Processing monitoring system

Abstract

To provide a machining monitoring system that accurately predicts information on a work piece such as a machining quality of the work piece, a material property of the work piece and a cutting resistance during machining.SOLUTION: A machining monitoring system is characterized by comprising: a machine tool that has a tool for machining a work piece and an AC motor for driving a tool; a signal conversion unit that converts an AC current of the AC motor into a DC amount; an information extraction unit that extracts at least one of machining speed information and cutting thickness information from the DC amount; and a work piece property information prediction unit that inputs the information into a pre-constructed model and predicts at least one of a machining quality, a material property, a type, and a cutting resistance of the work piece.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a process monitoring system. [Background technology]

[0002] In recent years, with the spread of additive manufacturing (AM), which creates products of desired shapes from raw materials such as metals and resins, new challenges have arisen in cutting processes that are carried out as a post-process. The first challenge is that the material properties of products manufactured by additive manufacturing (additively manufactured products) are different from those of materials manufactured by conventional manufacturing methods (strength, etc.). A similar challenge is also expected for products made of composite materials formed by mixing multiple materials. When multiple materials are mixed, the properties of the composite material formed may be unknown. In such a situation, it is not easy to set the processing conditions for the workpiece, and in order to optimize the cutting conditions, it is necessary to determine the conditions through repeated trial and error, which increases the processing time.

[0003] The second challenge is that the shapes of additively manufactured products are often complex, and the unevenness of the layered surfaces is large, making it difficult to accurately predict the cutting resistance value before processing. As a result, the frequency of chipping and breakage of processing tools and destruction of the additively manufactured products that are the processed products increases. In addition, since the cutting state of the tool and the workpiece (work material) assumed before processing cannot be maintained, the frictional force and heat conditions on the processed surface cannot be controlled, and the number of times the processing quality must be checked increases.

[0004] Thus, especially in the processing of additively manufactured products, the time required to adjust cutting conditions, reprocess, and reshape increases, and the processing time increases. Therefore, there is a growing need to understand the characteristics, cutting resistance, and processing quality of the processed product during processing, and to utilize them to reduce processing time and stabilize processing quality. These needs exist in the entire processing technology, regardless of additively manufactured products, and if they can be solved, they can be widely applied and useful. The type and characteristics of the material of the processed product, cutting resistance, processing quality, etc. are collectively referred to as "characteristic information of the processed product." The characteristic information of the processed product is a parameter that depends on the material of the processed product. In addition, "processing quality" is an evaluation of the shape of the processed product after processing, such as the thickness of the modified part, the dimensions after processing, and the surface roughness.

[0005] As an example of a technique for predicting machining dimensions during machining, there is the following Patent Document 1. Patent Document 1 discloses a method for predicting machining dimensions (machining quality) by calculating the average value, maximum value, minimum value, standard deviation, integral value, etc. of a specific machining section as feature values ​​using data on the machining section during machining of the machine tool, data indicating the operating state of the machine tool, and data acquired by sensors (current sensor, vibration sensor, temperature sensor) arranged on the machine tool, and inputting the average value, maximum value, minimum value, standard deviation, integral value, etc. of a specific machining section into a model constructed in advance.

[0006] Patent Document 2 discloses an AC motor monitoring device that monitors an AC motor driven by a drive power supply, characterized in that it performs coordinate conversion of the current detection values ​​using at least two-phase current detection values ​​of the AC motor detected by a current detection means for the AC motor, converts the current detection values ​​into DC amounts based on the phase of the coordinate-converted current detection values, and outputs a feature value based on the DC amount. Patent Document 2 describes that the DC amount is calculated from the current of an AC motor connected to a machine (such as a machine tool), and feature values ​​(values ​​or changes indicating changes over time or abnormal operation) are extracted from this DC amount to monitor the behavior of the AC motor. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] International Publication No. 2019 / 239606 [Patent Document 2] International Publication No. 2019 / 049188 Summary of the Invention [Problem to be solved by the invention]

[0008] However, the techniques described in the above Patent Documents 1 and 2 can appropriately correct the processing conditions based on the operation of the machine tool during processing, but do not disclose any means for appropriately correcting the processing conditions while considering the material properties of the workpiece during processing (the material's processing temperature dependency and the cutting thickness (removal volume) dependency). As described above, in particular in processing products made of composite materials and having complex shapes such as additively manufactured products, it is more difficult to predict the processing temperature change and processing volume in advance than in the past when processing products made of a single material with few uneven surfaces, and these change from moment to moment even during processing, so it is estimated that it is difficult to improve the prediction accuracy of the characteristic information of the workpiece (particularly the material properties and type of material) using conventional methods.

[0009] Therefore, an object of the present invention is to provide a processing monitoring system that accurately predicts information about a workpiece, such as the processing quality, material characteristics, type, and cutting resistance of the workpiece, during processing. [Means for solving the problem]

[0010] In order to achieve the above-mentioned object, one aspect of the present invention is a machining monitoring system comprising a machine tool having a tool for machining a workpiece and an AC motor (same meaning as the electric motor in the figure) for driving the tool, a signal conversion unit for converting the AC current of the AC motor into a DC current, an information extraction unit for extracting at least one of machining speed information and cutting thickness information from the DC current (taking into account machining temperature dependency and cutting thickness (removal volume) dependency), and a workpiece characteristic information prediction unit for inputting the above information into a pre-constructed model and predicting at least one of machining quality, material characteristics, type, and cutting resistance.

[0011] More specific configurations of the present invention are described in the claims. Effect of the Invention

[0012] According to the present invention, it is possible to provide a machining monitoring system that accurately predicts information about a workpiece, such as the machining quality of the workpiece, the material characteristics, type, and cutting resistance, during machining.

[0013] Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0014] [Figure 1] Configuration diagram of the machining monitoring system according to the first embodiment [Diagram 2] Schematic diagram showing the relative positions of the tool and the workpiece during milling [Diagram 3] Schematic diagram showing the relative positions of the tool and the workpiece during turning [Figure 4] Flow diagram of the processing performed in the signal conversion unit of Figure 1 [Diagram 5] Flow diagram of the process performed in the cutting speed and cutting thickness information extraction section [Figure 6] A graph showing example waveforms and average values ​​of the estimated spindle torque current Ia and the estimated spindle motor speed Wi at 0.1 second intervals. [Figure 7] A scatter plot showing the relationship between the average estimated spindle motor speed (W1_ave) and the cutting speed (command value) when machining a certain material, and the relationship between the average estimated spindle torque current (Ia_ave) and the feed amount per revolution (command value) [Figure 8] Flow diagram of the process performed in the workpiece characteristic information prediction unit [Figure 9] A graph showing a comparison between the measured cutting resistance value and the cutting resistance value calculated using equation (6) [Figure 10] A graph showing an example of the area (model) that classifies two workpiece materials A and B using SVM, with cutting speed information and cutting thickness information on two axes. [Figure 11] Configuration diagram of the machining monitoring system of the second embodiment [Figure 12] Configuration diagram of a machining monitoring system according to the third embodiment [Figure 13] A diagram showing an example of information showing the type of material of the workpiece [Figure 14] FIG. 11 is a configuration diagram of a machining monitoring system according to a fourth embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Representative embodiments of the present invention will be described in detail below. Reference numerals in the drawings are merely illustrative of the components to which they are attached. EXAMPLES

[0016] Fig. 1 is a configuration diagram of a machining monitoring system according to embodiment 1. In this embodiment, a milling machine (milling device) and a lathe device are assumed as machine tools, and a machining monitoring system using information on cutting speed and cutting thickness extracted from the phase current of a spindle motor will be described.

[0017] As shown in FIG. 1, a monitoring system 100a of the first embodiment includes a machine tool 5, a signal conversion unit 6, a cutting speed / feed rate information extraction unit 7, a workpiece characteristic information prediction unit 8, and a notification device 9.

[0018] The machine tool 5 has a spindle motor 2 that drives a tool to machine a workpiece. In this embodiment, monitoring is performed using phase currents (motor currents) acquired from the spindle motor 2 (spindle motor) that rotates the tool and workpiece, so only the spindle motor 2 is shown in the machine tool 5. In addition, a typical machine tool is equipped with multiple servo motors that move the workpiece and tool to any position, but these are omitted in the figure.

[0019] Here, the positional relationship between the spindle and the workpiece will be described. FIG. 2 is a schematic diagram showing the positional relationship between the tool and the workpiece in milling, and FIG. 3 is a schematic diagram showing the positional relationship between the tool and the workpiece in turning. As shown in FIG. 2, in milling, the tool 20 rotates around the spindle in a rotation direction 23 to machine the workpiece 21. The tool 20 or the workpiece 21 can be moved in the X-axis, Y-axis, and Z-axis directions by a servo motor (not shown). Also, as shown in FIG. 3, in turning, the workpiece 21 is machined by the tool 20 as it rotates around the spindle in a rotation direction 23. The tool 20 or the workpiece 21 can be moved in the X-axis, Y-axis, and Z-axis directions by a servo motor (not shown).

[0020] The procedure for acquiring current from the spindle motor 2 in FIG. 1 will be described below. The phase currents are acquired by the current sensors 3 and 4. In the figure, two-phase currents are acquired for one motor, but two or more phases are sufficient, and three-phase currents may be acquired. In this embodiment, the description will be given assuming that the currents of the U and V phases out of the three phases, UVW, are acquired. In addition, although FIG. 1 shows a diagram in which the current is acquired outside the inverter 1, information from a current sensor installed in the inverter 1 may be used. The acquired current information is input to the signal conversion unit 6 and converted into a DC amount. Here, the DC amount refers to a signal whose time series data is a constant value if the frequency component of the motor rotation period is removed and there is no influence of load fluctuation, rotation speed fluctuation, or inverter carrier frequency (conversely, if there is load or rotation speed fluctuation, the DC amount will fluctuate in accordance with the period). The DC amount of the spindle motor current output from the signal conversion unit 6 is input to the cutting speed / cutting thickness information extraction unit 7, which extracts information (feature amount) related to the cutting speed and cutting thickness during cutting. The cutting speed information and the cutting thickness information are then input to a workpiece characteristic information prediction unit 8, which estimates the processing quality, material (particularly the material characteristics and type), and cutting resistance (collectively defined as the workpiece characteristic information). The estimated workpiece characteristic information is input to a notification device 9. From here, each of the processing blocks 6 to 9 will be explained in detail.

[0021] First, the signal conversion unit 6 will be described in detail. Fig. 4 is a flow diagram of the process performed in the signal conversion unit of Fig. 1. Fig. 4 shows a DC conversion process called coordinate conversion using current phase. First, the acquired U-phase current Iu and V-phase current Iv are input to the W-phase current generation unit 10. This process is performed when the measured current is two-phase, and is not necessary when all three phases are acquired by current sensors. The W-phase current generation unit 10 performs the process shown in the following equation (1). Iw = -(Iu + Iv) ... (1) The three-phase current is input to a three-phase to two-phase converter 11, which performs the processes shown in the following equations (2) and (3). This obtains Iα and Iβ as orthogonal two-axis current components based on the α and β axes. Iα=(2 / 3){Iu-Iv / 2-Iw / 2} …(2) Iβ=(1 / √(3)){Iv-Iw} …(3) After that, the phase required for coordinate transformation (conversion to DC amount) is calculated by phase calculation unit 15. Phase calculation unit 15 is composed of instantaneous phase calculation unit 12, PLL unit (phase memory unit) 13, and integration unit 14, and among them, instantaneous phase calculation unit 12 performs the process shown in the following equation (4) to calculate instantaneous phase θi*. θi*=tan-1(Iβ / Iα) …(4) Instantaneous phase θi * is passed through a feedback loop consisting of a PLL unit 13 and an integrator unit 14, and finally a coordinate transformation phase θi is generated in the integrator unit 14. This value becomes an estimate of the motor rotor position. In this process, the PLL unit 13 provides an electrical angle estimate Wi (motor speed estimate) as a DC amount. Since this feedback loop has the role of removing noise, if the instantaneous phase θi * If the noise is not a concern, the signal does not need to go through the feedback loop.

[0022] The orthogonal two-axis current components Iα and Iβ based on the α-axis and β-axis calculated by the three-phase to two-phase conversion unit 11 and the coordinate transformation phase θi calculated by the phase calculation unit 15 are input to the coordinate transformation unit 16, and the coordinate transformation is performed by the process shown in the following equation (5). Ia=Iα·cos(θi)+Iβ·sin(θi) …(5) This coordinate transformation can be said to be a transformation of a current (AC) in a fixed coordinate system into a current (DC) in a rotating coordinate system. The calculated DC value Ia in the rotating coordinate system and the electrical angle estimate value Wi (motor speed estimate) output from the phase calculation unit 15 are DC amounts. Note that the DC amount Ia changes in a similar manner to the temporal change of the torque current used in motor control, and therefore is described as a torque current estimate in this specification.

[0023] Next, details of the cutting speed / chip thickness information extraction unit 7 will be described with reference to Fig. 5. Fig. 5 is a flow diagram of the process performed by the cutting speed / chip thickness information extraction unit. As shown in Fig. 5, the cutting speed information is calculated by taking the average value of the estimated motor speed value Wi of the spindle, and the chip thickness information is calculated by taking the average value of the estimated torque current value Ia of the spindle. The average values ​​are calculated by dividing the input time series data of Ia and Wi into certain time intervals and dividing the total value within the interval by the number of data points.

[0024] Figure 6 is a graph showing an example of waveforms and average values ​​of the estimated torque current Ia of the spindle and the estimated motor speed Wi of the spindle, divided by 0.1 seconds. Here, we will explain why the cutting speed and the chip thickness can be calculated by the average value of Ia and Wi. First, the cutting speed is the peripheral speed of the rotating tool blade or the workpiece, and is proportional to the rotation speed of the tool or the workpiece. In other words, if wear of the tool blade is not taken into consideration, it can be seen that the above-mentioned estimated electrical angle Wi can be used as an alternative value for the cutting speed. On the other hand, the chip thickness is a value that correlates with the thickness of the chip, and in milling, it increases with an increase in the feed rate per blade, and in turning, it increases with an increase in the feed rate per revolution. Here, the feed rate is a value that indicates how much the tool has moved relative to the workpiece, and for the spindle that rotates the tool or workpiece, increasing the feed rate leads to an increase in the load because a force acts in the direction in which the tool and the workpiece are pressed against each other. Therefore, the average value of the estimated torque current value Ia of the main shaft motor correlates with the cutting thickness.

[0025] Figure 7 is a scatter plot showing the relationship between the average value of the estimated spindle motor speed (W1_ave) and the cutting speed (command value) when turning a certain material, and the relationship between the average value of the estimated spindle torque current (Ia_ave) and the feed amount per revolution (command value) and the command value (set value) of the cutting thickness. As shown in Figure 7, there is a positive correlation between the cutting speed and W1_ave, and between the cutting thickness (feed amount) and Ia_ave, confirming that the above content is correct.

[0026] Further, the workpiece characteristic information prediction unit 8 in FIG. 1 will be described with reference to FIG. 8. FIG. 8 is a flow diagram of the process performed by the workpiece characteristic information prediction unit 8. As shown in FIG. 8, the workpiece characteristic information prediction unit 8 predicts at least one of the processing quality, the material characteristic or type, and the cutting resistance (workpiece characteristic information) by inputting at least one of the calculated cutting speed information and the cut thickness information into a model constructed in advance. When predicting the characteristic information of multiple workpieces, multiple models are prepared. Here, examples of the model include a regression equation, a cluster model, and a classification model.

[0027] Here, we will show an example of predicting cutting resistance using a regression equation. A possible way to predict cutting resistance is to use a formula that is a modification of Kronenberg's theoretical formula shown in equation (6). Here, F is the cutting resistance value, A, b, α, β, and γ are coefficients, a is the cutting depth, V is the cutting speed, and f is the chip thickness (feed amount), and α, β, and γ are determined in advance from the cutting resistance value obtained in advance, and the cutting speed information and chip thickness information extracted from the phase current. [Number 6] F={(A*ab)*Vβ*fγ}=α*Vβ*fγ …(6) In other words, the cutting resistance value can be calculated by inputting the values ​​calculated by the cutting speed / chip thickness information extraction unit 7 into V and f in equation (6). Figure 9 is a graph showing a comparison between the measured cutting resistance value and the cutting resistance value calculated using equation (6). If the plot is on the dashed line in the figure, it indicates that the predicted value and the measured value match. As can be seen from the figure, the cutting resistance calculated using equation (6) can be predicted with an average measurement error of about 10%. Note that equation (6) is just an example, and any equation containing terms for cutting speed information and chip thickness information can be used. Furthermore, regression equations can be used to predict processing quality such as the thickness of the processing-affected layer and the residual stress of the processing surface.

[0028] As an example other than the regression model, a classification model for classifying the type of workpiece is constructed using a support vector machine (SVM) or the like. Figure 10 is a graph showing an example of a region (model) for classifying two types of workpiece materials A and B calculated using SVM, with cutting speed information and cutting thickness information on two axes. The line in the figure indicates the separation boundary surface calculated by SVM, and the dark black (material A) and gray (material B) square marks indicate separate material data. As can be seen from the figure, a classification region (classification model) is created to effectively separate the dark black and gray data, and in this example, a model that can obtain a classification accuracy rate of 90% or more has been constructed. When actually using this model, the cutting thickness information and cutting speed information when machining an unknown material are input, and by determining which region the data is located in, the workpiece characteristic information prediction unit 8 outputs whether it is material A or B.

[0029] Furthermore, the workpiece characteristic information prediction unit 8 may output A or B as described above, or may output the class probability of each material being classified (for example, in the case of a two-material classification model, the probability of material A is x [%], and the probability of material B is 100-x [%]). Using the class probability makes it possible to grasp in more detail the material characteristics of a metal additively manufactured product constructed by mixing multiple materials (such as the extent to which the characteristics of material A are expressed). In this example, a model that classifies only two materials has been explained, but it is also possible to create a model that can classify more than two materials.

[0030] Finally, the notification device 9 in Fig. 1 will be described. The notification device 9 may be, for example, a device that displays the predicted results of the cutting resistance, cutting quality, and material of the workpiece directly on a screen, or may be a device that issues an alarm sound, turns on a patrol lamp, or displays text such as a warning on a screen if the cutting resistance or quality is above (or below) a certain value. In addition, the predicted results of the cutting resistance value, etc. may be notified to an NC control device that controls a machine tool, and used as an input value for a system that changes the operation command of the machine tool based on the cutting resistance value.

[0031] In this way, by using the embodiment 1, it is possible to construct a model that can predict the characteristic information of the workpiece with high accuracy from the phase current of the spindle motor during machining. This makes it possible to shorten the time required to determine cutting conditions, shorten the time required for rework due to machining failure, and shorten the inspection time required to stabilize the machining quality, thereby contributing to shortening the cutting machining time.

[0032] Conventionally, it has been possible to modify the machining conditions during machining while taking into account the state of the machining device, but it has not been possible to modify the machining conditions while taking into account the characteristics of the material of the workpiece during machining as in this embodiment. The present invention is useful when the material of the workpiece is unknown, particularly in the machining of additively manufactured products. EXAMPLES

[0033] Fig. 11 is a diagram showing the configuration of a machining monitoring system according to a second embodiment. In the second embodiment, another machining monitoring system is described, which uses at least one of the information on the motor current of the feed axis in the main force direction (set to the Y-axis direction in this example) and the chip temperature, together with the information on the cutting speed and the chip thickness extracted from the phase current of the main spindle motor, in order to improve the prediction accuracy of the material of the workpiece. Note that in this embodiment, the explanation of the same functional blocks as those in the first embodiment is omitted.

[0034] As shown in FIG. 11, the present embodiment differs from the first embodiment in that a torque current estimate is calculated from the phase current of the Y-axis motor 206 acquired by the current sensors 207 and 208 (DC signal conversion unit (Y-axis) 213), the torque current estimate is averaged (average value calculation unit 214), and the average value and the chip temperature are input to a workpiece characteristic information prediction unit 211 together with cutting speed information and chip thickness information.

[0035] In Fig. 11, both the average value of the Y-axis torque current estimate and the chip temperature are input to the workpiece characteristic information prediction unit 211, and the following description will be given on the assumption that the two additional signals are used, but it is also possible to use either one of the signals. The other processing block contents are the same as those explained in Fig. 1. The following describes the processing blocks and signals that have been added or changed.

[0036] First, the DC conversion unit (Y-axis) 213 added from the first embodiment performs the same processing as that described in FIG. 4, and the average calculation unit 214 performs the same processing as that described in FIG. 5. Next, the chip temperature is a value obtained by measuring the temperature of the chips generated during machining, for example, with a high-speed thermo camera or a radiation thermometer. Note that the hardness or color of the chips may be used instead of the chip temperature. This is because the hardness and color change depending on the chip temperature. As a result of the inventor's investigation, it was found that information on the material of the workpiece can be obtained more accurately by using the chip temperature, hardness or color rather than the temperature of the tool or workpiece.

[0037] The workpiece characteristic information prediction unit 211 constructs a classification model as a four-dimensional space by adding two dimensions of the average value of the Y-axis torque current and the chip temperature to the two-dimensional space shown in Fig. 10. This improves the prediction accuracy of material characteristics and types. This is because the thermal conductivity characteristics of each material can be grasped from the information on the chip temperature, and the average value of the Y-axis torque current can be used to grasp the load in the principal force direction during machining, making it possible to grasp information such as the hardness of the material more accurately.

[0038] In this way, by using the second embodiment, the prediction accuracy of the material of the workpiece can be improved, and therefore, compared to the first embodiment, it can contribute to shortening the cutting processing time. EXAMPLES

[0039] Fig. 12 is a configuration diagram of a machining monitoring system according to a third embodiment. In the third embodiment, another machining monitoring system is described, which uses information indicating the type of material of the workpiece together with information on the cutting speed and the thickness of the chip extracted from the phase current of the spindle motor in order to improve the prediction accuracy of the cutting resistance and machining quality of the cutting process. Note that in this embodiment, the description of the same functional blocks as those in the first embodiment is omitted.

[0040] As shown in FIG. 12, the present embodiment is different from the first embodiment in that, in addition to the cutting speed information and the chip thickness information, information indicating the type of material of the workpiece is input to the workpiece characteristic information prediction unit 108 to predict the material of the workpiece.

[0041] FIG. 13 is a diagram showing an example of information indicating the type of material of the workpiece. The information indicating the type of material of the workpiece in FIG. 12 is, for example, a list of numbers as shown in FIG. 13. Each digit of the number is called a dummy variable, and only one dummy variable is represented as one (1), and the rest are represented as zero (0). When processing material A, the number "100...0" is input, with only dummy variable A being 1. This number is input, for example, by the worker performing the processing into a monitoring device. Then, the list of numbers made of this dummy variable is used as in formula (7) by adding it to formula (6) shown in the first embodiment. Formula (7) represents the case where four materials are used. F is the predicted cutting resistance value, α, β, γ, δ, ε, and ζ are coefficients, V is cutting speed information, f is chip thickness (feed rate) information, DA, DB, and DC are dummy variables, and α, β, γ, δ, ε, and ζ are determined from the cutting resistance value, cutting speed information, and chip thickness information obtained in advance. Here, even though equation (7) targets four materials, only three dummy variables are used. This is because the case when DA, DB, and DC are all zero can be treated as the prediction equation for material D, and three dummy variables are sufficient. F=α*Vβ*fγ*(δ*DA+ε+DB+ζ*DC) …(7) In this way, by adding information indicating the type of workpiece material to the equation as a dummy variable, the coefficients δ, ε, and ζ applied to the dummy variables become values ​​equivalent to the machinability and machinability of each material (material properties taking into account hardness, thermoelectric coefficient, etc.), making it possible to predict the resistance value more accurately.

[0042] In other words, by using the third embodiment, the prediction accuracy of the cutting resistance and the processing quality can be improved, and therefore, compared to the first embodiment, it can contribute to a reduction in the cutting processing time. EXAMPLES

[0043] Fig. 14 is a configuration diagram of a machining monitoring system according to a fourth embodiment of the present invention. In the fourth embodiment, another machining monitoring system is described, which uses workpiece information indicating the type of material of the workpiece predicted from the phase current of the spindle motor together with information on the cutting speed and the thickness of the chip extracted from the phase current of the spindle motor in order to improve the prediction accuracy of the cutting resistance and the machining quality. Note that in this embodiment, the explanation of the same functional blocks as those in the third and fourth embodiments is omitted.

[0044] As shown in FIG. 14, the difference between this embodiment and the first and third embodiments is that the present embodiment includes workpiece characteristic information prediction units 308 and 310, and uses the results predicted by the workpiece characteristic information prediction unit (workpiece material) 308 as information indicating the type of material of the workpiece used in the workpiece characteristic information prediction unit (cutting resistance or machining quality) 310. Note that information on the chip temperature and the Y-axis torque current average value is input to the workpiece characteristic information prediction unit (workpiece material) 308 shown in FIG. 14, but these two signals may be omitted (configuration of the first embodiment). The output of the workpiece characteristic information prediction unit (workpiece material) 308 may be output in the form of dummy variables shown in FIG. 13, or may be a list of numbers by assigning class probability numbers to each dummy variable. The output workpiece material information indicating the type of material of the workpiece is input to the workpiece characteristic information prediction unit (cutting resistance or machining quality) 310. The processing of the workpiece characteristic information prediction unit (cutting resistance or machining quality) 310 is the same as in the fourth embodiment.

[0045] In this way, by using Example 4, it is possible to improve the prediction accuracy of the cutting resistance and processing quality without manually inputting information indicating the type of material of the workpiece, which can contribute to a greater reduction in cutting processing time than Example 3.

[0046] As described above, according to the present invention, it has been shown that it is possible to provide a processing monitoring system that accurately predicts information about a workpiece, such as the processing quality, material characteristics, and cutting resistance of the workpiece during processing.

[0047] The present invention is not limited to the above-mentioned embodiment, but includes various modified examples. For example, the above-mentioned embodiment has been described in detail to easily explain the present invention, and is not necessarily limited to those including all of the configurations described. Also, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Also, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration. [Explanation of symbols]

[0048] 1,101,201,301... Main shaft inverter, 2,102,202,302... Main shaft motor, 205,311... Y-axis inverter, 206,312... Y-axis motor, 3,4,103,104,203,204,207,208,303,304,313,314... Current sensor, 5,105,305... Machine tool, 6,106, 209, 213, 306, 315...signal conversion unit, 7, 107, 210, 307...cutting speed / cut thickness information extraction unit, 214, 316...average value calculation unit, 8, 108, 211, 308, 310...workpiece characteristic information prediction unit, 9, 109, 212, 309...notification device, 100a, 100b, 100c, 100d...processing monitoring system.

Claims

1. a machine tool having a tool for machining a workpiece and an AC motor for driving the tool; A signal conversion unit that converts the AC current of the AC motor into a DC current; an information extracting unit for extracting at least one of information on a cutting speed and information on a chip thickness from the DC amount; a workpiece characteristic information prediction unit that inputs the information into a pre-constructed model and predicts at least one of the processing quality, the characteristics or type of the material of the workpiece, and the cutting resistance; Equipped with The DC amount is a main shaft motor speed estimate and a main shaft motor torque current estimate, the information on the cutting speed is an average value of the estimated spindle motor speeds; The cutting thickness information is an average value of the main shaft motor torque current estimate. A processing monitoring system comprising:

2. A machine tool having a tool for machining a workpiece and an AC motor for driving the tool; A signal conversion unit that converts the AC current of the AC motor into a DC current; an information extracting unit for extracting at least one of information on a cutting speed and information on a chip thickness from the DC amount; a workpiece characteristic information prediction unit that inputs the information into a pre-constructed model and predicts at least one of the processing quality, the characteristics or type of the material of the workpiece, and the cutting resistance; Equipped with When predicting the characteristics or type of the material of the workpiece, an average value of the torque current estimate value of the AC motor responsible for the movement in the principal force direction and information on the chip temperature are input to the pre-constructed model together with information from the information extraction unit; When predicting the processing quality and the cutting resistance, information indicating a type of material of the workpiece is input into the pre-constructed model together with information from the information extraction unit; A processing monitoring system comprising:

3. 3. The machining monitoring system according to claim 1, wherein the signal conversion unit uses measurement values ​​of current sensors installed in at least two phases of a spindle motor that rotates the tool or the workpiece.

4. 4. The machining monitoring system according to claim 3, wherein the signal conversion unit calculates the DC amount by using a current phase calculated from the two-phase currents.

5. 5. The machining monitoring system according to claim 1, wherein the information indicating the characteristics or type of the material of the workpiece is a predicted value of the material of the workpiece predicted from the AC current.

6. The machining monitoring system according to any one of claims 1 to 5, wherein the pre-constructed model is a regression model, a cluster model or a classification model.

7. A machine tool having a tool for machining a workpiece and an AC motor for driving the tool; A signal conversion unit that converts the AC current of the AC motor into a DC current; an information extracting unit for extracting at least one of information on a cutting speed and information on a chip thickness from the DC amount; a workpiece characteristic information prediction unit that inputs the information into a pre-constructed model and predicts at least one of the processing quality, the characteristics or type of the material of the workpiece, and the cutting resistance; Equipped with The information indicating the characteristics or type of the material of the workpiece is a predicted value of the material of the workpiece predicted from the AC current. A processing monitoring system comprising:

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