool 6's performance characteristics has deteriorated to cause a decline in the machining quality of the workpiece W. Figure 18 shows the state in which the user-input-side factor estimation unit 204 has determined that the factors enclosed in rectangles are the factors causing the deterioration. Based on the knowledge model, the user-input-side factor estimation unit 204 may limit the factors causing the deterioration to one, or it may list multiple candidates without limiting it to one.

[0148] The display unit 212 displays the factors causing the decrease estimated by the user input factor estimation unit 204 (S9). For example, as shown in Figure 17, the display unit 212 displays the FTA 90 and may also distinguish the estimated factors causing the decrease, "decreased dynamic stiffness" and "decreased grinding wheel cutting performance," from other factors by enclosing them in shapes such as rectangles or making them bold.

[0149] Furthermore, as shown in Figure 18, the display unit 212 may display the knowledge model and distinguish other factors and nodes by enclosing factors estimated to be degrading factors with shapes such as rectangles, or by making the nodes connected to factors thicker than other nodes.

[0150] Furthermore, as shown in Figure 19, the display unit 212 may also display the results of the decline factor diagnosis along with the ranking of the likelihood of each decline factor. The likelihood of each decline factor may be calculated using methods such as adding points when the estimation by FTA90 matches the estimation by the knowledge model, and any method can be adopted.

[0151] If the analysis range determination unit 203 determines, based on the numerical data ND, that it should estimate the factors causing the decrease, the process proceeds to workpiece shape estimation (S5) or second numerical factor estimation (S6).

[0152] When the workpiece shape estimation process (S5) is executed, the first numerical factor estimation process (S7) or the third numerical factor estimation process (S8) is executed.

[0153] In the first numerical factor estimation process (S7), the first analysis parameter calculation unit 205 calculates the first analysis parameter AP1 (for example, the spring constant Kw of the workpiece W) based on the acquired numerical data ND (for example, servo sampling data).

[0154] The first numerical factor estimation unit 207 estimates the first numerical factor NF1 based on the first analysis parameter AP1 and the second analysis parameter AP2 acquired by the second analysis parameter acquisition unit 206.

[0155] If the estimated workpiece shape data calculation unit 208 can perform a simulation of machining the workpiece W with the machine tool 6 using only the calculated first analysis parameter AP1, it will perform the simulation based on the third analysis parameter AP3, which includes only the first analysis parameter AP1. If the estimated workpiece shape data calculation unit 208 cannot perform a simulation of machining the workpiece W with the machine tool 6 using only the calculated first analysis parameter AP1, it will perform the simulation based on the third analysis parameter AP3, which includes the first analysis parameter AP1 and the supplementary second analysis parameter SAP2.

[0156] The third analysis parameter update unit 210 modifies the third analysis parameter AP3 if the numerical data ND and the estimated workpiece shape data ESD are not at the same level, until the numerical data ND and the estimated workpiece shape data ESD are determined to be at the same level. The third analysis parameter AP3 to be modified may be the first analysis parameter AP1 or the supplementary second analysis parameter SAP2.

[0157] The third analysis parameter update unit 210 causes the estimated workpiece shape data calculation unit 208 to calculate the estimated workpiece shape data ESD based on the changed third analysis parameter AP3.

[0158] The first numerical factor update unit 211 updates the first numerical factor NF1 based on the updated third analysis parameter AP3 and the second analysis parameter AP2 when it is determined that the numerical data ND and the estimated workpiece shape data ESD are at the same level as a result of updating the third analysis parameter AP3.

[0159] The estimation result determination unit 209 determines whether the numerical data ND acquired by the numerical data acquisition unit 202 and the estimated workpiece shape data ESD calculated by the estimated workpiece shape data calculation unit 208 are at the same level. The estimation result determination unit 209 may also compare the numerical data ND and the estimated workpiece shape data ESD. Alternatively, the estimation result determination unit 209 may compare the analysis results obtained by harmonic analysis of the numerical data ND by the workpiece shape analysis unit 213 with the analysis results obtained by harmonic analysis of the estimated workpiece shape data ESD.

[0160] The display unit 212 displays a common factor CF that is common to the user input factor UF and the first numerical factor NF1 when it is determined that the numerical data ND and the estimated workpiece shape data ESD are at the same level.

[0161] If the user input-side factor estimation unit 204 estimates "decreased dynamic stiffness" and "decreased grinding wheel cutting performance" as factors causing the decrease (see Figure 17), and the first numerical factor estimation unit 207 also estimates "decreased grinding wheel cutting performance" as a factor causing the decrease, the display unit 212 may, as shown in Figure 17, explicitly indicate "decreased grinding wheel cutting performance" as a common factor CF for FTA90 by enclosing it in a rectangle or other shape, or by displaying it in bold. Alternatively, the display unit 212 may list the factors causing the decrease and explicitly indicate "decreased grinding wheel cutting performance" as a common factor CF by enclosing it in a rectangle or other shape, or by displaying it in bold, as shown in Figure 21.

[0162] Furthermore, as shown in Figure 22, the display unit 212 lists the factors causing the decrease, and clearly indicates "decreased grinding wheel cutting performance" as a common factor CF by enclosing it in a rectangle or other shape, or by displaying it in bold. In addition, as a measure to improve the factors causing the decrease, for example, "truing" may be listed, and "decreased grinding wheel cutting performance" and "truing" may be further emphasized by enclosing them in a rectangle or other shape, or by displaying them in bold.

[0163] When the third numerical factor estimation process (S8) is executed, the correspondence relationship acquisition unit 215 acquires the correspondence relationship data CD from the storage unit 4. Examples of the correspondence relationship data CD include data showing that the analysis parameter "grinding characteristics" corresponds to the reduction factor "reduction in grinding wheel cutting performance".

[0164] The analysis parameter comparison unit 216 determines whether the updated third analysis parameter AP3 and the analysis parameters included in the correspondence data CD are at the same level.

[0165] The third numerical factor estimation unit 217 estimates the factors that reduce the machining quality of the workpiece W, based on the analysis parameter determined to be equivalent to the third analysis parameter AP3, when the third analysis parameter AP3 and the analysis parameter are at equivalent levels. For example, if the third analysis parameter AP3, which is "grinding characteristics," and the analysis parameter "grinding characteristics" included in the correspondence relationship data CD are determined to be at equivalent levels, the third numerical factor estimation unit 217 estimates "grinding wheel sharpness reduction," which corresponds to the analysis parameter "grinding characteristics," as the factor that reduces the machining quality of the workpiece W.

[0166] The display unit 212 may also display the third numerical factor separately from the common factor CF.

[0167] When the second numerical factor estimation process (S6) is executed, the workpiece shape analysis unit 213 calculates analysis data by performing analyses such as harmonic analysis based on the numerical data ND. The second numerical factor estimation unit 214 estimates the second numerical factor NF2, which is a factor that reduces the machining quality of the workpiece W, based on the numerical data ND or the analysis data.

[0168] The second numerical factor estimation unit 214 estimates the second numerical factor NF2 as, for example, "vibration of the rotation source," "backlash of the rotation source," or "increase in external vibration."

[0169] The display unit 212 may also display the second numerical factor NF2 separately from the common factor CF.

[0170] With this, the operation of Embodiment 1 is completed.

[0171] 1.5 Effects of Embodiment 1 Next, the effects of Embodiment 1 will be described. The reduction factor estimation system 1 of Embodiment 1 comprises a user input data acquisition unit 201, a user input side factor estimation unit 204, a numerical data acquisition unit 202, a first analysis parameter calculation unit 205, a second analysis parameter acquisition unit 206, a first numerical factor estimation unit 207, an estimated workpiece shape data calculation unit 208, an estimation result determination unit 209, and a display unit 212.

[0172] The user input data acquisition unit 201 acquires user input data UD, which is data relating to a decrease in the machining quality of the workpiece W or a decrease in the performance of the machine tool 6 that processes the workpiece W, and which includes at least linguistic data or image data, and which is data entered by the user U of the machine tool 6. The user input side factor estimation unit 204 estimates the user input side factors UF, which are the factors causing the decrease in the machining quality of the workpiece W, based on the user input data UD.

[0173] The first analysis parameter calculation unit 205 acquires numerical data ND, which includes machine tool numerical data MND obtainable from the machine tool 6, or workpiece numerical data WND obtainable by actually measuring the shape of the workpiece W. Based on the numerical data ND, the first analysis parameter calculation unit 205 calculates a first analysis parameter AP1 to be used to perform a simulation of machining the workpiece W by the machine tool 6.

[0174] The second analysis parameter acquisition unit 206 acquires the second analysis parameter AP2, which is used to perform a simulation of machining the workpiece W by the machine tool 6 in the initial or normal state of the machine tool 6.

[0175] The first numerical factor estimation unit 207 estimates the first numerical factor NF1, which is a factor in the deterioration of the machining quality of the workpiece W, based on the first analysis parameter AP1 and the second analysis parameter AP2.

[0176] The estimated workpiece shape data calculation unit 208 calculates the estimated workpiece shape data ESD by performing a simulation of machining the workpiece W by the machine tool 6 using a third analysis parameter AP3 which includes a first analysis parameter AP1.

[0177] The estimation result determination unit 209 determines whether the numerical data ND and the estimated workpiece shape data ESD are at the same level based on the numerical data ND and the estimated workpiece shape data ESD. If the estimation result determination unit 209 determines that they are at the same level, the display unit 212 displays the common factor CF that is common to the user input side factor UF and the first numerical factor NF1.

[0178] According to Embodiment 1, a common factor CF, which is common to the user input factor UF estimated based on the user input data UD and the first numerical factor NF1 estimated based on the numerical data ND, can be displayed. This allows the user U to see the degrading factors for which the estimation accuracy has been improved.

[0179] The display unit 212 according to Embodiment 1 further displays a user input-side factor UF that is not common to the first numerical factor NF1, distinguishing it from the common factor CF.

[0180] According to Embodiment 1, user input factors UF that are not common to the first numerical factor NF1 can be displayed to user U separately from the common factor CF. This allows for the provision of diverse information to user U.

[0181] The display unit 212 according to Embodiment 1 further displays a first numerical factor NF1 that is not common with the user input factor UF, distinguishing it from the common factor CF.

[0182] According to Embodiment 1, the first numerical factor NF1, which is not common to the user input factor UF, can be displayed to the user U separately from the common factor CF. This allows the user U to be provided with a variety of information.

[0183] The degradation factor estimation system 1 according to Embodiment 1 further includes a second numerical factor estimation unit 214 that estimates a second numerical factor NF2, which is a factor in the degradation of the machining quality of the workpiece W, based on numerical data ND. The display unit 212 additionally displays the second numerical factor NF2, distinguishing it from the common factor CF.

[0184] According to Embodiment 1, the second numerical factor NF2 estimated from the numerical data ND can also be displayed.

[0185] The reduction factor estimation system 1 according to Embodiment 1 further includes a workpiece shape analysis unit 213 that generates workpiece shape analysis data WAD by analyzing the shape of the workpiece W based on numerical data ND. The numerical data ND includes roundness measurement data RMD obtained by actually measuring the roundness of the workpiece W.

[0186] The workpiece shape analysis unit 213 analyzes the roundness measurement data RMD as frequency domain data FD. The second numerical factor estimation unit 214 determines whether the frequency domain data FD has a specific peak value. If no peak value exists, the second numerical factor estimation unit 214 determines that the second numerical factor NF2 is an increase in external vibration; if a peak value exists, it determines that the second numerical factor NF2 is a decrease factor caused by a rotation source at the frequency corresponding to the peak value.

[0187] According to Embodiment 1, the second numerical factor NF2 can be estimated based on the degree of roundness.

[0188] The estimated workpiece shape data calculation unit 208 according to Embodiment 1 comprises an estimation unit 102, a dynamic stiffness storage unit 106, and a correction amount calculation unit 107.

[0189] The estimation unit 102 estimates the estimated workpiece shape data ESD using the third analysis parameter AP3. The dynamic stiffness storage unit 106 stores the workpiece dynamic stiffness (Mw, Cw, Kw), which is the dynamic stiffness of the workpiece W; the tool dynamic stiffness (Mt, Ct, Kt), which is the dynamic stiffness of the tool placed on the machine tool 6; and the contact dynamic stiffness (Ki, Ci), which is the dynamic stiffness at the contact point between the workpiece W and the tool. The correction amount calculation unit 107 calculates a correction amount for the relative displacement of the workpiece W and the machine tool 6 in the direction in which the workpiece W and the machine tool 6 approach each other, based on the workpiece dynamic stiffness (Mw, Cw, Kw), the tool dynamic stiffness (Mt, Ct, Kt), and the contact dynamic stiffness (Ki, Ci).

[0190] According to Embodiment 1, the accuracy of estimating the factors causing the decline can be further improved.

[0191] The reduction factor estimation system 1 according to Embodiment 1 further comprises a third analysis parameter update unit 210 and a first numerical factor update unit 211.

[0192] The third analysis parameter update unit 210 modifies the third analysis parameter AP3 if the numerical data ND and the estimated workpiece shape data ESD are not at the same level, until the numerical data ND and the estimated workpiece shape data ESD are determined to be at the same level, and then causes the estimated workpiece shape data calculation unit 208 to calculate the estimated workpiece shape data ESD based on the modified third analysis parameter AP3.

[0193] The first numerical factor update unit 211 updates the first numerical factor NF1 based on the updated third analysis parameter AP3 and the second analysis parameter AP2 when it is determined that the numerical data ND and the estimated workpiece shape data ESD are at the same level as a result of updating the third analysis parameter AP3.

[0194] According to Embodiment 1, the estimation accuracy of the first numerical factor NF1 can be improved.

[0195] The reduction factor estimation system 1 according to Embodiment 1 further comprises a correspondence relationship acquisition unit 215, an analysis parameter comparison unit 216, and a third numerical factor estimation unit 217.

[0196] The correspondence relationship acquisition unit 215 acquires correspondence relationship data CD relating to the correspondence between analysis parameters used to perform a simulation of machining the workpiece W by the machine tool 6 and factors that reduce the machining quality of the workpiece W. The analysis parameter comparison unit 216 determines whether the third analysis parameter AP3 updated by the third analysis parameter update unit 210 and the analysis parameters included in the correspondence relationship data CD are at the same level.

[0197] The third numerical factor estimation unit 217 estimates the factors causing a decrease in the machining quality of the workpiece W, based on the analysis parameter determined to be equivalent to the third analysis parameter AP3, when the third analysis parameter AP3 and the analysis parameter are at equivalent levels.

[0198] According to Embodiment 1, the factors causing a decrease in the machining quality of the workpiece W can be estimated based on the updated third analysis parameter AP3 and the corresponding relationship data CD.

[0199] The third analysis parameter AP3 further includes a supplementary second analysis parameter SAP2. The supplementary second analysis parameter SAP2 is used to supplement the first analysis parameter AP1 when the estimated workpiece shape data calculation unit 208 cannot perform the simulation with only the first analysis parameter AP1.

[0200] According to Embodiment 1, the estimation accuracy of the first numerical factor NF1 can be improved by updating the supplementary second analysis parameter SAP2.

[0201] The degradation factor estimation system 1 according to Embodiment 1 further includes a communication unit 7 that transmits the data displayed on the display unit 212 to the user terminal 5 of user U.

[0202] According to Embodiment 1, the factors causing the decrease can be easily communicated to the user U.

[0203] (Embodiment 2) 2.1 Outline configuration of Embodiment 2 Embodiment 2 will be described with reference to Figures 23 to 24. As shown in Figure 23, the processing unit 2 of Embodiment 2 differs from Embodiment 1 in that it includes a provisional reduction factor estimation unit 221 instead of the first numerical factor estimation unit 207, a provisional reduction factor update unit 222 instead of the first numerical factor update unit 211, and a reduction factor setting unit 223.

[0204] The provisional reduction factor estimation unit 221 estimates a provisional reduction factor HF, which is a factor in the reduction of the machining quality of the workpiece W, based on the first analysis parameter AP1 and the second analysis parameter AP2, which is a second analysis parameter CAP2 that can be compared with the first analysis parameter AP1.

[0205] The provisional reduction factor estimation unit 221 compares the value of the first analysis parameter AP1 with the value of the second analysis parameter CAP2 for comparison. If there is a significant discrepancy between the value of the first analysis parameter AP1 and the value of the second analysis parameter CAP2 for comparison, the provisional reduction factor estimation unit 221 estimates the factors influencing the first analysis parameter AP1 and the second analysis parameter CAP2 that show a significant discrepancy as factors contributing to the reduction in the machining quality of the workpiece W.

[0206] However, if there is no second analysis parameter CAP2 for comparison that can be compared with the first analysis parameter AP1, the provisional reduction factor estimation unit 221 may estimate the provisional reduction factor HF using only the first analysis parameter AP1.

[0207] The provisional reduction factor update unit 222 updates the provisional reduction factor HF based on the updated third analysis parameter AP3 and second analysis parameter AP2.

[0208] The provisional reduction factor update unit 222 compares the updated third analysis parameter AP3, which has been updated until the numerical data ND and the estimated workpiece shape data ESD are judged to be at the same level, with the second analysis parameter AP2. The provisional reduction factor update unit 222 compares the updated third analysis parameter AP3 with the second analysis parameter AP2 to identify the analysis parameter where a numerical discrepancy has occurred. The provisional reduction factor update unit 222 determines whether the numerical discrepancy is significant or not.

[0209] Whether or not the deviation is significant can be determined by comparing its magnitude to a predetermined threshold, or by using a ratio of the values ​​in which deviations have occurred; any method can be adopted. The provisional decline factor update unit 222 estimates the analysis parameters in which the deviation of the values ​​is determined to be significant as decline factors. The provisional decline factor update unit 222 updates the analysis parameters estimated to be decline factors as provisional decline factors HF.

[0210] Furthermore, the provisional reduction factor update unit 222 compares the third analysis parameter AP3 before the update with the third analysis parameter AP3 after the update. If there are analysis parameters in the third analysis parameter AP3 before the update that deviated significantly from the second analysis parameter AP2, the provisional reduction factor update unit 222 excludes those analysis parameters from the provisional reduction factor HF if the deviation from the second analysis parameter AP2 is no longer significant in the updated third analysis parameter AP3.

[0211] The reduction factor setting unit 223 sets the provisional reduction factor HF as reduction factor FC when the numerical data ND and the estimated workpiece shape data ESD are at equivalent levels. Whether the numerical data ND and the estimated workpiece shape data ESD are at equivalent levels is determined in the same way as in Embodiment 1.

[0212] The display unit 212 according to Embodiment 2 displays the degrading factor FC. It may display the degrading factor FC that is common to both the user input factor UF and the hypothetical degrading factor HF, or it may display the degrading factor FC that is caused only by the hypothetical degrading factor HF.

[0213] The storage unit 4 according to Embodiment 2 is identical to that of Embodiment 1, except that, as shown in Figure 24, it includes a provisional reduction factor HF instead of the first numerical factor NF1 in Figure 2 of Embodiment 1, and also includes a reduction factor FC. Therefore, redundant explanations will be omitted.

[0214] The configuration other than that described above is substantially the same as in Embodiment 1, so the same reference numerals are used for the same components, and redundant explanations are omitted.

[0215] 2.2 Effects of Embodiment 2 The reduction factor estimation system 1 according to Embodiment 2 includes a numerical data acquisition unit 202, a first analysis parameter calculation unit 205, a second analysis parameter acquisition unit 206, a provisional reduction factor estimation unit 221, an estimated workpiece shape data calculation unit 208, an estimation result determination unit 209, a third analysis parameter update unit 210, a provisional reduction factor update unit 222, a reduction factor setting unit 223, and a display unit 212.

[0216] The numerical data acquisition unit 202 acquires numerical data ND, which includes machine tool numerical data MND obtainable from the machine tool 6 that processes the workpiece W, or workpiece numerical data WND obtainable by actually measuring the shape of the workpiece W. The first analysis parameter calculation unit 205 calculates a first analysis parameter AP1 used to perform a simulation of processing the workpiece W by the machine tool 6 based on the numerical data ND. The second analysis parameter acquisition unit 206 acquires a second analysis parameter AP2 used to perform a simulation of processing the workpiece W by the machine tool 6 in the initial state or normal state of the machine tool 6.

[0217] The provisional reduction factor estimation unit 221 estimates provisional reduction factors that are the causes of the reduction in machining quality of the workpiece W, based on the first analysis parameter AP1 and the second analysis parameter AP2, which is a comparison second analysis parameter CAP2 that can be compared with the first analysis parameter AP1. The estimated workpiece shape data calculation unit 208 calculates the estimated workpiece shape data ESD by performing a simulation of machining the workpiece W with the machine tool 6 using the third analysis parameter AP3, which includes the first analysis parameter AP1. The estimation result determination unit 209 determines whether the numerical data ND and the estimated workpiece shape data ESD are at the same level, based on the numerical data ND and the estimated workpiece shape data ESD.

[0218] The third analysis parameter update unit 210 updates the third analysis parameter AP3 if the numerical data ND and the estimated workpiece shape data ESD are not at the same level, until the numerical data ND and the estimated workpiece shape data ESD are determined to be at the same level, and causes the estimated workpiece shape data calculation unit 208 to calculate the estimated workpiece shape data ESD based on the updated third analysis parameter AP3. The provisional reduction factor update unit 222 updates the provisional reduction factor based on the updated third analysis parameter AP3 and the second analysis parameter AP2. The reduction factor setting unit 223 sets the provisional reduction factor as the reduction factor if the numerical data ND and the estimated workpiece shape data ESD are at the same level.

[0219] According to Embodiment 2, the third analysis parameter AP3 can be updated by repeating the simulation. This brings the estimated workpiece shape data ESD closer to the numerical data ND. This improves the accuracy of estimating the factors that reduce the machining quality of the workpiece W. In addition, according to Embodiment 2, the display unit 212 displays the factors that reduce the quality.

[0220] (Embodiment 3) 3.1 Outline configuration of Embodiment 3 Embodiment 3 will be described with reference to Figures 25 to 29. As shown in Figure 25, the processing unit 2 of Embodiment 3 differs from that of Embodiment 2 in that it includes a comparison data generation unit 231.

[0221] As shown in Figure 26, the comparison data generation unit 231 according to Embodiment 3 comprises a first comparison data generation unit 232, a reference data generation unit 233, and a first machining accuracy calculation unit 234.

[0222] The first comparison data generation unit 232 calculates the first comparison data CD1 by having the estimated workpiece shape data calculation unit 208 perform a simulation of machining the workpiece W using the machine tool 6, using only one of the third analysis parameters AP3 related to the reduction factor and a second analysis parameter AP2 of a different type from the third analysis parameter AP3 related to the reduction factor.

[0223] Figure 27 schematically shows the simulation results of roundness as an example of the first comparison data CD1. In the first comparison data CD1, only the analysis parameters related to the degradation factors in the estimated workpiece shape data ESD differ from the shape of the workpiece W when machined according to the command values. Therefore, the estimated cross-sectional shape of the workpiece W is close to a circular shape.

[0224] The reference data generation unit 233 calculates the reference data RD by having the estimated workpiece shape data calculation unit 208 perform a simulation of machining the workpiece W using the machine tool 6, using the second analysis parameter AP2.

[0225] Figure 28 schematically shows the simulation results of roundness as an example of reference data RD. Reference data RD is the shape of the workpiece W when machined according to the command value, which is the second analysis parameter AP2. Therefore, the cross-sectional shape of the workpiece W is close to a circular shape.

[0226] The first machining accuracy difference calculation unit calculates the first machining accuracy difference data AD1, which is caused by the degradation factor, based on the first comparison data CD1 and the reference data RD. In other words, in Embodiment 3, the unit estimates which analysis parameter has the greatest impact on the reference data RD, using the data of the workpiece W when machined by the machine tool 6 in a normal or initial state as a reference.

[0227] In Embodiment 3, for each of the third analysis parameters AP3 estimated as a cause of reduction, a first comparison data CD1 is calculated, and the calculated first comparison data CD1 is compared with the reference data RD to calculate the first machining accuracy difference data AD1. In this way, the first machining accuracy difference data AD1 is calculated for each of the third analysis parameters AP3 estimated as a cause of reduction. As a result, the third analysis parameters AP3 can be quantified based on the magnitude relationship of the first machining accuracy difference data AD1.

[0228] Furthermore, as shown in Figure 29, the storage unit 4 of Embodiment 3 differs from Embodiment 2 in that it stores the first comparison data CD1, the reference data RD, and the first machining accuracy difference data AD1.

[0229] The display unit 212 can display multiple third analysis parameters AP3 in a ranked state based on the first machining accuracy difference data AD1 (see Figure 21).

[0230] The configuration other than that described above is substantially the same as in Embodiment 2, so the same reference numerals are used for the same components, and redundant explanations are omitted.

[0231] 3.2 Effects of Embodiment 3 The reduction factor estimation system 1 according to Embodiment 3 further comprises a first comparison data generation unit 232, a reference data generation unit 233, and a first processing accuracy difference calculation unit.

[0232] The first comparison data generation unit 232 calculates the first comparison data CD1 by having the estimated workpiece shape data calculation unit 208 perform a simulation of machining the workpiece W using the machine tool 6, using only one of the third analysis parameters AP3 related to the reduction factor and a second analysis parameter AP2 of a different type from the third analysis parameter AP3 related to the reduction factor.

[0233] The reference data generation unit 233 calculates reference data RD by having the estimated workpiece shape data calculation unit 208 perform a simulation of machining the workpiece W using the machine tool 6, using the second analysis parameter AP2. The first machining accuracy difference calculation unit calculates first machining accuracy difference data AD1, which is caused by the degradation factor, based on the first comparison data CD1 and the reference data RD.

[0234] According to Embodiment 3, the influence of a single analytical parameter on the factors causing the decline can be quantified.

[0235] (Embodiment 4) 4.1 Outline configuration of Embodiment 4 Embodiment 4 will be described with reference to Figures 30 to 32. As shown in Figure 30, the comparison data generation unit 241 of Embodiment 4 differs from Embodiment 3 in that it includes a second comparison data generation unit 242 and a second machining accuracy difference calculation unit 243.

[0236] The second comparison data generation unit 242 calculates the second comparison data CD2 by having the estimated workpiece shape data calculation unit 208 perform a simulation of machining the workpiece W using the machine tool 6, using the third analysis parameter AP3 other than the third analysis parameter AP3 related to the decrease factor, and the second analysis parameter AP2 of the same type as the third analysis parameter AP3 related to the decrease factor among the second analysis parameters AP2.

[0237] Figure 31 schematically shows the simulation results of roundness as an example of the second comparison data CD2. The second comparison data CD2 uses the second analysis parameter AP2 as the analysis parameter estimated as a factor causing the decrease, and the third analysis parameter AP3 is used for the other analysis parameters. As a result, the estimated cross-sectional shape of the workpiece W has greater irregularities compared to the reference data RD (see Figure 27). In other words, in Embodiment 4, the data of the workpiece W processed by the current machine tool 6 is used as a reference to estimate which analysis parameter has the greatest impact on the reference data RD.

[0238] In Embodiment 4, for each of the third analysis parameters AP3 estimated as a cause of reduction, a second comparison data CD2 is calculated, and the calculated second comparison data CD2 is compared with the reference data RD to calculate the second machining accuracy difference data AD2. In this way, the second machining accuracy difference data AD2 is calculated for each of the third analysis parameters AP3 estimated as a cause of reduction. As a result, the third analysis parameter AP3 can be quantified based on the magnitude relationship of the second machining accuracy difference data AD2.

[0239] Furthermore, as shown in Figure 32, the storage unit 4 of Embodiment 4 differs from Embodiment 3 in that it stores the second comparison data CD2 and the second machining accuracy difference data.

[0240] The display unit 212 can display multiple third analysis parameters AP3 in a ranked state based on the second machining accuracy difference data AD2 (see Figure 21).

[0241] The configuration other than that described above is substantially the same as in Embodiment 4, so the same reference numerals are used for the same components, and redundant explanations are omitted.

[0242] 4.2 Effects of Embodiment 4 The reduction factor estimation system 1 according to Embodiment 4 further comprises a second comparison data generation unit 242, a reference data generation unit 233, and a second processing accuracy difference calculation unit 243.

[0243] The second comparison data generation unit 242 calculates the second comparison data CD2 by having the estimated workpiece shape data calculation unit 208 perform a simulation of machining the workpiece W using the machine tool 6, using the third analysis parameter AP3 other than the third analysis parameter AP3 related to the decrease factor, and the second analysis parameter AP2 of the same type as the third analysis parameter AP3 related to the decrease factor among the second analysis parameters AP2.

[0244] The reference data generation unit 233 calculates reference data RD by having the estimated workpiece shape data calculation unit 208 perform a simulation of machining the workpiece W using the machine tool 6, using the second analysis parameter AP2. The second machining accuracy difference calculation unit 243 calculates second machining accuracy difference data AD2, which is caused by the degradation factor, based on the second comparison data CD2 and the reference data RD.

[0245] According to the above configuration, the influence of a single analytical parameter on the factors causing the decline can be quantified.

[0246] (Embodiment 5) 5.1 Outline configuration of Embodiment 5 Embodiment 5 will be described with reference to Figures 33 to 39. As shown in Figure 33, the processing unit 2 of Embodiment 5 differs from Embodiment 2 in that it includes a temporary machining condition setting unit 251, a temporary estimated workpiece shape data acquisition unit 252, a reference estimated workpiece shape data acquisition unit 253, an estimated workpiece shape data determination unit 254, an improved machining condition setting unit 255, an improved estimated workpiece shape data setting unit 256, and an improvement amount calculation unit 257. In addition, the functions of the estimated workpiece shape data calculation unit 208 and the display unit 212 are different.

[0247] The temporary machining condition setting unit 251 sets the temporary machining condition TPC, which is used when causing the estimated workpiece shape data calculation unit 208 to perform a simulation based on the first analysis parameter AP1.

[0248] The provisional estimated workpiece shape data acquisition unit 252 acquires provisional estimated workpiece shape data TESD by causing the estimated workpiece shape data calculation unit 208 to perform a simulation based on the first analysis parameter AP1 and the provisional machining condition TPC.

[0249] The reference estimated workpiece shape data acquisition unit 253 acquires the reference estimated workpiece shape data RESD by causing the estimated workpiece shape data calculation unit 208 to perform a simulation based on the first analysis parameter AP1 or the second analysis parameter AP2 and the current machining conditions.

[0250] The estimated workpiece shape data determination unit 254 determines whether the provisional estimated workpiece shape data TESD has improved compared to the reference estimated workpiece shape data RESD.

[0251] The improved machining condition setting unit 255 sets the provisional machining condition TPC as the improved machining condition IPC when the provisional estimated workpiece shape data TESD is improved compared to the standard estimated workpiece shape data RESD.

[0252] When the temporarily estimated workpiece shape data TESD is improved compared to the reference estimated workpiece shape data RESD, the improved estimated workpiece shape data setting unit 256 sets the temporarily estimated workpiece shape data TESD as the improved estimated workpiece shape data IESD.

[0253] The improvement amount calculation unit 257 calculates an improvement amount AI by which the improved estimated workpiece shape data IESD is improved compared to the reference estimated workpiece shape data RESD based on the improved estimated workpiece shape data IESD and the reference estimated workpiece shape data RESD.

[0254] The display unit 212 according to Embodiment 5 displays the improved processing conditions IPC, the improved estimated workpiece shape data IESD, the reference estimated workpiece shape data RESD, and the improvement amount AI.

[0255] The estimated workpiece shape data calculation unit 208 according to Embodiment 5 calculates the estimated workpiece shape data ESD by executing a simulation of machining the workpiece W by the machine tool 6 based on the analysis parameters AP and the processing conditions. The analysis parameters AP include the first analysis parameter AP1, the second analysis parameter AP2, and the third analysis parameter AP3.

[0256] As shown in FIG. 34, the storage unit 4 of Embodiment 5 is different from that of Embodiment 1 in that it stores the temporary processing conditions TPC, the improved processing conditions IPC, the temporarily estimated workpiece shape data TESD, the reference estimated workpiece shape data RESD, the improved estimated workpiece shape data IESD, and the improvement amount AI.

[0257] For the configurations other than those described above, they are substantially the same as those in Embodiment 1. Therefore, the same members are denoted by the same reference numerals, and duplicate descriptions are omitted.

[0258] 5.2 Operation of Embodiment 5 As shown in FIG. 35, the display unit 212 may display the value of the estimated workpiece shape data ESD (e.g., 3.2 μm) related to the current workpiece W with respect to the roundness of the workpiece W, and may also display the value of the simulation (e.g., 1.2 μm) when the workpiece W is processed by a normal machine tool 6. Further, the display unit 212 may display the improvement amount AI (e.g., 2.0 μm) of the roundness when the current machine tool 6 is made in a normal state by repair or part replacement or the like.

[0259] Furthermore, as shown in FIG. 36, the display unit 212 may display the value of the cycle time (e.g., 45 s) as an example of the analysis parameter AP.

[0260] Also, as shown in FIG. 37, the display unit 212 displays the value of the current estimated workpiece shape data ESD (e.g., 3.2 μm) with respect to the roundness of the workpiece W, and as the data after improvement, displays the value of the simulation (e.g., 0.9 μm) when the analysis parameter AP is improved in the current machine tool 6. Further, the display unit 212 displays the improvement amount AI (e.g., 2.3 μm) of the roundness when the analysis parameter AP is changed in the current machine tool 6.

[0261] Furthermore, as shown in FIG. 38, the display unit 212 may display the value of the current cycle time (e.g., 45 s) and the value of the cycle time after improvement (e.g., 55 s) as an example of the analysis parameter AP.

[0262] Also, as shown in FIG. 39, the display unit 212 may display the improved machining conditions such as the replacement in each process, the feed rate in each process, the spark-out time, etc. However, the improved machining conditions are not limited to the above. The black-filled round marks in FIG. 39 indicate arbitrary numbers.

[0263] 5.3 Effects of Embodiment 5 The reduction factor estimation system 1 according to Embodiment 5 comprises an estimated workpiece shape data calculation unit 208, a first analysis parameter calculation unit 205, a second analysis parameter acquisition unit 206, a provisional machining condition setting unit 251, a provisional estimated workpiece shape data acquisition unit 252, a reference estimated workpiece shape data acquisition unit 253, an estimated workpiece shape data determination unit 254, an improved machining condition setting unit 255, an improvement amount calculation unit 257, and a display unit 212.

[0264] The estimated workpiece shape data calculation unit 208 calculates the estimated workpiece shape data ESD by performing a simulation of machining the workpiece W using the machine tool 6 based on the analysis parameters AP and machining conditions. The numerical data acquisition unit 202 acquires numerical data ND, which includes machine tool numerical data MND obtainable from the machine tool 6, or workpiece numerical data WND obtainable by actually measuring the shape of the workpiece W.

[0265] The first analysis parameter calculation unit 205 calculates a first analysis parameter AP1 based on numerical data ND, which is used to perform a simulation of machining a workpiece W by the machine tool 6. The second analysis parameter acquisition unit 206 acquires a second analysis parameter AP2, which is used to perform a simulation of machining a workpiece W by the machine tool 6 in the initial state or normal state of the machine tool 6.

[0266] The temporary machining condition setting unit 251 sets the temporary machining condition TPC, which is used when causing the estimated workpiece shape data calculation unit 208 to perform a simulation based on the first analysis parameter AP1. The temporary estimated workpiece shape data acquisition unit 252 acquires the temporary estimated workpiece shape data TESD by causing the estimated workpiece shape data calculation unit 208 to perform a simulation based on the first analysis parameter AP1 and the temporary machining condition TPC.

[0267] The reference estimated workpiece shape data acquisition unit 253 acquires the reference estimated workpiece shape data RESD by having the estimated workpiece shape data calculation unit 208 perform a simulation based on the first analysis parameter AP1 or the second analysis parameter AP2 and the current machining conditions. The estimated workpiece shape data determination unit 254 determines whether the provisional estimated workpiece shape data TESD has improved compared to the reference estimated workpiece shape data RESD.

[0268] The improved machining condition setting unit 255 sets the provisional machining condition TPC as the improved machining condition IPC when the provisional estimated workpiece shape data TESD is improved compared to the reference estimated workpiece shape data RESD. The improved estimated workpiece shape data setting unit 256 sets the provisional estimated workpiece shape data TESD as the improved estimated workpiece shape data IESD when the provisional estimated workpiece shape data TESD is improved compared to the reference estimated workpiece shape data RESD. The improvement amount calculation unit 257 calculates the improvement amount AI, which is the improvement in the improved estimated workpiece shape data IESD compared to the reference estimated workpiece shape data RESD, based on the improved estimated workpiece shape data IESD and the reference estimated workpiece shape data RESD.

[0269] According to Embodiment 5, it is possible to determine the amount of improvement AI based on changes in processing conditions.

[0270] The estimated workpiece shape data calculation unit 208 according to Embodiment 5 calculates estimated workpiece shape data ESD, which is data relating to the machining accuracy of the workpiece W, data relating to the machining quality of the workpiece W, or data relating to the productivity of the workpiece W.

[0271] According to Embodiment 5, it is possible to improve data related to the machining accuracy of the workpiece W, data related to the machining quality of the workpiece W, or data related to the productivity of the workpiece W.

[0272] The temporary machining condition setting unit 251 sets the temporary machining condition TPC based on the second analysis parameter AP2 which corresponds to at least one of the first analysis parameters AP1 from among the second analysis parameters AP2, and at least one first analysis parameter AP1. The temporary estimated workpiece shape data acquisition unit 252 acquires the temporary estimated workpiece shape data TESD by having the estimated workpiece shape data calculation unit 208 perform a simulation based on the second analysis parameter AP2 which corresponds to at least one of the first analysis parameters AP1 from among the second analysis parameters AP2, at least one first analysis parameter AP1, and the temporary machining condition TPC.

[0273] According to Embodiment 5, it is possible to estimate the amount of improvement AI of the workpiece W after measures such as repairs have been taken on the machine tool 6.

[0274] According to Embodiment 5, the display unit 212 displays the improved machining conditions IPC, the improved estimated workpiece shape data IESD, the reference estimated workpiece shape data RESD, and the improvement amount AI. The communication unit 7 transmits the improved machining conditions IPC, the improved estimated workpiece shape data IESD, the reference estimated workpiece shape data RESD, and the improvement amount AI to the user terminal 5.

[0275] The present invention is not limited to the embodiments described above, and can be applied to various embodiments without departing from its spirit. [Explanation of symbols]

[0276] 1: Degradation Factor Estimation System, 2: Processing Unit, 3: Network, 4: Memory Unit, 5: User Terminal, 6: Machine Tool, 7: Communication Unit, 102: Estimation Unit, 106: Dynamic Stiffness Memory Unit, 107: Correction Amount Calculation Unit, 202: Numerical Data Acquisition Unit, 203: Analysis Range Determination Unit, 205: First Analysis Parameter Calculation Unit, 206: Second Analysis Parameter Acquisition Unit, 208: Estimated Workpiece Shape Data Calculation Unit, 209: Estimation Result Determination Unit, 210: Third Analysis parameter update unit, 212: Display unit, 213: Workpiece shape analysis unit, 214: Second numerical factor estimation unit, 215: Correspondence relationship acquisition unit, 216: Analysis parameter comparison unit, 217: Third numerical factor estimation unit, 221: Provisional reduction factor estimation unit, 222: Provisional reduction factor update unit, 223: Reduction factor setting unit, 231: Comparison data generation unit, 232: First comparison data generation unit, 233: Reference data generation unit, 234: First machining accuracy calculation unit ,241: Comparison data generation unit,242: Second comparison data generation unit,243: Second machining accuracy difference calculation unit,AD1: First machining accuracy difference data,AD2: Second machining accuracy difference data,AP1: First analysis parameter,AP2: Second analysis parameter,AP3: Third analysis parameter,ASD: Shipment data,CAP2: Second analysis parameter for comparison,CD: Correspondence relationship data,CD1: First comparison data,CD2: Second comparison data,ESD: Estimated workpiece shape data,FD: Frequency domain data,HF: Provisional reduction factor,MND: Machine tool numerical data,ND: Numerical data,NF1: First numerical factor,NF2: Second numerical factor,NSD: Normal state data,RD: Reference data,FC: Reduction factor,RMD: Roundness measurement data,SAP2: Second analysis parameter for supplementation,T: Tool,W: Workpiece,WAD: Workpiece shape analysis data,WND: Workpiece numerical data

Claims

1. A numerical data acquisition unit that acquires numerical data including machine tool numerical data obtainable from a machine tool that processes a workpiece, or workpiece numerical data obtainable by actually measuring the shape of the workpiece, A first analysis parameter calculation unit calculates first analysis parameters used to perform a simulation of machining the workpiece using the machine tool based on the numerical data, A second analysis parameter acquisition unit acquires second analysis parameters used to perform a simulation of machining the workpiece by the machine tool in the initial or normal state of the machine tool, A provisional reduction factor estimation unit estimates provisional reduction factors that are factors causing a decrease in the machining quality of the workpiece, based on the first analysis parameter and a second comparison analysis parameter among the second analysis parameters that can be compared with the first analysis parameter. An estimated workpiece shape data calculation unit calculates estimated workpiece shape data by performing a simulation of machining the workpiece using the machine tool, using a third analysis parameter including the first analysis parameter. An estimation result determination unit determines whether the numerical data and the estimated workpiece shape data are at the same level, based on the numerical data and the estimated workpiece shape data. If the numerical data and the estimated workpiece shape data are not at the same level, the third analysis parameter update unit updates the third analysis parameter until the numerical data and the estimated workpiece shape data are determined to be at the same level, and causes the estimated workpiece shape data calculation unit to calculate the estimated workpiece shape data based on the updated third analysis parameter. A provisional reduction factor update unit updates the provisional reduction factor based on the updated third analysis parameter and the second analysis parameter, A reduction factor setting unit sets the provisional reduction factor as the reduction factor when the numerical data and the estimated workpiece shape data are at the same level, A system for estimating factors causing a decline, comprising the following components.

2. The aforementioned third analysis parameter further includes a supplementary second analysis parameter, The reduction factor estimation system according to claim 1, wherein the supplementary second analysis parameter is used to supplement the first analysis parameter when the estimated workpiece shape data calculation unit cannot perform the simulation with only the first analysis parameter.

3. The estimated workpiece shape data calculation unit is: An estimation unit that estimates the estimated workpiece shape data using the third analysis parameter, A dynamic stiffness storage unit stores the workpiece dynamic stiffness, which is the dynamic stiffness of the workpiece; the tool dynamic stiffness, which is the dynamic stiffness of the tool placed on the machine tool; and the contact dynamic stiffness, which is the dynamic stiffness at the contact portion between the workpiece and the tool. A correction amount calculation unit calculates a correction amount for the relative displacement of the workpiece and the machine tool in the direction in which the workpiece and the machine tool approach each other, based on the workpiece dynamic stiffness, the tool dynamic stiffness, and the contact dynamic stiffness. A reduction factor estimation system according to claim 1, comprising:

4. moreover, A first comparison data generation unit calculates first comparison data by causing the estimated workpiece shape data calculation unit to perform a simulation of machining the workpiece using the machine tool, using only one of the third analysis parameters related to the aforementioned reduction factor and a second analysis parameter of a different type from the third analysis parameter related to the aforementioned reduction factor among the second analysis parameters. A reference data generation unit calculates reference data by causing the estimated workpiece shape data calculation unit to perform a simulation of machining the workpiece using the machine tool using the second analysis parameters, A first machining accuracy difference calculation unit calculates a first machining accuracy difference data caused by the reduction factor based on the first comparison data and the reference data, A reduction factor estimation system according to claim 1, comprising:

5. moreover, A second comparison data generation unit calculates second comparison data by causing the estimated workpiece shape data calculation unit to perform a simulation of machining the workpiece using the machine tool, using a third analysis parameter different from the third analysis parameter related to the aforementioned reduction factor, and a second analysis parameter of the same type as the third analysis parameter related to the aforementioned reduction factor among the second analysis parameters. A reference data generation unit calculates reference data by causing the estimated workpiece shape data calculation unit to perform a simulation of machining the workpiece using the machine tool using the second analysis parameters, A second machining accuracy difference calculation unit calculates a second machining accuracy difference data caused by the reduction factor based on the second comparison data and the reference data. A reduction factor estimation system according to claim 1, comprising:

6. Furthermore, it is equipped with a display unit that shows the factors causing the decrease. A system for estimating factors causing a decrease in performance according to any one of claims 1 to 5.

7. moreover, A system for estimating the cause of degradation according to any one of claims 1 to 5, comprising a communication unit for transmitting the aforementioned degradation cause to the user's terminal.

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